Investment Decision-making Research Articles

Impact of Anchoring, Herding and Loss-Aversion on Working Women’s Investment Decision-Making

Impact of Anchoring, Herding and Loss-Aversion on Working Women’s Investment Decision-Making

Relationship Between ESG Scores and Stock Performance after the COVID-19 Crisis

The essay titled Relationship between ESG Scores and Stock Performance After the COVID-19 Crisisdelves into how Environmental, Social, and Governance (ESG) factors influenced stock performance in the wake of the COVID-19 pandemic. By analyzing a broad range of data across various industries and geographical regions, the study explores the correlation between companies ESG scores and their stock performance during and after the crisis. The research reveals that firms with higher ESG scores generally outperformed their counterparts, suggesting that robust ESG practices may have bolstered resilience and investor confidence during turbulent times. This trend underscores the growing importance of ESG considerations in investment decision-making, as investors increasingly recognize the potential of ESG factors to mitigate risks and drive long-term value. The findings of this study add to the expanding body of literature on the interplay between ESG factors and financial performance, particularly under the strain of a global crisis such as the COVID-19 pandemic, highlighting the relevance of sustainable and responsible business practices in the modern investment landscape.

Factors affecting the market value of football players in the Indonesian League

The development of accounting science, especially in human resource accounting (HRA), has progressed rapidly. The understanding of human resources (HR) as a vital asset that has an important role in the success of an organization is growing. This research focuses on measuring the market value of professional football players in the Indonesian League by considering individual performance, especially for players who play as central midfielders. Football players, recognized as intangible assets, have a market value that is closely related to their contribution on the field. The research method used is quantitative with a descriptive approach, examining the relationship between performance variables (goals, assists, number of appearances, yellow cards, and red cards) to player market value. The research data was obtained through secondary data sources from the transfermarkt.co.id website with a sample of 52 Indonesian central midfield players in the 2023 season. The results showed that goals, assists, and number of appearances had a significant influence on the player's market value. Assists and number of appearances show a very strong influence on player prices, while yellow cards and red cards do not show a significant influence. This research provides important insights for football club management in determining the market value of players as part of financial reporting and investment decision-making, and enriches the study of human resource accounting in the football industry. The findings also show the importance of players' positive on-field contributions, such as assists and performances, in determining their market value, beyond disciplinary factors such as yellow and red cards.

How Does Loss Aversion Mediate the Relationship Between Personality Traits and Efficiency of Skills in Investment Decision- Making?

This study examines how loss aversion mediates the connection between personality factors and the effectiveness of investment decision-making skills among individual investors at the Colombo Stock Exchange. This research challenges the neoclassical paradigm that presumes investor rationality. Instead, it accords with behavioural finance, which emphasizes deviations from rationality in both beliefs and preferences. Using Prospect Theory as a framework, this study examines how the five major personality traits -conscientiousness, extraversion, agreeableness, neuroticism, and openness to experience influence investment decision-making. Analyzing data from 351 investors, a structured questionnaire and purposive sampling were used. The analysis was conducted using Structural Equation Modelling with SmartPLS4. The results demonstrate notable correlations: individuals with a higher level of openness to experience and agreeableness tend to have a positive impact on their investment decision-making, whereas extraversion, openness to experience, and conscientiousness are highly associated with loss aversion. Loss aversion plays a crucial role in connecting extraversion, conscientiousness, and openness to experience with investment decision-making. This study enhances the field of behavioural finance by providing a detailed understanding of how personality traits and loss aversion influence investing choices.

The Investment Puzzle: Unveiling Behavioral Finance, Risk Perception, and Financial Literacy

Abstract Contemporary financial theory upholds the market efficiency hypothesis, asserting that stock prices inherently embody all available information. In contrast, behavioral finance theory challenges this premise by proposing that psychological and emotional factors can wield influence over stock prices. This study delves into the repercussions of behavioral finance factors on investment decisions within the Saudi equity markets, utilizing risk perception and financial literacy as mediating variables. An online questionnaire was disseminated to 350 individual investors, yielding 315 completed responses deemed suitable for analysis. Employing Structural Equation Modeling (SEM), the collected data underwent rigorous scrutiny. The findings reveal that herding, the disposition effect, blue-chip bias, and overconfidence significantly and positively impact both risk perception and financial literacy. Furthermore, all four behavioral finance factors exhibit a substantial positive indirect effect on investment decision-making through the intermediaries of risk perception and financial literacy. It is imperative to acknowledge the study’s specific focus on these factors while recognizing the potential influence of other elements on risk perception, financial literacy, and investment decision-making. The research underscores the critical importance of incorporating an individual’s perception of risk into investment decisions, as it markedly shapes their proclivity for risk-taking and, consequently, the performance of their investment portfolio. The findings highlight the necessity for investors to recognize and address their behavioral biases. Additionally, the study advocates for advisors and policymakers to formulate strategies aimed at mitigating the impact of these biases. The comprehensive exploration of behavioral finance factors and their implications for investment decisions yields valuable insights for individual investors and stakeholders in financial advisory and policymaking roles.

Current Research Progress of ESG and Its Systematic Analysis from the Perspective of Economics

As global environmental, social and governance issues gradually become key factors for enterprise sustainable development and investment decision-making, the research progress of Environment, Social Responsibility and Governance (ESG) has received wide attention. This article will systematically summarize the existing ESG research based on the existing ESG research environment, society and governance, based on the existing research on ESG enterprise value, corporate performance and social responsibility, systematically summarize the existing ESG research, find problems in ESG research and propose solutions through summary. The construction of ESG system in accordance with economics is discussed, and the research found that, The ESG system provides new evaluation indicators and decision-making tools for enterprises and investors. By quantifying the performance of the environment, society and governance, ESG evaluation not only helps enterprises better internalize social costs, improve brand value and employee loyalty, but also alleviates information asymmetry and agency problems, and enhances the long-term risk resistance of enterprises. Based on the discussion of the ESG system in this article, it provides a program discussion to solve the problems in the current ESG research. At the same time, it provides more possibilities for better achieving the coordinated development of society, economy and the environment, and provides a feasibility plan for global sustainable development.

The Impact of Social Influences on Investment Decision-Making: A Multi-Method Analysis of Vietnamese Investors

This study explores the impact of social influences on investment decision-making, focusing on Vietnamese investors and employing a multi-method approach. Drawing on Social Contagion Theory, Behavioural Finance Theory, and Information Cascade Theory, the research examines how social media, peer pressure, and expert opinions shape investment behaviour. A representative sample of 368 individual investors was analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA), revealing significant direct effects of social influences, moderated by investment self-efficacy and risk perception. Notably, novice investors were more susceptible to peer pressure and social media, while experienced investors relied heavily on expert advice. The findings extend theoretical frameworks by elucidating the mediating and moderating mechanisms of social influences and providing boundary conditions based on investor experience. Methodological contributions include the integration of quantitative and qualitative approaches and advanced analytical techniques. Practical implications highlight the need for investment platforms to provide tailored decision-support tools, financial advisors to enhance investor self-efficacy, and regulators to address misinformation risks in digital platforms. Despite limitations such as reliance on self-reported data and a cross-sectional design, the study offers a robust foundation for future research into the dynamic interaction of social and cognitive factors in financial decision-making.

Improving realised volatility forecast for emerging markets

AbstractAccurate forecasting of realised volatility is essential for financial risk management and investment decision-making in emerging markets, taking the South African financial market as a benchmark. This study examines the predictive performance of four prominent models: HAR (Heterogeneous AutoRegressive), realised GARCH (Generalized AutoRegressive Conditional Heteroscedasticity), Recurrent Conditional Heteroskedasticity (RECH), and the Rough Fractional Stochastic Volatility (RFSV) models. These models are specifically tailored to capture the complex dynamics and long-range dependence observed in financial time series. We illustrate the challenges and limitations of these models outside the context of established markets. Our empirical findings reveal unique strengths for each model. The HAR model excels in capturing long-term volatility patterns, while realised GARCH models effectively capture volatility clustering and persistence. RECH model showcases their ability to forecast Value-at-Risk, while the RFSV model successfully captures irregular and long-memory characteristics. We provide empirical evidence that the South African financial market is rough. Moreover, this study provides valuable insights into forecasting realised volatility in the South African market, and the findings can assist practitioners and investors in making informed decisions and developing robust risk management strategies.

EVA and EBITDA: How such Metrics Can Help in the Investment Decision-Making Process

Objective: EBITDA is the most widely disclosed non-accounting metric in reference forms and tends to be the most used by analysts. However, EVA is little publicized, despite its qualitative superiority and well-founded theoretical framework. Therefore, it was verified whether EBITDA can better explain the stock returns of Brazilian companies listed on B³. Employing two hypothetical portfolios, it was also verified which decision reference is more efficient and delivers a higher shareholder return, whether portfolios based on the companies' EVA or portfolios based on the companies' EBITDA. Method: The analysis is based on data from the companies listed on B³ from 2010 to 2022, collected from the Economatica® database. Through panel data regressions the study hypotheses were tested. Two hypothetical portfolios were built based on firms' EBITDA and EVA, to empirically verify which of the two indicators is the most efficient in terms of generating investors’ returns. Results: Results indicate that EBITDA better explains the firm’s return on the Brazilian stock market than EVA. Conversely, the portfolio built based on EVA obtained a higher return over the period studied. Contributions: A comparative study of the explanatory power of both metrics in Brazil is relevant to the transformation experienced in the Brazilian capital market. Moreover, evaluating the explanatory power between them helps existing and potential investors to make their investment decisions.

Leadership Traits and Investment Behavior: Examining the Influence of Leadership Characteristics on Behavioral Biases in Financial Decision-Making

This study investigates the relationship between leadership traits and behavioral biases in investment decisionmaking. Through a quantitative survey of 172 investors, the research examines how leadership characteristics influence susceptibility to overconfidence bias, anchoring bias, herding behavior, and loss aversion in investment contexts. The findings reveal a moderate negative correlation (-0.256) between leadership traits and behavioral biases, suggesting that stronger leadership qualities may help mitigate investment biases. However, regression analysis indicates this relationship is complex, with leadership traits explaining only a small portion of bias variance. The study finds that strategic decision-making capacity has the strongest negative correlation (-0.32) with overconfidence bias, while proactive management skills show consistent negative correlations across all behavioral biases. These findings contribute to both leadership and behavioral finance literature by establishing a novel connection between leadership traits and investment behavior, while highlighting the multifaceted nature of this relationship. The research has practical implications for investment advisors and leadership development programs, suggesting the need for integrated approaches that consider both leadership qualities and behavioral aspects in improving investment decision-making.

Impact of Human Psychology on Investment Decision-Making: A Study of Mutual Fund Investors

Purpose: The purpose of this study is to investigate the impact of cognitive factors such as Herd, Representativeness, Availability, Mental Accounting, Conservatism, Familiarity, Anchoring, and Overconfidence biases on mutual fund investment decision making. Methodology: The study used quantitative methods to evaluate behavioral factors effect on mutual fund investments. 592 questionnaires were distributed to retail investors in Karnataka using the snowball sampling technique. Of these, 543 responses were received, and 520 were valid, achieving an 87% response rate. Findings: The result of the study revealed that cognitive factors such as herd behavior, representativeness, availability, mental accounting, conservatism, familiarity, anchoring, and overconfidence biases have a significant impact on the investment decision-making of mutual fund investors. The R-squared value of 0.616 indicates that 61.67% of the variation in investment decision-making can be explained by these cognitive factors.

Harnessing LLMs for Financial Forecasting: A Systematic Review of Advances in Stock Market Prediction and Portfolio Optimization

Abstract: This review paper examines the rapidly evolving landscape of Large Language Models (LLMs) in financial analysis, synthesizing recent advances and applications in this transformative field. We trace the progression from traditional natural language processing (NLP) methods to contemporary language models in financial applications, showing how these technologies are reshaping market analysis, risk assessment, fraud detection, and investment decision-making across various financial sectors. The paper offers an in-depth overview of LLM architectures and approaches used in finance, covering both general-purpose models adapted for financial tasks and specialized models tailored for industry needs. Through systematic analysis of the latest research and empirical studies, we assess the capabilities of LLMs in processing diverse financial data sources, including real-time market data, news articles, social media sentiment, earnings calls, and regulatory filings, enhancing insights and predictive accuracy. We identify key challenges in LLM implementation, such as the need for real-time data processing, high accuracy, and interpretability, crucial for trust and adoption in high-stakes contexts. Additionally, we explore emerging trends and future research directions, highlighting both the transformative potential and limitations of LLMs as they redefine analytical frameworks and decision support in finance. This review underscores the need for ongoing research to bridge gaps and fully realize LLMs’ potential in reshaping financial analysis and practices

Analisis Faktor-Faktor Equity Risk Premium Perusahaan Sektor Kesehatan yang Terdaftar di Bursa Efek Indonesia

This study aims to analyze the determinants of equity risk premium among healthcare sector companies listed on the Indonesia Stock Exchange during the period from 2011 to 2022. The equity risk premium, a crucial concept in investment decision-making, represents the additional return investors require for holding risky assets compared to risk-free assets. The study uses independent variables such as book to market equity ratio, beta, leverage, and firm size to understand their influence on the equity risk premium. This research adopts a quantitative approach, utilizing panel data analysis to evaluate the relationships between the variables. The sample is selected using purposive sampling, focusing on healthcare companies listed on the Indonesia Stock Exchange that have published financial statements for the years 2011 to 2022. The results of the study indicate that beta significantly affect the equity risk premium in the healthcare sector. However, book to market equity ratio, leverage, and firm size do not show a significant impact on the equity risk premium. The findings contribute to the literature by providing insights into the unique determinants of the equity risk premium in the healthcare sector, particularly in the context of the Indonesian market. These insights are valuable for investors seeking to optimize their portfolios and manage risk effectively, as well as for companies aiming to improve their financial performance and investor relations.

Bitcoin price movement prediction by news sentiment using machine learning approach

This study investigates the potential of news sentiment, derived from Google News, in predicting Bitcoin price movements. It explores the correlation between sentiment in news headlines and Bitcoin's market behavior. Employing a data set of news headlines related to Bitcoin from Google News, this research applies sentiment analysis and various machine learning algorithms, including Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Naïve Bayes, and KNN. The approach involves extracting sentiment scores and correlating these with historical Bitcoin price data. The analysis revealed that Decision Tree and Random Forest algorithms provide a balanced prediction for Bitcoin price movements. Logistic Regression and Support Vector Machine showed high AUC scores but with unbalanced class predictions. Naïve Bayes and KNN were less effective. Overall, sentiment analysis of news headlines can predict short-term Bitcoin price movements to a reasonable degree. This research offers a novel tool for investors and market analysts, enhancing understanding of the impact of news sentiment on cryptocurrency prices. It provides a predictive model to assist in investment decision-making and market analysis. This study contributes to the literature on financial forecasting by integrating sentiment analysis with machine learning for cryptocurrency price prediction. It's one of the few studies to analyze the impact of news sentiment on Bitcoin price, offering a new perspective in the realm of financial technology and digital currencies.

Optimizing Cryptocurrency Investment Decisions Using Plithogenic Hypersoft Sets in MCDM

This study explores the integration of Plithogenic Hypersoft Sets (PHSS) in Multi-Criteria Decision Making (MCDM) for cryptocurrency investment analysis. Given the volatile and unpredictable nature of the cryptocurrency market, traditional decision-making models often fail to capture the complexities and uncertainties present. By incorporating the advanced concept of PHSS, which accommodates multiple membership degrees (fuzzy, intuitionistic fuzzy, neutrosophic), we propose a more robust framework for investment decision-making. The study normalizes cryptocurrency data and applies PHSS to a set of cryptocurrencies (e.g., BTC, ETH, BNB, SOL, and XRP), analyzing key attributes such as price changes, market cap, volume, and circulating supply. Our results demonstrate that PHSS can enhance the accuracy and reliability of financial decision-making, providing valuable insights for investors.

Which opinion is more trustworthy: An analysts’ earnings forecast quality assessment framework based on machine learning

Analysts’ Earnings Forecast (AEF) is a crucial reference in investment decision-making and significantly impact capital market efficiency. While much research has focused on the factors influencing AEF, the variability and disparity in its quality have often been overlooked. This study presents a machine learning (ML)-based framework for assessing and forecasting AEF quality, including multi-perspective feature generation, rank aggregation-based heterogeneous ensemble feature selection, and quality forecasting. We validate this framework on a real-world dataset and use an explainable approach to identify the key features affecting AEF quality from a data-driven perspective. Our analyses reveal the unique characteristics of the China’s A-share market in terms of AEF quality forecasting and investigate the sensitivity of feature combinations from the perspectives of state ownership and industry. On the basis of our assessment, we develop an investment strategy to demonstrate economic value. Our findings offer insights for regulators and brokerage houses, helping investors mitigate the risks associated with low-quality opinions.

Behavioural Biases in Investment Decision-Making and Their Impact on Investment Performance

This paper provides an in-depth discussion of behavioural bias in investment decision-making and its impact on investment performance. Current research mostly focuses on theories and lacks specific coping strategies, while behavioural biases are prevalent in investment decision-making, affecting investment performance and the stable development of financial markets. By analysing cognitive bias, emotional bias and volitional bias, their manifestations and negative impacts on investment performance are elaborated, such as overconfidence leading to frequent trading and loss aversion making investors short-sighted. In order to reduce the behavioural biases, coping strategies such as searching for counter evidence, timely detection of errors and stop loss, broadening horizons and reducing noise are proposed. Investors can make rational decisions by collecting information through multiple channels, verifying the reasonableness of viewpoints, establishing a stop-loss mechanism, enriching thinking paths, and avoiding noise interference. In conclusion, investors should recognise the existence of behavioural biases, adopt effective strategies to improve the accuracy and rationality of investment decisions and achieve better investment performance, and financial institutions and regulators should also strengthen investor education and guidance.

Decoding Investor Sentiments in the Indian Stock Market: A Structural Equation Modelling Approach

Background of the study This research examines how psychological and social biases affect individual investors’ investing decisions. Investor sentiment significantly influences financial markets, frequently causing stock prices to deviate from their intrinsic values. In rising economies such as India, where retail investors are significantly affected by psychological factors, comprehending these attitudes is crucial. Methods This study analyses data from a comprehensive questionnaire that was conducted throughout the nation and included 552 retail investors. The investigation employed structural equation modelling (SEM) to identify the elements that influence the decision-making of individuals who invest in the Indian stock market. Findings The research offers insight on the influence that investor attitude has on investment decision-making as well as the factors that precede it. The study demonstrates that investors make financial decisions based on sentiment. In addition to assessing the efficacy of the Indian financial market, this study sought to ascertain the rationality of investors’ choices by exploring the factors that influence their decision-making process. Conclusion The outcome of the study shows that information seeking, anchoring, herding, representativeness, and overconfidence all have a big impact on investors. Moreover, the study has proven investors’ irrationality and stock market inefficiency. The findings may be employed to further examine the trading practices of international investors and encourage further study in the field of behavioural finance.

Stock Portfolio Analysis with Machine Learning Algorithmic Approach for Smart Investment Decisions

This study investigates the application of machine learning algorithms in stock portfolio analysis within the Indonesia Stock Exchange (IDX) and their impact on investment decision-making. By engaging 500 respondents from diverse market segments, including retail investors, institutional investors, and stock traders, the research provides a comprehensive overview of adopting and utilising machine learning technologies in the Indonesian stock market. The findings reveal that over 80% of respondents have integrated machine learning algorithms into their investment strategies. The algorithms are applied in various capacities: 45% of respondents use them for portfolio risk analysis, 30% for stock price prediction, and 25% for identifying new investment opportunities. Preferences for specific algorithms vary, with regression, Support Vector Machines (SVM), and Random Forest emerging as the most used tools. The integration of machine learning was strongly associated with improved investment decisions, as more than 60% of respondents reported enhanced portfolio performance and greater accuracy in their decision-making. These results highlight the transformative potential of machine learning algorithms in enabling more innovative and more adaptive investment strategies.

Theoretical Frameworks for Integrating Sustainability Factors into Institutional Investment Decision-Making

This paper explores key theoretical frameworks instrumental in understanding the relationship between sustainability and institutional investment decisions. The study identifies and analyzes various theories, including Behavioral Finance Theory, Modern Portfolio Theory, Risk Management Theory, and others, to explain how sustainability considerations increasingly influence investment choices. By examining these frameworks, the paper highlights how investors integrate Environmental, Social, and Governance (ESG) factors to optimize financial outcomes and align with broader societal goals.