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Purpose
A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypo...
Contexts in source publication
Context 1
... volatility effect variation of sentiment extracted from unincorporated news is analyzed by plotting the co-movement graph of the unincorporated news sentiment polarity values with the stock returns of NIFTY LOW VOLATILITY(low volatile stocks) and NIFTY HIGH VOLATILITY (high volatile stocks). The relative sensitivity of the low volatile stocks and high volatile stocks to unincorporated news sentiment is examined by plotting the comovement graph and computing the correlations between low volatile stocks and news sentiment and similarly concerning high volatile stocks in Figure 4. Figure 4 shows that HIGH VOLATILITY stocks vary with unincorporated news sentiment (correlation = 0.35) to a greater extent than LOW VOLATILITY stocks (correlation = 0.15), suggesting that highly volatile stocks are prone to news sentiment and absorb news faster and are more sensitive than low volatile stocks. ...
Context 2
... volatility effect variation of sentiment extracted from unincorporated news is analyzed by plotting the co-movement graph of the unincorporated news sentiment polarity values with the stock returns of NIFTY LOW VOLATILITY(low volatile stocks) and NIFTY HIGH VOLATILITY (high volatile stocks). The relative sensitivity of the low volatile stocks and high volatile stocks to unincorporated news sentiment is examined by plotting the comovement graph and computing the correlations between low volatile stocks and news sentiment and similarly concerning high volatile stocks in Figure 4. Figure 4 shows that HIGH VOLATILITY stocks vary with unincorporated news sentiment (correlation = 0.35) to a greater extent than LOW VOLATILITY stocks (correlation = 0.15), suggesting that highly volatile stocks are prone to news sentiment and absorb news faster and are more sensitive than low volatile stocks. ...
Context 3
... (Figure 4), we infer that HIGH VOLATILITY stocks vary with unincorporated news sentiment (correlation = 0.35) to a greater extent than LOW VOLATILITY stocks (correlation = 0.15), indicating that highly volatile stocks are prone to news sentiment, absorb news faster and are more sensitive than low volatile stocks. Thus, Volatility is directly proportional to the rate of news assimilation in the stock market, confirming Prasanna and Menon (2012). ...
Citations
... It is not only related to the large volume of text, but also to the diversity of topics, the complex structure of the documents, and the interconnectedness of the documents. [3]- [5]. In this context, efficient and effective methods are needed to extract topics and model them in large-scale text corpora. ...
This research compares unsupervised learning methods in topic extraction and modeling in large-scale text corpora. The methods used are Singular Value Decomposition (SVD) and Latent Dirichlet Allocation (LDA). SVD is used to extract important features through term-document matrix decomposition, while LDA identifies hidden topics based on the probability distribution of words. The research involves data collection, data exploratory analysis (EDA), topic extraction using SVD, data preprocessing, and topic extraction using LDA. The data used were large-scale text corpora. Data explorative analysis was conducted to understand the characteristics and structure of text corpora before topic extraction was performed. SVD and LDA were used to identify the main topics in the text corpora. The results showed that SVD and LDA were successful in topic extraction and modeling of large-scale text corpora. SVD reveals cohesive patterns and thematically related topics. LDA identifies hidden topics based on the probability distribution of words. These findings have important implications in text processing and analysis. The resulting topic representations can be used for information mining, document categorization, and more in-depth text analysis. The use of SVD and LDA in topic extraction and modeling of large-scale text corpora provides valuable insights in text analysis. However, this research has limitations. The success of the methods depends on the quality and representativeness of the text corpora. Topic interpretation still requires further understanding and analysis. Future research can develop methods and techniques to improve the accuracy and efficiency of topic extraction and text corpora modeling.
... The main scope of the scientific articles in this theme is the intersection between behavioral finance and AI techniques and algorithms, to analyze and predict market trends and investor behavior. The specific topics include measuring investor sentiment using machine learning and news photos [97], using sentiment analysis and options volume to anticipate future returns [98], examining differences in investors' behavior across different financial markets [99], analyzing the relationship between social moods and the stock market [100], using artificial neural networks to examine behavioral biases in retail investors during the pandemic [101], bankruptcy modeling using neural trace [102], media sentiment and international asset prices, accounting for unadjusted news sentiment for asset pricing [103] and applications of AI in commercial banks for behavioral finance. ...
Behavioral economics and artificial intelligence (AI) have been two rapidly growing fields of research over the past few years. While behavioral economics aims to combine concepts from psychology, sociology, and neuroscience with classical economic thoughts to understand human decision-making processes in the complex economic environment, AI on the other hand, focuses on creating intelligent machines that can mimic human cognitive abilities such as learning, problem-solving, decision-making, and language understanding. The intersection of these two fields has led to thrilling research theories and practical applications. This study provides a bibliometric analysis of the literature on AI and behavioral economics to gain insight into research trends in this field. We conducted this bibliometric analysis using the Web of Science database on articles published between 2012 and 2022 that were related to AI and behavioral economics. VOSviewer and Bibliometrix R package were utilized to identify influential authors, journals, institutions, and countries in the field. Network analysis was also performed to identify the main research themes and their interrelationships. The analysis revealed that the number of publications on AI and behavioral economics has been increasing steadily over the past decade. We found that most studies focused on customer and consumer behavior, including topics such as decision-making under uncertainty, neuroeconomics, and behavioral game theory, combined mainly with machine learning and deep learning techniques. We also identified several emerging themes, including the use of AI in nudging and prospect theory in behavioral finance, as well as undeveloped themes such as AI-driven behavioral macroeconomics. The findings suggests that there is a need for more interdisciplinary collaboration between researchers in behavioral economics and AI. We also suggest that future research on AI and behavioral economics further consider the ethical implications of using AI and behavioral insights in decision-making. This study can serve as a valuable resource for researchers interested in AI and behavioral economics.
... As mentioned above in Section 4, research from China has been significant. Notably, over the past decade, researchers in emerging nations (especially China) have been making efforts to work in this area (Eachempati and Srivastava 2021;Gong et al. 2022;He et al. 2022;Y. Sun et al. 2021;Xiong et al. 2020). ...
... Sun et al. 2021;Xiong et al. 2020). However, there is very little empirical evidence on ISIs in an Indian setting (Dash and Maitra 2018;Eachempati and Srivastava 2021;Goel and Dash 2021). This creates a research gap for future studies. ...
The Investor Sentiment Index (ISI) is widely regarded as a useful measure to gauge the overall mood of the market. Investor panic may result in contagion, causing failure in financial markets. Market participants widely use the ISI indicator to understand price fluctuations and related opportunities. As a result, it is imperative to systematically review the compiled literature on the subject. In addition to reviewing past studies on the ISI, this paper attempts a bibliometric analysis (BA) to understand any related publications. We systematically review over 100 articles and carry out a BA on a set of information based on the publication year, the journal, the countries/territories, the deployed statistical tools and techniques, a citation analysis, and a content analysis. This analysis further strengthens the study by establishing interesting findings. Most articles use the Baker and Wurgler index and text-based sentiment analysis. However, an Internet-search-based ISI was also used in a few of the studies. The results reveal the lack of direct measures or a robust qualitative approach in constructing the ISI. The findings further indicate a vast research gap in emerging economies, such as India’s. This study had no limit on the period for inclusion and exclusion. We believe that our current work is a seminal study, jointly involving a systematic literature review and BA, that will enormously facilitate academicians and practitioners working on the ISI.
... Matsubara et al. (2012) and concluded that the news influence would diminish over time. Eachempati and Srivastava (2021) argued that investors were inclined to ignore old news that may not stir the market sentiments, but they were sensitive to new news. In this article, our study will further analyze whether bond investors have different reactions to new news and old news. ...
The literature has widely studied the market response to the financial news or events but mainly focused on the stock market. This article associates the concept of internet news with the bond market response and attempts to examine how credit rating agencies (CRAs) and bond investors, two important bond participants, react to financial news on the internet with a range of multiply regressions. Our empirical study leads to several findings. First, CRAs tend to ignore the warnings of financial news on the internet, whereas bond investors strongly react to such news. Second, there is an asymmetry in bond investors’ reactions to good news compared to bad news, with investors being more sensitive to bad news. Third, there is heterogeneity in the psychological reaction where bond investors do not react to the news about central state-owned enterprises (SOEs) but to the news about other enterprises. Finally, there is an asymmetric response driven by news timeliness that bond investors are more sensitive to the latest news articles than old ones. Overall, our study confirms the existence of psychological reactions to the financial news on the internet in China’s bond market, which has significance for keeping bond market participants from overreacting or underreacting to market news.
This research builds upon the analysis of curriculum vitae (CV) through text mining techniques, including natural language processing (NLP), to evince digital and creative KSC supply and skill gaps in the CCIs in Italy. This chapter is organized into three sections, the first two dealing with theoretical research on CV analysis and NLP techniques, and the third explaining our research methodology. In particular, the first section compiles existing literature on CV (or resume) analysis, with a particular focus on the analysis of CV in the CCI, while the second is a selected review of past literature dealing with NLP analysis with insights on its applications in multiple research areas, including human resource management (HRM) and CV analysis. The third, and final, section describes our original empirical strategy, explaining the methodological steps we followed to analyze the skills, competences, and knowledge of the candidates in our CV dataset.KeywordsNatural language processingCurriculum vitae analysisResume analysisText miningClusteringWord2VecSkill extractionESCO taxonomyATECO
Orientation: Responsible investment (RI) is gaining prominence globally. As South African asset managers increasingly apply environmental, social and governance (ESG) criteria, they require improved guidance on the integration thereof in mainstream investment practices.
Research purpose: A best-practice framework for ESG integration is proposed based on empirical results and international standards.
Motivation for the study: The mainstreaming of ESG is examined in South Africa, a country where integrated disclosure by listed companies is well established and asset managers are challenged to meet international standards while facing emerging market realities.
Research approach/design and method: Selected South African asset managers completed a questionnaire and semi-structured interviews were conducted with national and international asset managers to gauge their views on ESG integration best practices.
Main findings: The findings highlight the limited local opportunity set and scope to increase client demand for RI in South Africa. Inconsistencies were noted in how the local asset managers incorporated material ESG information in their investment policies and practices.
Practical/managerial implications: ESG integration should form part of the entire investment process. To effectively apply the proposed ESG framework, Investment managers should formalise and publish comprehensive RI policies and develop internal ESG data provision systems covering material subjects defined by international ESG disclosure standards. Invest managers are encouraged to conduct ongoing engagement with investee companies on key ESG issues.
Contribution: While the need for ESG integration is recognised, local asset managers require improved guidance on the incorporation of complex ESG issues. A best-practice framework for ESG integration is hence proposed.
