Investment Decision-making Research Articles

Simpson�s Paradox: Conceptual Foundational Implications in Real-Life Decision-Making in Quantitative Investment and Trading

Pankaj Mani writes that a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined raises foundational issues in concluding the significance of statistical relationships among different variables reliably in real-world decision-making for trading, investing, risk management.

Internet use and household financial market participation: evidence from China

ABSTRACT This paper examines the effect of internet use on household financial market participation and its underlying mechanism. Using data from the 2018 China Family Panel Studies (CFPS), we find that households with internet use are 4.7% more likely to participate in financial market investments than those without. The finding remains robust after accounting for endogeneity concerns using the instrumental variables approach and a fresh double machine learning approach. We highlight the phenomenon of ‘digital divide’ that the positive effects of internet use on making financial investments are more substantial among families living in economically developed regions, and families with higher income and education levels than their counterparts. We identify three novel mechanisms through which internet use affects household investment decision-making, namely the internet information mechanism, the internet social mechanism, and the internet enabling mechanism. These findings suggest that expanding digital infrastructure building and increasing the number of internet users are important ways to enhance households’ participation in financial markets.

Aligning Enterprise Risk Management into Risk Disclosure of the IPO Prospectus

This concept paper aims to review the potential of aligning Enterprise Risk Management (ERM) elements into risk disclosure of the IPO prospectus. This concept paper explores the potential for aligning ERM elements within the risk disclosure section of IPO prospectuses. The paper emphasizes the regulatory requirement of the risk disclosure in the prospectus. It highlights the importance of risk disclosure in investment decision-making. It contends the best practices of risk disclosure in the IPO prospectus for the benefit of investors and also reinforces the issuing company’s commitment to effective risk management and corporate governance. By incorporating ERM principles, the prospectus not only improves transparency but also strengthens the issuing company’s commitment to effective risk management and corporate governance. The key research objective is to evaluate the integration of ERM into IPO risk disclosures, with particular emphasis on regulatory compliance, investor decision-making, and the enhancement of corporate governance practices. This research endeavors to fill the gap in the literature by enhancing the ERM elements in the existing practices of IPO disclosure.

“Exploring Sustainable Investment Frontiers: A Bibliometric Analysis on current trends and hurdles in incorporating ESG into Investment decision-making”

Integrating environmental, social, and governance (ESG) factors into investment decision-making has emerged as a critical area of interest in both academic and industry circles. This literature review aims to comprehensively explore and synthesize existing research on this topic by providing valuable insights into current trends, challenges, and prospects in sustainable investing. Articles, reports, and case studies published in relevant databases from 2019 to 2023 are reviewed for this research. A comprehensive search strategy encompassing keywords related to ESG integration in investment decisions has been used in the review process. The integration of ESG factors in investment decisions has evolved significantly in recent years. However, the knowledge landscape is scattered, and it is imperative to consolidate this body of research to gain a holistic understanding thus this bibliometric analysis addresses the research agenda on the impact of the integration of ESG factors on investment decision-making and the challenges and opportunities associated with this evolving paradigm. Our review highlighted that ESG integration has been shown to mitigate risk and enhance long-term returns, supporting the argument that sustainability considerations are not only ethical but also financially prudent. As per the analysis, hurdles range from the lack of awareness regarding standardized ESG metrics and reporting to concerns about greenwashing. The complexity of ESG factors and the need for robust data and analytics pose significant hurdles to investors and practitioners. The synthesis of findings from the study highlights the need for standardized ESG reporting, enhanced data quality, and increased stakeholder engagement. Future research should explore the integration of ESG factors in specific asset classes and regions, as well as investigate the effectiveness of various ESG integration strategies.

Climate policy uncertainty and its impact on real estate market dynamics: A sectoral and regional analysis.

This study explores the impact of Climate Policy Uncertainty (CPU) on real estate market volatility, utilizing the CPU index to assess how climate policy affects various real estate segments. It highlights the significant impact of CPU on sectors with high energy demand and emissions, such as industrial and residential. A multi-horizon analysis reveals the long-term sensitivity of CPU's influence, with significant sensitivity noted in coastal regions prone to climate risks. The findings provide crucial insights for investors and policymakers, emphasizing the importance of integrating CPU considerations into strategic decision-making for real estate investment.

Decision-Making Amid Economic Uncertainty: Exploring the Key Considerations of Commercial Property Investors

This study explored the key considerations of commercial property investors acting under conditions of economic uncertainty across the following three dimensions: market fundamentals, institutional, and behavioural factors. Over the past few decades, a series of exogenous shocks to the global economy has impacted property performance, investment volumes, and investor perceptions. Acknowledging that uncertainty further complicates investment decision-making, a mixed-methods research approach was adopted to examine the perspectives of 5 experts and 412 property investors. The findings revealed that, while most investors express negative responses to uncertainty and adopt more cautious attitudes, others are more aggressive and attempt to capitalise on emerging opportunities. Market fundamentals are not the only key consideration; access to information and investors’ behaviour all impact how decisions are made under these conditions. In particular, wealthy and more experienced investors make more comprehensive decisions, considering all three dimensions, while aggressive investors may disregard data in favour of intuition. Behavioural biases such as the bandwagon effect and fear of missing out (FOMO) all influence decisions and are sometimes exacerbated by media narratives. Practically, these considerations underscore the complexity of decision-making under conditions of uncertainty and how different investors attempt to navigate market volatility.

Similarity Measures of Probabilistic Interval Preference Ordering Sets and Their Applications in Decision-Making

The concept of probabilistic interval preference ordering sets (PIPOSs) provides a scientific and intuitive framework for solving real-life multi-criteria group decision-making problems. In some areas such as investment decision-making and supplier selection, PIPOSs have a wider application space, and the development of similarity and distance measures based on PIPOSs holds great significance. Similarity measure is a basic and prominent tool for dealing with imperfect and ambiguous information in fuzzy sets, but it can also be used to deal with uncertain information in preference ordering. These metrics play an important role in the actual decision-making process, as they effectively quantify the degree of similarity between two PIPOSs, and further allow for the prioritization of different scenarios. In this article, we sort out the definitions and arithmetic rules of PIPOSs, and creatively propose several new similarity measures based on PIPOSs. Then, we propose a group decision-making method based on similarity measures and conduct a comparative study with three existing similarity measures to illustrate its advantages over existing metrics. Finally, we confirm its validity through numerical illustrations in the case study, and also conduct a comparative assessment to verify the scientific validity and effectiveness of the newly introduced measure against the existing metrics.

Exploring Gender Differences in Investment Decision-Making: The Role of AI in Identifying and Addressing Behavioral Biases

This study investigates how Gender effects investment decision-making and AI's potential to detect and reduce behavioral biases. We utilized a 5-point Likert scale to score demographics, investment decision-making, and behavioral bias identification in 354 people who completed a self-administered online survey. The study examined gender, investment choices, and AI's impact on Behavioral Biases using descriptive correlation and SEM. Men scored slightly higher in Investment Decision-Making than women. (M = 3.7230 vs. M = 3.6784). There is no statistical significance for this difference. (t = 0.550, p = 0.583). SEM indicates gender affects behavioral biases. (β = 0.022, p < 0.05) these biases were significantly good for investment performance (β = 0.821, p < 0.05). The findings reveal how gender and behavioral biases interact, suggesting AI could improve financial decision-making.

A Comparative Study of The Application of NPV And IRR To Real Estate Investment Decision- Making

The China real estate industry is undergoing a fluctuation nowadays. And this paper is going to investigate this industry by researching a leading enterprise, CHINA VANKE CO., LTD. with the assistance of two financial instruments, Net Present Value (NPV) and Internal Rate of Return (IRR). Furthermore, the paper has another purpose that is comparing the properties between NPV and IRR. This paper uses literature research method and case study to analyse the situation of Vanke and list the possible considerations while having the analysis. After a series of research, the author considers NPV as a better indicator compared to IRR. NPV has the advantage of simple marginal determination, however, the related indicator, discount rate cannot be determined easily and accurately in the real estate analysis since it can be affected by many factors. For advantage of IRR, it is simple to calculate the value because it is only decided by the cashflows. On the other hand, IRR cannot clarify the real estate investment decision-making by itself, it must have the assistance of NPV versus discount rate graph. Consequently, the mixed employment would present a clearer and more comprehensive analysis for investors.

Research on Quantitative Investment Strategies of Securities Investment Funds

This paper investigates the quantitative investment strategies of securities investment funds, aiming to explore their applications in financial markets and their impact on investment decision-making. Through a review of relevant literature and empirical data, the paper first outlines the basic concepts of quantitative investing and its importance in securities investment funds. Then, it discusses various quantitative strategies, including multi-factor models and machine learning algorithms, and validates their effectiveness through case studies. The research finds that quantitative investing can enhance the scientific and accurate nature of investment decisions while effectively reducing investment risks. Finally, the paper provides suggestions for optimizing quantitative strategies and discusses future trends, offering practical references for the investment industry.

IMPACT ASSESSMENT OF INFORMATION TECHNOLOGY ON INVESTMENT CHOICE DECISIONS OF ACADEMIC PROFESSIONALS

The substantial impact of information technology on investment decision-making is explored in this study, and strong empirical support is provided through a significant regression model. Understanding the extent to which technology influences investment choices is of utmost relevance in a time of fast technological advancement. The research makes use of a varied dataset that includes a variety of economic and market characteristics, employing robust statistical analysis to evaluate the relationship between technology and investment decisions. Our findings show that information technology and investing choices are statistically significantly correlated. The regression model illustrates how technology is increasingly being used into investment strategies and how it influences investors' decisions. This study emphasizes the importance of incorporating technical expertise into investment plans, with a focus on data analytics, artificial intelligence, and long-term tech investments. Furthermore, the ease with which mutual funds can be invested in, with alternatives for both novice and experienced investors, has made them a practical and accessible option as the study points mutual funds as the most preferred option for investment. Their liquidity and transparency also resonate with investors who appreciate the ability to monitor their investments easily and access their funds when needed. The results of the study highlight how crucial it is to comprehend investor preferences for financial goods. The popularity of mutual funds not only demonstrates their benefits but also emphasizes the necessity for financial institutions and advisors to customise their services and products in order to meet investor needs. KEYWORDS: Information Technology, Investment Choice Decisions, Mutual Funds

IMPLEMENTASI MACHINE LEARNING ALGORITMA NEURAL NETWORK DALAM MEMPREDIKSI HARGA SAHAM MENGGUNAKAN RAPIDMINER

The research is aimed at implementing the Neural Network algorithm in predicting stock prices using RapidMiner. Historical stock price data is analyzed to train neural network models in identifying patterns and trends that can be used to predict future stock prices. The research phases include data collection, data preprocessing, Neural Network model development, model training, and model performance evaluation. Data used include daily closing prices, trade volumes, and other technical indicators. The data is processed through several stages of preprocesing, including normalization and filling of missing values, before being used to train Neural network models. The model was built with several layers and the number of neurons adjusted based on early experiments to find the optimal configuration. Models are trained using datasets that are divided into training sets and testing sets. The predicted result is compared to the actual value to evaluate the performance of the model. The results showed that the Neural Network model was able to predict stock prices with a fairly high accuracy rate, with an RMSE of 0.989 indicating a low prediction error rate. The research concludes that the Neural Network algorithm implemented in RapidMiner is effective in predicting stock prices and has significant potential to be used in investment decision-making. The use of RapidMiner as a data processing tool has proven to be efficient and facilitating the development and evaluation of predictive models. Keywords: Neural Network, Stock Price Prediction, RapidMiner, Machine Learning, Investment

The Role of Artificial Intelligence in Investment Decision-Making: Opportunities and Risks for Financial Institutions

The Role of Artificial Intelligence in Investment Decision-Making: Opportunities and Risks for Financial Institutions

AN ANALYSIS OF BEHAVIOURAL FINANCE AND ITS INFLUENCE ON INVESTMENT DECISION

This research delves into the fascinating realm of Behavioural Finance, exploring its significant influence on investment decision-making among residents in India's National Capital Region (NCR). By drawing on insights from relevant studies, this work seeks to uncover the intricate link between human behaviour and financial decision-making within this demographic. The study begins by examining the unique financial behaviours of investors in the region, aiming to identify the key investor profiles that shape the investment environment. As a multidisciplinary field combining elements of psychology, sociology, and finance, Behavioural Finance provides the primary framework for this exploration. The research distinguishes between its micro and macro perspectives: the detailed analysis of individual behavioural traits of investors and the broader identification of inefficiencies in the market. Recognizing the pervasive impact of behavioural biases on investment behaviours and decisions, this study offers an in-depth review of diverse research within the Behavioural Finance domain. It sheds light on how human psychology and social dynamics interact with financial decision-making, highlighting the crucial role of Behavioural Finance in contemporary financial practices. In an era where financial markets are not merely governed by rationality but are also shaped by complex human behaviours, this research offers valuable insights into the dynamics of Behavioural Finance. By illuminating the distinct investment choices, the study aims to deepen our understanding of the factors influencing these decisions, ultimately equipping investors and financial professionals with more informed decision-making strategies.

SUSTAINABLE FINANCE AND USE OF ARTIFICIAL INTELLIGENCE IN INVESTMENT DECISION MAKING

Artificial Intelligence (AI) is widely used across various industries to enhance efficiency, optimize decision-making, and create innovative solutions and it is now transforming the field of investment decision-making. As companies and investors place a greater emphasis on ethical and sustainable practices, sustainable finance-which incorporates environmental, social, and governance (ESG) factors into financial activities - is gaining global traction. Artificial Intelligence (AI) is becoming a vital instrument in the sustainable finance, because of its ability to analyse enormous volumes of data and spot patterns that are beyond human comprehension. This research paper examines the use of artificial intelligence (AI) tools like predictive analytics, machine learning, and natural language processing,in the financial decision-making. AI can quickly evaluate ESG aspects, predict long-term sustainability risks, and pinpoint high-impact investment opportunities that are in line with sustainability objectives by automating data gathering and analysis. Furthermore, AI-driven insights promote more ethical and knowledgeable financial decision-making by helping investors better manage risks related to social inequity, resource depletion, and climate change. Even though artificial intelligence (AI) has many benefits for sustainable finance, there are drawbacks as well, including data biases, opaque algorithms, and ethical issues. The article concludes that, artificial intelligence helps in creating more precise, effective, and socially conscious financial strategies.Artificial Intelligence (AI) is becoming increasingly important in sustainable finance decision-making due to its ability to handle complex, large-scale data, identify patterns, and optimize decision-making processes in ways that can advance sustainability goals. However, to fully realise AIs potential in forming a sustainable future for global finance, careful governance and ethical concerns are indispensable.

Impact of Heuristic Behavior and Risk Perception on Investment Decisions by Young Investors

This research examines how young investors' heuristic behaviour and risk perception influence their decision-making in investments within the Faculty of Economics and Business at Lambung Mangkurat University. The variables under investigation in this study include heuristic behaviour and risk perception as independent factors, with investment decision-making as the dependent variable. The study population encompassed 2,901 students in the Faculty of Economics and Business at Lambung Mangkurat University. The sample size was determined through purposive sampling, resulting in a final sample of 66 participants with investment experience. Primary data was collected via a questionnaire. The analysis employed multiple linear regression using the SPSS version 26 software. The findings demonstrate that heuristic behaviour and risk perception significantly influence investment decision-making. The implication of this research is to provide an understanding that it is essential to consider fundamental and technical analysis in investment decisions

Women in C-suite: Does Top Management Team gender diversity matter? Evidence from firm investment efficiency

Firms with relatively more female executives in their top management team exhibit superior investment efficiency. Economically, female non-CEO executives increase investment efficiency by almost 11 % of the cross-sectional mean of our investment efficiency measure. Further analysis shows that this increase in efficiency is driven by the reduction in both overinvestment and underinvestment. In addition, efficiency is more pronounced in less competitive markets, during industry shock periods, and in firms with less competent or outsider CEOs. Our findings highlight that women in C-suite play a significant positive role in effective investment decision-making.

LOSS AVERSION: THE UNSEEN FORCE SHAPING INVESTMENT DECISIONS

This research paper aims to investigate the profound impact of loss aversion on individual investment decision-making, with a specific focus on its influence on risk preferences. Loss aversion, a key concept in behavioral economics, posits that individuals experience a greater psychological impact from losses than equivalent gains, thereby shaping their decision-making processes. The study employs an empirical approach, utilizing a diverse sample of individual investors to analyze how loss aversion interacts with risk preferences and influences investment choices. The research methodology involves a combination of surveys and data analysis to explore the intricate relationship between loss aversion and risk preferences. By examining real-world investment decisions in various market conditions, the study seeks to identify patterns and behavioral tendencies that emerge when individuals confront potential losses in their investment portfolios. The research design also considers demographic factors, financial literacy, and other relevant variables to provide a comprehensive understanding of the factors influencing investment choices. Ultimately, this study aims to enhance our comprehension of the psychological factors driving individual investment decisions and to inform the development of strategies that mitigate the negative impacts of loss aversion on financial outcomes.

Evaluating ESG Investment Profitability: From the Perspective of Sophistication in Investment Decision-Making

Environmental, Social, and Governance (ESG) investments are widely discussed in the stock market. Further, the debate on ESG investment profitability has garnered significant scholarly attention. This study examines the relationship between ESG investments and equity investment return (EIR) and whether this relationship is affected by investors' sophistication. The results suggest that while ESG investors generally gain relatively lower profits than others, sophisticated investors, characterized by greater financial knowledge, diversified investments, contrarian investment strategies, and not being influenced by familiar biases, can mitigate these lower returns. These findings enhance our understanding of ESG investments and offer valuable insights to guide investors in making sophisticated ESG investment decisions.

The Impact of Prospect Theory on Asset Forecasting: A Comparative Study Based on Random Forest and Linear Regression

This study focuses on the application of predictive models and prospect theory in the field of machine learning to predict financial asset prices. Here, the linear regression and random forest algorithms were employed to forecast the price values and fluctuations of two different financial assets: the S&P 500 index and Bitcoin prices. Meanwhile, this study compared the performance of different financial asset datasets before and after the introduction of prospect theory models. Through calculation and comparative analysis, this article provides an in-depth discussion on the predictive performance of regression models and classification models. The research results indicate that in most cases, simple linear regression models have high prediction accuracy. Meanwhile, this paper also found that introducing prospect theory can effectively improve the accuracy of prediction models for specific financial assets. This result has a positive impact on the financial industry, helping to optimize risk management and investment decision-making, improving efficiency at the market level and promoting information transparency, while also promoting the development of emerging financial formats in financial innovation