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Bankruptcy prediction has been extensively investigated by data mining techniques since it is a critical issue in the
accounting and finance field. In this paper, a new hybrid algorithm combining switching particle swarm optimization
(SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. In particular, a
rec...
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Citations
... here = ( , , ) indicates the RBM parameter; signifies the relationship value between visible units (v) and hidden units (h); and and indicates bias terms of the visible as well as hidden units. The hidden units' h conditional distributions and the visible units' v conditional distributions are written as (14) and (15). ...
In the regions of southern Andhra Pradesh, rice brown spot, rice blast, and rice sheath blight have emerged as the most prevalent diseases. The goal of this research is to increase the precision and effectiveness of disease diagnosis by proposing a framework for the automated recognition and classification of rice diseases. Therefore, this work proposes a hybrid approach with multiple stages. Initially, the region of interest (ROI) is extracted from the dataset and test images. Then, the multiple features are extracted, such as color-moment-based features, grey-level cooccurrence matrix (GLCM)-based texture, and shape features. Then, the S-particle swarm optimization (SPSO) model selects the best features from the extracted features. Moreover, the deep belief network (DBN) model trained by SPSO is based on optimal features, which classify the different types of rice diseases. The SPSO algorithm also optimized the losses generated in the DBN model. The suggested model achieves a hit rate of 94.85% and an accuracy of 97.48% with the 10-fold cross-validation approach. The traditional machine learning (ML) model is significantly less accurate than the area under the receiver operating characteristic curve (AUC), which has an accuracy of 97.48%.
... Another optimization option was proposed by Shi et al. (2009), showing that bagging as an ensemble learning method can also improve the prediction accuracy for predicting a company's bankruptcy. Based on this, Lu et al. (2015) show that hybrid algorithms, such as using a support vector machine combined with particle swarm optimization, can also substantially improve the accuracy and robustness of bankruptcy predictions. The predictive power of support vector machines was also evaluated by Antunes et al. (2017) by comparing them with the logistic regression and Gaussian processes, with the result that Gaussian processes effectively improved the forecast performance. ...
Purpose
Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.
Design/methodology/approach
The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.
Findings
The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.
Research limitations/implications
Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.
Practical implications
Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.
Originality/value
To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.
... The switching PSO algorithm has been proposed by (Tang et al., 2011), in which a mode-dependent velocity updating Eq. 7 is introduced to realize a balance between the local search algorithm and the global one. Lu et al. (2015) have employed the SPSO algorithm to solve the optimization problem with constraints by converting it into an optimization problem without constraints and have gained a better performance. ...
... In the study, the BP layer of DBN is replaced by the SPSO-SVM classifier (For detail of switching PSO to optimize the parameters of SVM, refer to the study by Lu et al. (2015). ...
Rice blast, rice sheath blight, and rice brown spot have become the most popular diseases in the cold areas of northern China. In order to further improve the accuracy and efficiency of rice disease diagnosis, a framework for automatic classification and recognition of rice diseases is proposed in this study. First, we constructed a training and testing data set including 1,500 images of rice blast, 1,500 images of rice sheath blight, and 1,500 images of rice brown spot, and 1,100 healthy images were collected from the rice experimental field. Second, the deep belief network (DBN) model is designed to include 15 hidden restricted Boltzmann machine layers and a support vector machine (SVM) optimized with switching particle swarm (SPSO). It is noted that the developed DBN and SPSO-SVM can simultaneously learn three proposed features including color, texture, and shape to recognize the disease type from the region of interest obtained by preprocessing the disease images. The proposed model leads to a hit rate of 91.37%, accuracy of 94.03%, and a false measurement rate of 8.63%, with the 10-fold cross-validation strategy. The value of the area under the receiver operating characteristic curve (AUC) is 0.97, whose accuracy is much higher than that of the conventional machine learning model. The simulation results show that the DBN and SPSO-SVM models can effectively extract the image features of rice diseases during recognition, and have good anti-interference and robustness.
... SVM method can solve regression and pattern recognition problems effectively and can also be used to make predictions and stability assessments [28]. According to Lu et al. [7], SVM has many advantages when utilized to solve small samples, nonlinear, and high -dimensional pattern recognition problems when compared to other algorithms. ...
The Support Vector Machine is one of the artificial intelligence techniques that can be applied to forecast the stability of cantilever retaining walls. The selection of the right Kernel function is very important so that the Support Vector Machine model can make good predictions. However, there are no general guidelines that can be used to select Kernel functionality. Therefore, the Kernel function which consists of Linear, Polynomial, Radial Basis Function and Sigmoid has been evaluated to determine the optimal Kernel function by using 10 cross-validation (V-fold cross-validation). The achievement of each function is evaluated based on the mean square error value and the squared correlation coefficient. The mean square error value is closer to zero and the squared correlation coefficient closer to the value of one indicates a more accurate Kernel function. Results show that the Support Vector Machine model with Radial Basis Function Kernel can successfully predict the stability of cantilever retaining walls with good accuracy and reliability in comparison to the various other Kernel functions.
... SVM, firstly developed by Vapnik in 1995(Vapnik 1995, is a supervised learning model with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis (Burges 1998). According to Lu et al. (2015), compared with other algorithms, SVM has many unique advantages when applied in solving small sample, nonlinear, and high-dimensional pattern recognition problem. The concept of a neural network has been developed in biology and psychology, but its use goes to other areas, such as business and economics (Vochozka 2017). ...
Bankruptcy prediction is always a topical issue. The activities of all business entities are directly or indirectly affected by various external and internal factors that may influence a company in insolvency and lead to bankruptcy. It is important to find a suitable tool to assess the future development of any company in the market. The objective of this paper is to create a model for predicting potential bankruptcy of companies using suitable classification methods, namely Support Vector Machine and artificial neural networks, and to evaluate the results of the methods used. The data (balance sheets and profit and loss accounts) of industrial companies operating in the Czech Republic for the last 5 marketing years were used. For the application of classification methods, TIBCO’s Statistica software, version 13, is used. In total, 6 models were created and subsequently compared with each other, while the most successful one applicable in practice is the model determined by the neural structure 2.MLP 22-9-2. The model of Support Vector Machine shows a relatively high accuracy, but it is not applicable in the structure of correct classifications.
... But the author also indicated that a hybrid between PSO and SVM could yield a good balance between short-and long-term prediction accuracy. This was consequently done by Lu et al. [102], who combined switching PSO (SPSO) and SVM. The SPSO was employed in searching the optimal parameter values of radial basis function (RBF) kernel of the SVM. ...
Computational finance is an emerging application field of metaheuristic algorithms. In particular, these opti-misation methods are becoming the solving approach alternative when dealing with realistic versions of several decision-making problems in finance, such as rich portfolio optimisation and risk management. This paper reviews the scientific literature on the use of metaheuristics for solving NP-hard versions of these optimisation problems and illustrates their capacity to provide high-quality solutions under scenarios considering realistic constraints. The paper contributes to the existing literature in three ways. Firstly, it reviews the literature on metaheuristic optimisation applications for portfolio and risk management in a systematic way. Secondly, it identifies the linkages between portfolio optimisation and risk management and presents a unified view and classification of both problems. Finally, it outlines the trends that have gradually become apparent in the literature and will dominate future research in order to further improve the state-of-the-art in this knowledge area.
... In Balcaen and Ooghe (2006), an overview of classic statistical methodologies that analyze business failure prediction and their related problems is shown. More recent works about bankruptcy prediction were published by Yang et al. (2011), Jeong et al. (2012, Lu et al. (2015) and Liang et al. (2016). ...
Predicting corporate failure is an important problem in management science. This study tests a new method for predicting corporate failure on a sample of Spanish firms. A GRASP (Greedy Randomized Adaptive Search Procedure) strategy is proposed to use a feature selection algorithm to select a subset of available financial ratios, as a preliminary step in estimating a model of logistic regression for predicting corporate failure. Selecting only a subset of variables (financial ratios) reduces the costs of data acquisition, increases prediction accuracy by excluding irrelevant variables, and provides insight into the nature of the prediction problem allowing a better understanding of the final classification model. The proposed algorithm, that it is named GRASP-LOGIT algorithm, performs better than a simple logistic regression in that it reaches the same level of forecasting ability with fewer accounting ratios, leading to a better interpretation of the model and therefore to a better understanding of the failure process.
... By doing so, all the particles can be randomly generated among the search space discussed in Section 2.3. All the velocity components are assigned the initial value of 0. The parameters of PSO are determined in line with the recommendations in [41][42][43][44]. The final parameters of PSO are shown in Table 3. ...
This paper examines the interval forecasting of carbon futures prices in one of the most important carbon futures market. Specifically, the purpose of this study is to present a novel hybrid approach, which is composed of multioutput support vector regression (MSVR) and particle swarm optimization (PSO), in the task of forecasting the highest and lowest prices of carbon futures on the next trading day. Furthermore, we set out to investigate if considering some potential predictors, which have strong influence on carbon futures prices, in modeling process is useful for achieving better prediction performance. Aiming at testing its effectiveness, we benchmark the forecasting performance of our approach against four competitors. The daily interval prices of carbon futures contracts traded in the Intercontinental Futures Exchange from August 12, 2010, to November 13, 2014, are used as the experiment dataset. The statistical significance of the interval forecasts is examined. The proposed hybrid approach is found to demonstrate the higher forecasting performance relative to all other competitors. Our application offers practitioners a promising set of results with interval forecasting in carbon futures market.
... )과 부스팅(boosting) (Freund and Schapire, 1996) (Beaver, 1966;Altman, 1968;Meyer and Pifer, 1970;Ohlson, 1980 (Bryant, 1997;Shaw and Gentry, 1988;Zhang et al., 1999;Kim and Jhee, 2012;Lu et al., 2015;Yu, 2014;Zhang et al., 2013 (Dietterich, 1997). Kim and Kim(2007) 1, 1, 1, 1, 1, 1, 0, 0, 0), C' 2 = (1, 1, 1, 1, 0, 0, 0, 1, 1, 1), C' 3 = (0, 0, 0, 0, 1, 1, 1, 1, 1, 1)가 있다고 하자. ...
Ensemble classification is an approach that combines individually trained classifiers in order to improve prediction accuracy over individual classifiers. Ensemble techniques have been shown to be very effective in improving the generalization ability of the classifier. But base classifiers need to be as accurate and diverse as possible in order to enhance the generalization abilities of an ensemble model. Bagging is one of the most popular ensemble methods. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. In this study we proposed a new bagging variant ensemble model, Randomized Bagging (RBagging) for improving the standard bagging ensemble model. The proposed model was applied to the bankruptcy prediction problem using a real data set and the results were compared with those of the other models. The experimental results showed that the proposed model outperformed the standard bagging model.
The rapid expansion of artificial intelligence (AI) technologies presents novel technical solutions to traditional accounting and finance problems. Despite this, scholars in accounting and finance frequently encounter difficulties navigating the extensive and intricate domain knowledge of AI and its continuously evolving literature. To address this gap, this paper conducts a qualitative survey of the implementation of AI methods in accounting and finance. The paper is structured into four sections. Firstly, we examine the conventional accounting and finance issues and their requirement for AI techniques. Secondly, to inform accounting and finance researchers about the potential of AI, we present broad categories of AI applications. Thirdly, we explore recent research on AI solutions to conventional problems. Finally, we highlight emerging trends and possible research directions.
