Stock Investment Research Articles

Valuation and Returns on Stock Return Volatility

ABSTRACT This paper provides an accounting-based valuation model that predicts that cross-sectional variation in firm-level returns to investments in both stock and stock return volatility are related to cross-sectional variation in firm-level fundamentals. The model predicts that expected stock returns have a positive quadratic relation with stock return variance and a negative quadratic relation with gains to trading in stock return variance. Consistent with these predictions, firms with high model-implied expected stock returns have high future stock return variance, and the relation is roughly quadratic. In contrast, firms with high expected stock returns have low future returns to trading in stock return variance through option contracts because these firms have high option-implied variance relative to future realized variance, i.e., low variance risk premia (VRP). The study provides a framework for using fundamentals for trading in individual stocks and options. JEL Classifications: G12; G14; G17.

Reinforcement Learning-Based Multimodal Model for the Stock Investment Portfolio Management Task

Machine learning has been applied by more and more scholars in the field of quantitative investment, but traditional machine learning methods cannot provide high returns and strong stability at the same time. In this paper, a multimodal model based on reinforcement learning (RL) is constructed for the stock investment portfolio management task. Most of the previous methods based on RL have chosen the value-based RL methods. Policy gradient-based RL methods have been proven to be superior to value-based RL methods by a growing number of research. Commonly used policy gradient-based reinforcement learning methods are DDPG, TD3, SAC, and PPO. We conducted comparative experiments to select the most suitable method for the dataset in this paper. The final choice was DDPG. Furthermore, there will rarely be a way to refine the raw data before training the agent. The stock market has a large amount of data, and the data are complex. If the raw stock market data are fed directly to the agent, the agent cannot learn the information in the data efficiently and quickly. We use state representation learning (SRL) to process the raw stock data and then feed the processed data to the agent. It is not enough to train the agent using only stock data; we also added comment text data and image data. The comment text data comes from investors’ comments on stock bars. Image data are derived from pictures that can represent the overall direction of the market. We conducted experiments on three datasets and compared our proposed model with 11 other methods. We set up three evaluation indicators in the paper. Taken together, our proposed model works best.

Expression of Concern: “Perceived Behavioral Factors and Individual Investor Stock Market Investment Decision: Multigroup Analysis and Major Stock Markets Perspectives”

Expression of Concern: “Perceived Behavioral Factors and Individual Investor Stock Market Investment Decision: Multigroup Analysis and Major Stock Markets Perspectives”

The Role of Social Cognitive Perspective on Investment Intentions in Sharia Stocks in Indonesia

The majority of Indonesia's population recorded in 2024 will be Muslims, reaching 85% with a figure of 263 million out of a total population of more than 278 million people. However, the level of public investment in shares is still low, especially in sharia shares. The aim of this research is to examine the impact of attitudes, social influence, and self-confidence on Indonesian investors' intentions to invest in sharia shares. This will be achieved by exploring further with Social Cognitive Theory. This research uses a quantitative methodology which is used to collect data from a minimum sample size of 85 respondents. This research was conducted by giving questionnaires to investors who have knowledge about sharia shares in Indonesia. The expected result of this research is to increase understanding of the determining factors that influence investment intentions in sharia shares. Therefore, this research is expected to provide a significant contribution to policy makers in their efforts to increase the number of investors in Islamic stocks.

Analysis of the Application of the Capital Asset Pricing Model (CAPM) Method in Making Islamic Stock Investment Decisions

This study aims to analyze the application of the Capital Asset Pricing Model (CAPM) in sharia stock investment decision making, especially JII70. The research design used is a quantitative approach. The type of research used is descriptive research. This research leads to the shares of companies included in the Jakarta Islamic Index 70 (JII70) on the Indonesia Stock Exchange. The population in this study were all shares of companies listed on the Jakarta Islamic Index 70 (JII70) in the period January 2019-January 2024. The research sample was 32 JII70 stock issuers who consistently entered JII70 during the period January 2019-January 2024. The results showed that there were 14 issuers of JII70 stocks in the efficient category because they had individual stock returns greater than the expected rate of return [Ri > E(Ri)] with positive individual returns.

Analysis of Factors Influencing The Proportion of Stock Investments in Millennial Generation Investors

This study aims to analyze the factors that influence the proportion of stock investment among millennial generation investors, especially millennial generation students in Yogyakarta City. The research method used is quantitative with a survey approach. Data collection was carried out through questionnaires distributed to 100 students from various universities in Yogyakarta who actively invest in the stock market. Data analysis was carried out using SPSS version 25 software, with multiple regression analysis techniques to identify significant factors that influence the proportion of stock investment. The independent variables tested include financial literacy, income, investment risk and social influence, while the dependent variable is the proportion of stock investment. The results showed that financial literacy and income have a positive and significant effect on the proportion of stock investment. Meanwhile, investment risk has a negative and significant effect, and social influence does not show a significant effect on the proportion of stock investment.

Stock Market Participation Awareness among University Students in Nepal

Financial Literacy amongst students is very important aspects. Therefore, as part of Financial Literacy, a study was conducted amongst 120 students of various colleges and fields to explore the awareness level of stock market investments in the Kathmandu valley. With a growing number of young investors entering the financial market, this research seeks to understand the factors that influence students' engagement with the stock market, particularly in a developing economy like Nepal. The study employs both qualitative and quantitative methodologies, including surveys distributed to students across various colleges in Nepal. Findings reveal that while there is moderate awareness about the stock market among students, actual participation remains limited. The main barriers include a lack of financial literacy, limited disposable income, and perceived risks associated with stock investments. Furthermore, cultural attitudes towards saving and investing also play a significant role in shaping students' engagement with the stock market. The research underscores the importance of integrating financial education from the school level itself to equip students with the knowledge and confidence to participate in the stock market. This study contributes to the existing literature on financial literacy and investment behaviors, offering insights into the unique challenges faced by students in developing countries like Nepal. It emphasizes the need for targeted interventions to enhance financial literacy and foster a more inclusive financial ecosystem.

PENENTUAN VALUE AT RISK (VAR) PADA PORTOFOLIO BIVARIAT DENGAN PENDEKATAN COPULA GUMBEL

One way to minimize risk in stock investment is stock portfolio. Value at Risk (VaR) is a calculation method that can be used to estimate the risk of a stock portfolio. VaR can be measured by parametric and non-parametric approaches. Calculation of VaR with Monte Carlo simulation assumes the data is normally distributed. Stock return data generally has high volatility so that the residual variance of the model is not constant (heteroscedasticity) and not normally distributed. The ARIMA-GARCH model can be used to solve heteroscedasticity problems. Copula is a tool used to model the combined distribution of residuals from the ARIMA-GARCH model which does not require normality assumptions. Gumbel's copula is copula that has the best sensitivity to high risk. This study uses stock data of PT Bukit Asam Tbk (PTBA) and PT Chandra Asri Petrochemical Tbk (TPIA) for the period April 1 2020 – December 1 2022. The initial step of this research is model stock returns using the ARIMA-GARCH method and then calculate portfolio VaR using the Gumbel’s copula. The results showed that the best model for PTBA is ARIMA(2,0,2) GARCH(1,1) and for TPIA is ARIMA(1,0,0) GARCH (1,1). At the 95% confidence level, the portfolio risk is 2,41%.

Does ICT Investment Affect Market Share and Customer Acquisition Cost? A Comparative Analysis of Domestic and Foreign Banks Operating in India

Competitive banks aggressively invest in information and communication technologies (ICT) to enhance their market share and reduce Customer Acquisition Costs (CAC). This study examines the impact of cumulative stock of ICT investment on (a) deposit and loan market share and (b) CAC of banks operating in India. The analysis uses a longitudinal dataset of 84 domestic and 70 foreign banks from 2000 to 2020, employing a two-step system Generalized Method of Moment (GMM). It is found that ICT investment adversely affects the market share of domestic banks, indicating a need for these banks to strategically invest more in CAC. Conversely, foreign banks are able to increase their market share through ICT investment and reduced CAC, thereby demonstrating greater efficiency in utilizing ICT. The study underscores the strategic importance of cumulative stock of ICT investment for banks. Nonetheless, it is emphasized that ICT investment must be complemented with innovative marketing strategies to enhance customer experience, reduce CAC, and increase market share. Overall, while foreign banks are able to leverage ICT to boost efficiency, domestic banks must leverage ICT to implement targeted marketing strategies and strive to enhance their customer service.

Navigating Market Movements: The Role of Herd Behaviour in Indian Stocks

When an individual elects to allocate their financial resources towards the stock market for the initial occasion or on a consistent basis, yet possesses insufficient knowledge pertaining to market analysis and investment strategies, the primary issue or inquiry that emerges is the appropriate allocation of capital. In more straightforward terms, this involves determining whether to invest in equity shares, preference shares, debentures, or to allocate resources to mutual funds, and if so, in which specific equity/preference shares, debentures, or mutual funds the investment should be made. Individuals may either engage the services of a stock broker to manage their investments on their behalf or choose to undertake the investment process independently. In both scenarios, their decision-making processes are likely to be influenced, either wholly or partially, by herd behaviour, commonly referred to as the bandwagon effect. Individuals tend to adhere to prevailing trends. When a substantial cohort of individuals engages in investment in a particular stock, there exists a propensity among others to similarly invest in that stock, often without conducting thorough independent analysis. They possess a belief that a significant collective cannot err. The bandwagon effect is an inherent phenomenon that is relatively innocuous to a certain degree; however, it can occasionally be strategically manipulated to influence market movements in a preferred direction, an action that may constitute unfair trade practices and is frequently observed within the stock market. While herd behaviour in everyday situations may be benign, it can prove detrimental when related to investment decisions.

Cognitive Ability and Stock Investment among Chinese Middle-aged and Older Population

This study uses the Wave 2 (2013), Wave 3 (2015) and Wave 4 (2018) released by Harmonized China Health and Retirement Longitudinal Study (CHARLS) to examine the effect of cognitive ability on stock investment in the middle-aged and older populations in China. This study investigates the relationship between subjective and objective aspects of cognitive ability on stock ownership and shares of stock holdings in financial investment over time. We further compare this relationship between subsamples (older adults vs. middle-aged adults). The findings in this study will provide implications for policymakers, financial professionals as well as investors.

Enhancing the sustainable development of investment funds: a study of the stock market in Vietnam

Stock investment funds play an important role in the development of the stock market by creating demand for the market, while increasing the professionalism of market activities. With expertise, the ability to access and process information effectively, investment funds will bring benefits to all participants. The role of stock investment funds becomes even more important in developing countries where market information is often imperfect and the state's warning systems and control capabilities are limited. In Vietnam, the strong growth of the stock market over the past 10 years has been the foundation for the formation and development of stock investment funds. However, Vietnam's fund industry has only started to take shape in the last 5 years. Up to now, there are only 24 domestic closed-end stock investment funds operating on the Vietnamese stock market, with limited size and diversity of fund products compared to the market potential. At the same time, the stock investment funds themselves have not yet promoted their role in attracting investment capital, leading individual investors, and contributing to the stability of the stock market during their existence. Keywords: Investment Fund, Stocks, Vietnam.

ESG investment performance and global attention to sustainability

We analyze ESG-based investments in stocks across 23 developed markets using daily data from 2004 to 2022. The findings suggest a weak relationship between the ESG ratings and expected returns, with some evidence of modest underperformance of high ESG stocks compared to lower-rated ones in specific periods. This outcome indicates that stock prices have already reflected ESG information, and well-known asset pricing factors can effectively capture the returns of portfolios based on ESG ratings. However, the strength of this relationship depends on global attention to sustainability, where high ESG-rated stocks tend to gain advantages during unexpected attention increases, highlighting the dynamic, nonlinear nature of this relationship.

The Dynamic Relationship Between Exchange Rate Volatility and Stock Prices in the Egyptian Real Estate Market and the Moderating Effect of Interest Rates

Among different channels of the financial market, the exchange rates and stock markets play a vital role in the domestic and international markets. This study aims to explore the dynamic relationship between the exchange rate (USD/EGP) and the real estate sector listed companies of the Egyptian Stock Exchange (EGX) from 2013 to 2023. Additionally, the study examines the moderating effect of interest rates on the relationship. To achieve this, the study employs several econometric methods such as Augmented Dicky Fuller (ADF) test, Granger Causality test to determine the direction of the relationship, Vector Autoregressive (VAR) model, and more. The results demonstrate that a long-run relationship exists between the variables. Furthermore, there is a significant unidirectional causality that runs from the exchange rate to real estate stock prices; thus, fluctuations in the exchange rate can significantly affect the profitability of investments in real estate stocks. Therefore, investors should closely monitor the movements in the exchange rate level to predict changes in real estate stock prices. When including the interest rate as a moderator, the outcome changes to bidirectional causality, meaning that the exchange rate and real estate stock price movements affect each other at a 1% significance level. Hence, interest rate could be used as an instrument to control demand in both the stock market and foreign exchange market. Finally, it is recommended that policymakers use the monetary policy while considering this bidirectional relationship in the presence of interest rate as a moderating variable.

Whether ARIMA-based Portfolio Can Beat the Market Index

Portfolio optimization is a long-term topic in the financial field, which can maximize returns while minimizing risks. It is widely used in production and daily life, such as stock investment, production optimization, and engineering models. This article selects data from 10 stocks, namely KO, PG, PEP, CL, MDLZ, STZ, PM, KMB, GIS, and K, from January 24, 2024, to June 14, 2024. Firstly, this article uses the ARIMA model to learn the first 70% of the data and predict the last 30% of the data, and then uses portfolio strategy to evaluate the results. This article obtained the maximum Sharpe ratio model on the Monte Carlo effective boundary. By combining the maximum Sharpe ratio model with the SP 500 index within the same time frame, this article concludes that the market performs better. This article provides different portfolio allocation strategies for risk averse investors seeking stable positive returns in turbulent markets.

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.

All are interesting to invest, I fear of missing out (FOMO): a comparative study among self-employed and salaried investors

PurposeThis paper aims to examine how fear of missing out (FOMO) and investment intention mediate the relationship between behavioral biases and investment decisions of retail investors in the Indian stock market.Design/methodology/approachThe present research comprises two cross-sectional quantitative studies, where Study A involves data from 405 self-employed and Study B involves 393 salaried investors. Data was attained through questionnaires – the partial least squares structural equation modeling was used for data analysis.FindingsThe outcomes show that herding, overconfidence and loss aversion bias significantly impact investment intention and FOMO on both studies. Furthermore, the outcomes also indicate that herding and loss aversion bias significantly influence investment decisions in studies (A and B); however, overconfidence bias insignificantly affects the investment decisions in Study A. Besides, the results also reveal a substantial relationship between FOMO, investment intention and investment decision.Practical implicationsThe findings of this paper assist practitioners (financial analysts and retail investors) in considering the various ways of analyzing investment decision outcomes by considering the joint effect of several biases.Originality/valueThis paper is an initial attempt to propose a new theoretical framework and empirically examine the impact of behavioral biases on investment decisions by considering the FOMO and investment intention among self-employed and salaried investors. This study also contributes to the behavioral finance literature; other researchers may find it valuable to attain their goals.

A study on Factors Influencing the Herding Behavior of Indian Stock Market

Behavioral finance proposes that cognitive traits of investors impact their investment decisionswhich are not always rational, in contradiction to traditional finance. These cognitive traits of stockinvestors are influenced by their demographical profile and the financial information that they receive fromvarious sources which in turn influences their stock investment decisions. Investors with similardemographic profile tend to follow a similar pattern with regard to their investment behavior biases. Themain objective is to study the Factors Influencing the Herding Behavior of Indian Stock Market. Using thestate space model, calculate the herding measure using the log cross-sectional standard deviation of betaof the market component (using the kalman filter approach).

Determinants of mobile stock investment application adoption and its impact on intention to recommend the applications in emerging countries: a case study of Indonesia

Abstract Commonly used research models analysing technological adoption, such as the Technology Adoption Model, Theory of Planned Behaviour, and Unified Theory of Acceptance and Use of Technology, mostly emphasise technology-related variables. In the context of mobile stock investment application adoption, this study extends the existing technological adoption models by adding digital financial service-related variables. The purpose of this study is to investigate the main determinants of mobile stock investment application adoption in emerging countries, specifically in Indonesia. The study deployed a quantitative type of research with an online survey questionnaire by recruiting 256 respondents of stock investors who have used mobile applications for a minimum of one year. Data was analysed using partial least squares structural equation modelling (PLS-SEM) with advanced analysis tests. The results confirm the significant influence of performance expectancy, finfluencers, perceived financial risks, perceived financial benefits, perceived technology security, financial literacy, and e-reputation on adoption behaviour. The results also find a significant influence of adoption behaviour on the intention to recommend. Meanwhile, effort expectancy and facilitating conditions were insignificant toward adoption behaviour. These findings signify that the comprehensive research model could contribute to enriching studies on adoption of the mobile technology by extending TPB and UTAUT with specific variables related to stock investment and its impact on the intention to recommend the applications. Finally, the implications of the proposed new model for future research and FinTech practice are discussed.

Stock market index prediction based on market trend using LSTM

The stock market data analysis has received interest as a result of technological advancements and the investigation of new machine learning models, since these models provide a platform for traders and business people to choose gaining stocks. The business price prediction is a challenging and extremely complex process due to the impact of several factors on company prices. The numerous patterns that the stock market goes, they have been the focus of extensive research and analysis by numerous experts. There are several large data sets accessible, an artificial intelligence and machine learning techniques are developing quickly, and because of the machine’s improved computational power, complex stock price prediction algorithms can be developed. This paper presents stock market index prediction based on market trend using long short-term memory (LSTM). Using built-in application programmable interface (API), Yahoo Finance offers a simple method to programmatically retrieve any historical stock prices of an organization using the ticker name. The standard and poor’s 500 index (S&P 500 index) include the firms that have been taken into consideration here. Utilizing the selected input variable, single-layer and multi-layer LSTM models are implemented, and the measurement parameters of mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) are used to compare each performance. Nearly all of the real closing price’s curve and the prediction curve’s closing price for test data overlap. A potential stock investor may benefit significantly from such a prediction by using it to make well-informed choices that would increase his earnings.