Investment Outcomes Research Articles

Empowering Investors: Reducing Herd Bias through Financial Literacy and Self-reflective Thinking

Herd behavior, where investors mimic the trades of others rather than relying on their own information or analysis, is a widespread phenomenon that can lead to suboptimal investment decisions and market inefficiencies. Hence, understanding how to reduce this behavior is crucial for improving individual investment outcomes and sustainable market performance. Though the literature suggests that self-reflection and financial literacy have potential to mitigate herd bias, it is unclear as to how these two perspectives interact to minimize this irrational behavior. By drawing on insights from the adaptive market hypothesis, transformative learning theory, concept of bounded rationality and dual process theory, this study attempts to enhance the understanding of this phenomenon by introducing a conditional mediation model that promotes self-directed learning. It predicts that investors can reduce their herd bias through self-reflection on their past stock trading experiences, which they can practice by engaging intuitive logical thinking and strengthen further through their financial literacy. Since herding is generally predicted to be more prevalent in frontier stock markets, the study was conducted in a frontier market - the Colombo Stock Exchange. The data was obtained through a self-administered questionnaire from 253 active individual investors, and analyzed by applying the PROCESS procedure. The findings reveal the significance of self-reflection as a mediating variable across its antecedents, which provides strong evidence for its central role in reducing herd bias. Notably, the findings indicate that self-reflection has a stronger effect on reducing herd bias of investors with lower level of financial literacy. It implies that investors with low financial literacy are more susceptible to herd bias, and self-reflection facilitates them to recognize and minimize their herd bias. Accordingly, the study concludes that promoting self-reflection can empower individual investors to become more financially literate and thereby mitigate their herd bias.

Psychological Determinants of Investment Decisions: Analyzing Financial Behavior in Personal Investments

Understanding the psychological factors that influence investor behavior is critical in the dynamic world of financial markets. Financial conduct encompasses the decisions and behaviors individuals exhibit in managing their finances, including investments in various asset classes. Factors such as risk tolerance, cognitive biases, emotional influences, and financial knowledge significantly shape investment outcomes. Gaining long-term financial success requires mastery over these behavioral aspects. This study investigates the influence of three psychological factors—information asymmetry, problem framing, and risk propensity—on the investment decisions of 220 active investors trading on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). A quantitative research approach was employed, utilizing a structured questionnaire to collect data. Statistical analyses, including regression and correlation analysis, were used to assess the relationships between these psychological variables and investment behaviors. The results reveal significant correlations between psychological factors and investment decisions. Information asymmetry, problem framing, and risk propensity were found to strongly influence individual investor choices. These findings shed light on the intricate role that cognitive biases and psychological processes play in shaping financial decision-making. The study's findings offer valuable insights into the psychological drivers behind investment behavior. By highlighting the impact of these factors, the research contributes to both academic understanding and practical applications for financial professionals. The results underscore the importance of enhancing financial literacy and investor education, enabling more informed decision-making and promoting improved financial outcomes.

Forecasting Orange Juice Futures: LSTM, ConvLSTM, and Traditional Models Across Trading Horizons

This study evaluated the forecasting accuracy of various models over 5-day and 10-day trading horizons to predict the prices of orange juice futures (OJ = F). The analysis included traditional models like Autoregressive Integrated Moving Average (ARIMA) and advanced neural network models such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), and Convolutional Long Short-Term Memory (ConvLSTM), incorporating factors like the Commodities Index and the S&P500 Index. We employed loss function metrics and various tests to assess model performance. The results indicated that for the 5-day horizon, the LSTM and ConvLSTM consistently outperformed the other models. LSTM achieved the lowest error rates and demonstrated superior capability in capturing temporal dependencies, especially in single-factor and S&P500 Index predictions. ConvLSTM also performed strongly, effectively modeling spatial and temporal data patterns. In the 10-day horizon, similar trends were observed. LSTM and ConvLSTM models had significantly lower errors and better alignment with actual values. The BPNN model performed well when all factors were included, and the SVR model maintained consistent accuracy, particularly for single-factor predictions. The Diebold–Mariano (DM) test indicated significant differences in forecasting accuracy, favoring advanced neural network models. In addition, incorporating multiple influencing factors further improved predictive performance, enhancing investment outcomes and reducing risk.

The Ripple Effects of Financial Fraud: Investor Trust and Risk Appetite Post-Kangmei Pharmaceutical

The financial fraud at Kangmei Pharmaceutical (stock code: 600518) has had profound implications for retail investors. This paper, through data analysis, explores the correlation between information asymmetry and investment outcomes for retail investors. Information asymmetry often leaves retail investors unable to access the true financial state and operational conditions of companies, leading them to make speculative investments. Post-exposure, as information transparency increased, retail investors became aware of the company's financial issues and rushed to sell, causing a stock plunge. This highlights how information asymmetry not only affects investment decisions but also amplifies market volatility. To mitigate risks from information asymmetry, regulators must strengthen oversight and penalties, improve disclosure transparency and authenticity, while investors need to enhance self-protection awareness and information gathering and analysis capabilities. Only then can we minimize the impact of information asymmetry on retail investors and uphold fairness and stability in financial markets. This paper suggests that by implementing a robust regulatory framework and encouraging proactive investor education, we can better safeguard retail investors from the detrimental effects of information asymmetry, thereby fostering a more resilient and equitable financial market ecosystem.

The Impact of Financial Literacy on Selected Nigerian Public Sector Employees Savings Culture and Investment

Financial literacy ensures that an individual is able to match his income with his expenditure, lives within his means and forestall going broke or bankrupt. Like general or health literacy, financial literacy could be conceptualized as having two dimensions: understanding (personal finance knowledge) and use (personal finance application). In this study, we reviewed how financial literacy is measured in the current literature and examine how well the existing studies addresses whether financial literacy improves employees savings culture and investment. We review the literature on alternative policies to improve financial outcomes and compare the evidence on whether financial literacy improves employees spending habits, savings, investments and standard of living. The sample size of this survey based study consists of 110 working men and women from non-financial public sector in Nigeria. Data was analyzed using simple percentage and frequency distribution. The Statistical Package for Social Sciences (SPSS) was used in the analysis of the data. The major challenge in financial education has been how to measure the impact of financial education on the recipient savings culture. Investment and standard of living. On the other hand what is the influence of financial literacy on employees’ savings and investment outcome. It can be observed that in some instances those who did not have formal financial education and those who are primary or secondary school dropout have risen to own successful business empire. However, some who have tertiary financial education are struggling to survive. A large proportion of the sampled employees are deficient in financial literacy notwithstanding their exposure to financial education. Contrary to popular perception this research shows that there is no significant relationship between financial literacy and education level, savings culture and investments, of Nigerian public sector employees. Finally, we discuss directions ........

Strategies for Mitigating and Managing Risks in Bank Investments

This paper suggests that while studies of banking risk management often emphasize analytics and stress testing to improve risk governance, there is too little emphasis on historical case studies to generate comprehensive risk management strategies. Using numerous case studies, the paper suggests four categories of risk - market risks, credit risks, regulatory risks, and operational risks - that impact overall investment outcomes. By extensively reviewing both historic failures and historic successes, the paper highlights the importance of understanding different types of risks and adopting strategic measures to manage them effectively. Emphasis is placed on the necessity of a diversified investment portfolio to mitigate market volatility and credit exposure. Additionally, the paper provides actionable strategies for navigating regulatory complexities and operational challenges, supported by real-world case studies and practical examples. This research aims to equip investors with valuable insights and tools to optimize investment opportunities while minimizing potential risks in the banking sector.

The Impact of Financial Literacy on Investment Decisions: The Moderating Role of Financial Technology

This study investigates the impact of financial literacy on investment decisions and explores the moderating role of financial technology (Fintech). Conducted among small and medium-sized enterprises (SMEs) in Bali Province, Indonesia, the research seeks to understand how financial literacy influences investment choices and how Fintech enhances this relationship. A quantitative approach was used, involving the distribution of questionnaires to 100 SME owners who received business legality through decentralization funds. The data, collected via primary sources and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) 4.0, included measures of financial literacy, investment decisions, and Fintech usage. The findings reveal that higher financial literacy positively impacts investment decisions, with SMEs demonstrating better investment management and decision-making capabilities. Additionally, Fintech was found to strengthen this relationship by providing real-time data and advanced analytical tools that enhance investment decisions for those with strong financial literacy. However, the benefits of Fintech were more significant for individuals with higher financial knowledge, indicating that technology alone cannot replace the need for financial education. This study underscores the importance of integrating financial literacy with technological tools to improve investment outcomes and suggests that future research should explore diverse geographic contexts and longitudinal studies to better understand the evolving dynamics between financial literacy, Fintech, and investment decisions

Impact of Behavioral Biases Observed on Investors’ Performance in The Nepalese Stock Market

The purpose of this research article is to clarify how behavioral biases affect the performance of investors within the framework of the Nepalese stock market. This study employs a quantitative analysis utilizing a purposive and causal research design. Data was collected between April and May of 2024 through a survey conducted with 124 respondents who actively trade in the Nepalese stock market. Secondary data was gathered from existing research papers to support the findings. The analysis reveals that emotional biases, specifically Loss Aversion Bias and Overconfidence Bias, significantly impact investor performance. In contrast, cognitive biases, such as Anchoring Bias, Availability Bias, and Representativeness Bias, showed no significant effect on investor performance. This suggests that while cognitive biases can be identified and adapted, emotional biases may have a more profound influence on trading decisions. Implications the significance of this study extends beyond individual and institutional investors. It provides insights into the importance of understanding various biases that affect investor performance in the Nepalese context. By addressing these biases, investors can improve their decision-making processes and potentially enhance their investment outcomes. This research contributes to the existing literature on behavioral finance by focusing on the specific biases affecting Nepalese investors. It highlights the need for further exploration of these biases to mitigate inconsistencies in investor performance and promote more informed trading strategies in the Nepalese stock market.

Mastering Market Movements: Technical Indicators in IDX30 Stock Selection

This study aims to analyze the role of trading volume, foreign money flows, and technical chart patterns in shaping investment decisions for IDX30 companies. Utilizing descriptive quantitative research, data was collected from Stockbit and IPOT, focusing on stock price charts, trading volume, and broker summaries from January 1, 2020, to December 31, 2022. The findings reveal that purchase decisions were made when stock prices broke out of a chart pattern, accompanied by increased trading volume and foreign accumulation. Conversely, sell decisions occurred when stock prices broke down from a pattern, followed by increased volume and foreign distribution. These results underscore the importance of technical analysis in guiding informed investment decisions, helping investors avoid irrational practices and potential losses. The study highlights the practical need for investors to integrate technical indicators, such as volume and foreign flows, into their decision-making processes to enhance investment outcomes.

A Novel Stock Price Prediction and Trading Methodology Based on Active Learning Surrogated with CycleGAN and Deep Learning and System Engineering Integration: A Case Study on TSMC Stock Data

Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial or inaccurate results. This study introduces the application of Cycle Generative Adversarial Network (CycleGAN) alongside Deep Learning (DL) models, such as Residual Neural Network (ResNet) and Long Short-Term Memory (LSTM), to assess the joint effects of stock price and trading volume on prediction accuracy. By incorporating these models into system engineering (SE), the research aims to decode short-term stock market trends and improve investment decisions through the integration of predicted stock prices with Bollinger Bands. Thereby, active learning (AL) is employed to avoid over-and under-fitting and find the hyperparameters for the overall system model. Focusing on TSMC’s stock price prediction, the use of CycleGAN for analyzing 30-day stock data showcases the capability of ResNet and LSTM models in achieving high accuracy and F-1 scores for a five-day prediction period. Further analysis reveals that combining DL predictions with SE principles leads to more precise short-term forecasts. Additionally, integrating these predictions with Bollinger Bands demonstrates a decrease in trading frequency and a significant 30% increase in average Return on Investment (ROI). This innovative approach marks a first in the field of stock market prediction, offering a comprehensive framework for enhancing predictive accuracy and investment outcomes.

Bridging The Gap: Leveraging Augmented And Virtual Reality To Improve Retail Investment Outcomes In Equity Trading

Bridging The Gap: Leveraging Augmented And Virtual Reality To Improve Retail Investment Outcomes In Equity Trading

Do Real Estate Investments Yield the Highest Returns in Pakistan?

The investment choices of economic agents are critical in determining the value of capital stock in different sectors of the economy. These choices depend upon the available investment options in society and the expected rate of returns. In the case of Pakistan, it is commonly perceived that real estate investment in the form of buying a piece of land or buying a house comparatively yields the highest returns. This study aims to devolve on this presumption and estimate returns from different available investment avenues. Our findings, contrary to common perception, suggest investment in stocks yields the highest real returns while real estate returns stand second. However, the associated risk with stock investment is higher than in the real estate sector. Therefore, one can argue that real estate investment offers better returns comparatively. Surprisingly, we also find that the real returns from treasury bills, national savings accounts, and silver are negative. We also delve into the potential determinants of real estate returns and their impact on investment outcomes. The estimation results reveal that GDP growth, interest rate, and inflation pandemic are significantly affecting the rate of returns from real estate, while the Covid pandemic has negatively affected the returns from housing.

The Role of Actively Open-Minded Thinking and Control Orientation in VC Investment Outcomes

The Role of Actively Open-Minded Thinking and Control Orientation in VC Investment Outcomes

Innovation investment and performance: the moderating role of university collaborations

Purpose Considering the historical evolution of innovation dynamics, and its paradoxical state, and answering Nelson (2008) and Winter (2014), this paper aims to analyze the dynamics of innovation within the context of the Evolutionary Economic Theory. Specifically, this study looks to unravel the moderating influence of university cooperation on the relationship between innovation expenditure and innovation results. This study aims to provide valuable insights and evidence that can inform strategic decision-making for policymakers and businesses striving to foster innovation-driven economic growth in an ever-evolving global landscape. Design/methodology/approach The quantitative methodology adopted in this study involved harvesting data from the Latin American Innovation Survey (LAIS) database and cleaning it up using Python to ensure data integrity. Subsequently, SPSS, coupled with the PROCESS macro, was employed to conduct moderation analysis. This methodological approach enabled the examination of the intricate interplay between innovation expenditure, university cooperation and innovation outcomes within a large sample of firms, thereby easing a robust exploration of the hypothesis. Findings The research highlights the moderating role of university cooperation, showing that collaborative partnerships amplify the impact of innovation spending on innovation results, advancing the understanding of the impact of university-business collaborations. Additionally, the results revealed a positive relationship between innovation expenditure and innovation results, underscoring the significance of R&D investments. Practical implications This study highlights the role of university-industry collaborations in enhancing innovation investment outcomes in Latin America. It suggests that managers should proactively engage with universities to access advanced research and foster a culture of innovation. These partnerships can significantly boost a firm's competitive edge and innovation success, marking them as crucial in the rapidly evolving economic environment. Originality/value The originality of the paper relies on bridging theoretical concepts from the Evolutionary Economic Theory framework with empirical insights of the moderating role of university cooperation. It addresses a theoretical gap, with a new methodology and offers insights into the complex relationship between universities, businesses and innovation in a constantly changing economic environment, making it clearer how these connections can help boost innovation in practical ways.

Cash Versus Kind: Benchmarking a Child Nutrition Program Against Unconditional Cash Transfers in Rwanda

Abstract We develop a methodology to benchmark in-kind programs against cost-equivalent cash transfers. Our application compares a multi-dimensional child nutrition intervention to unconditional cash transfers, using randomised variation in transfer amounts and regression adjustment of expenditures to estimate impacts of cash transfers at identical cost as well as to estimate the return to increasing cash transfer amounts. While neither the in-kind program nor a cost-equivalent transfer costing ${\$}$124 per household moves core child outcomes within a year, cash transfers create significantly greater consumption and asset accumulation. A larger cash transfer costing ${\$}$517 substantially improves consumption and investment outcomes and drives modest improvements in dietary diversity and child growth.

Stock Trend Prediction with Machine Learning: Incorporating Inter-Stock Correlation Information through Laplacian Matrix

Predicting stock trends in financial markets is of significant importance to investors and portfolio managers. In addition to a stock’s historical price information, the correlation between that stock and others can also provide valuable information for forecasting future returns. Existing methods often fall short of straightforward and effective capture of the intricate interdependencies between stocks. In this research, we introduce the concept of a Laplacian correlation graph (LOG), designed to explicitly model the correlations in stock price changes as the edges of a graph. After constructing the LOG, we will build a machine learning model, such as a graph attention network (GAT), and incorporate the LOG into the loss term. This innovative loss term is designed to empower the neural network to learn and leverage price correlations among different stocks in a straightforward but effective manner. The advantage of a Laplacian matrix is that matrix operation form is more suitable for current machine learning frameworks, thus achieving high computational efficiency and simpler model representation. Experimental results demonstrate improvements across multiple evaluation metrics using our LOG. Incorporating our LOG into five base machine learning models consistently enhances their predictive performance. Furthermore, backtesting results reveal superior returns and information ratios, underscoring the practical implications of our approach for real-world investment decisions. Our study addresses the limitations of existing methods that miss the correlation between stocks or fail to model correlation in a simple and effective way, and the proposed LOG emerges as a promising tool for stock returns prediction, offering enhanced predictive accuracy and improved investment outcomes.

Tax Implications of Real Estate Investments: A Comprehensive Analysis

Real estate investments constitute a significant portion of many investors' portfolios, offering the potential for long-term wealth accumulation and diversification. However, beyond considerations of property location, market trends, and financing options, investors must also navigate the intricate landscape of tax implications associated with real estate investments. Taxes play a critical role in shaping investment decisions, impacting cash flow, profitability, and overall returns. Therefore, understanding and effectively managing tax obligations is paramount for maximizing investment success in the real estate market. This comprehensive analysis aims to delve into the multifaceted realm of tax implications related to real estate investments. By examining various tax strategies, structures, regulations, and their implications, this study seeks to provide investors with valuable insights and tools to optimize their tax position and enhance investment outcomes. From the tax treatment of rental income and capital gains to the utilization of tax-efficient investment structures like REITs and LLCs, every aspect of real estate taxation will be explored in detail. Moreover, with the ever-evolving nature of tax legislation and economic conditions, it is imperative for investors to stay abreast of changes that may impact their tax liabilities and investment strategies. Through a thorough analysis of historical trends and current tax laws, this study aims to equip investors with the knowledge and foresight necessary to adapt and thrive in a dynamic tax environment. In essence, this comprehensive analysis endeavors to empower real estate investors with the tools and understanding needed to navigate the complex terrain of tax implications effectively. By unraveling the intricacies of real estate taxation and providing actionable insights, this study aspires to assist investors in making informed decisions, optimizing their tax position, and ultimately realizing the full potential of their real estate investments.

A Study on Behavioural Finance

This research paper delves into the intriguing field of behavioral finance, exploring how psychological factors influence financial decision-making processes. Behavioral finance seeks to understand why people make irrational financial decisions despite being aware of the consequences. It bridges the gap between economics and psychology, offering insights into human behavior that traditional finance models often overlook. The study examines various aspects of behavioral finance, including cognitive biases, market anomalies, and investor behavior under different market conditions. Through a comprehensive review of existing literature and empirical studies, this paper aims to shed light on the complex interplay between human emotions, cognitive limitations, and financial markets. The findings contribute to a deeper understanding of investor behavior, potentially informing strategies for financial advisors and investors alike. This research underscores the importance of incorporating psychological principles into financial analysis, highlighting the potential for improved investment outcomes through a more nuanced understanding of human behavior.

Catalyzing Sustainable Investment: Revealing ESG Power in Predicting Fund Performance with Machine Learning

AbstractIn today’s dynamic financial landscape, the integration of environmental, social, and governance (ESG) principles into investment strategies has gained great significance. Investors and financial advisors are increasingly confronted with the crucial question of whether their dedication to ESG values enhances or hampers their pursuit of financial performance. Addressing this crucial issue, our research delves into the impact of ESG ratings on financial performance, exploring a cutting-edge machine learning approach powered by the Extreme Gradient algorithm. Our study centers on US-registered equity funds with a global investment scope, and performs a cross-sectional data analysis for annualized fund returns for a five-year period (2017–2021). To fortify our analysis, we synergistically amalgamate data from three prominent mutual fund databases, thereby bolstering data completeness, accuracy, and consistency. Through thorough examination, our findings substantiate the positive correlation between ESG ratings and fund performance. In fact, our investigation identifies ESG score as one of the dominant variables, ranking among the top five with the highest predictive capacity for mutual fund performance. As sustainable investing continues to ascend as a central force within financial markets, our study underscores the pivotal role that ESG factors play in shaping investment outcomes. Our research provides socially responsible investors and financial advisors with valuable insights, empowering them to make informed decisions that align their financial objectives with their commitment to ESG values.

Risk Management Strategies in Facing Global Financial Market Volatility

Global financial market volatility refers to the unpredictable fluctuations in the prices of financial assets such as stocks, bonds, currencies, and commodities. This volatility can stem from various factors such as economic indicators, geopolitical events, policy changes, technological advancements, and investor sentiment. This research aims to analyze effective risk management strategies in facing global financial market volatility. Market volatility often triggers anxiety, uncertainty, and irrational investment decisions. In this situation, it is important for investors to adopt the right strategies to minimize risks and maximize long-term investment outcomes. In this study, data is collected from various sources related to global financial market volatility and risk management strategies adopted by investors during periods of uncertainty. Qualitative and quantitative analyses of this data are conducted to identify patterns of investor behavior and the effectiveness of applied risk management strategies. The research findings indicate that maintaining a long-term perspective, retaining portfolio diversification, and taking an active approach to risk management are effective strategies in dealing with market volatility. Open communication with financial professionals is also proven to be essential in helping investors stay calm and make rational decisions.