Stock Return Rate Research Articles

On the partial impact of uncertainties on the nexus between macroeconomic fundamentals in West Africa

We investigate the degree of interconnectedness between stock returns and exchange rate returns, and the influence of some selected global uncertainty indices on such a relationship within a time-frequency domain in West Africa through the bi and partial wavelet approaches. The analysis was based on monthly observations from February 2013 to June 2023. The results highlight a negative correlation between stock return and exchange rates. The partial wavelet analysis evidence a significant effect of the global economic policy uncertainty, the implied oil market volatility, and the United States volatility index in driving the co-movements observed in the currency and stock markets. We also find a significant impact of the stock market on the currency market, underscoring the need for robust stock market policies. It is recommended that policymakers prioritize strategies aimed at boosting stock market stability and depth which can positively affect the currency markets. The significant influence of global uncertainties or shocks should not be disregarded in the formulation of policies regarding exchange rates and stock return integration at various investment horizons.

A study of impact of climate change on the U.S. stock market as exemplified by the NASDAQ 100 index constituents

This paper employs an innovative event study methodology to demonstrate the impact of climate change on the NASDAQ index from the unique perspective of extreme weather events. This is achieved through the application of the event study methodology to a total of 526 biological, climatic, geological, hydrological, and meteorological disasters of climate change occurring in the U.S. during the period of 2000–2019. The results of the study demonstrate that: ① it can be generally observed that the five dimensions of climate change have a significant impact on stock returns. ② Empirical evidence indicates that the impact of different climate change dimensions on the return rate of stocks from NASDAQ stocks varies. In contrast, the biological and hydrological dimensions have a significantly negative impact on the return rate of stocks from the NASDAQ index, while the climate dimension has a significantly positive impact on the return rate of stocks from the NASDAQ index. ③ From the perspective of time, the impact of the five dimensions of climate change on the stock yield exhibits certain non-linear characteristics. This can be observed in the phenomenon of shock reversal, which occurs before and after the event.

Research on Stock Market Risk Contagion of Major Debt Crises Based on Complex Network Models—The Case of Evergrande in China

After a major debt crisis occurs in a listed company, the stock prices of related enterprises may also fluctuate sharply, resulting in the spread of debt risk to more enterprises. Taking the stocks of listed companies as network nodes, we constructed a complex stock market network over three periods of time through the logarithmic return rate of stocks for the three periods of prophase, metaphase, and anaphase of the debt crisis. We studied the topological characteristics of the network and destructiveness over the three periods. Finally, the minimum spanning tree was used to construct a network and the community structure of the network. The empirical analysis took the debt crisis of the China Evergrande Company as an example to analyze the impact of its major debt crisis on the Chinese stock market. The research findings were as follows: First, the debt crisis increased the inter-industry connections within the network, that is, the correlations between enterprises in different industries was enhanced. Second, the closeness of the cross-industry connections increased the connectivity efficiency of the network, but compared with the other two periods, the debt crisis in the metaphase was less stable. Third, community research showed that in the metaphase of the debt crisis, the enterprises became the core nodes of the network.

The Impact of Trading Volume on Stock Price Volatility

In the capital markets, research on the impact of market trading volume on asset prices and returns has been ongoing since the 1960s, and remains a focus of attention for scholars and investors both domestically and internationally. Studies on the relationship between volume and price primarily concentrate on the volatility of stock prices in relation to trading volume, and the correlation between stock return rates and trading volume. In empirical research on the volume-price relationship, the main theories are inseparable from the study of market information. Trading volume encompasses the market's reaction speed to information and the extent of information's impact on the market, which are significant reasons why trading volume can affect stock market prices and returns.

Exploring the Relationship Between Patent Forward Citation and Stock Return Rate Using Empirical Data of China Stock Market

Exploring the Relationship Between Patent Forward Citation and Stock Return Rate Using Empirical Data of China Stock Market

Influence of scissors difference of money growth rate on stock market return in different financial cycles

We use TVP-VAR model to study the relationship between the scissors difference of money growth and stock returns in different financial cycles from February 2001 to October 2022. The results show that the scissors difference of money growth rate has a positive effect on the stock return rate that lags behind for one month, but a negative effect on the stock return rate that lags behind for three months. The impact on the half-year lag is almost negligible. In addition, during the financial cycle contraction, the scissors difference of money growth rate has a greater impact on stock returns; In the period of financial cycle expansion, its impact is small. During the financial shock, its impact is between the first two.

Analysis of the relationship between sustainability scores and financial performance of the companies in the BIST sustainability index

Purpose- The inclusion of companies in the BIST Sustainability Index and the disclosure of sustainability scores provide not only benefits in terms of making companies’ sustainability practices better known by stakeholders and the public, but also may increase stock returns. Therefore, this study aims to analyse the relationship between sustainability scores and financial performance of companies in the BIST Sustainability Index. Methodology- In order to analyse the relationship between the sustainability scores of 62 companies in the BIST Sustainability Index and their financial performances, the sustainability scores of the companies and the companies' firm value, return on assets and stock rate of return as the financial performance indicators is examined by multiple regression analysis method over the data of 2022. Findings- A negative relationship was found between the sustainability scores of the companies and the firm value, while no significant relationship was found between the sustainability scores and the other financial performance indicators, namely return on assets and stock rate of return. Conclusion- The reason for the negative relationship between the sustainability scores of the companies and the firm value may stem from the investors’ perception that the environmental investments to be made by the the company will increase the company's costs, which will have a negative impact on the company's financial statements and thus on the stock return. Keywords: Sustainability, sustainability scores, financial performance, multiple regression analysis. JEL Codes: E44, G11, G32

주가와 환율 변동성에 관한 실증연구

China's tourism industry has mainly focused on inbound tourism. Starting in 1995, the Chinese government introduced a weekend holiday system, and this policy served as an opportunity to develop China's domestic tourism industry. Thanks to the golden week system introduced in 1999, domestic tourism in China has grown explosively. Accordingly, social overhead capital such as railways, aviation, and highways was rapidly expanded. From then on, the tourism industry developed simultaneously into inbound tourism, domestic tourism, and outbound tourism. It is examine the volatility spillover effect between stock returns and exchange rates for Chinese tourism companies. For the empirical analysis, we use the BEKK-GARCH model of Engle and Kroner (1995). From the empirical analysis, we find the following results. First, the shock that occurred in the previous year was found to have a statistically significant positive effect on the current stock return rate and exchange rate change rate. Second, all conditional volatility spillover effect coefficients were found to have a statistically significant positive effect. Third, a negative spillover effect was found to occur between exchange rate volatility and Shanghai Composite Stock Index returns.

Do financial markets react to emerging economies’ asset purchase program? Evidence from the COVID-19 pandemic period

We employ the daily data on bond yield, stock return, and exchange rate to explore their reaction to the large-scale asset purchase program for 8 emerging market economies during the COVID-19 pandemic period. Using event study analysis, we find that: (1) out of 8 emerging economies, 6 economies’ financial markets react to the asset purchase announcements. (2) Most economies witnessed a rise in stock return, and a fall in the yield rate. (3) 2 economies witnessed currency appreciation, while 3 economies experience currency depreciation. Overall, the asset purchase announcements have a mixed effect on the stock, bond, and foreign exchange markets. Hence, these should be included in the central bank’s toolbox as complementary instruments applied to address certain unprecedented economic circumstances.

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.

Determinan Penting Stock Return pada Jakarta Islamic Index (JII) ditinjau dari Faktor Makroekonomi

Purpose: This study analyzes the effect of macroeconomic factors, including inflation, interest rates, exchange rates, and money supply, on the stock return rate of the Jakarta Islamic Index (JII). Methodology/approach: The data in this study were processed using the multiple linear regression approach in SPSS 26.0. The sample includes monthly data on the Jakarta Islamic Index (JII) Sharia index for ten years from January 2013 to January 2023. Results: The analysis results prove that the exchange rate and inflation have a significant negative effect on JII stock returns. In contrast, the interest rate and money supply do not have a significant relationship with stock returns. Limitations: A low adjusted R-squared value indicates that the independent variables can only explain the limited scope of the dependent variable. Thus, it can be concluded that additional variables are required to be included in this investigation. Contribution: This study is expected to provide a reference for further research. In addition, using one of the Sharia indices in this study is expected to provide a new view to novice investors that not only shares of conventional companies are traded but also shares of Sharia companies. It is hoped that this study’s results will also help investors prospect JII stock returns to help them find the best investment decision. Novelty: Many previous studies have conducted research on stock prices using the Jakarta Islamic Index (JII). However, research on stock returns using this index has not been found.

EXPLORING THE ROLE OF STOCK EXCHANGES AND EXCHANGE RATES FOR SUSTAINABLE ECONOMIC DEVELOPMENT: A CROSS CULTURE STUDY OF EMERGING AND DEVELOPED MARKETS

The study seeks to analyze the developed and emerging countries forecasting the business market strategy of Stock Markets index and Exchange rate index and impact on the country's economy from 1991 to 2021. Annually date of nine developed and nine emerging countries has been considered as sample data for this study. Unit-root-test for stationary, Johansen’s Co-integration, VECM, Granger Causality, ARDL, GMM (panel data) applied to test the short run/long-run impact, association, and behavior of variables among the economic development, stock return and exchange rates. Result of stock returns and exchange rate indicates highly significant and asymmetrical relationships exist each other and with countries growth. Robustness test validates the finding of study. Recommendation and implication are cleared; investment efficiency, portfolio diversification and exchange management to make significant investment decision and predict market behavior, preventive actions for validate both markets.

An Equilibrium Strategy for Target Benefit Pension Plans with a Longevity Trend and Partial Information

This paper considers the problem of portfolio selection and adjustment for target benefit plans (TBP) with longevity trends and partial information. The longevity trends are modeled by a time-varying force function. The financial market consists of risk-free assets and stocks, in which the return rate of stocks is a stochastic process and cannot be completely observed. This paper adopts the mean-variance utility model as an optimization criterion. The aim is to maximize the terminal value of the pension fund and the excess pension benefit after the participant’s retirement. The optimization equations are developed in game theory to obtain explicit solutions for the equilibrium strategies. Finally, the influence of the longevity trend on the internal structure of the pension system and the sensitivity of the equilibrium strategies to the related parameters are explored by numerical analysis. The conclusion shows that this model’s results can provide stable and adequate retirement benefits for participants.

ANALISI TINGKAT PROBABILITAS INVESTOR DALAM MEMBELI SAHAM SYARIAH DI PASAR MODAL DENGAN PENDEKATAN LOGISTIK

This study discusses the probability of investors buying Islamic shares in the capital market with the aim of knowing what factors influence investors in buying Islamic shares, to determine the characteristics of natural investors buying Islamic shares and to determine the probability level of investors in buying Islamic shares. The variables used are stock prices, transaction costs, return rates, risk levels and sharia compliance levels as independent variables and sharia stock purchases as the dependent variable. The research approach used is the Islamic finance approach using the logistic regression method. The data used in this research is secondary data.The results showed that the stock price had a significant effect on the purchase of sharia shares with a value of sig 0.016 0.05 and transaction costs had a significant effect on the purchase of sharia shares with a sig value of 0.017 0.05. Based on the characteristics of the stock price, namely the greater the stock price, the smaller the purchase of shares, while the characteristics of the transaction costs, namely the lower the transaction costs, the greater the purchase of shares. The probability level of investors in buying Islamic shares is 24%.

Exploring the impact of online news sentiment and relevance on stock market risks: A signalling theory perspective

AbstractBased on the frame theory and signalling theory, this article uses financial technology (fintech) to analyse online news sentiment and proposes the relevant hypothesis of the influence of news relevance on stock market risk, and conducts an empirical study on the VAR model of the stock return rate by using the news relevance and news sentiment data of the Uqer database. The results show a two‐way Granger causality between news sentiment and stock returns. News relevance is not Granger causality of yield but Granger causality of news sentiment. In addition, combined with the impulse response, variance decomposition, and relevance analysis, it is found that the news relevance degree positively affects news sentiment and then indirectly affects the stock return rate. Our findings indicate that news relevance is an essential variable in analysing online text sentiment and stock market volatility, and online news will increase the volatility of stock market risk. These results contribute to the financialization literature and guide fintech enterprises to balance market risk by combining signalling and frame theory to improve news relevance.

Impact of Pandemic Outbreak and Targeted Required Reserve Ratio Cut Policy on Stock Return Rate: Evidence from China

Abstract This study analyzed the impact of COVID-19 outbreak and targeted required reserve ratio cut policy on stock returns of Chinese listed companies. This paper uses the data of 3,449 A-share listed companies from February 3, 2020 to December 31, 2020 for research, the empirical results showed that stock prices of private enterprises with stronger debt-paying ability and looser financing constraints, and state-owned enterprises with less supply chain credit risks performed better, in the central and western regions, enterprises with stronger solvency and looser financing constraints have better stock price performance during the early stages of pandemic. After the implementation of the targeted RRR cut policy, the stock prices of enterprises with poor solvency, private enterprises, and enterprises in central and western regions with strong financing constraints, state-owned enterprises, and enterprises in eastern region with high credit risks all showed significant reversals, and the stock prices reflected the effect of the targeted RRR cut policy in the short and medium term. Over time, the pandemic has been controlled, and the resumption of work and production has freed most enterprises from financial difficulties. However, due to sporadic outbreaks, large private enterprises and eastern enterprises with strong risk resistance and loose financing constraints enjoy better stock price performance. This study is helpful for enterprises to understand the value of financial flexibility and solvency and provides a reference for enterprises to make financial decisions: how to balance the benefits and costs of solvency.

Analysis of Influencing Factors of Stock Return Rate in China

In recent years, the decline of China's stock market prices, as reflected in the decline of the Shanghai Composite Index and Shenzhen Composite Index in the late 2020s, has led to an economic downturn in China. The article analysis the statistics from 2016 to 2021 and introduces the possible reasons and political background and the reasons why these factors lead to the share value. Through the method of linear regression, the possible factors were simulated on the statistical system SPSS, and a more reliable data conclusion was obtained. To analyze the causes of the economic downturn by factoring the relationship between the consumer consumption index, per capita GDP growth, Gini coefficient, the Exchange rate of RMB against the US dollar, and the Shenzhen Index. In addition, it focuses on the impact of real-life factors on the stock market, focusing on the relationship between fixed asset investment, Hong Kong factors, the economic cycle, and the stock market.

The influence of non-controlling major shareholder's exit threat on firm's idiosyncratic risk

Non-controlling major shareholders can effectively restrain controlling shareholders and management by exit threat, so as to participate in corporate governance. Traditional research holds that the exit threat of non-controlling major shareholders can make the company have a more stable development prospect, but it has not paid enough attention to the hidden dangers. Based on 23,243 samples of Shanghai and Shenzhen A-shares from 2010 to 2021, this paper analyzes the influence of non-controlling major shareholder's exit threat on idiosyncratic risk of enterprises. It is found that the exit threat of non-controlling major shareholders will significantly increase the idiosyncratic risk of enterprises. Overall, exit threat lead to increased idiosyncratic risk. This conclusion is still robust after controlling for endogenous problems. The reason why the exit threat amplifies the idiosyncratic risk is that the idiosyncratic volatility is the market's response to the increased uncertainty of the future return rate of individual stocks, while the high exit threat of non-controlling major shareholders of a company exposes the future uncertainty of the company to investors more. This paper reveals that the threat of exit is the key factor affecting the idiosyncratic risk of firms, which has important reference value for regulators and investors.

Stock Return Prediction Based on CNN Model and Genetic Evolutionary Algorithm For A-share Medical Sector

Since the outbreak of the epidemic in December 2019, the real economy has been seriously affected, where the stock market has experienced a short-term crash. In this context, the financial market has become more turbulent and unpredictable. Previously, the traditional prediction method only captures the linear relationship between both the dependent and the independent variable. With the growing advancement of technology and science, the application of deep learning models to learn the nonlinear relationship between high latitude indicators and predictors has become the mainstream trend of stock prediction. Therefore, a convolutional neural network (CNN) model with fusion genetic algorithm is proposed in this paper to predict and analyze the stock return rate of China’s A-share medical sector from January 1,2020 to February 2020. In order to facilitate the operation, this paper chooses the GPU with better performance to process the large-scale calculation in the neural network and builds the experimental environment under the Huawei cloud model arts notebook system. This paper selects the Sequential model belonging to the tensor flow library Keras package. Subsequently, this paper optimizes the parameters in the model by using genetic algorithm. According to the analysis, the mean square error of the optimal model is 0.0017, and the prediction results of the model in the test set are validated with well performances, which is of great significance to investors' decision-making. These results shed light on guiding further exploration of stock trend prediction.

Forecasting movements of stock time series based on hidden state guided deep learning approach

Stock movement forecasting is usually formalized as a sequence prediction task based on time series data. Recently, more and more deep learning models are used to fit the dynamic stock time series with good nonlinear mapping ability, but not much of them attempt to unveil a market system’s internal dynamics. For instance, the driving force (state) behind the stock rise may be the company’s good profitability or concept marketing, and it is helpful to judge the future trend of the stock. To address this issue, we regard the explored pattern as an organic component of the hidden mechanism. Considering the effective hidden state discovery ability of the Hidden Markov Model (HMM), we aim to integrate it into the training process of the deep learning model. Specifically, we propose a deep learning framework called Hidden Markov Model-Attentive LSTM (HMM-ALSTM) to model stock time series data, which guides the hidden state learning of deep learning methods via the market’s pattern (learned by HMM) that generates time series data. What is more, a large number of experiments on 6 real-world data sets and 13 stock prediction baselines for predicting stock movement and return rate are implemented. Our proposed HMM-ALSTM achieves an average 10% improvement on all data sets compared to the best baseline.