Stock Investment Research Articles

The Effect of Overconfidence Bias, Risk Tolerance, and Herding Bias on Stock Investment Decisions with Financial Literacy as a Moderation Variable

This study aims to analyze the effect of Overconfidence Bias, Risk Tolerance, and Herding Bias on Stock Investment Decisions with Financial Literacy as a Moderating Variable. The subjects in this study were: (1) Students aged >17 years to 27 years, (2) having financial assets in the stock sector, (3) had done trading, and (4) domiciled in DIY, (5) or joined KSPM (Capital Market Study Group). KSPM members were chosen as research subject because they are considered as individuals who understand more about stocks and follow the development of the capital market. In this study, the sample amounted to 120 respondents selected using purposive sampling analysis technique, then the data was processed using the SPSS 25 software. Data were collected through a survey by distributing questionnaires online via Google Form to various social media platforms such as WhatsApp, line, telegram, Instagram, and twitter (now X). Based on the analysis that has been done, the results show that Overconfidence Bias, Risk Tolerance, and Herding Bias have a positive and significant effect on stock investment decisions, while Financial Literacy moderates a negative and significant effect on the influence of Overconfidence Bias, Risk Tolerance, and Herding Bias on stock investment decisions. Keywords: Overconfidence Bias, Risk Tolerance, Herding Bias, Financial Literacy, Investment Decisions

Pengaruh Kinerja Keuangan Terhadap Return Saham Pada Perusahaan Manufaktur Sektor Aneka Industri Yang Terdaftar di Bursa Efek Indonesia Periode Tahun 2018-2022

Stock returns are the results obtained from stock investments. Financial performance is said to have a big influence on stock returns, therefore it is necessary to pay attention to information and carry out an analysis of the condition of the company's financial statements using financial ratios. The aim of this research is to determine the influence of Liquidity, Leverage, Activity and Profitability on stock returns both partially and simultaneously. The company population in this study was 15 companies and 65 samples, using secondary data and purposive sampling techniques with the results of annual financial reports of manufacturing companies in various industrial sectors listed on the BEI for the 2018-2022 period. Data analysis in this research uses descriptive analysis, classical assumption tests consisting of data normality tests, multicollinearity tests, autocorrelation tests and heteroscedasticity tests, multiple regression tests and hypothesis tests consisting of t tests, F tests and coefficient of determination tests. Based on the results of this research, it shows that liquidity and leverage have a significant effect on stock returns. Activity and profitability have no effect on stock returns. And Liquidity, Leverage, Activity and Profitability simultaneously have a significant effect on stock returns.

Fostering the Ummah's Economy Through the Stockinvestment System: The Views of the Mui for Sustainabledevelopment Goals (SDGS)

Purpose: This study aims to investigate the role of the stock investment system in fostering the people's economy, focusing on the views of the Indonesian Ulema Council/ Majelis Ulama Indonesia (MUI) on its contribution to the Sustainable Development Goals (SDGs). This study also aims to identify factors that support or hinder the implementation of the stock investment system in the economic context of the people in PT Suryamu. Theoretical Framework: This study uses the Islamic economic framework and principles advocated by the Indonesian Ulema Council (MUI). The stock investment system is seen as a potential instrument to promote people's economic participation and support sustainable economic development, in accordance with Islamic values that prioritize justice, transparency, and common prosperity. Methods: The data in this study was collected through the analysis of fatwa documents or guidelines issued by the MUI related to stock investment, as well as related literature studies. The collected data were analyzed thematically to identify key patterns and significant findings. This research is a type of field research using a qualitative approach, where the data will be analyzed with the fatwa of the National Sharia Council of the Indonesian Ulema Council NO: 135 / DSN-MUI / V / 2020 concerning stocks. Results and Conclusion: The results of this study show that: 1) There is clarity of the contract at the beginning of the transaction, investors use the shirkah contract, some investors are chosen to be representatives of several investors using the wakalah bil istitsmar contract and the employees who work according to the work contract set by the company use the ijarah contract. 2) The parties who are associated are Muhammadiyah investors who already have a Muhammadiyah membership card as a condition to invest their funds in PT Suryamu. 3) The object of the contract is in the form of shares in the form of letters whose initial value of shares per share is Rp 100.000,-. From the point of view of the fatwa of the National Sharia Council of the Indonesian Ulema Council NO: 135/DSN-MUI/V/2020 regarding shares is by the criteria set by the fatwa. Research implications: The Company's stock investment meets sharia criteria with well-managed potential risks. So the implications and recommendations are to continue taking investment. Originality/value: Research on sharia stock investment with the fatwa approach of the Indonesian ulema council is very unique, specially studied in companies that carry out the concept of sharia.

Indonesian Stock Price Prediction Using Neural Basis Expansion Analysis for Interpretable Time Series Method

The stock market is an attractive investment venue for many individuals and companies. However, unexpected share price fluctuations can cause significant financial losses. In stock investment, predicting stock price movements is the most frequently discussed topic because it allows investors to make the right investment decisions to make big profits. Therefore, a model is needed to predict future stock prices, one strategy for maximising investment profits. New state-of-the-art deep learning architectures for time series forecasting are being developed yearly, making them more accurate than ever. The most commonly used network for such a solution is Long Short-Term Memory (LSTM) architecture, but it has limitations such as long training time and interpretability. This study aims to evaluate another state-of-theart solution, Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), in comparison with LSTM by utilising historical data of PT Bank Central Asia Tbk (one of the banking companies in Indonesia) from 25 March 2013 to 21 March 2023. N-BEATS is a relatively new variable method that can produce accurate predictions using neural networks. This architecture has advantages such as interpretability, seamless applicability across diverse target domains without requiring modifications, and fast training. Based on tests carried out with prediction errors measured using the Mean Average Percentage Error (MAPE), it was found that the N-BEATS model outperformed the LSTM model with a MAPE value of 1.05 percent. In conclusion, this research shows the use of a new method of deep learning algorithms to predict stock prices, which contributes to facilitating stock buying and selling decisions by investors.

Infrastructure Investment and Finance in the Global South: Private Participation and Its Determinants

While the public sector is traditionally the sole provider for much of infrastructure, the pendulum is shifting in light of the enormous investment gap. Across the Global South, public utilities and planning agencies are engaging with the private sector to help bridge the gap. What are the key patterns of private participation in infrastructure (PPI) across countries in the Global South? What macro factors underscore such private participation? These research questions motivate this paper, focusing on five infrastructure sectors across the Global South and its regions. Following a historical synthesis of the changing balance of public and private provision, the paper outlines recent patterns of PPI. Drawing from a World Bank database, a regression analysis includes key macro-level predictors for private sector financing, for both the number of projects and stock of investment. Key results point to the importance of market size and in turn the potential for risk diversification, as well as a set of governance factors underlining investor confidence and stable environment.

Comparison of Commercial Investment Value in the Food Industry

This article aims to provide investment advice to investors by comparing and analyzing four representative companies in the food industry. This article can help investors choose the most suitable stocks and gain more benefits. This article identified four representative companies and conducted data organization and financial analysis on them. Based on the results, the most suitable companies for investors to invest in were identified, along with their corresponding advantages and risks. This article analyzes and calculates the data released by the four major companies, and concludes that Starbucks (SBUX) is the most suitable stock for investors to choose because it has high investment returns and low investment risks. And the other three companies will all have some flaws to some extent. This article provides a method of analyzing data to find the most suitable stocks for investment, which can help investors make decisions, it can also help investors have the ability to distinguish between good and bad stocks themselves.

NORMA SUBYEKTIF DAN SELF-EFFICACY DALAM PENERIMAAN APLIKASI REKSA DANA DAN SAHAM

Mutual funds and shares are investment elements that are more volatile compared to gold, but in the long term they are quite profitable elements. This can be seen during the COVID-19 pandemic, the Russian-Ukrainian war crisis, and the bankruptcy of a large bank in America, this investment element still provides profits. The financial technology industry also provides various service applications on mobile phones or smartphones that make it easier for people to invest in mutual funds and shares. This research analyzes the determinants of Indonesian society's acceptance of stock mutual fund applications using the Technology Acceptance Model (TAM) and adding two external variables, namely subjective norms and self-efficacy. This research uses Structural Equation Model-Partial Least Square (SEM-PLS) to test the hypothesis. The results show that all TAM variables and subjective norms have an influence, but self-efficacy does not influence interest in using mutual fund and stock applications. In order to increase public acceptance of mutual fund and stock investment applications, and then interest in using them, service providers should improve the ease of operation and usefulness of their applications. Efforts should also be made to encourage people to recommend each other to invest through this application.

Potential Predictors of Psychologically Based Stock Price Movements

Investment in stocks is increasingly dependent on artificial intelligence (AI), but the psychological and social factors that affect stock prices may not be fully covered by the measures currently used in AI training. Here, we search for additional measures that may improve AI predictions. We start by reviewing stock price movements that appear to be affected by social and psychological factors, drawing on stock market behaviour during the COVID-19 pandemic. A review of processes that are likely to produce such stock market movements follows: the disposition effect, momentum, and the response to information. These processes are then explained by regression to the mean, negativity bias, the availability mechanism, and information diffusion. Taking account of these processes and drawing on the consumer behaviour literature, we identify three factors which may not be covered by current AI training data that could affect stock prices: publicity in relation to capitalization, stock-holding penetration in relation to capitalization, and changes in the penetration of stock holding.

A Study of the Impact of Investor Sentiment on Stock Investment Returns

This study examines the influence of investor sentiment on stock investment returns in China's rapidly developing yet volatile securities market. Utilizing principal component analysis, a comprehensive investor sentiment index is constructed from six key indicators. The dynamic relationship between investor sentiment and stock market return volatility is explored through a Vector Autoregression (VAR) model and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The findings reveal a significant bidirectional correlation, with positive optimism exerting a greater impact on stock returns than negative pessimism. The findings of this study indicate that investor sentiment exhibits a heightened sensitivity to declining returns as opposed to rising ones, offering valuable insights for both investors and policymakers.

Enhancing the accuracy of stock return movement prediction in Indonesia through recent fundamental value incorporation in multilayer perceptron

PurposeThe study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.Design/methodology/approachThe study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.FindingsIncorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.Research limitations/implicationsThe study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.Practical implicationsGovernments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.Originality/valueStudies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.

Can multi-period auto-portfolio systems improve returns? Evidence from Chinese and U.S. stock markets

Current portfolios often underperform due to limited utilization of stock selection and a lack of attention to multi-period trading. To address this issue, we propose an auto-portfolio system that addresses these problems by integrating multi-class stock selection with portfolio optimization based on technical indicators. For stock selection, we combine Two-dimensional Convolutional Neural Network with Long and Short-term Memory to forecast the future trends of stocks and select potentially profitable stocks for investment. We then develop two portfolio models based on two technical indicators, which automatically perform multi-period investment. We establish a many-objective optimization problem including return, Conditional Value-at-Risk, skewness, kurtosis, and cost. To solve the optimization problem, we employ Non-dominated Sorting Genetic Algorithm III. The data of Chinese and the U.S. stock markets is used for verification, and a comparative analysis is discussed. In the out-of-sample period, two proposed multi-period portfolio models outperform the other models in both single and multi period, achieving higher Sharpe ratio of 1.021 and 1.052 in China, and 1.116 and 1.236 in the U.S., respectively.

Does Contrarian Investing Beat the Conventional Strategies and the Index?

Traditional momentum strategies on the stock market are implemented in accordance with the efficient market hypothesis and involve making investments in accordance with market trends. However, this hypothesis has been repeatedly criticized by supporters of behavioral finance, who allow the irrational nature of investment decisions, which led to the emergence of contrarian investment strategies, based on the overreaction hypothesis and the reversal effect (over a longer horizon, loser stocks outperform winners), according to which investments are made in opposite direction to the market, involves buying the losing stocks and selling the winners. According to contrarian investment strategies, when buying shares, investors prefer a lower level of P/E ratios (Price – earnings ratio), which indicates the valuation multiple placed to the company’s market price vis-à-vis its net profits. The article examines the hypothesis that P/E ratio and contrarian investment are related, based on data for 2010–2022 for 50 shares of India, whose stock market is developing rapidly, examines the differences between the top ten P/E stocks and the bottom ten P/E stocks using inflation adjusted return on investment (ROI) to determine, whether or not, an investment in such stocks would generate sufficient returns to outpace inflation over a period of ten years. The article tests 3 hypotheses that investing in stocks with low P/E ratios in the Nifty 50 index (a live benchmark that illustrates how each industry contributes to India’s GDP) will generate higher returns than investing in stocks with high P/E ratios, over a long-term horizon (H1), inflation (Н2), the Nifty 50 index (Н3). The sources of data were finalized to be BSE Official Website, NSE Official Website, Yahoo Finance, Screener, Annual Reports, etc. The article calculates the profits for top 10 P/E stocks (conventional strategy) and bottom 10 P/E stocks (contrarian investing), and determining the returns from top 10 P/E stocks and the bottom 10 P/E stocks (when calculating for 10 years, the same steps were repeated: synchronously, once a year, both types of shares were bought at Purchase Price and sold a year later at Market Price). The calculations confirmed all 3 proposed hypotheses. Contrarian investing delivered returns of around 3.64 times the initial investment, whereas conventional investing produced returns of about 77.5% when comparing the top 10 P/E stocks to the bottom 10 P/E stocks in the Nifty 50 index. An outstanding return of 84.11%, which is a strong return when adjusted for inflation, was achieved using the contrarian strategy. As a result of the conventional strategy’s loss of 10.45% when adjusted for inflation, the study also shows that contrarian investing can assist an investor in hedging against inflation. This emphasizes how crucial it is to take a contrarian attitude when making stock market investments. At the same time, contrarian investment returns of 3.64 times the initial investment over this period surpass the benchmark Nifty 50 Index returns, which is 3.24 times.

Do Muslim Investors Consider Sharia literacy in Their Behavior When Trading Stocks? Experiences of Indonesian Stock Investors

The purpose of this study is to explain how the economic behavior of the Indonesian Muslim community in stock transactions influenced by their understanding of sharia. This research uses the Theory of Planned Behavior with exogenous variables being attitude, subjective norm, and perceived behavior control as predictors of intention as the endogenous variable. The ethical domain in this study is indicated by the presence of sharia literacy as a consideration in economic behavior. This research uses a quantitative approach with primary data obtained by distributing questionnaires to respondents who are sharia stock investors in Indonesia. The number of valid questionnaires amounted to 499. The data were processed using Partial Least Squares (PLS) with SmartPLS software. The results of this study show that each of the variables of attitude, subjective norm, and perceived behavior control has a significant positive effect on the intention to invest in sharia stocks. Sharia literacy is proven to moderate the influence of attitude on behavioral intention, but this does not occur in the variables of subjective norm and perceived behavior control.

Nhân tố ảnh hưởng đến thái độ sử dụng dịch vụ AI trong đầu tư chứng khoán của các nhà đầu tư thiếu kinh nghiệm

Using the partial least squares Structural Equation Modelling (SEM) on primary data collected from surveys of 466 inexperienced investors in Vietnam during September and October 2023, our study explores the factors that influence investor attitudes towards the use of AI services in six distinct tasks within the stock investment process. The results indicate that perceived usefulness, subjective norms, and trust significantly affect these investors’ attitudes towards AI use in stock investments. However, the perceived ease of use impacts only the tasks that involve straightforward processing. Based on these findings, we recommend that financial service providers tailor their product development strategies to attract potential customers in this emerging market.

Green stock investment preferences among adult investors in east Malaysia

Abstract In light of increasing concerns about environmental issues and a heightened focus on green stocks prompted by global warming and the COVID-19 pandemic, this research study aims to analyse the determinants influencing the investment intentions of working adults in Sarawak, Malaysia, regarding green stocks. This study emphasises comprehending how factors such as attitudes, social influences, affect, and perceived customer effectiveness impact the investment intentions of working adults in green stocks. This research adopts a quantitative methodology, employing hypothesis testing to analyse data obtained from 150 respondents in East Malaysia, who were selected using a convenience sampling technique and responded through a Google Form. Data analysis was conducted using SPSS version 27, with a comprehensive assessment of variable validity and reliability through various tests. Six hypotheses were formulated to establish a significant correlation between the dependent variable intention to invest in green stocks and the independent variables’ social factors, attitude, affect, perceived customer effectiveness, and financial literacy. Four independent variables significantly influence the dependent variable when investing in green stocks. Further, perceived customer effectiveness directly impacts investors’ intentions. Thus, financial literacy indirectly influences the intention to invest in green stocks through its mediation with attitude. The findings result in practical implications related to the determinants of green stock investment intention among working-age adults in Sarawak. To begin with, this study contributes to the body of knowledge concerning green investments by investigating the factors influencing the Sarawak residents’ intention to invest in green stocks, utilising the theory of interpersonal behaviour. Previous research has predominantly relied on the theory of reasoned action and the “theory of planned behaviour” to elucidate the factors impacting green investment intention. Therefore, this study’s adoption of the theory of interpersonal behaviour offers a special perspective and potential reference for future researchers. The knowledge gained from this study is pertinent to a broad spectrum of stakeholders, encompassing policymakers, government, professionals, and educators. It offers valuable insights that can shape decision-making and the development of strategic initiatives within green investments.

Utilizing Genetic Algorithms in Conjunction with ANN-Based Stock Valuation Models to Enhance the Optimization of Stock Investment Decisions

Navigating the stock market’s unpredictability and reducing vulnerability to its volatility requires well-informed decisions on stock selection, capital allocation, and transaction timing. While stock selection can be accomplished through fundamental analysis, the extensive data involved often pose challenges in discerning pertinent information. Timing, typically managed through technical analysis, may experience delays, leading to missed opportunities for stock transactions. Capital allocation, a quintessential resource optimization dilemma, necessitates meticulous planning for resolution. Consequently, this thesis leverages the optimization attributes of genetic algorithms, in conjunction with fundamental analysis and the concept of combination with repetition optimization, to identify appropriate stock selection and capital allocation strategies. Regarding timing, it employs deep learning coupled with the Ohlson model for stock valuation to ascertain the intrinsic worth of stocks. This lays the groundwork for transactions to yield favorable returns. In terms of experimentation, this study juxtaposes the integrated analytical approach of this thesis with the equal capital allocation strategy, TAIEX, and the Taiwan 50 index. The findings affirm that irrespective of the Taiwan stock market’s bullish or bearish tendencies, the method proposed in this study indeed facilitates investors in making astute investment decisions and attaining substantial profits.

Characteristics of Domestic Individual Investors in Vietnam Stock Market

Individual investors in the Vietnamese stock market have many different characteristics from individual investors in stock markets in other countries, especially developed countries. Outstanding features include: (1) domestic individual investors always occupy the leading position and dominate the market; (2) the transactions of this type of investors have the potential to change the market structure; However (3) they are strongly influenced by stock market sentiment and (4) most investors are still very young, have little experience, and have limited investment and capital management knowledge. leading to major fluctuations in the market that stem from this group of investors.

Analysis of Factors Affecting the Performance of Stock Investment Portfolios in Indonesia

Indonesia's capital market is experiencing rapid growth, attracting investors to invest in stocks. The performance of a stock investment portfolio is influenced by a variety of internal and external factors. Understanding these factors is important for investors in making informed investment decisions. This study aims to analyze the factors that affect the performance of stock investment portfolios in Indonesia using the literature review method. The literature review method allows for a systematic review of relevant literature and produces comprehensive findings. The results of this study are expected to provide a clear picture of the factors affecting the performance of stock investment portfolios in Indonesia and become a reference for investors in making more informed and measurable investment decisions.

Estimated Value-at-Risk Using the ARIMA-GJR-GARCH Model on BBNI Stock

Stocks are investment instruments that are much in demand by investors as a basis in financial storage. Return and risk are the most important things in investing. Return is a complete summary of investment and the return series is easier to handle than the price series. The movement of risk of loss is obtained from stock investments with profits. One way to calculate risk is value-at-risk. The movement of stocks is used to form a time series so that the calculation of risk can use time series. The purpose of this study was to find out the Value-at-Risk value of BBNI Shares using the ARIMA-GJR-GARCH model. The data used in this study was the daily closing price for 3 years. The time series method used is the model that will be used, namely the Autoregressive Integrated Moving Average (ARIMA)-Glosten Jagannathan Runkle - generalized autoregressive conditional heteroscedastic (GJR-GARCH) model. The stage of analysis is to determine the prediction of stock price movements using the ARIMA Model used for the mean model and the GJR-GARCH model is used for volatility models. The average value and variants obtained from the model are used to calculate value-at-risk in BBNI shares. The results obtained are the ARIMA(1,0,1)-GJR-GARCH(1.1) model and a significance level of 5% obtained value-at-risk of 0.0705.

One-way ticket to the moon? An NLP-based insight on the phenomenon of small-scale neo-broker trading

We present an Natural Language Processing based analysis on the phenomenon of “Meme Stocks”, which has emerged as a result of the proliferation of neo-brokers like Robinhood and the massive increase in the number of small-scale stock investors. Such investors often use specific Social Media channels to share short-term investment decisions and strategies, resulting in partial collusion and planning of investment decisions. The impact of online communities on the stock prices of affected companies has been considerable in the short term. This paper has two objectives. Firstly, we chronologically model the discourse on the most prominent platforms. Secondly, we examine the potential for using collaboratively made investment decisions as a means to assist in the selection of potential investments.. To understand the investment decision-making processes of small-scale investors, we analyze data from Social Media platforms like Reddit, Stocktwits and Seeking Alpha. Our methodology combines Sentiment Analysis and Topic Modelling. Sentiment Analysis is conducted using VADER and a fine-tuned BERT model. For Topic Modelling, we utilize LDA, NMF and the state-of-the-art BERTopic. We identify the topics and shapes of discussions over time and evaluate the potential for leveraging information of the decision-making process of investors for trading choices. We utilize Random Forest and Neural Network Models to show that latent information in discussions can be exploited for trend prediction of stocks affected by Social Network driven herd behavior. Our findings provide valuable insights into content and sentiment of discussions and are a vehicle to improve efficient trading decisions for stocks affected from short-term herd behavior.