Article

The Impact of Multinationality on Firm Value: A Comparative Analysis of Machine Learning Techniques

January 2013
Decision Support Systems 59(1)

January 2013
59(1)

DOI:10.1016/j.dss.2013.11.001

Authors:

Cemil Kuzey

Murray State University

Ali Uyar

La Rochelle Business School

Dursun Delen

Oklahoma State University - Stillwater

Managerial Overconfidence with Its Cash Holding Decision and Firm Performance: Evidence from Vietnam

Conference Paper

Full-text available

Dec 2023

Managerial overconfidence plays an important role in firm performance. It is stated that the greater the overconfidence level of managers, the higher risk, and the greater the probability of loss in firm value. It is also supported by evidence that overconfident managers tend to hold less cash, hence increasing the risk of getting a lower firm performance. Interestingly, the empirical evidence from this study indicates a different result whereby managerial overconfidence is shown to have a positive impact on firm value, however, firms with the combination of both managerial overconfidence and low cash holdings tend to have a worse firm performance than others. The result is illustrated through empirical research from a sample size of 648 listed firms on the Vietnamese stock exchange market.

Do Strategic Human Resources and Artificial Intelligence Help to Make Organisations More Sustainable? Evidence from Non-Governmental Organisations

Article

Full-text available

Jun 2022

Uncertainty and a lack of stability are among the difficulties non-governmental organisations face. However, certain strategies for ensuring their performance's sustainability have not been empirically demonstrated in the literature. Using strategic resource management practises and artificial intelligence, this study examines the effect of organisational learning and corporate social responsibility on the sustainability of non-governmental organisations' performance. The survey gathered data from 171 participants representing 21 United Nations organisations and 70 non-governmental organisations in Jordan to accomplish this goal. The data were analysed using WarpPLS and PLS-SEM. The study demonstrates that organisational learning, artificial intelligence, strategic human resource management practises, and corporate social responsibility all contribute to the long-term viability of non-governmental organisations. Furthermore, the study discovered that strategic resource management practises and artificial intelligence significantly mediate the relationship between organisational learning and sustainable organisational performance on the one hand, and corporate social responsibility on the other. Finally, the study provides theoretical and practical guidance on how to apply the findings to assist non-profit organisations' management in utilising organisational learning, corporate social responsibility, artificial intelligence, and strategic resource management practices to help them run their internal operations in a more efficient and sustainable manner over time.

effect of financial ratios on company value in oil and gas mining subsectors in Indonesia stock exchange

Article

Full-text available

Feb 2021

Firm value is influenced by elements outside and within the organization. . They were selected by purposive examination technique. The examination procedure used is Panel Data Regression Analysis. The consequences of such examinations lead to the demonstration that Return on Equity has a substantial beneficial return on firm value, suggesting that return on capital through increased benefits will build financial support certainty. Conversely, the Debt to Asset Ratio has a critical negative impact on firm value. This implies that the use of extreme liabilities can sustain the business. Owners and top administrative organizations should be careful about the use of obligations. Operational productivity and expansion of the number of items must be the primary concern to build Return on Equity. Different factors, such as Asset Growth, Total Asset Turn Over, and Current Ratio, have no impact on firm value.

Influence of Artificial Intelligence (AI) on Firm Performance: The Business Value of AI-based Transformation Projects

Article

Full-text available

Mar 2020

Purpose: The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (i) analysis of AI and AI concepts/technologies; (ii) in-depth exploration of case studies from a great number of industrial sectors; (iii) data collection from the databases (websites) of AI-based solution providers; and (iv) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations. Design/methodology/approach: This study has called on the theory on IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase. Findings: AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can therefore enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes. Implications: AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared towards a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations and, at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships, and scalable infrastructure. Originality/Value: This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 cases studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.

Influence of Artificial Intelligence (AI) on Firm Performance: The Business Value of AI-based Transformation Projects

Article

Full-text available

Mar 2020
Bus Process Manag J

The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (i) analysis of AI and AI concepts/technologies; (ii) in-depth exploration of case studies from a great number of industrial sectors; (iii) data collection from the databases (websites) of AI-based solution providers; and (iv) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations. This study has called on the theory on IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase. AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can therefore enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes. AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared towards a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations and, at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships, and scalable infrastructure. This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 cases studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.

Energy Firm Value Prediction using Machine Learning

Article

Full-text available

Nov 2019

For decades, a high prediction error rate of firm value assessment has been reported by using traditional financial evaluation methods, therefore develop a suitable assessment tool to improve firm value prediction accuracy is in urgent. This paper provides a comprehensive review and statistical comparison of six machine learning models: K-Nearest Neighbor, Decision Trees, Support Vector Regression, Artificial Neutral Network, AdaBoost, and Random Forest in oil firm and power firm value prediction. Based on nearly 5000 M&A items, this paper finds that for both oil and power industries, the prediction error of ANN is the lowest in all the three measurement terms. ANN performs better than the other five ML models by 18% at least for oil industry, and outperforms the others by 19% for power industry. It shows that ANN models can produce both accurate and reasonably understandable prediction results. ANN can be applied to a wide range of M&A decisions and value assessment for energy firms.

Analyzing initial public offerings' short-term performance using decision trees and SVMs

Article

Feb 2015

Corporate Attributes and Value of Listed Manufacturing Firms in Nigeria: A Comparative Analysis

Article

Full-text available

Mar 2024

Firms are increasingly striving to improve their value through various corporate strategies as well as exploiting their unique attributes to stimulate value. The extent to which firms create value given their attributes remains a subject of discuss among scholars with mix conclusions. This study therefore investigates the comparative effect of corporate attributes (Firm size, leverage, Institutional ownership, multi-nationality and Research and development) on the value (Tobin’s Q and Market value of equity) of 24 listed Consumer and Industrial goods firms for a period of 14 years (2009-2022). The study utilized a positivist research philosophy and employed correlational research design. Data for the study were quantitatively retrieved from the annual reports and accounts of the firms. Variables were described using descriptive statistics and relationships were ascertained via correlation analysis. Both random effect (FE) and OLS robust regressions were used to analyze the data having carried out some robustness and diagnostic tests. Results from the study revealed firm size, multi-nationality and research and development have significant positive effects on firm value. However, while leverage has significant negative effect on firm value, institutional shareholding effect on firm value was found to be negative and insignificant.

The Road to corporate sustainability: The importance of artificial intelligence

Article

Dec 2023
Tech Soc

Artificial intelligence (AI) is playing an increasingly important role in the corporate strategic development, and this study explores the relationship between AI and corporate sustainable development for the first time. By analyzing data from Chinese listed companies from 2011 to 2020 and employing a series of robust methods, this study makes the following findings: First, AI promotes corporate sustainable development, with a greater impact on environmental governance and social responsibility. Second, this study focuses on the moderating effects of internal and external pressures. Confucian culture and public pressure hinder the positive effects of AI, while technological proficiency and data security awareness amplify this positive impact. Finally, this study explores the potential pathways of AI based on corporate resource allocation and stakeholders. AI achieves corporate sustainable development by alleviating financing constraints, reducing agency costs, improving supply chain performance, labor productivity, resource utilization efficiency, and reducing corporate risks. These findings have significant implications for the application of AI technology and corporate sustainable development in emerging countries, including China.

Scalable Neural Network Algorithms for High Dimensional Data

Article

Jan 2023

The boundary for machine learning engineers lately has moved from the restricted data to the algorithms' failure to involve every one of the data in the time permitted. Due of this, scientists are presently worried about the adaptability of machine learning algorithms notwithstanding their exactness. The key to success for many computer vision and machine learning challenges is having big training sets. A few published systematic reviews were taken into account in this topic. Recent systematic reviews may include both more recent and older research on the subject under study. Thus, the publications we examined were all recent. The review utilized information that were gathered somewhere in the range of 2010 and 2021. System: In this paper, we make a modified brain organization to eliminate possible components from extremely high layered datasets. Both a totaled level and an exceptionally fine-grained level of translation are feasible for these highlights. It is basically as easy to grasp non-straight connections as it is a direct relapse. We utilize the method on a dataset for item returns in web based shopping that has 15,555 aspects and 5,659,676 all out exchanges. Result and conclusion: We compare 87 various models to show that our approach not only produces higher predicted accuracy than existing techniques, but is also interpretable. The outcomes show that feature selection is a useful strategy for enhancing scalability. The method is sufficiently abstract to be used with many different analytics datasets

A Machine Learning Based Funding Project Evaluation Decision Prediction

Article

Full-text available

Jan 2023
COMPUT SYST SCI ENG

How Fair Is IS Research?

Chapter

Full-text available

Jan 2021

While both information systems and machine learning are not neutral, the identification of discrimination is more difficult if a system learns from data and discrimination can be introduced at several stages. Therefore, this article investigates if IS Research has taken up with this topic. A literature analysis is conducted and its discussion shows that technology, organization, and human aspects have to be considered, making it a topic not only for data scientist or computer scientist, but for information systems researchers as well.

How do data scientists and managers influence machine learning value creation?

Article

Full-text available

Jan 2021

Corporations are leveraging machine learning (ML) to create business value (BV). So, it becomes relevant to not only ponder the antecedents that influence the ML BV process but also, the main actors that influence the creation of such value within organizations: data scientists and managers. Grounded in the dynamic-capabilities theory, a model is proposed and tested with 319 responses to a survey. While for both groups, platform maturity and data quality are equally important factors for financial performance, information intensity is an equally important factor for organizational performance. On one hand, data scientists care more about the catalytic effect of data quality on the relationship between platform maturity and financial performance, and the compatibility factor for organizational performance. On the other hand, managers care more about the feasibility factor for financial performance. The findings presented here offer insights on how data scientists and managers perceive the ML BV creation process.

Internationalisation and financial performance: in the case of global automotive firms

Article

Jan 2021

Purpose Given the mixed evidence on the relationship between internationalisation and firm performance, the purpose of this study is to investigate the effect of internationalisation on the financial performance in the setting of a matured and stagnant market, the global automotive industry. Design/methodology/approach The study uses 37 automotive manufacturers covering from 2000 to 2015. Panel regression analyses were used to estimate the relationship between four financial performance variables (return on equity [ROE], return on asset [ROA], return on capital [ROC] and return on sales [ROS]) and three main independent variables (foreign assets to total assets [FATA], research and development intensity [RNDi], advertising intensity [ADVi]), controlling for product diversification, firm size, age and risk. Findings The findings reveal that automotive firms with a lower FATA ratio, lower RNDi and higher ADVi tend to achieve higher financial performance. However, the intensity of product diversification does not influence the financial performance of global automakers. Ceteris paribus, larger firms in terms of market capitalisation and new entrants into the market tend to have higher financial performance relative to smaller and older firms. Originality/value This study contributes to the literature first by examining the relationship between internationalisation and firm performance in the setting of a matured market, i.e. the automotive industry. Secondly, the paper uses a multinational sample at a global level; and third, it analyses financial performance on a comprehensive basis via four measures, namely, ROA, ROE, ROC and ROS, as the dependent variables.

An investigation of the factors influencing cost system functionality using decision trees, support vector machines and logistic regression

Article

Feb 2019
Int J Account Inform Manag

Purpose The paper aims to identify and critically analyze the factors influencing cost system functionality (CSF) using several machine learning techniques including decision trees, support vector machines and logistic regression. Design/methodology/approach The study employed a self-administered survey method to collect the necessary data from companies conducting business in Turkey. Several prediction models are developed and tested; and a series of sensitivity analyses is performed on the developed prediction models to assess the ranked importance of factors/variables. Findings Certain factors/variables influence CSF much more than others. The findings of the study suggest that utilization of management accounting practices require a functional cost system, which is supported by a comprehensive cost data management process (i.e., acquisition, storage and utilization). Research limitations/implications The underlying data was collected using a questionnaire survey; thus, it is subjective which reflects the perceptions of the respondents. Ideally, it is expected to reflect the objective of the practices of the firms. Secondly, we have measured CSF it on a “Yes” or “No” basis which does not allow survey respondents reply in between them; thus, it might have limited the choices of the respondents. Thirdly, the Likert scales adopted in the measurement of the other constructs might be limiting the answers of the respondents. Practical implications Information technology plays a very important role for the success of CSF practices. That is, successful implementation of a functional cost system relies heavily on a fully-integrated information infrastructure capable of constantly feeding CSF with accurate, relevant and timely data. Originality/value In addition to providing evidence regarding the factors underlying CSF based on a broad range of industries interesting finding, this study also illustrates the viability of machine learning methods as a research framework to critically analyze domain specific data.

Journal on Artificial intelligence .pdf

Preprint

Full-text available

Jan 2024

p> This study examined artificial intelligence adoption and marketing performance of quoted manufacturing firms in Nigeria. The study adopted the positivism research philosophy and correlational research design. The population of the study consisted of 426 managers drawn from the 71 quoted manufacturing firms in Nigeria. The managers include branch managers, operational managers, production managers, marketing managers and sales managers of the firms. A sample size of 206 managers was used for the study. The sample size was determined mathematically using the Taro Yamene’s formula. A structured questionnaire was used to obtain data from the respondents. The data collected were analyzed statistically while the hypotheses were tested using Spearman Rank Order Correlation Coefficient (rho). The SPSS version 23.0 was used to perform the bivariate analysis. The findings revealed that the application of artificial intelligence technologies in marketing operations has a significant relationship with sales growth of quoted manufacturing firms in Nigeria. The study also revealed that the application of artificial intelligence technologies in marketing operations has a strong and significant relationship with market share growth of quoted manufacturing firms in Nigeria. The study equally confirmed that artificial intelligence capabilities have a strong and significant relationship with sales growth of quoted manufacturing firms in Nigeria. The study also reported that artificial intelligence capabilities has a strong and significant relationship with market share growth of quoted manufacturing firms in Nigeria. Based on these findings, it was concluded that artificial intelligence adoption significantly relate to marketing performance of quoted manufacturing firms in Nigeria. Based on these findings and conclusion, it was recommended that quoted manufacturing firms in Nigeria especially those that are experiencing poor marketing performance should adopt artificial intelligence technologies in their marketing operations as it would improve their marketing performance. </p

Journal on Artificial intelligence .pdf

Preprint

Full-text available

Jan 2024

Artificial Intelligence and Service Quality of Telecommunication Firms in Nigeria

Article

Full-text available

Jun 2023

Globally, artificial intelligence (AI) technology spans various industries, but relatively little attention is given to the use of AI technologies by telecommunication industries. This study evaluated the effect of AI on the service quality of telecommunications companies in Nigeria, specifically the effect of data mining, machine learning, and chatbots on the service quality of these firms. The research employed a survey research design, and its population was heterogeneous. A sample size of 400 participants was chosen using Taro Yamane's formula, and the Cronbach alpha test yielded an average of 75%, confirming the reliability of the instrument. To analyze the data collected, descriptive and ordinary least squares regression methods were used. The study revealed that data mining and chatbots exhibited a significant positive effect while machine learning showed a negative relationship to the service quality of the telecommunications industry. Based on these findings, it is concluded that artificial intelligence affects service quality in Nigeria, with strong reference to data mining and chatbot, which enhance the quality of service to customers in Nigeria. It is therefore recommended that telecommunication firms in Nigeria should embrace the philosophy of AI to improve their quality of service. | KEYWORDS Artificial intelligence, service quality, data mining, machine learning, chatbot, Telecommunications. JEL Classification Codes: M49, L87, L96.

ROA and ROE Forecasting in Iron and Steel Industry Using Machine Learning Techniques for Sustainable Profitability

Article

Full-text available

Apr 2023

Return on equity (ROE) and return on assets (ROA) are important indicators that reveal the sustainability of a company’s profitability performance for both managers and investors. The correct prediction of these indicators will provide a basis for the strategic decisions made by the company managers. The estimation of these signs is a significant factor in supporting the decisions and up-to-date knowledge of potential investors. In this study, return on equity and return on assets were estimated using artificial neural networks (ANNs), multiple linear regression (MLR), and support vector regression (SVR) on the financial data of thirteen companies operating in the iron and steel sector. The success of predicting ROA in the designed model was 86.4% for ANN, 79.9% for SVR, and 74% for MLR. The success of estimating the ROE of the same model was 85.8% for ANN, 80.9% for SVR, and 63.8% for MLR. It is concluded that ANN and SVR can produce successful prediction results for ROA and ROE both accurately and reasonably.

Model of Neural Networks: Probabilistic Prediction of Floods in Banana Agricultural Field

Article

Full-text available

Jan 2023

During the latest events caused by climate change and the current of the child, Peru has been affected by these natural disasters, such as the flood, which directly affect the Peruvian economy and especially the department of Piura. To prevent and mitigate the problems that affect the department of Piura with respect to flooding, the development of a probabilistic system has been proposed with the use of machine learning that will allow us to prevent possible climatic changes and avoid material damage to the area based on predictions. Likewise, the data found in the repository of the free data web page provided by SENAMHI will be extracted to be reused internally and can contribute to the development of the application through neural networks that will facilitate the use of the data. Given this, it has been decided to use the data scientific method, which consists of 10 phases that allow us to identify the main points that contribute to the model of the proposal. This allows us to carry out the necessary validations to make the proposed system feasible. To obtain, as a result, a model that can predict and give warning about the threat of flooding based on the weather behavior of the area. In addition, it is concluded that the prediction models with the help of artificial intelligence tools have better efficiency in terms of forecasts.

Factores influyentes en el valor percibido en una organización deportiva sin ánimo de lucro en la ciudad de Medellín mediante el uso de redes neuronales artificiales. Influencing factors in the perceived value in a non-profit sports organization in Medellín through the use of artificial neural networks.

Conference Paper

Full-text available

Nov 2019

En el contexto de cualquier empresa, los administradores y responsables de la alta dirección centran sus esfuerzos en conocer el estado futuro de sus áreas de negocio, con el fin de minimizar los riesgos y tomar decisiones de forma más acertada. Es por esta razón que actualmente se han centrado los esfuerzos organizacionales en entender y dar valor a los datos generados al interior de la organización con el fin de encontrar soluciones más competitivas. Por esta razón se han aplicado diversos modelos estadísticos y computacionales para tratar estos datos y convertirlos en información valiosa para la organización. El objetivo de esta investigación es proponer un modelo de red neuronal artificial para eficientemente entender cuáles son las variables que impactan en el valor percibido por los clubes adscritos de una organización deportiva sin ánimo de lucro en la ciudad de Medellín. Esta investigación consta de una primera parte introductoria, donde se exploran los referentes conceptuales sobre el valor percibido y las redes neuronales artificiales (RNA), seguido, se plantea la metodología utilizada para la investigación, en un tercer momento se presenta el desarrollo por medio de las RNA, finalmente se obtienen los resultados obtenidos de la RNA que apuntan a que 3 de las 10 variables más importantes para diagnosticar el valor percibido son FIA1, FIA4 y CR3 pertenecientes a la categoría de la calidad de servicio, otras variables en menor medida se encuentran en las categorías expectativas y compromiso. Se espera que los hallazgos presentados sirvan a administradores y personal encargado en mejorar el proceso de toma de decisiones.

El índice global de innovación en Colombia: un análisis y selección de los factores influyentes mediante el uso de redes neuronales artificiales The global innovation index in Colombia: An analysis and selection of influencing factors through the use of artificial neural networks

Article

Full-text available

Feb 2021

Julián Alberto Uribe Gómez

This research proposes a selection and classification model to understand what are the factors that impact innovation processes in the case of Colombia. The conceptual referents on the global innovation index and the technique of artificial neural network optimization are analyzed, the framework for the analysis and development through the networks is presented. Finally, the results obtained are shown, where it is found that 5 of the 133 most important variables to diagnose global innovation index are: intensity in local competition, foreign investment, credit, tertiary education, human capital and research, other variables discarded by the model for its minor importance are: GDP per unit of energy used or amount of total shares traded. The findings are expected to improve the decision-making process in prioritizing frameworks for regional and national innovation systems.

THE EFFECT OF INFORMATION SYSTEMS ON PERFORMANCE, PROFITABILITY, AND GROWTH OF FOOD INDUSTRIES OF GREECE

Article

Sep 2020

Improving Crime Count Forecasts Using Twitter and Taxi Data

Preprint

Sep 2020

Crime prediction is crucial to criminal justice decision makers and efforts to prevent crime. The paper evaluates the explanatory and predictive value of human activity patterns derived from taxi trip, Twitter and Foursquare data. Analysis of a six-month period of crime data for New York City shows that these data sources improve predictive accuracy for property crime by 19% compared to using only demographic data. This effect is strongest when the novel features are used together, yielding new insights into crime prediction. Notably and in line with social disorganization theory, the novel features cannot improve predictions for violent crimes.

Missing care: A framework to address the issue of frequent missing values;The case of a clinical decision support system for Parkinson's disease

Article

Full-text available

Jun 2020
DECIS SUPPORT SYST

Saeed Piri

In recent decades, the implementation of electronic health record (EHR) systems has been evolving worldwide, leading to the creation of immense data volume in healthcare. Moreover, there has been a call for research studies to enhance personalized medicine and develop clinical decision support systems (CDSS) by analyzing the available EHR data. In EHR data, usually, there are millions of patients records with hundreds of features collected over a long period of time. This enormity of EHR data poses significant challenges, one of which is dealing with many variables with very high degrees of missing values. In this study, the data quality issue of incompleteness in EHR data is discussed, and a framework called ‘Missing Care’ is introduced to address this issue. Using Missing Care, researchers will be able to select the most important variables at an acceptable missing values degree to develop predictive models with high predictive power. Moreover, Missing Care is applied to analyze a unique, large EHR data to develop a CDSS for detecting Parkinson's disease. Parkinson is a complex disease, and even a specialist's diagnosis is not without error. Besides, there is a lack of access to specialists in more remote areas, and as a result, about half of the patients with Parkinson's disease in the US remain undiagnosed. The developed CDSS can be integrated into EHR systems or utilized as an independent tool by healthcare practitioners who are not necessarily specialists; therefore, making up for the limited access to specialized care in remote areas.

A comprehensive survey of data mining

Article

Full-text available

Feb 2020

Data mining plays an important role in various human activities because it extracts the unknown useful patterns (or knowledge). Due to its capabilities, data mining become an essential task in large number of application domains such as banking, retail, medical, insurance, bioinformatics, etc. To take a holistic view of the research trends in the area of data mining, a comprehensive survey is presented in this paper. This paper presents a systematic and comprehensive survey of various data mining tasks and techniques. Further, various real-life applications of data mining are presented in this paper. The challenges and issues in area of data mining research are also presented in this paper.

A Contrastive Study of Machine Learning on Funding Evaluation Prediction

Article

Full-text available

Jul 2019

Winning a granting is critical in helping young and innovative firms to reduce financial burden, yet successfully get a funding is not easy. Granting applicants are eager to find out the funding evaluator’s decision pattern and fully prepare for the fund application. In such condition, supervised machine learning models seem to be a suitable tool. Based on nearly 5000 Beijing Innofund applicants, we find that supervised machine learning models, like support vector machines (SVM), K-nearest neighbors (KNN), decision tree, logistic regression, and Artificial Neutral Network (ANN) can produce both accurate and reasonably understandable funding prediction results with their average accuracy rate over 80%. Yet, the comparison results also reveal that SVM model produces the most accurate forecasts in terms of average accuracy rate (86%) and F-score (82%). The findings indicate that SVM is an effective and reliable classification algorithm that can perform tasks well with small datasize. Based on the selected attributes and their weights, the funding applicants can get ready for the grants, by making up for the disadvantages and enhancing the advantages.

Using Hybrid Classifiers to Conduct Intangible Assets Evaluation

Chapter

Jun 2018

Traditional financial reporting usually ignores intangible assets, even though these assets play an increasingly important role in today's knowledge-based economy. As such, the valuation of intangible assets, while typically overlooked in traditional reporting, has nonetheless garnered widespread interest. This paper uses data-mining technologies to identify important valuation factors and to determine an optimal valuation model. In the feature selection process, the paper focus on three methods, namely, decision trees, association rules, and genetic algorithms in data mining, to identify important valuation factors. The results show that decision trees have approximately 75% prediction accuracy and select seven critical variables. In the prediction process, the paper constructs and compares many kinds of evaluation and prediction models. The results show that hybrid classifiers (i.e., k-means + k-NN) perform best in terms of prediction accuracy (91.52%), Type I and II errors (11.17% and 7.15%, respectively), and area under ROC curve (0.908).

Improving Crime Count Forecasts Using Twitter and Taxi Data

Article

Full-text available

Aug 2018
DECIS SUPPORT SYST

Crime prediction is crucial to criminal justice decision makers and efforts to prevent crime. The paper evaluates the explanatory and predictive value of human activity patterns derived from taxi trip, Twitter and Foursquare data. Analysis of a six-month period of crime data for New York City shows that these data sources improve predictive accuracy for property crime by 19% compared to using only demographic data. This effect is strongest when the novel features are used together, yielding new insights into crime prediction. Notably and in line with social disorganisation theory, the novel features cannot improve predictions for violent crimes.

Modeling Funding for Industrial Projects Using Machine Learning: Evidence from Morocco

Article

Full-text available

Apr 2024

Moroccan manufacturing companies investing in the metallurgical, mechanical, and electromechanical industries sector are among the contributors to the growth of the national economy. The projects they are awarded do not have the same specific features as those of operating activities within other companies. They share several common features, making them particularly complex to fund. In such circumstances, supervised machine learning seems to be a suitable instrument to help such enterprises in their funding decisions, especially given that linear regression methods are inadequate for predicting human decision making as human thinking is a complicated system and not linear. Based on 5198 industrial projects of 53 firms operating in the said sector, four machine learning models are used to predict the funding method for some industrial projects, including are decision tree, random forest, gradient boosting, and K-nearest neighbors (KNN). Among the four machine learning methods, the gradient boosting method appears to be most effective overall, with an accuracy of 99%.

The pathway to curb greenwashing in sustainable growth: The role of artificial intelligence

Article

Apr 2024
ENERG ECON

Dongyang Zhang

Şirket Değerlemesinde Makine Öğrenmesi Algoritmalarının Kullanımına Yönelik Bir Uygulama: Holding Şirketleri Örneği /// An Application for the Use of Machine Learning Algorithms in Company Valuation: The Case of Holding Companies

Article

Full-text available

Apr 2024

Bu araştırmanın amacı, şirket değerini etkileyen unsurların tespit edilmesi, bu unsurlardan hareketle finansal oranlar/veriler kullanılarak Türkiye’de Borsa İstanbul Yatırım ve Holding Endeksi’nde (BİST XHOLD) işlem gören holding şirketlerinin şirket değerlerini tahmin etme aracı olarak makine öğrenimi algoritmalarından Yapay Sinir Ağları (YSA), Destek Vektör Makineleri (DVM), Karar Ağaçları (KA) ve Rastgele Orman (RO) ile uygulanabilirliğini ortaya koymaktır. Belirtilen algoritmalar ile dört adet model kurulmuş ve bu modellerin tahmin gücü sınanmıştır. Bulgulara göre piyasa değerini R2, MAE ve RMSE ölçütleri baz alınarak YSA algoritmasının daha güçlü tahmin ürettiği görülmüştür. Bu araştırma ile şirket değerinin tahminine ve gelecek fiyatların öngörüsüne yönelik literatür incelenmiş, finansal oranlar/verileri içeren bütüncül bir yapı ortaya koyularak, yatırımcılara ve analistlere hisse senedi yatırımlarında ve şirket değerleme süreçlerinde makine öğrenimi algoritmaları ile geleneksel değerleme yaklaşımlarına kıyasla farklı bir bakış açısı sunulmuştur.

How administrative AI applications enhance organizational innovation and quality of work life for disabled employees: a case study of a Saudi University

Article

Dec 2023

Hyperbole or reality? The effect of auditors' AI education on audit report timeliness

Article

Dec 2023
Int Rev Financ Anal

İŞLETMELERDE FİNANSAL BAŞARISIZLIK ÖNGÖRÜSÜNDE MAKİNE ÖĞRENMESİNİN KULLANIMI: BİST UYGULAMASITHE USE OF MACHINE LEARNING IN PREDICTING FINANCIAL FAILURE IN BUSINESSES: ISTANBUL STOCK EXCHANGE APPLICATION

Article

Sep 2023

ismail fatih Ceyhan

Çalışmanın amacı işletmelerin finansal başarısızlık riski ile ilgili tahmin yapay zekâ tekniklerinden makine öğrenmesi kullanılarak yapılmasıdır. Bu kapsamda, Borsa İstanbul Ulusal Pazar’da yer alan 14 firma ile Borsa İstanbul Yakın İzleme Pazarı’nda yer alan14 firmanın 2022 yılı 12 aylık gelir tabloları ve bilançolarından elde edilen 43 adet finansal oran kullanılmış makine öğrenmesi yöntemlerinden NaiveBayes, J48, RandomForest, LinearRegression, RandomTree kullanılmıştır. Şirketlerin mali tabloları kullanılarak elde edilen veriler ile, makine öğrenmesi uygulama modellerinden hangisinin daha iyi sınıflandırma doğruluğu sağladığı araştırılmıştır. Ayrıca 2022 yılında yakın izleme pazarında yer alan bir şirketin bir sonraki sene için finansal durumunun makine öğrenmesi ile öngörüsünün mümkün olup olmadığı test edilmiştir. En yüksek sınıflandırma doğruluğu oranına RandomForest algoritması ve 10 kat çapraz doğrulama tekniğinin birlikte uygulanması ile ulaşıldığı, tek yıl için yapılan öngörü modelinde ise NaiveBayes algoritması ve 10 kat çapraz doğrulama tekniğinin çok yüksek bir oranda başarı sağladığı sonuçlarına ulaşılmıştır.

New Trends in Organizational Learning and Its Impact on Business

Chapter

Jun 2023

Ayşe Meriç Yazici

This study examines emerging trends in organizational learning and their profound impact on modern businesses. In an era marked by rapid technological developments, globalization, and changing customer expectations, businesses must constantly adapt to remain competitive. Organizational learning as a strategic tool plays a crucial role in fostering employee development, supporting innovation, and improving overall business performance. The paper begins by providing an overview of traditional approaches to learning in organizations and highlights the limitations that hinder their effectiveness in the dynamic business world. In conclusion, this study shows how adopting the latest trends in organizational learning can increase competitive advantage and foster a culture of innovation in businesses. By empowering employees with personalized, collaborative, and data-driven learning experiences, businesses can adapt more effectively to change and unlock their full potential in an ever-evolving business environment.

Karar Ağaçları Algoritması ile Modigliani-Miller Teorilerinin Testi: Holding Şirketleri Üzerine Bir Uygulama-Testing of Modigliani-Miller's Theories with Decision Trees Algorithm: An Application on Holding Companies

Article

Full-text available

Oct 2022

Modern finans anlayışı kapsamında yirminci yüzyılın yarısından sonra tartışmaya açılan şirket amacı, hissedar değerinin maksimize edilmesi yönüne evrilmiştir. Finansın 1950’li yılların başından itibaren geçirdiği teorik yönelim, hissedar değerini maksimize edecek gerekli varlık ve kaynakların seçimine yönelik kararlar ve bu kararların temel alındığı analitik çalışmalardır. Hissedar değeri stratejileri temelde, şirketin büyüme fırsatlarını görebilmeyi ve sektöründe rekabetçi üstünlük yaratmaya odaklanmaktadır. Günümüzde değer yaratmanın, şirket paydaşlarınca stratejik bir faktör olduğunun farkına varılması ile şirketler, varlık ve kaynaklarını değer odaklı bir perspektifle yönetme sürecine girmiştir. Bu süreç doğrultusunda, şirketlerin sermaye yapısı ve temettü dağıtma kararları değer yaratımının itici güçleri olarak finans yazınında hâlâ tartışmalı konumunu sürdürmektedir. Yapılan bu tartışmaların fitilini de Merton Miller ile Franco Modigliani 1958 yılında kaleme aldıkları makale ateşlemiştir. Sürdürülen bu tartışmalara katkı sunmak amacı ile makine öğrenimi algoritmalarından Karar Ağaçları ile Borsa İstanbul (BİST) Yatırım ve Holding Endeksi’ne (XHOLD) kote 12 holding şirketi üzerinde değer yaratan finansal unsurlar tespit edilmiş ve değer ile ilişkili Modigliani-Miller’in Sermaye Yapısı ve Temettü İlgisizliği Teorileri test edilmiştir. Elde edilen bulgularda, holding şirketleri üzerinde etkisi en yüksek finansal oran/veriler özsermaye, likit oran, hisse başı temettü oranı ve kısa vadeli borç/aktif olarak tespit edilmiştir. Modigliani-Miller’in sermaye yapısı ve temettü dağıtım kararlarının şirket piyasa değeri üzerinde bir etkisinin olmadığına dair teorileri, holding şirketleri nezdinde geçerli bulunmamıştır. Modigliani-Miller’in teorilerinin yapay zekâ/makine öğrenmesi temelli algoritmalar ile test edilmesi, bu araştırmanın orijinalliğine katkı sunmaktadır.

Literature review: Anomaly detection approaches on digital business financial systems

Article

Aug 2022

Anomaly detection approaches have become critically important to enhance decision-making systems, especially regarding the process of risk reduction in the economic performance of an organisation and the consumer costs. Previous studies on anomaly detection have examined mainly abnormalities that translate into fraud, such as fraudulent credit card transactions or fraud in insurance systems. However, anomalies represent irregularities in system patterns data, which may arise from deviations, adulterations or inconsistencies. Further, its study encompasses not only fraud, but also any behavioural abnormalities that signal risks. This paper proposes a literature review of methods and techniques to detect anomalies on diverse financial systems using a five-step technique. In our proposed method, we created a classification framework using codes to systematize the main techniques and knowledge on the subject, in addition to identifying research opportunities. Furthermore, the statistical results show several research gaps, among which three main ones should be explored for developing this area: a common database, tests with different dimensional sizes of data and indicators of the detection models' effectiveness. Therefore, the proposed framework is pertinent to comprehending an existing scientific knowledge base and signals important gaps for a research agenda considering the topic of anomalies in financial systems.

Decision information for auditors to assess litigation risk: Application of machine learning techniques

Article

Full-text available

May 2022

Fraud cases have become more common in recent years, highlighting the role of auditors’ legal liability. The competent authorities have called for stricter control and disciplinary measures for auditors, increasing auditors’ legal liability and litigation risk. This study used machine learning (ML) techniques to construct a litigation warning model for auditors to assess audit risk when they evaluate whether accept or terminate an engagement, thus improving audit quality and preventing losses due to litigation. Otherwise, a sample matching method comprised of 64 litigated companies and 128 non-litigated companies was used in this study. First, feature selection technology was used to extract six important influencing factors among the many variables affecting auditors’ litigation risk. Then a decision tree was used to establish a litigation warning model and a decision table for auditors’ reference. The results indicated that the eight outcomes provided by the decision table could effectively distinguish the level of a litigation risk with an accuracy rate of 92.708%. These results can provide useful information to aid auditors in assessing engagement decisions.

A study of artificial intelligence on employee performance and work engagement: the moderating role of change leadership

Article

May 2022
INT J MANPOWER

Abstract Purpose – This paper aims to explore employee perceptions of companies engaged in services and banking of the role of change leadership on the application of artificial intelligence (AI) that will impact the performance and work engagement in conditions that are experiencing rapid changes. Design/methodology/approach – This study has used a quantitative research approach, and data analysis uses an approach structural equation modeling (SEM) supported by program computer software AMOS 22.0. A total of 357 respondents were involved in this study, but only 254 were qualified. In this study, the respondent is an employee of companies engaged in the services and banking sector in the East Java, Indonesia region. Findings – The results reveal that AI has a significant positive effect on employee performance and work engagement. Change leadership positively moderates the influence of AI on employee performance and work engagement. Originality/value – The development of this model has a novelty by including the moderating variable of the role of change leadership because, in conditions that are experiencing rapid changes, the role of leaders is essential. After all, leaders are decision-makers in the organization. The development of this concept focuses on studies of companies engaged in services and banking. Employee performance is an essential determinant in the organization because it will improve organizational performance. In addition, the application of AI in organizations will experience turmoil, so that the critical role of leaders is needed to achieve success with employee work engagement. Keywords: Artificial intelligence, Change leadership, Employee performance, Work engagement Paper type Research paper

Firm Performance Prediction for Macroeconomic Diffusion Index using Machine Learning

Article

Full-text available

Jul 2021

Utilizing firm performance in the prediction of macroeconomic conditions is an interesting research trend with increasing momentum that supports to build nowcasting and early warning systems for macroeconomic management. Firm-level data is normally high volume, with which the traditional statistics-based prediction models are inefficient. This study, therefore, attempts to assess achievements of Machine Learning on firm performance prediction and proposes an emerging idea of applying it for macroeconomic prediction. Inspired by “micro-meso-macro” framework, this study compares different machine learning algorithms on each Vietnamese firm group categorized by the Vietnamese Industry Classification Standard. This approach figures out the most suitable classifier for each group that has specific characteristics itself. Then, selected classifiers are used to predict firms’ performance in the short term, where data was collected in wide range enterprise surveys conducted by the General Statistics Office of Vietnam. Experiments showed that Random Forest and J48 outperfomed other ML algorithms. The prediction result presents the fluctuation of firms’ performance across industries, and it supports to build a diffusion index that is a potential early warning indicator for macroeconomic management.

Enhancing the Competitive Advantage of the Germinated Parboiled Rice

Article

Jun 2021

Objective - This research aims to; 1) study the competitive advantage and firm performance of the geminated parboiled rice community enterprise group, 2) investigate the differences in competitive advantage and firm performance when classified by organizational factors, 3) study the difference in the influence of competitive advantage on firm performance, and 4) determine proposals to enhance competitive advantage. Methodology/Technique - This research uses mixed research methods. The quantitative method was used to study the influence of competitive advantage on firm performance of community enterprises, and a qualitative method was used to determine proposals to enhance competitive advantage. In Step 1, a quantitative research method was used to identify the population which includes management groups of 36 geminated parboiled rice community enterprises consisting of a chairperson, a vice-chairperson and a supervisor of 208 people, and 137 samples. Proportional sampling and simple random sampling were used in this process. Data was collected using questionnaires. The statistics used for data analysis was frequency, percentage, mean, standard deviation, F-test (one way ANOVA), Pearson's correlation coefficient, and hierarchical regression analysis. In step 2, data was collected from in-depth interviews with key informants, using a purposive sampling method. Data analysis from the interviews using the information obtained from the interview into categories and using content analysis to determine proposals. Findings - The research results found that competitive advantage and firm performance was high. The hypothesis testing results show that only the age of the community enterprise has an effect on the image of the community enterprise with the statistical significance of 0.05. All 3 independent variables composed of cost leadership, differentiation, and market focus had an influence on firm performance, by standardized coefficients (β) was .398, .426, .654 respectively. Novelty - The proposals from this research include the establishment of newly geminated parboiled rice community enterprises to accelerate the creation of the organization image to be known and accepted by consumers. Further, community enterprises should focus on developing a competitive advantage that comes from a market focus strategy. This will have a positive impact on firm performance. Type of Paper - Empirical. Keywords: Geminated Parboiled Rice; Community Enterprise; Competitive Advantage; Firm Performance JEL Classification: URI: http://gatrenterprise.com/GATRJournals/GJBSSR/vol9.2_5.html DOI: https://doi.org/10.35609/gjbssr.2021.9.2(5) Pages 152 – 164

Enhancing Prediction of Student Success: Automated Machine Learning Approach

Article

Full-text available

Feb 2021
COMPUT ELECTR ENG

Students' success has recently become a primary strategic objective for most institutions of higher education. With budget cuts and ramping operational costs, academic institutions are paying more attention to sustaining students' enrollment in their programs without compromising rigor and quality of education. With the proliferation of big data analytics and Machine Learning, universities are increasingly relying on students' data to predict students' performance. Many initiatives and research projects addressed the use of students' behavioral and academic data to classify students and predict their future performance using advanced statistics and Machine Learning. To allow for early intervention, this paper proposes the use of Automated Machine Learning to enhance the accuracy of predicting student performance using data available prior to the start of the academic program.

Selección de los factores influyentes en la predicción de camas hospitalarias en una institución de salud de alto nivel de complejidad, mediante el uso de redes neuronales artificiales Selection of the influential factors in the prediction of hospital beds in a health institution of high complexity level, through the use of artificial neural networks

Article

Full-text available

Jun 2020

Background: In the context of any hospital institution, health administrators focus their efforts on knowing the future state of their areas, in order to minimize risks and make better decisions. Currently, organizations have focused on understanding and giving value to the data generated within their processes, through the use of optimization models and computational tools, in order to convert them into valuable information and find optimal and competitive solutions. Methods: 5 indicators were analyzed in a monthly institution. These were collected through files obtained in Microsoft EXCEL-2016 format from January 2004 to December 2018, later, 3 Multilayer Perceptron RNAs were modeled with the help of IBM SPSS version 19 software. Results: It is shown by the RNA that demand is the most important variable for the prediction of hospital beds, this with an importance of 63%, 45% and 51% for each RNA. Similarly, model 1 is presented with the most successful. On the other hand, the average length of stay is the least important variable to forecast. Conclusions: Through this study it was possible to show that the institution should give greater importance to factors such as demand and the bed spin indicator. These must be constantly managed within the administrative work of the institution.

COVID‐19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions

Article

Full-text available

Nov 2020

Although scholars in management recognize the value of harnessing big data to understand, predict and respond to future events, there remains little or very limited overview of how various analytics techniques can be harnessed to provide the basis for guiding scholars in studying contemporary management topics and global grand challenges raised by the COVID‐19 pandemic. In this Methodology Corner, we present a review of the methodological innovations in studying big data analytics and how they can be better utilized to examine contemporary organizational issues. We provide insights on methods in descriptive/diagnostic, predictive and prescriptive analytics, and how they can be leveraged to study ‘black swan’ events such as the COVID‐19‐related global crisis and its aftermath's implications for managers and policymakers.

The use of neural networks to determine value based drivers for SMEs operating in the rural areas of the Czech Republic

Article

Full-text available

Jun 2020

Jaromír Vrbka

Research background: In the past, the main objective of a company was to generate sufficient profit. Nowadays, a company must seek to achieve much broader objectives. To be successful in this pursuit, it must not only measure financial performance, but also monitor internal and external developments, increase shareholders’ wealth and protect the interests of other stakeholders, i.e. to analyze and act on those factors that affect company value. Purpose of the article: The objective of the contribution is to determine through the use of artificial neural networks the relationship between business value drivers, or value based drivers (VBD), and EVA Equity, which is economic value added (EVA), of small and medium-sized enterprises operating in the rural areas of the Czech Republic. Methods: The data was obtained from the Bisnode´s Albertina database. The data set consists of the profit and loss accounts for 2013 to 2017 of small and medium-sized enterprises operating in rural areas of the Czech Republic. Two scenarios are analyzed. In the first, the independent variables are only the value drivers, whereas in the second, company location (region) is included. The objective is to find the dependence of EVA Equity on individual VBD and company location. A sensitivity analysis is conducted, on the basis of which the importance of individual value drivers and company location is determined. Findings & Value added: The output is a set of value drivers, which could be used by company managers to regulate the growth of EVA Equity, i.e. value for shareholders. The findings reveal that the difference between successful and unsuccessful companies is determined by the level of involvement of human capital; companies use a large number of substitutes for factors of production, whereby the involvement of borrowed capital is likely to cause a positive financial leverage effect.

Assessing the drivers of machine learning business value

Article

Sep 2020
J BUS RES

Machine learning (ML) is expected to transform the business landscape in the near future completely. Hitherto, some successful ML case-stories have emerged. However, how organizations can derive business value (BV) from ML has not yet been substantiated. We assemble a conceptual model, grounded on the dynamic capabilities theory, to uncover key drivers of ML BV, in terms of financial and strategic performance. The proposed model was assessed by surveying 319 corporations. Our findings are that ML use, big data analytics maturity, platform maturity, top management support, and process complexity are, to some extent, drivers of ML BV. We also find that platform maturity has, to some degree, a moderator influence between ML use and ML BV, and between big data analytics maturity and ML BV. To the best of our knowledge, this is the first research to deliver such findings in the ML field.

Mali Tablo Denetiminde Ön Analitik İnceleme Tekniği Olarak Veri Madenciliğinin Kullanımı

Article

Full-text available

Jan 2014

Auditors will have to be reached the judgment about the firm that will be supervised to perform a good quality audit as much as possible in a short time. To reach this judgment, the auditors benefit from the company's financial statements. Financial statements arranged in accordance with International Financial Reporting Standards are important indicators that provide information about companies. These statements is one of the instruments the auditor can use it during the analytical review activities in stage of the audit program. In our study, we aimed to classify 40 companies listed in Borsa İstanbul as financially successful or financially unsuccessful with data mining algorithms using datas of the financial statement of these companies. As a result, correct classification estimation was achieved at a rate as high as 95% with the k-nearest neighbor algorithm and 10-fold cross-validation technique.

Sustainable development carbon pricing initiative and voluntary environmental disclosures quality

Article

Mar 2019

Although corporate environmental disclosures have been researched extensively, empirical evidence regarding the indirect impact of carbon pricing on firms' voluntary disclosures is scarce. The objective of this study is to identify the indirect impact of carbon pricing initiatives on the voluntary environmental disclosures (VEDs) of electricity generating companies, analyzed through the lens of institutional theory. This study adds to the growing literature on the determinants of VED, investigating the impact of adoption of the Global Reporting Initiative and ISO 14001 on VED. Secondary data were collected from 2015 annual reports and/or standalone sustainability reports of electricity generating companies from 53 countries around the world. Content analysis approach was adopted for measuring the extent of the quality of VED. Findings of multiple regression analysis suggest that there is an indirect institutional impact of carbon pricing on the quality of VED. This study also finds that, as the form of nongovernment guidance, the Global Reporting Initiative adoption and ISO 14001 certification also have an institutional influence on the VED. VED is also affected by company size although this study reveals no significant relationship of leverage with VED.

Inherent Virtue or Inevitable Evil: The Effects of Directors' and Officers' Insurance on Firm Value

Article

Sep 2018

Whether directors’ and officers’ (D&O) insurance improves firm value is a controversial issue. We perform a literature review about the effect of D&O insurance and find mixed results. The proponents of D&O insurance believe it enhances corporate monitoring and improves firm value, while the opponents of D&O insurance argue that it creates a moral hazard problem and diminishes firm value. Against this backdrop, we argue that the trade‐off between the monitoring and moral hazard effects depends on the information acquired by the outside directors. Using a sample of listed Canadian firms, we find that (1) a change in D&O insurance coverage has no net effect on a firm's subsequent value when we ignore the information acquired by outside directors, (2) an increase in D&O insurance coverage improves a firm's subsequent value when the outside directors are well informed, and (3) an increase in D&O insurance coverage reduces a firm's subsequent value when the outside directors are poorly informed. Our findings are robust to endogeneity checks and have important implications for the regulation of D&O insurance.

How Do Family Ownership, Control, and Management Affect Firm Value?

Article

Full-text available

Jan 2004

Using proxy data on all Fortune-500 firms during 1994–2000, we find that family ownership creates value only when the founder serves as CEO of the family firm or as Chairman with a hired CEO. Dual share classes, pyramids, and voting agreements reduce the founder's premium. When descendants serve as CEOs, firm value is destroyed. Our findings suggest that the classic owner-manager conflict in nonfamily firms is more costly than the conflict between family and nonfamily shareholders in founder-CEO firms. However, the conflict between family and nonfamily shareholders in descendant-CEO firms is more costly than the owner-manager conflict in nonfamily firms.

Glossary of terms. Special issue of applications of machine learning and the knowledge discovery process

Article

Full-text available

Jan 1998

A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

Conference Paper

Full-text available

Jan 1995

Ron Kohavi

We review accuracy estimation methods and compare the two most common methods: crossvalidation and bootstrap. Recent experimental results on arti cial data and theoretical results in restricted settings have shown that for selecting a good classi er from a set of classiers (model selection), ten-fold cross-validation may be better than the more expensive leaveone-out cross-validation. We report on a largescale experiment|over half a million runs of C4.5 and a Naive-Bayes algorithm|to estimate the e ects of di erent parameters on these algorithms on real-world datasets. For crossvalidation, we vary the number of folds and whether the folds are strati ed or not � for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, the best method to use for model selection is ten-fold strati ed cross validation, even if computation power allows using more folds. 1

The value impact of international and industrial diversifications on public‐listed firms in Malaysia

Article

Full-text available

Sep 2012
Emerg Market Rev

This study examines the relationship between firm value and both international and industrial diversification involvements for a sample of 267 listed firms in Malaysia over 2001–2009. We find no evidence that international diversification has any significant impact on the firm value but industry diversification locally slightly increases firm value, even after controlling for the degree of ownership concentration. Our research further indicates that without any diversification involvement, family ownership presents lower value than foreign and government ownerships; and with industrial diversification family ownership presents significant higher value than foreign and government ownerships.

Witten IH, Frank E: Data Mining: Practical Machine Learning Tools and Techniques

Article

Full-text available

Jan 2006

Value relevance of voluntary disclosure: evidence from Turkish firms

Article

Full-text available

Jul 2012

Purpose The purpose of this paper is to examine whether or not listed Turkish companies’ voluntary disclosure practices are value‐relevant in the capital market. Design/methodology/approach The sample consists of 129 manufacturing companies listed in the Istanbul Stock Exchange (ISE) for the year 2010. Data regarding voluntary disclosure level and other variables have been collected by analyzing the contents of annual reports. Through multiple regression analysis, the authors investigated whether or not voluntary disclosure level impacts firm value. Findings The main finding of the study is that voluntary disclosure is value‐relevant; i.e. impacts firm value. This implies that market participants value voluntary disclosure. The more information firms disclose voluntarily, the higher value they have in the eyes of investors. Therefore, this finding might be accepted as a signal to corporations to disclose more information to the stakeholders. However, the finding varied based on the dependent variable used; hence, the result was not supported by all models. Research limitations/implications The findings of this paper are based on the study conducted on the ISE including manufacturing industry. Thus, the results might not be valid for non‐listed and non‐manufacturing companies. Originality/value There is a scarcity of studies regarding how the marketplace perceives voluntary disclosure. The paper evaluates comprehensively the value relevance of overall voluntary disclosure as well as of 12 sub‐categories of disclosure. It contributes to the relevant literature, particularly in an emerging market context.

Effects of intellectual capital information disclosed in annual reports on market capitalization: Evidence from Bursa Malaysia

Article

Full-text available

Jun 2011

Purpose This paper aims to examine the effects of intellectual capital (IC) disclosure in the annual reports of listed companies in Bursa Malaysia (BM) on their market capitalization (MCAP). Design/methodology/approach The paper uses secondary data for listed companies on BM for the years 2002 and 2006. A disclosure index was used to measure the extent of IC information disclosed in the annual reports. The MCAP data were obtained from the Bloomberg database. The data were analyzed using correlation and regression analyses. Findings The paper finds that the extent of IC disclosure by Malaysian‐listed companies has a positive significant effect on their MCAP. In addition, the paper found that there is significant positive impact of the control variables (i.e. book value, net profit, firm size and leverage) on the MCAP. Research limitations/implications Although the paper was focused on the IC information and MCAP data for two years (i.e. 2002 and 2006), it provides empirical evidence that IC disclosure does affect the MCAP of companies. Hence, it means that the IC information is picked up by the market. Future research may incorporate more control variables and years. Practical implications The findings provide empirical evidence that IC information disclosed by the Malaysian‐listed companies positively affects their MCAP. These findings can be considered to be useful for these companies and work as a signal towards the need for more IC disclosure. In addition, the findings could be useful for the regulatory bodies, e.g. the Malaysian Accounting Standards Board and BM, perhaps to develop guidelines on IC disclosure to enhance transparency and increase confidence in the capital market. Originality/value The paper is considered the first empirical study to examine the effects of IC disclosure in the annual reports of Malaysian‐listed companies on their MCAP.

Do Multinational Operations Influence Firm Value? Evidence from the Triad Regions

Article

Full-text available

Jan 2005

This paper examines the effect of multinational operations on firm value using a sample of firms from the three triad regions of the world. Using the Transnational Index (reported by the United Nations), a newer measure of multinational operations, this study helps explain some of the conflicting findings reported in the literature and highlights the importance of location of the firm as a factor in influencing the premium or discount in firm value due to multinational operations.

Measuring Marketing Productivity: Current Knowledge and Future Directions

Article

Full-text available

Nov 2004
J MARKETING

For too long, marketers have not been held accountable for showing how marketing expenditures add to share-holder value. As time has gone by, this lack of accountability has undermined marketers' credibility, threatened the standing of the marketing function within the firm, and even threatened marketing's existence as a distinct capa-bility within the firm. This article proposes a broad framework for assessing marketing productivity, cataloging what is already known, and suggesting areas for further research. The authors conclude that it is possible to show how marketing expenditures add to shareholder value. The effective dissemination of new methods of assessing mar-keting productivity to the business community will be a major step toward raising marketing's vitality in the firm and, more important, toward raising the performance of the firm itself. The authors also suggest many areas in which further research is essential to making methods of evaluating marketing productivity increasingly valid, reli-able, and practical.

Does corporate diversification by business groups create value? Evidence from Korean chaebols

Article

Full-text available

Nov 2011
Pac Basin Finance J

We investigate the valuation effects of diversification activities by Korean business groups known as chaebols. Employing an extensive dataset of Korean firms and accounting for the endogeneity problem associated with firms' diversification decisions, we offer reconciling evidence that unrelated diversification by Korean firms erodes firm value, but their related diversification does not decrease firm value. Undocumented in the existing literature, we further provide new evidence that a firm's affiliation to a large business group amplifies the valuation effect of firm-level diversification, such that the non-negative or weak positive valuation effect of related diversification is stronger for large business group firms. These findings are supportive of the notion that the unique characteristics of large business group firms such as stronger internal factor markets enable these firms to take advantage of the synergic benefits associated with related diversification. Our findings are robust to different model and sample specifications.

Valuation of Loss Firms in a Knowledge-Based Economy

Article

Full-text available

Mar 2007

Recent research in accounting has documented a substantial increase in the number of loss firms. Existing theories on the valuation of loss firms are based on adaptation/abandonment options or limited liability, assuming that these firms are operationally distressed. In this paper, we show that many loss firms do not fit this stereotype and identify the primary value drivers of this new type of loss firms. Our analysis helps resolve the puzzling negative relation between earnings and market value documented in prior research. Overall, our findings underscore the importance of “hidden assets” or intangibles in the study of loss firms.

Agency costs of free cash flow, corporate finance, and takeovers

Chapter

Mar 1996

Michael C. Jensen

This collection is the first comprehensive selection of readings focusing on corporate bankruptcy. Its main purpose is to explore the nature and efficiency of corporate reorganisation using interdisciplinary approaches drawn from law, economics, business, and finance. Substantive areas covered include the role of credit, creditors' implicit bargains, non-bargaining features of bankruptcy, workouts of agreements, alternatives to bankruptcy, and proceedings in countries other than the United States, including the United Kingdom, Europe, and Japan.

Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure

Article

Jan 1998

This paper integrates elements from the theory of agency, the theory of property rights and the theory of finance to develop a theory of the ownership structure of the firm. We define the concept of agency costs, show its relationship to the 'separation and control' issue, investigate the nature of the agency costs generated by the existence of debt and outside equity, demonstrate who bears the costs and why, and investigate the Pareto optimality of their existence. We also provide a new definition of the firm, and show how our analysis of the factors influencing the creation and issuance of debt and equity claims is a special case of the supply side of the completeness of markets problem.

Data Mining: Practical Machine Learning Tools and Techniques

Book

Nov 2016

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book.

Optimal capital structure under corporate taxation

Article

Jan 1980
J FINANC ECON

Classification and regression trees

Book

Jan 1984

L Breiman

Data Mining Model Comparison

Chapter

Jan 2005

Paolo Giudici

The aim of this contribution is to illustrate the role of statistical models and, more generally, of statistics, in choosing a Data Mining model. After a preliminary introduction on the distinction between Data Mining and statistics, we will focus on the issue of how to choose a Data Mining methodology. This well illustrates how statistical thinking can bring real added value to a Data Mining analysis, as otherwise it becomes rather difficult to make a reasoned choice. In the third part of the paper we will present, by means of a case study in credit risk management, how Data Mining and statistics can profitably interact.

Statistical modeling: The two cultures - Comments and rejoinders

Article

Aug 2001
STAT SCI

On learning the past tenses of English verbs. Parallel distributed processing: explorations in the microstructure of cognition

Article

Jan 1986

Industrial Economics of Foreign Investment: The Case of the International Corporation

Article

Jun 1971
ECONOMICA

R.E. Caves

Neural nets versus logistic regression: A comparison of each model's ability to predict commercial bank failures

Article

Jan 1990

Theory of the Firm: Managerial Behavior, Agency Costs, and Ownership Structure

Article

Oct 1976

In this paper we draw on recent progress in the theory of (1) property rights, (2) agency, and (3) finance to develop a theory of ownership structure for the firm.1 In addition to tying together elements of the theory of each of these three areas, our analysis casts new light on and has implications for a variety of issues in the professional and popular literature, such as the definition of the firm, the “separation of ownership and control,” the “social responsibility” of business, the definition of a “corporate objective function,” the determination of an optimal capital structure, the specification of the content of credit agreements, the theory of organizations, and the supply side of the completeness-of-markets problem.

Split selection methods for classification trees

Article

Oct 1997
STAT SINICA

Classification trees based on exhaustive search algorithms tend to be biased towards selecting variables that afford more splits. As a result, such trees should be interpreted with caution. This article presents an algorithm called QUEST that has negligible bias. Its split selection strategy shares similarities with the FACT method, but it yields binary splits and the final tree can be selected by a direct stopping rule or by pruning. Real and simulated data are used to compare QUEST with the exhaustive search approach. QUEST is shown to be substantially faster and the size and classification accuracy of its trees are typically comparable to those of exhaustive search.

CART: Classification and Regression Trees

Book

Jan 1983

Corporate Governance and Firm Value: International Evidence

Article

Jan 2010

In this paper, we investigate the relation between firm-level corporate governance and firm value based on a large and previously unused dataset from Governance Metrics International(GMI) comprising 6663 firm-year observations from 22 developed countries over the period from 2003 to 2007. Based on a set of 64 individual governance attributes we construct two alternative additive corporate governance indices with equal weights attributed to the governance attributes and one index derived from a principal component analysis. For all three indices we find a strong and positive relation between firm-level corporate governance and firm valuation. In addition, we investigate the value relevance of governance attributes that document the companies' social behavior. Regardless of whether these attributes are considered individually or aggregated into indices, and even when “standard” corporate governance attributes are controlled for, they exhibit a positive and significant effect on firmvalue. Our findings are robust to alternative calculation procedures for the corporate governance indices and to alternative estimation techniques.

An Exploratory Technique for Investigating Large Quantities of Categorical Data

Article

Jan 1980

G. V. Kass

The technique set out in the paper, CHAID, is and offshoot of AID (Automatic Interaction Detection) designed for a categorized dependent variable. Some important modifications which are relevant to standard AID include: built-in significance testing with the consequence of using the most significant predictor (rather than the most explanatory), multi-way splits (in contrast to binary) and a new type of predictor which is especially useful in handling missing information.

Business Intelligence: Data Mining and Optimization for Decision Making

Article

Mar 2009

Carlo Vercellis

Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.

Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models

Article

Jun 2004

IntroductionHow to Obtain and Install SIMLABSIMLAB Main PanelSample GenerationHow to Execute ModelsSensitivity Analysis

The effects of the degree of internationalization on firm performance

Article

Jun 1998
INT BUS REV

This study developed and tested a model that attempts to describe the influence of internationalization on firm performance. Degree of internationalization was measured by foreign revenues/total revenues. Results based on data from a cross-sectional set of U.S. multinational firms find evidence of a nonmonotonic relationship between degree of internationalization and firm performance. The rate of return on assets declines, then increases, and finally decreases slightly as the degree of internationalization increases.

An elusive antecedent of superior firm performance: The knowledge management factor

Article

Dec 2011
DECIS SUPPORT SYST

Knowledge management (KM) concepts, principles, and technologies provide a foundation for understanding and building systems for acquiring, assimilating, selecting, generating, and emitting knowledge-a crucial resource of the firm. In the knowledge management community, it is commonly contended that knowledge, and capabilities for processing it, comprise a major resource that can differentiate one firm from another in the sense of yielding better performance or a competitive edge. However, aside from anecdotes, there has been little to substantiate this contention. Can any empirical link be discovered between a firm's KM success and that firm's financial performance? To develop an answer to this question, we use an independent research company's reports of firms judged to be highly successful in their KM initiatives, plus related data reported by COMPUSTAT. As an initial investigation of the linkage between KM performance and firm performance, as measured by financial ratios, this study uses the Matched Sample Comparison Group methodology to evaluate research hypotheses. The analysis reveals a heretofore elusive antecedent of firm performance-evidence that superior KM performance is indeed a predictor of superior bottom-line performance. This study contributes to the information systems (IS) literature by demonstrating that KM, a basic foundation for IS, is an important factor to consider from the standpoint of achieving strong financial performance. As such, it suggests that KM furnishes an important context for understanding designs, applications, and possibilities for IS with respect to achieving such performance. In the context of devising and executing KM initiatives, both technological and human treatments of knowledge need to be cultivated and integrated in ways that lead to superior KM performance. This study also contributes to the management literature by going beyond anecdotes and case studies in buttressing the proposition that a firm's KM competencies are an important ingredient in that firm's performance. It solidifies the raison d'etre for investigating KM phenomena and methods (computer-based and human), both within and across modern organizations. It gives practicing managers evidence that bottom-line benefits are indeed associated with superior KM strategy and execution.

Using genetic algorithm based knowledge refinement model for dividend policy forecasting

Article

Dec 2012
EXPERT SYST APPL

Dividend policy is one of most important managerial decisions affecting the firm value. Although there are many studies regarding decision-making problems, such as credit policy decisions through bankruptcy prediction and credit scoring, there is no research, to our knowledge, about dividend prediction or dividend policy forecasting using machine learning approaches in spite of the significance of dividends. For dealing with the problems involved in literature, we suggest a knowledge refinement model that can refine the multiple rules extracted through rule-based algorithms from dividend data sets by utilizing genetic algorithm (GA). The new technique, called “GAKR (genetic algorithm knowledge refinement)”, aims to combine the advantages of both knowledge consolidation and GA. The main result of the cross-validation procedure is the average accuracy rate of prediction in the five sets over the five iterations. The experiments show that GAKR model always outperforms other models in the performance of dividend policy prediction; we can predict future dividend policy more correctly than any other models. The major advantages of GAKR model can be summarized as follows: (1) Classification process of GAKR can be very fast with a compact set of rules. In other words, fast training mechanism of GAKR is possible regardless of data set sizes. (2) Multiple rules extracted by GAKR development process are much simpler and easier to understand. Moreover, GAKR model can discriminate redundant rules and inconsistent rules.

Determinants of intangible assets value: The data mining approach

Article

Jul 2012
KNOWL-BASED SYST

It is very important for investors and creditors to understand the critical factors affecting a firm’s value before making decisions about investments and loans. Since the knowledge-based economy has evolved, the method for creating firm value has transferred from traditional physical assets to intangible knowledge. Therefore, valuation of intangible assets has become a widespread topic of interest in the future of the economy. This study takes advantage of feature selection, an important data-preprocessing step in data mining, to identify important and representative factors affecting intangible assets. Particularly, five feature selection methods are considered, which include principal component analysis (PCA), stepwise regression (STEPWISE), decision trees (DT), association rules (AR), and genetic algorithms (GA). In addition, multi-layer perceptron (MLP) neural networks are used as the prediction model in order to understand which features selected from these five methods can allow the prediction model to perform best. Based on the chosen dataset containing 61 variables, the experimental result shows that combining the results from multiple feature selection methods performs the best. GA ∩ STEPWISE, DT ∪ PCA, and the DT single feature selection method generate approximately 75% prediction accuracy, which select 26, 22, and 7 variables respectively.

Are firms from developed markets more international than firms from emerging markets?

Article

Jan 2013
Res Int Bus Finance

Regionalist supporters’ claim that most of the world's largest firms are regional rather than global and that managers should be encouraged to ‘think regional, act local and forget global’ (Rugman and Moore, 2004, p. 67). We apply the matrix of multinationality proposed by Aggarwal et al. (2011) to a sample of the world's 500 largest corporations, the Fortune Global 500. We show that these firms range from purely domestic to regional, trans-regional and entirely global with most lying in the trans-regional and global categories. Our results imply that global strategies are essential to international trade and management in today's business environment. We compare multinationality results by market type (developed versus emerging market), industry, size and age. We find that firms from more advanced economies tend to be older, larger and more multinational than firms from emerging markets. We find no relationship between multinationality and age or multinationality and size, and conclude that developed market firms are not more multinational as a result of size, age or industrial structure.

Form versus substance: The effect of ownership structure and corporate governance on firm value in Thailand

Article

Jun 2012
J BANK FINANC

We examine the relation between the quality of corporate governance practices and firm value for Thai firms, which often have complex ownership structures. We develop a comprehensive measure of corporate governance and show that, in contrast to conventional measures of corporate governance, our measurement, on average, is positively associated with Tobinâ€™s q. Furthermore, we find that q values are lower for firms that exhibit deviations between cash flow rights and voting rights. We also find that the value benefits of complying with â€œgoodâ€ corporate governance practices are nullified in the presence of pyramidal ownership structures, raising doubts on the effectiveness of governance measures when ownership structures are not transparent. We conclude that family control of firms through pyramidal ownership structures can allow firms to seemingly comply with preferred governance practices but also use the control to their advantage.

Data mining and statistics: What's the connection?

Article

Jan 1998

J. H. Friedman

A Discriminant Analysis of Predictors of Business Failure

Article

Apr 1972

Elizabeth Deakin

The Value Relevance of International Financial Reporting Standards: Empirical Evidence in an Emerging Market

Article

Mar 2012

This study examines the value relevance of accounting information under International Financial Reporting Standards (IFRS) in the Abu Dhabi Stock Exchange (ADX, henceforth). Based on models developed by Easton and Harris (1991), and Ohlson (1995) and using monthly market data from 2000 to 2006, this paper investigates the value relevance of accounting information of firms traded on the ADX. Our overall results show that earnings scaled by beginning of period price are positively and significantly related to cumulative returns and that earnings per share and book value per share are positively and significantly related to price per share. We also find that value relevance of accounting information has changed since the market inception in 2000. The results documented herein extend the literature on value relevance accounting information in an emerging market that requires the use of IFRS. The study therefore contributes to the debate over the mandatory adoption of IFRS and the value relevance of accounting information reported under IFRS.

Hybrid Neural Network Models for Bankruptcy Predictions

Article

Sep 1996
DECIS SUPPORT SYST

The objective of this paper is to develop the hybrid neural network models for bankruptcy prediction. The proposed hybrid neural network models are (1) a MDA-assisted neural network, (2) an ID3-assisted neural network, and (3) a SOFM(self organizing feature map)-assisted neural network. Both the MDA-assisted neural network and the 11)3-assisted neural network are the neural network models operating with the input variables selected by the MDA method and 1133 respectively. The SOFM-assisted neural network combines a backpropagation model (supervised learning) with a SOFM model (unsupervised learning). The performance of the hybrid neural network model is evaluated using MDA and ID3 as a benchmark. Empirical results using Korean bankruptcy data show that hybrid neural network models are very promising neural network models for bankruptcy prediction in terms of predictive accuracy and adaptability.

Classification and Regression Trees

Article

Jan 1984

Capital expenditures and corporate earnings: Evidence from the Taiwan Stock Exchange

Article

Nov 2006
Manag Finance

Purpose – The purpose of this paper is to examine the relationship between capital expenditures and corporate earnings for 357 manufacturing firms listed on the Taiwan Stock Exchange over the sample period 1992-2002. Design/methodology/approach – The sample period of 11 years is divided into capital investment period and performance period. The sample firms are first grouped into eight portfolios ranked by capital investment ratio estimated from the investment period. Corporate earnings in the performance period for the eight portfolios are examined to see if any positive association exists. Regressions are then estimated to test the relationship between capital expenditures and corporate earnings. Findings – The results indicate a significantly positive association between capital expenditures and future corporate earnings even after controlling for current corporate earnings. Practical implications – The results indicate that the unexpected announcements of capital expenditures are good news for investors in the investment practice. Originality/value – Previous studies on the relationship between capital expenditures and corporate earnings are based mainly on developed countries. Empirical evidence from the manufacturing firms listed on the Taiwan Stock Exchange would provide further insights regarding this important issue.

Parallel Distributed Processing: Explorations in the Microstructures of Cognition

Book

Jan 1986

The degree of internationalization and the value of the firm: Theory and evidence

Article

Dec 1999
J Int Account Audit Taxat

Ahmed Riahi-Belkaoui

A valuation approach is used to examine the effects of the degree of internationalization on the relation between the market value of a firm’s stock and the book value of equity. Degree of internationalization was measured by both foreign revenues over total revenues and foreign assets over total assets. Results on the “Most International” 100 U.S. firms indicate a consistent and positive relation between each measure of the degree of internationalization and the value of equity.

Corporate governance and firm value: International evidence

Article

Jan 2011
J Empir Finance

In this paper, we investigate the relation between firm-level corporate governance and firm value based on a large and previously unused dataset from Governance Metrics International (GMI) comprising 6,663 firm-year observations from 22 developed countries over the peri-od from 2003 to 2007. Based on a set of 64 individual governance attributes we construct two alternative additive corporate governance indices with equal weights attributed to the governance attributes and one index derived from a principal component analysis. For all three indices we find a strong and positive relation between firm-level corporate governance and firm valuation. In addition, we investigate the value relevance of governance attributes that document the companies' social behavior. Regardless of whether these attributes are considered individually or aggregated into indices, and even when "standard" corporate governance attributes are controlled for, they exhibit a positive and significant effect on firm value. Our findings are robust to alternative calculation procedures for the corporate govern-ance indices and to alternative estimation techniques.

The Capital Structure Puzzle

Article

Jul 1984

Stewart C. Myers

This paper contrasts the "static tradeoff" and "pecking order" theories of capital structure choice by corporations. In the static tradeoff theory, optimal capital structure is reached when the tax advantage to borrowing is balanced, at the margin, by costs of financial distress. In the pecking order theory, firms preferinternal to external funds, and debt to equity if external funds are needed. Thus the debt ratio reflects the cumulative requirement for external financing. Pecking order behavior follows from simple asymmetric information models. The paper closes with a review of empirical evidence relevant to the two theories.

The Effect of Board Structure on Firm Value: A Multiple Identification Strategies Approach Using Korean Data

Article

Jul 2011
J FINANC ECON

Outside directors and audit committees are widely considered to be central elements of good corporate governance. We use a 1999 Korean law as an exogenous shock to assess how board structure affects firm market value. The law mandates 50% outside directors and an audit committee for large public firms, but not smaller firms. We study how this shock affects firm market value, using event study, difference-in-differences, and instrumental variable methods, within a regression discontinuity approach. The legal shock produces large share price increases for large firms, relative to mid-sized firms; share prices jump in 1999 when the reforms are announced.In a companion paper, Bernard Black, Woochan Kim, Hasung Jang and Kyung-Suh Park, How Corporate Governance Affects Firm Value: Evidence on Channels from Korea (working paper 2011), http://ssrn.com/abstract=844744, we provide evidence on the channels through which governance may affect firm value. For our earlier cross-sectional research on Korean corporate governance, see:Bernard Black, Hasung Jang and Woochan Kim, Does Corporate Governance Affect Firms' Market Values? Evidence from Korea,: 22 Journal of Law, Economics and Organization 366-413 (2006), nearly final version at http://ssrn.com/abstract=311275Bernard Black, Hasung Jang & Woochan Kim, Predicting Firms' Corporate Governance Choices: Evidence from Korea, 12 Journal of Corporate Finance 660-691 (2006), nearly final version at http://ssrn.com/abstract=428662

Geographic Diversification and Firm Value in the Financial Services Industry

Article

Oct 2009
J Empir Finance

This paper investigates whether geographic diversification is value-enhancing or value-destroying in the financial services sector, broadly defined. Our dataset comprises approximately 3,579 observations over the period from 1985 to 2004 and covers the entire range of U.S. financial intermediaries – commercial banks, investment banks, insurance companies, asset managers, and financial infrastructure services firms. We use two alternative measures of geographic diversification: (1) a dummy variable whether the firm reports more than one geographic segment and (2) the percentage of sales from non-domestic operations. Our results indicate that geographic diversification is not associated with a significant valuation discount in financial intermediaries. However, when accounting for the firms’ main activity-areas, we find evidence of a significant discount associated with geographic diversification in securities firms and a premium in credit intermediaries and insurance companies. All these results are robust after taking into account functional diversification of the firms as well as a potential endogeneity of both functional and geographic diversification.

Do corporate governance mechanisms matter for cash holdings and firm value?

Article

Nov 2011
Pac Basin Finance J

Yuanto Kusnadi

This paper examines the relationships between firm-level corporate governance mechanisms and cash holdings; along with their combined effects on firm value for a sample of firms listed in Singapore and Malaysia. Firms with less effective governance attributes are found to be more inclined to accumulate cash than those with more effective governance. The results support the flexibility hypothesis in that an increase in agency conflicts between managers and minority shareholders leads to entrenched managers having more discretion to hoard cash reserves. In addition, the incremental value of holding excess cash is shown to be negative for firms with a single leadership structure, firms with a pyramidal ownership structure, as well as family-controlled firms. The discounts associated with these firms may reflect investors' recognition of the possibility of managerial entrenchment.

Can minority state ownership influence firm value? Universal and contingency views of its governance effects

Article

Aug 2011
J BUS RES

Hsueh-Liang Wu

Drawing on the literature of corporate governance and privatization, this study explores the elusive roles of a specific owner identity, namely, state ownership in its minority. With a sample of 68 Taiwanese companies with 5 to 49% state ownership during 1999-2003, the study examines the value-shaping effects of minority state ownership (MSO) and, furthermore, seeks to establish a contingency perspective suggesting that the internal and external contexts may moderate the influence of MSO on firm value. Using first-order autoregressive models to mitigate the problems of endogeneity, the study shows that the governance effect of MSO associates not only in a curvilinear relationship with firm value but also strengthened by corporate ownership ties and market competition. The non-monotonic performance effect and the context-dependent nature of MSO yield significant implications for government investments in the private sector.

Does Multinationality Lead to Value Enhancement? An Empirical Examination of Publicly Listed Corporations from Germany

Article

Dec 2010
INT BUS REV

We analyse the impact of multinationality on shareholder value in the case of German firms for the time span from 1990 to 2006. Based on a sample of 13,130 firm-year observations, we find that multinational companies perform worse in terms of shareholder value than domestic companies. This relationship remains stable even after controlling for industrial diversification. However, using a multivariate regression model, the impact of multinationality on shareholder value turns out to be positive. Obviously, the relationship between multinationality and shareholder value seems to be a classical example of Simpson's paradox. Therefore, bivariate analysis of the effects of multinationality on shareholder value must be considered as methodologically inappropriate. We find that the effect of multinationality on shareholder value depends on the existence of intangible assets either related to research and development or on the existence of intangible assets related to marketing and management skills. Hence, our findings support the results of Morck and Yeung (1991). Furthermore, our findings tend to support the view that the effect of mulinationality depends on the potential to realize economies of scale. The implication is that multinationality is not a value in itself. The multinational company has to have either intangible assets that can be capitalized abroad or the potential to realize economies of scale through internationalization in order for multinationality to lead to value enhancement.

Data Mining Model Comparison

Chapter

Jul 2010

Paolo Giudici

The Impact of Multinationality on Firm Value: A Comparative Analysis of Machine Learning Techniques

No full-text available

Recommended publications

Comparative analysis of regression and machine learning methods for predicting fault proneness model...

MACHINE LEARNING TEHNIQUES IMPLEMENTATION FOR CLASSIFICATION OF PATIENTS AFTER MYOCARDIAL INFARCTION

Prediction Model for Bollywood Movie Success: A Comparative Analysis of Performance of Supervised Ma...

A comparative analysis of data mining methods in predicting NCAA bowl outcomes