Stock Price Returns Research Articles

Corporate resilience during crises and the role of ESG factors: Evidence from the COVID-19 pandemic

This study explores the relationship between environmental, social, and governance (ESG) factors and investor behavior during the COVID-19 crisis, utilizing a sample of S&P 500 companies. Findings suggest that these factors may not have had a significant influence on investment decisions during this period. Despite a notable overall market response to major COVID-19 related events, our findings reveal that ESG factors provide minimal explanatory power for individual stock price returns. This observation invites further investigation into the conditions under which ESG considerations are prioritized by investors.

A Comparison of Parameters of Weibull Distribution on Nigeria Stock Prices

This paper compared 2-parameter and 3-parameter Weibull Distribution using monthly average stock price returns of five different bottling companies enlisted in the Nigerian Stock market comprising Nigerian Breweries PLC, Coca-Cola Bottling Company, Guinness Nigeria PLC, Seven Up Bottling Company and International Breweries from 2012 to 2016 obtained from www.market.ft.com/data/equities/tearsheet/historical. The parameters were obtained using the Method of Moments (MOM) and were compared by the means of minimum Mean Squared Error; the result shows that Coca-Cola bottling company stock price return performed better under 2-parameter Weibull estimate while International Breweries was a choice under 3-parameter Weibull estimate. Also, the goodness-of-fit test was conducted using Kolmogorov-Smirnov Test and the result reviewed that the price of the stock returns does not follow a Weibull Distribution (P-value<0.05).

The Exploring the Relationship between Financial Performance Indicators, ESG, and Stock Price Returns: A Rough Set-based Bipolar Approach

Using a Rough-Set-based approach, this study investigates the connection between financial performance, Environmental, Social, and Governance (ESG) factors, and stock returns. It analyzes data from various companies and industries to examine how ESG metrics interact with financial indicators to influence stock prices. Those findings underscore the importance of considering financial and ESG factors when evaluating a company's performance and market valuation. The Rough-Set-based bipolar model comprises eight decision rules, and the DEMATEL (Decision-Making Trial and Evaluation Laboratory) analysis reveals the influence relationship among the core attributes. Furthermore, the ranking result, based on the bipolar model, is consistent with the market returns, which suggests the validity of this study.

Consensus Analysts Target Price and Stock Price Returns in Nigeria

This study focuses on the effect of an Investment Valuation technique (Consensus Analysts Target Price) on Stock Price Return of listed companies in the Nigerian Exchange between the period of 2011 and 2023. The ex-post facto research design was employed in this research due to the use of historic (secondary) data from the selected fifteen (15) companies across 3 major sectors on the Nigeria Exchange (NGX). Panel autoregressive distribution lag model was the estimation techniques and inference made on 5% significance level. Prior to the data analysis, a number or pre-estimation test were carried out such as co-integration, homogeneity, and cross-sectional dependence in addition to basis correlation and descriptive analysis. Findings from the short run reveals a significant negative coefficient (-0.273) for lagged Stock Price Return (L.STR), indicating a substantial short-term correction effect. Conversely, the lagged Consensus Analyst Target Price (L.CATP) shows a positive coefficient (0.141) without statistical significance, suggesting a modest positive relationship without an immediate impact on Stock Price Return. Transitioning to the long run, the highly significant negative coefficient (-1.273) for long-run Stock Price Return (lr_STR) emphasizes a robust and persistent negative adjustment effect. Over an extended period, Consensus Analyst Target Price (lr_CATP) introduces a marginally significant positive coefficient (0.077), implying a modest positive impact on Stock Price Return. Based on the results, one recommendation concerning Consensus Analyst Target Price (CATP) and Stock Price Return is to exercise caution in relying solely on CATP for short-term decision-making. Investors should be aware that, in the short term, deviations from the lagged return may not be significantly influenced by CATP. Therefore, considering additional factors or indicators alongside CATP could enhance the accuracy of short-term decision-making in understanding stock price movements.

Comparative Analysis of Risk and Return of IT Companies

The study focuses on evaluating the historical performance of selected IT companies in terms of risk-adjusted returns, utilizing both quantitative and qualitative research methods. Various financial metrics, including stock price volatility, beta coefficients, and return on investment (ROI), are examined to assess the risk and return profiles of IT companies. Additionally, the study explores the relationship between economic and financial indicators and stock returns to provide insights for investors, financial analysts, and policymakers. The research methodology involves data collection from primary and secondary sources, selection of IT companies based on specific criteria, and statistical analysis techniques such as regression analysis and correlation analysis. The findings highlight the diversity in financial performance and risk profiles among IT companies, emphasizing the importance of informed decision-making for investment strategies and market predictions in the dynamic landscape of the IT sector. The findings offer actionable insights for investors seeking to optimize their investment outcomes within the IT Companies

Which Subjective Expectations Explain Asset Prices?

Abstract We present a method for determining whether errors in expectations explain asset pricing puzzles without imposing assumptions about the error mechanism. Using accounting identities and survey forecasts, we find that errors in expected long-term inflation explain price variation, return predictability, and the rejection of the expectations hypothesis for aggregate stock and bond markets. Errors in short-term (long-term) nominal earnings growth expectations explain (do not explain) stock price variation and return predictability. The relevant errors are consistent with mistakes about the persistence of forecasted variables and the response to surprises. A simple framework based on fundamental extrapolation successfully replicates these findings. (JEL G40, G12, G14, E71)

Predictive modeling of return volatility in sustainable investments: An in-depth analysis of ARIMA, GARCH, and ARCH techniques

This paper aims to forecast the stock price and analyze the return volatility of India’s top three socially responsible companies. This study used ARIMA and GARCH models to forecast the stock price and analyze return volatility. For the analysis, the required time series data are collected from Yahoo Finance from 01-08-2012 to 29-07-2022 of the companies’ Monthly and daily closing stock prices. The socially responsible companies are selected based on India’s sustainability indices. The findings of the study show that the ARIMA (9,1,9) model for HDFC Ltd, ARIMA (10,1,7) for Reliance Industries Ltd, and ARIMA (2,1,2) are suitable models to forecast the stock price. Also, the study’s findings forecasted stock prices from August 2022 to July 2023. The forecasted stock price for July 2023 of HDFC Ltd is INR 2,613.78, Reliance industries Ltd is INR 3,073.75, and ICICI Bank Ltd is INR 857.73. Reliance Industries Ltd (σ2t = 0.9270586) is less volatile, and HDFC Ltd (σ2t = 0.9665041) is more volatile among the three companies, ICICI Bank Ltd (σ2t = 0.9507527) is the second high volatile company. The present study is limited to the top three companies that were selected from the three sustainability indices of BSE. The study is also limited to analysis of past volatility of stock price returns.

The Choice of Maximum Likelihood Estimate among the Lognormal, Weibull and Mixed-Lognormal-Weibull Distributions: An Empirical Examination of Stock Price Returns

This paper compared the maximum likelihood estimates (MLE) of lognormal, Weibull and Mixed-lognormal-weibull distributions. The data for this study were Coca-cola stock price returns obtained from https://ng.www.investing.com/equities/cocacola-bottle-historical-data and the result with the help of Excel package shows that Weibull distributionhas the minimum Mean Squared Error among the lognormal and Mixed-lognormal-weibull distributions; hence, the maximum likelihood estimate of the Weibull distribution is the choice.

The Non-Seasonal Holt-Winters Method for Forecasting Stock Price Returns of Companies Affected by BDS Action

The non-seasonal Holt-Winters method is one of the methods of smoothing theory. This method can be implemented on time series data that does not have a seasonal component. In this study, this method is used to forecast the stock price returns of companies affected by the Boycott, Divestment, and Sanctions (BDS) action. Forecasting gets very good results that can be seen from the MAPE value of modeling the six stocks affiliated with Israel that continue to carry out Zionism against Palestine is not more than 10%. This method can also accommodate the limitations of existing data while still obtaining good forecasting results. In addition, the use of several transformations of stock price returns in this case is very useful in modeling to obtain appropriate error assumptions. The forecasting results of the model formed as a whole follow the trend in the stock price of each company. To produce good forecasting results using this method, it is recommended to do forecasting in the short term. The forecasting results show that of the six company stocks, almost all of them experienced a decrease in stock price returns. Only one stock of PT Map Boga Adiperkasa Tbk has increased. This also illustrates that the BDS action influences on these companies.

How does Shanghai crude oil futures affect top global oil companies: The role of multi-uncertainties

The international influence of Shanghai crude oil futures (INE) has been increasing year by year. This study adopts the connectedness network method to construct a risk spillover network between INE and the top 20 global oil companies and measures the static characteristics and dynamic trends of return and risk spillovers. wMoreover, the core factors affecting the systemic risk are identified from the dimensions of crude oil implied volatility, geopolitical risk, US economic policy uncertainty, and climate concern. The results reveal that the overall connectedness between INE returns and global oil company stock price returns is high, with oil companies located in the Americas and at the upstream end of the chain experiencing greater spillovers in the system. Furthermore, INE is a net recipient of risk spillovers in the system, being subject to spillovers from most oil companies. In a dynamic scenario, the total system spillover index is susceptible to unexpected events, and there is asymmetry in the system risk spillover between INE and oil company returns. Meanwhile, crude oil implied volatility and geopolitical risk are the main influences on the systematic risk of INE and oil company returns.

The JOBS Act and IPO underpricing

In the realm of Initial Public Offerings (IPOs), the initial day stock price return, known as underpricing, poses a considerable financial burden on capital issuers. This study scrutinizes two hypotheses regarding IPO underpricing: the general information asymmetry theory, positing that ‘underpricing’ compensates for investor uncertainty about firm quality, and the practical underwriter-institutional investor collusion, potentially explaining the recent upswing in underpricing.Our findings indicate that the JOBS Act contributes to an increase in overall IPO underpricing post its 2012 enactment, aligning with the traditional adverse selection phenomenon. Notably, empirical analysis reveals a corresponding upsurge in underpricing tied to the increasing proportional shares of institutional investors in the post-Act period, supporting the collusion hypothesis. In summary, this paper underscores a notable side effect of the JOBS Act, revealing a pronounced increase in underpricing for issuers and subsequently enhancing the attractiveness of the underwriting business.

Tesla stock prediction and analysis based on LSTM model

Now that new energy vehicles are developing well, Teslas stock forecast has research value. This report focuses on predicting and analysing Tesla stock price returns using Long Short-Term Memory (LSTM) models. Deep learning models like LSTM can handle large amounts of data and make predictions about future stock dynamics. In this research, historical stock prices of Tesla Inc. are utilized as input data. The LSTM model is used to train and test the data, and subsequently provides results on its accuracy. For comparison, both Linear Regression and Random Forest models have also been used. The results indicate that the LSTM model has better performance than the other models in predicting short-term stock price movements. The result is evaluated by MSE, MAE and RMSE. However, Stock prices are extremely susceptible to economic, market, and political factors, so the predictions of the LSTM model cannot play an important role in actual investment.

A NEW GENERALIZATION OF CAUCHY DISTRIBUTION

A new heavy-tailed distribution is considered in this paper: the p-generalized Cauchy distribution. This new 5-parameter distribution family is substantially more flexible than than the classical Cauchy distribution, it contains, in particular, asymmetric distributions. Various numerical characteristics of the new distribution are considered, among them are fractional order moments and “twice incomplete” moments. There were obtained also (using numerical methods) values of quantile-based standardized moments of higher orders: Bowley’s skewness and Moors’ kurtosis. Suitability of the p-generalized Cauchy distribution for modeling real data was confirmed by fitting this distribution to a series of log returns of stock prices. The p-generalized Cauchy distribution had the smaller value of the AIC statistic than the Cauchy distribution, the skew Cauchy distribution, the generalized logistic distribution and the hyperbolic distribution.

Measuring the impact of ESG investing on the stock performance of JSE-listed financial service providers during the Covid-19 pandemic

The study's objective is to examine the impact of ESG investing on the stock performance of financial service providers in South Africa (SA) during the COVID-19 pandemic. This has significant ramifications for the usefulness of ESG investing during a crisis period. To this end, the study employs the panel nonlinear autoregressive distributed lags (PNARDL). The study finds that ESG investing had a significant impact on stock performance. The error term in the PNARDL reveals that the share price of financial service providers in SA was significantly affected by ESG investing. The PNARDL shows that, on average, a 1 percent increase in ESG investing increased stock price returns by 5 percent, cetiris paribus. This crucial finding has significant implications. It implies that ESG was another significant resilience factor behind the stock performance of financial service providers. Moreover, the PNARDL found that reducing ESG investing negatively affected stock price returns, while increasing ESG investing positively affected stock price returns during COVID-19. Hence, the study recommends that financial service providers increase ESG investing during a crisis period.

A-DCC GARCH모형을 이용한 주식, 채권 및 외환시장의 변동성 간의 동적 관계

Purpose - This research aims to analyze the interrelationship among the volatility of stock price, interest rate, and exchange rate in Korea. The main topics are volatility spillovers and the asymmetric behaviors of each market’s volatility and correlation.
 Design/Methodology/Approach - This research adopts Engle et al.’s (2006) asymmetric dynamic conditional correlation (A-DCC) GARCH models with student t error distribution. The models are a GJR GARCH model with spillovers and a revised Koutmos (1996) EGARCH model. The full sample is divided into four sub-sample periods, including the global financial crisis and the COVID-19 pandemic.
 Findings - The results show no volatility spillovers among the returns of stock price, interest rate, and exchange rate. However, there are unidirectional spillovers originating from the exchange rate in the global crisis period, and from stock price to exchange rate in the COVID-19 period. Volatility asymmetry is consistently shown in stock price, while the others vary by period. Finally, the asymmetric correlation effects are found only in recent times.
 Research Implications - In the long-run, the volatility spillovers among financial markets are not shown. However, there is also evidence that volatility spillovers are time-varying, intensifying in times of stress. In the global financial crisis period, the exchange rate influences other markets, while in recent the COVID-19 period stock price seems to play that role. Asymmetric volatility is also time-varying, except in stock price, and recently correlation asymmetry is shown very significantly.

Market Reaction to the Covid-19 Pandemic: Evidence from Countries in the Asia Pacific

The covid-19 pandemic spread rapidly in the Asia Pacific region, causing economic losses, exacerbating market volatility, and disrupting economic chains in Asia Pacific countries. This study aims to analyze the differences in capital market reactions before and after the announcement of covid-19 in the Asia Pacific Region. This study uses an event study approach using a variable stock price index, return, trading volume, and exchange rate. The research sample was 11 stock exchanges in Asia Pacific countries with purposive sampling techniques. Data testing using the data normality test and two average difference tests using a Paired Sample T-Test for normally distributed data and the Wilcoxon Singed Rank-Test test for data that is not normally distributed. The results showed that (1) there are differences in stock price indices before and after the announcement of covid-19 cases in Indonesia, Malaysia, Singapore, Mexico, Taiwan, Hong Kong, and New Zealand. Meanwhile, Thailand, Australia, Japan, and South Korea showed no differences. (2) there are differences in returns during covid-19 cases in Malaysia and Taiwan. While Indonesia, Singapore, Thailand, Australia, Japan, South Korea, New Zealand, Mexico, and Hong Kong showed no difference. (3) There are differences in stock trading volumes during covid-19 cases in Malaysia, Australia, Mexico, and Hong Kong. Meanwhile, Indonesia, Singapore, Thailand, Japan, South Korea, New Zealand, and Taiwan showed no difference. (4) There are differences in exchange rates during covid-19 cases in Indonesia, Malaysia, Singapore, Thailand, Australia, South Korea, New Zealand, Taiwan, and Hong Kong. While in Japan, it shows no difference.

The Effect Of Economic Value Added (Eva), Market Value Added (MVA), Refined Economic Value Added (REVA) On Stock Prices And Stock Returns In Manufacturing Companies Listed In Indonesia Stock Exchange

The development of stock returns of manufacturing companies shows fluctuating movements from year to year. Return movements like this indicate that the condition of the manufacturing company is unstable so that it has an impact on the resulting stock returns. This uncertainty will certainly create its own concerns for investors who will invest in shares in manufacturing companies. With this, it is important for investors to analyze the company's condition first so that the investment made can provide returns. One aspect of the company that can be assessed by investors is the company's performance through financial statements. Helfert (2012: 98) grouped company performance measurements into three groups, namely (1) Earning Measures, (2) Cash Flow Measures, (3) Value Measures. Performance measurement based on value measures is economic value added (EVA), market value added (MVA), and refined economic value (REVA) This performance measurement holds the view that increasing value means also increasing long-term shareholder investment returns. Steward (2013: 115) states that value measures are considered to have capabilities that exceed Another performance measurement, because it takes into account all factors related to the creation (value) of the company that have an impact on the increasing prosperity of shareholders. The purpose of this study is to test EVA, MVA, and REVA against stock prices and stock returns. In this study using a research sample of 104 manufacturing companies taken through random sampling in the period 2014-2016. Data analysis and hypothesis testing in this study used the Partial Least Square Path Modeling (PLS-SEM) method. The results of this study show that EVA, MVA, and REVA have a significant positive relationship with stock price and stock return . By looking at the results of this study, it shows that performance appraisal using the EVA, MVA, and REVA methods has a strong influence on stock prices and stock returns . This shows that value-based valuation is important because every investment cannot escape the consequences of the incurring cost of capital as compensation for the funds used to finance the investment. The return on an investment will only mean that if the amount of return exceeds the cost of capital incurred to realize the investment, measurement certainly requires an accurate valuation method.

Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?

This paper proposes a new forecast method of stock price return and volatility by GAVMD-Carbon-BiLSTM. First, based on the data from eight carbon markets in China, the logarithmic rate of return of carbon emission rights price is constructed. Second, the TVP-VAR model is used to investigate the impact effect of carbon emission rights trading, stock price return, and volatility. It is used as a predictor of return and volatility rationality. Third, the genetic algorithm is used to optimize the parameters of the variational mode decomposition. Fourth, the return and volatility of stock price are decomposed into multiple intrinsic modes, effectively reducing the data’s complexity. Finally, GAVMD is combined with BiLSTM, and the logarithmic return of carbon emission trading price is used as input to predict the stock price return and volatility. The results show that carbon emissions trading impacts the rate of return and volatility at different lead times and time points. Therefore, using it to predict stock price return and volatility can improve prediction accuracy. At the same time, combining GAVMD with BiLSTM, the prediction performance of carbon emission trading price logarithmic return rate as input is much better than other machine learning models.

Macroeconomic Variables and the Returns on Stock Prices: Empirical Evidence from Nigeria

In spite of the vital roles performed by the stock exchange market for the overall national development, it is still performing below expectations in Nigeria owing to several macroeconomic factors. Accordingly, the main objective of this study is to investigate the effects of selected macroeconomic variables on returns of stock prices in Nigeria. The study employed time series data obtained from the Central Bank of Nigeria statistical bulletin and World Development Indicators. Returns of stock price was measured using all-share index while the identified macroeconomic variables include GDP growth, broad money supply, exchange rate, interest rate and inflation rate. Autoregressive Distributive Lag (ARDL) estimation technique was used to establish the long run relationship among the variables and it was revealed that long run relationship exists among the variables in the estimated model. The result of the Error Correction Mechanism (ECM) within the framework of the ARDL shows that macroeconomic variables such as gross domestic product, broad money supply, exchange rate and interest rate have positive effect on returns of stock prices in Nigeria. On the other hand, the results showed that inflation rate has negative effect on stock prices in Nigeria. Predicated on the result, the study recommended that policies to increase gross domestic product, exchange rate, interest rate and money supply should be implemented because they can lead to improvement in the returns of stock prices, while inflation rate should be maintained at single digit to prevent its negative effect on the performance of the stock market in Nigeria.

The Stock Market Reaction to the 2020 Tokyo Olympics: Evidence from Japanese Sponsors and Taiwanese Olympic-Related Firms

Abstract: This study aimed to examine the impact of a major sporting event, the 2020 Tokyo Olympics, on abnormal returns of Japanese sponsors and Taiwanese Olympic-related stock prices during three different periods, including the hosting announcement, the postponement in light of the COVID-19 pandemic, and the holding as scheduled. Using the event study approach, secondary data were collected to investigate the fluctuation of abnormal returns of official Japanese sponsors and Taiwanese Olympic-related stocks. The empirical evidence showed that Olympics-related news disclosures significantly affected the abnormal returns of official sponsors’ stock prices during both the periods of the hosting announcement and the postponement announcement but not during the period of holding as scheduled. This study also verified that the postponement announcement of the 2020 Tokyo Olympics and its holding as scheduled would generate positive and negative spillover effects on the stock market of a non-host country.