Stock Portfolio Returns Research Articles

The Impact of Asymmetric Price Limits on Stock Portfolio Returns: Evidence from Indonesian Markets

This study examines the effect of asymmetric price limits policy on stock portfolio returns represented by equity mutual funds. An event study methodology was used to calculate abnormal returns (ARs) across four estimation models: constant mean, market-adjusted return, market model, and capital asset pricing model. The sample consists of 237 equity funds in Indonesia, with 7,968 observations in the estimation window and 4,977 in the event window. Statistically significant Average Abnormal Returns (AARs) and Cumulative Average Abnormal Returns (CAARs) were found through parametric and non-parametric tests. The findings are consistent with panel data analysis results. An increase in average returns and a decrease in standard deviation were found for sampled equity funds following the policy implementation. These findings highlight the importance of policy interventions in stabilizing financial markets during crises and offer valuable insights into stock portfolio performance before and after the policy.

The Impact of Asymmetric Price Limits on Stock Portfolio Returns: Evidence from Indonesian Markets

This study examines the effect of asymmetric price limits policy on stock portfolio returns represented by equity mutual funds. An event study methodology was used to calculate abnormal returns (ARs) across four estimation models: constant mean, market-adjusted return, market model, and capital asset pricing model. The sample consists of 237 equity funds in Indonesia, with 7,968 observations in the estimation window and 4,977 in the event window. Statistically significant Average Abnormal Returns (AARs) and Cumulative Average Abnormal Returns (CAARs) were found through parametric and non-parametric tests. The findings are consistent with panel data analysis results. An increase in average returns and a decrease in standard deviation were found for sampled equity funds following the policy implementation. These findings highlight the importance of policy interventions in stabilizing financial markets during crises and offer valuable insights into stock portfolio performance before and after the policy.

CNN-BiLSTM-Attention Based Stock Price Prediction and Quantitative Investment Strategy

With the rapid development of technology and the increasing maturity of financial markets, stock price prediction has become a hot topic and an important trend in the financial field. However, the stock market has complexity and volatility, in order to reduce the investment risk and ensure the maximization of benefits, selecting the optimal stock to predict the stock price and formulating the quantitative trading strategy are extremely important issues for financial academics and investors. For the research of stock price prediction and quantitative trading, firstly, the quantitative stock selection model combining XGBoost and multi-factor stock selection model is constructed, and four stocks are screened out, and then the CNN-BiLSTM-Attention model is proposed to predict the stock price trend of the selected stocks, and it is found from the prediction results of the four stocks that the prediction accuracies all reach more than 95%, which is higher than that of the single model prediction. accuracy and passed the validity test of the fitted model. Secondly, based on the quantitative investment portfolio given in the above analysis and the quantitative stock trading strategy of MACD, the prediction results are verified, and the total return of most stocks based on the strategy is higher than 40% at the highest, and the loss is controlled within 10%, which indicates that the trading strategy in this paper is effective and feasible. The study concludes that greater returns can be obtained by calculating the total returns of stock portfolios based on different portfolio approaches.

Determination of Optimal Stock Portfolio Return by Single Index Model (Case Study on Banking Sector Stocks in Indonesia)

The optimal portfolio is a portfolio chosen by investors from the many options available in the collection of efficient portfolios. To get the optimal proportion, which is the maximum return and minimum risk, it is necessary to analyze the stocks to be selected in the investment model. The research objective is to determine the optimal return, risk, and proportion for each banking stock portfolio in Indonesia in the period February - July 2023. The method used is the Single Index Model. The process of determining the optimal proportion of stocks with the Single Index Model requires stock and market return data as the main basis for applying this method. This study involves the formation of an optimal portfolio of daily closing prices of 46 banking stocks. As a result of this research, there are 5 optimal stocks that meet the criteria for optimal portfolio formation with each fund proportion of 21.43% (BNII), 13.52% (BDMN), 35.02% (BBRI), 23.69% (BTPN), and 6.34% (BBCA). Expected return from optimal stocks is 0.152% and the risk that will be borne by investors is 0.0011% per day.

Statistical analysis of stable fundamental anomalies in stock pricing on the Russian market

Subject. The article considers stable anomalies in the dynamics of stock prices on the Russian stock market, which are caused by specific characteristics of issuing companies. Objectives. The aim is to perform a cross-sectional analysis of excess returns of stock portfolios reflecting new stable fundamental anomalies of the Russian stock market. Methods. The study employs the data of accounting (financial) statements of issuers. I considered the groups of indicators related to operational efficiency, expectation of slowdown in operational efficiency, threat of insolvency (bankruptcy) of the issuer, and the size of capital investments of issuers. The groups of indicators identified four working hypotheses on consideration of corresponding systematic risk factors in the prices of financial instruments. Analytical procedures were carried out in the RStudio development environment. The sample population covers the period from June 2012 to December 2021. Results. I tested the working hypotheses and formed special portfolios, the excess profitability of which simulates systematic risk factors associated with specific characteristics of issuing companies. The test of risk factors for redundancy, using auxiliary regressions and the Sharpe ratio for various combinations of risk factors, demonstrated that the risk factor associated with operational efficiency of issuers can be considered as redundant, since it is fully explained by other risk factors. The risks of slowing the growth of efficiency and the risk of bankruptcy of the issuer in the Russian stock market are investigated for the first time. Conclusions. The revealed price anomalies contribute to the development of theoretical and empirical prerequisites for factor investment strategies in the Russian stock market. The findings may help investors to increase portfolio strategy’s profitability in the Russian stock market.

Carbon dioxide and asset pricing: Evidence from international stock markets

We use carbon dioxide (CO2) emissions growth to measure consumption risk within a consumption-based capital asset pricing model framework. Given the comprehensive worldwide coverage of CO2 emissions, this measure allows us to use the full history of stock market data in the US, Europe, the world, and fifteen international markets. For the US (Europe/the world), we are able to explain the observed equity market premium with a relative risk aversion of 6 (10/12), which is less than half the size of that estimated using the canonical expenditures-based consumption growth measure. The average estimated relative risk aversion across fifteen other international markets is 5. We also find evidence that the growth of CO2 emissions is a priced risk factor that captures the cross section of stock portfolio returns.

The Investigation of China Stock in Fama-French Model Based on High-end Equipment Manufacturing Industry

This study investigates the elements of return and chance in the very good-quality hardware manufacturing sector by directing empirical research and far-reaching analysis. The research centers around building six portfolios considering market esteem scale and book-to-market ratio to explore the relevance of the Fama-French factor analysis model. Our discoveries uncover important patterns, including a backward connection between the book-to-market ratio and rates of return, as well as higher paces of return for bigger scope portfolios, featuring a scale impact. The meaning of positive month-to-month yields across all portfolio blends underlines the business' general profitability from 2015 to 2021. Thorough stationarity tests approve our relapse analysis, where market risk factor(MKT), scale factor (SMB), and book-to-market ratio factor (HML) altogether impact stock portfolio returns. By adjusting our discoveries to past writing and featuring the model's viability in various sectors, our review adds to a more extensive comprehension of asset pricing and illuminates powerful investment methodologies custom-fitted to explicit market elements.

Analysis of Stock Portfolio with Global Economic Factors using Dynamic Data Modelling

This article analyzes stock portfolios and worldwide economic challenges. This study uses APIs to retrieve data and analyze correlations. To determine and measure how economic indicators affect stock performance. This work introduces an autoencoder-based model to better comprehend the complex relationship between economic conditions and stock portfolio dynamics. The analysis begins with an API retrieval of a wide range of macroeconomic information. These indicators include global economic metrics like GDP, unemployment, CPI, federal funds rates, and treasury bill rates. After collection, data is carefully curated and prepared for analysis. This study uses correlation analysis to understand economic variables and stock portfolio performance. This study explores how economic conditions affect stock prices and portfolio returns. This study seeks to discover trends, dependencies, and future issues that may affect investment decisions. This study also introduces an autoencoder-based neural network model to capture complex nonlinear relationships between economic variables and stock portfolio behavior. Deep learning improves interpretability and prediction, allowing a better understanding of the complex financial ecosystem dynamics. The inquiry provides valuable insights for investors, financial experts, and regulators. This study advances data-driven investment and risk management solutions. The autoencoder-based approach also reveals latent structures and hidden factors that affect stock portfolios. This novel approach opens new study options. In conclusion, this study provides a thorough stock portfolio analysis approach for global economic challenges. API data retrieval, correlation analysis, and a novel autoencoder model are used in this work to better understand the complicated relationships between economic indicators and financial markets. These insights can improve investment and policy decisions in a more integrated and dynamic global economy.

Real-time transition risk

We develop a point-in-time index to approximate changes in transition risk from climate-related news events. By explicitly considering news to signal an increase or a decrease in the external pressure towards a shift to a lower-carbon economy, we overcome the assumption that “no news is good news on climate” inherent in previous research. We evaluate the return sensitivity of publicly available climate portfolios that apply different approaches to measure a firm's environmental performance based on investors’ objectives. Our results show that short-term transition risk tends to affect returns of stock portfolios based on firms’ business activity but not emissions.

Portfolio rebalancing based on a combined method of ensemble machine learning and genetic algorithm

PurposeThis paper presents a combined method of ensemble learning and genetics to rebalance the corporate portfolio. The primary purpose of this paper is to determine the amount of investment in each of the shares of the listed company and the time of purchase, holding or sale of shares to maximize total return and reduce investment risk.Design/methodology/approachTo achieve the goals of the problem, a two-level combined intelligent method, such as a support vector machine, decision tree, network Bayesian, k-nearest neighbors and multilayer perceptron neural network as heterogeneous basic models of ensemble learning in the first level, was applied. Then, the majority vote method (weighted average) in the second stage as the final model of learning was collectively used. Therefore, the data collected from 208 listed companies active in the Tehran stock exchange (http://tsetmc.com) from 2011 to 2015 have been used to teach the data. For testing and analysis, the data of the same companies between 2016 and 2020 have been used.FindingsThe results showed that the method of combined ensemble learning and genetics has the highest total stock portfolio yield of 114.12%, with a risk of 0.905%. Also, by examining the rate of return on capital, it was observed that the proposed method has the highest average rate of return on investment of 110.64%. As a result, the proposed method leads to higher returns with lower risk than the purchase and maintenance method for fund managers and companies and predicts market trends.Research limitations/implicationsIn the forthcoming research, there were no limitations to obtain research data were easily extracted from the site of Tehran Stock Exchange Technology Management Company and Rahvard Novin software, and simulation was performed in MATLAB software.Practical implicationsIn this paper, using combined machine learning methods, companies’ stock prices are predicted and stock portfolio optimization is optimized. As companies and private organizations are trying to increase their rate of return, so they need a way to predict stock prices based on specific indicators. It turned out that this algorithm has the highest stock portfolio return with reasonable investment risk, and therefore, investors, portfolio managers and market timers can be used this method to optimize the stock portfolio.Social implicationsThe homogeneous and heterogeneous two-level hybrid model presented in the research can be used to predict market trends by market timers and fund managers. Also, adjusting the portfolio with this method has a much higher return than the return on buying and holding, and with controlled risk, it increases the security of investors’ capital, and investors invest their capital in the funds more safely. And will achieve their expected returns. As a result, the psychological security gained from using this method for portfolio arrangement will eventually lead to the growth of the capital market.Originality/valueThis paper tries to present the best combination of stock portfolios of active companies of the Tehran Stock Exchange by using the two-level combined intelligent method and genetic algorithm.

Ambiguity and asset pricing: An empirical investigation for an emerging market

This study explores the impact of ambiguity on returns of both individual stocks and stock portfolios in an emerging market setting. First, an ambiguity index is derived and then the sensitivity of stock returns to ambiguity is analyzed while controlling for the other risk factors commonly cited in the literature. Results show that stocks with a high (low) sensitivity to ambiguity generate higher (lower) excess returns. These results are intuitive in the sense that investors seem to ask for lower returns from those stocks that serve as a natural hedge against ambiguity. Our findings are also in line with the earlier studies that provide similar evidence from the US stock markets.

The Impact of Sudden Public Health Events on the Insurance Companies' Investment Returns: Based on the Investors' Sentiment Perspective.

This study analyzes the conflicting effects of investors' sentiment caused by public health emergencies and uses event analysis methods and linear regressions to examine the impact of such emergencies on the stock prices of insurance companies. The study shows that public health emergencies have a positive and significant impact on insurance companies' portfolios through investors' sentiment, which is persistent. However, the investor fear index triggered by public health emergencies is negatively associated with insurance stock portfolio returns. Meanwhile, insurers with smaller market capitalization are more strongly influenced by investors' sentiment than those with larger market capitalization.

Analysis Investor Index Indonesia with Capital Asset Pricing Model (CAPM)

This study aimed to compare composition of the optimal portfolio of stocks, the proportion of funds in each of these stocks and calculate risk and return portfolio from Investor33 (INV33) Index and Jakarta Islamic Index (JII) in research period January 2016-December 2018. The method used in this research is a quantitative descriptive method. Sample in this study using purposive sampling were 24 stock from INV33 Index and 17 stock from JII Index. The results of the study were as follows : (1) The optimal portfolio of stocks by using capital asset pricing model from INV33 Index are CPIN (Charoen Pokphand Indonesia Tbk), ITMG (Indo Tambangraya Megah Tbk), BBCA (Bank Central Asia Tbk), UNTR (United Tractor Tbk), (TLKM) Telekomunikasi Indonesia (Persero) Tbk, ICBP (Indofood CBP Sukses Makmur Tbk), BBTN (Bank Tabungan Negara Persero Tbk and from JII Index are ADRO (Adaro Energy Tbk), ICBP (Indofood CBP Sukses Makmur Tbk), INCO (Vale Indonesia Tbk), INDF (Indofood Sukses Makmur Tbk), TLKM (Telekomunikasi Indonesia Persero Tbk), UNTR (United Tractor Tbk). (2) The composition of the proportion of funds in optimal portfolio formed by INV33 Index are BBCA (46,49%), CPIN (20,11%), ICBP (12,78%), ITMG (8,59%), UNTR (6,95%), TLKM (4,11%) and BBTN (0,97%) and from JII Index are ICBP (34,96%), ADRO (19,47%), UNTR (16,26%), INCO (10,88%), TLKM (10,43%) and INDF (8,00%). (3) The optimal portfolio of stocks return from INV33 Index was greater than stock portfolio return from JII Index and the optimal portfolio of stocks risk from INV33 Index was lower than stock portfolio risk from JII Index.

Treynor, Sharpe, and Jensen Ratio of Health Sector Companies on The Indonesia Stock Exchange Before and During Covid-19 Pandemic Period

This research aims to compare the return and risk in investment at the stock portfolio of Health Sector Companies in period before the COVID-19 pandemic and during the COVID-19 pandemic in Indonesia. This research conducted using quantitative method with descriptive approach with secondary data with samples of the stock of Health Sector Companies listed in the Indonesia Stock Exchange ("IDX") which then formulated into a stock portfolio. The research period used is the period March 2019-Feb 2020 for before COVID19 pandemic, and the period March 2020 - Feb 2021 for during the COVID19 pandemic. The stock portfolio return and risk measurement is measured by Sharpe, Treynor, and Jensen Ratio which then will be statistically tested to see if there are significant differences of ratios between the two conditions.

Treynor, Sharpe, and Jensen Ratio of Health Sector Companies on The Indonesia Stock Exchange Before and During Covid-19 Pandemic Period

This research aims to compare the return and risk in investment at the stock portfolio of Health Sector Companies in period before the COVID-19 pandemic and during the COVID-19 pandemic in Indonesia. This research conducted using quantitative method with descriptive approach with secondary data with samples of the stock of Health Sector Companies listed in the Indonesia Stock Exchange ("IDX") which then formulated into a stock portfolio. The research period used is the period March 2019-Feb 2020 for before COVID19 pandemic, and the period March 2020 - Feb 2021 for during the COVID19 pandemic. The stock portfolio return and risk measurement is measured by Sharpe, Treynor, and Jensen Ratio which then will be statistically tested to see if there are significant differences of ratios between the two conditions.

Stock market tail risk, tail risk premia, and return predictability

AbstractIn this study, we use the S&P 500 options prices to derive various tail risk indexes. We then decompose the option‐implied tail risk indexes into the conditional tail risk of stock returns and equity tail risk premia. We examine the predictive power of the conditional tail risks and equity tail risk premia for various stock portfolio returns. The results demonstrate that the tail risk indicators possess additional predictive power for stock returns in the presence of extant risk indicators and other return predictor variables.

ACCOUNTING INFORMATION IN THE FAMA AND FRENCH THREE-FACTORS MODEL

The purpose of this study is to reexamine the ability of the Fama-French Three Risk Factor Model to explain stock portfolio returns in countries with different economic levels, as well as examine the effect of accounting information derived from book-to-market on stock portfolio returns. The sample used was a manufacturing company on the Indonesia Stock Exchange and the Tokyo Stock Exchange from 2013-2018. The results show that the three risk factors of the Fama-French model apply consistently to explain the variation in stock portfolio returns in developed markets. For the portfolio of shares in the emerging market, model Fama-French does not consistently assess stock portfolio returns. This research also provides empirical evidence that accounting information contained in book-to-market risk factors is only retained earnings, which has a contribution to the valuation of stock portfolio returns. The results of this study indicate that investors in developed markets are more rational and knowledgeable than emerging markets.

The effects of Fama-French five factor and momentum factor on Islamic stock portfolio excess return listed in ISSI

Purpose – The purpose of this study is to examine the effect of Fama-French five-factor and momentum factor on Islamic stock portfolio excess returns listed in the Indonesia Sharia Stock Index (ISSI). Methodology – This study used return data from ISSI group, starting from January 2013 to December 2017, which are then formed into time series data with excess monthly stock portfolio. This study adapted the Fama and French (2015) methodology using 2x3 and 2x2 to form the portfolio and applied Ordinary Least Square (OLS) with monthly data frequency to test the relevance of the model to the expected stock return of 183 companies. Findings – The results showed that the risk premium, the book-to-market ratio which is proxied by High Minus Low (HML), the investment that is proxied by Conservative Minus Aggressive (CMA), and the momentum which is proxied by Up Minus Down (UMD) has a positive effect on the excess return of the company's stock portfolio registered in Indonesia Sharia Stock Index (ISSI) during the period. While, the size and profitability variable do not affect the expected stock return. Research limitations – The results of this study provides relevant information about the relationship between risk and stock return using Fama and French five-factor model and momentum. However, future researchers can expand the scale of the research by adding research periods and using daily return research data. It is intended that the results are more representative of the actual market conditions at the moment. Originality – Researches on the factors that influence the selection of Islamic stock portfolios based on excess return using Fama-French five-factor including the momentum factor are still limited. This study contributes to the asset pricing development by investigating factors influencing performance of ISSI’s portfolio excess return using five-factor model and momentum factor.

Come together: trust, sociability and individual investors' stock-portfolio returns

PurposePrevious studies have found that trusting and sociable individuals are more likely to participate in the stock market and hold risky assets. The purpose of this paper is to explore if trust and sociability also are related to individual investors' stock-portfolio returns.Design/methodology/approachThe authors study the questions in the paper by linking survey measures of trust and sociability to investors' actual stock portfolios.FindingsThe authors find that trusting investors acquire higher raw and risk-adjusted stock-portfolio returns, but that the returns do not differ depending on how sociable investors are. These results suggest that trust is important for investors' stock-portfolio decisions, and that trusting investors tend to perform better in the stock market than less-trusting investors.Originality/valueThis is, to the best of the authors’ knowledge, the first paper that relates survey measures of trust and sociability to investors' actual stock-portfolio holdings. This is important to increase the understanding for how trust and sociability are related to the financial decisions individuals makes.

An Overview of Stock Portfolio Returns and Return Premia in Japan: The Case of Size and Book-to-Market Portfolios

This article explores Japanese stock portfolio returns and return premia by focusing on size- and book-to-market (BM)-sorted portfolios over the period of 1990 to 2020. As a result of our investigations, we derive the following useful findings. (1) In general, the value and/or size effects are continuously seen in the Japanese stock market. However, (2) these effects much depend on the economic and business background: for the performance of size- and BM-sorted portfolios in Japan, the value effect is stronger in some sub-periods; while the size effect is clearer in other sub-periods. Furthermore, (3) this study employs the data in US dollars, and computes various statistics and measures for both our full sample period and many different sub-periods, whose economic circumstances are rather different. Therefore, not only for academic researchers but also for international investors, our findings shall be highly beneficial for enriching the understanding of Japanese stock portfolio returns and return premia.