Stock Price Volatility Research Articles

Capital market liberalization and corporate environmental performance: Evidence from the Shanghai-Hong Kong Stock Connect.

The implementation of Shanghai-Hong Kong Stock Connect marks the maturity of China's capital market, and the effect of the implementation has been the focus of academic attention. Based on this quasi-natural experiment, We select 3248 samples of heavily polluting enterprises listed in China in 2010-2020 to examine the impact of capital market liberalization and on corporate environmental performance.The results show that capital market liberalization significantly improves the environmental performance of heavy polluting enterprises. The results of the heterogeneity analysis indicate that the positive effect varies across firms with different ownership and internal controls. Finally, mechanism analysis results find that capital market liberalization promotes the environmental performance of heavily polluting firms by increasing environmental assets,reducing stock price volatility,and improving the quality of information disclosure.

Corporate political connection building and stock price volatility: evidence from Chinese-listed companies

Corporate political connection building and stock price volatility: evidence from Chinese-listed companies

Asian Rainbow Option Pricing Problem Based on Uncertain Fractional Differential Equations

Option pricing is a core issue in option trading, which directly affects investors’ trading decisions and risk management. With the growing maturity of uncertainty theory, the research on option pricing problems based upon uncertainty theory has been gradually deepened. However, the volatility of stock price is not only related to the price at the current moment, but also related to all the previous stock price changes. Uncertain fractional differential equations can well portray the memory and history dependence of financial markets. Its nonlocal nature can more accurately describe the long-term memory effect and the phenomenon of spiky fat tails of stock market fluctuations. Thus, this paper mainly explores the pricing of Asian rainbow option in uncertain financial environments, where the underlying asset price is viewed as uncertain fractional processes. First, two models are presented for discussing the price of Asian rainbow option in two-asset cases, respectively. Then the pricing problems of different types of Asian rainbow options under [Formula: see text]-assets case are further discussed. Finally, empirical analysis is conducted on the proposed models with real stock data to verify the validity of the pricing formulas.

Integrating Twitter Sentiment with ML Models for Enhanced Stock Forecasting

This research investigates the integration of Twitter sentiment analysis with machine learning models, specifically Long Short-Term Memory (LSTM), Facebook Prophet, and Neural Prophet, to enhance the accuracy of stock market predictions. While these models are accurate in capturing long-term trends and seasonality, they often fail when exploring the short-term volatility in stock prices. By using real-time social media sentiments, we aim to improve short-term prediction accuracy. Sentiment data from Twitter, extracted and classified using NLP techniques, was integrated with the models to predict stock prices for Apple Inc. (AAPL), Paramount Global (PARA), and BP plc (BP) over the period 2016-2024. The study compares the performance of the models using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Results show that Neural Prophet stood the best in terms of its accuracy and efficiency in stock predictions when compared to LSTM and Facebook Prophet, reducing RMSE by 25% and MAE by 20%. While the integration of sentiment analysis improved prediction accuracy, limitations remain in predicting the scale of sudden market changes. The findings highlight the potential of sentiment analysis in stock prediction but also call for further model enhancements to fully address short-term market volatility.

IMPACT OF STOCK PRICE FLAKINESS ON DEVELOPMENT OF NIGERIAN EXCHANGE GROUP

This study is aimed to investigate what is happening in the Nigeria Stock Market under its new name, “Nigeria Exchange Group”, with respect to the uncertainty nature of share price behaviour in the capital market. Hence, the month end stock prices of four listed consumer companies from the period January 2017 to December 2022 were used as proxies. The study made use of the Autoregressive Conditional Heteroskedasticity (ARCH) to estimate and find out the presence of the volatility. The study identified the presence of volatility in all the four consumer stock prices used, while the volatility of stock price was then regressed against stock prices to determine their certainty. The results however revealed that out of the four consumer goods companies, only two consumer goods companies’ stock prices were predicted by volatility, while past stock prices predicted current stock prices implying that the market does not follow a random walk. The study therefore recommended that activities of corporate insiders should be critically scrutinized to reduce the predictability of stock prices. Also, that Information should be made available and in clear terms to stakeholders and all investors in particular. Also, that policy makers should carefully review their economic policies in their use of the Nigerian bourse as a barometer to measure performance in the general economy to avoid misleading the investing public.

Market Reaction to ESG Information Disclosure A Case Study of Midea Group

Abstract: As global environmental issues become increasingly severe, investors are paying more attention to corporate sustainability. They realize that focusing solely on financial performance is insufficient; a company's environmental, social, and governance (ESG) performance must also be considered. In China, with the goal of carbon neutrality highlighting the importance of ESG, concepts such as ESG investment, reporting, and rating are flourishing. Corporate ESG actions are becoming a new focal point for investors. This paper takes the ESG information disclosure of Midea Group as a case study, using an event study model to explore the stability of the companys stock price and the variation in its returns during the event window, thus analyzing the market's short-term reaction to ESG information. The study finds that the ESG reports issued by Midea Group have a positive impact on the stock market to some extent, significantly reducing stock price volatility and inducing a positive trend in abnormal returns.

The Effect of Dividend Yield on The Volatility of Stock Prices of IDX30 Index Companies Listed on the Indonesia Stock Exchange for the 2019-2023 Period

This study investigates the influence of Dividend Yield on Stock Price Volatility in companies included in the IDX30 index on the Indonesia Stock Exchange (IDX) during the 2019-2023 period. Utilizing secondary data from annual reports of IDX30 companies, panel data regression analysis was conducted to examine the relationship between the variables. The findings reveal that Dividend Yield positively affects Stock Price Volatility, contradicting the initial hypothesis. The results indicate that an increase in Dividend Yield leads to higher Stock Price Volatility, while a decrease in Dividend Yield results in lower Stock Price Volatility. This research provides valuable insights for investors in understanding the dynamics between dividend policies and stock price fluctuations.

Dividend Policy and Share Price Volatility of Listed Construction Firms at the Nairobi Securities Exchange, Kenya

The inherent volatility in all stock market scares low risk takers from the financial market and is capable of deterring investments. One of the factors that drives investors’ reactions to stock pricing is the dividend policy. This study examined the influence of dividend policy on the volatility of stock prices of construction firms listed at the Nairobi Securities Exchange for the period spanning five (5) years from 2019 to 2023. The study was dealt with on the following objectives; to examine the influence of dividend pay-out on share price volatility of listed construction firms at the NSE, Kenya, to determine the influence of dividend yield on share price volatility of listed construction firms at the NSE, Kenya. The study was guided by the: Dividend relevance theory, and dividend irrelevance theory. This study adopted a correlational research design while dealing with the research objectives. A census method was employed in data collection before analysis of the audited financial statements that was downloaded from the firm websites in arriving at detailed and well informed conclusions by employing Multiple regression, Correlation and ANOVA techniques using the Statistical Package for Social Sciences (SPSS) to establish the existence (or lack of it) of the relationship between the independent variables and the dependent variable of the study. The findings revealed that dividend policy influenced share price volatility by; dividend pay-out ratio (r=0.111, β=0.036, t=4.7240); and dividend yield (r= -0.018, β= -0.003, t= 2.7130). The study recommended that potential and existing investors should opt for listed firms in the construction sector with a high dividend payout ratio compared to the earnings generated by the firms; Investors to invest in firms with high dividend payouts. The study achieved its main objective. Keywords: Dividend Policy, Dividend Pay-out, Dividend Yield, Share Price Volatility

Exploring the principal factors driving the volatility structure of stock trade levels in New Zealand: a Covid-19 perspective

This paper explores factors that drive the volatility structure of New Zealand stock trade levels, with the aim of understanding unusual volatility of stock trade levels that may pose a risk to the critical operations of a trading venue. Weekly number of trades from 2014–2024 for the sampled stocks were examined within principal axis factoring (PAF) and GARCH models. The results showed that three factors accounted for around 60% of volatility in the number of stock trades over the sampled period; they were driven primarily by electricity generation stocks, consumer discretionary stocks, and commercial property stocks respectively. During the sampled period, two subperiods of market shock showed unusual levels of volatility in all principal factors, with the second subperiod, which coincided with the Covid-19 outbreak, being particularly notable. An implication of the study is that given the volatilities of stock trade levels are little studied in the literature, the findings of this study could be a useful complement to existing studies examining stock price volatility for the purposes of mitigating unusual stock trading volatility.

Research on the Impact of ESG Performance on Stock Price Volatility of Enterprises: Empirical Evidence from A-share Listed Companies

As socially responsible investment and sustainable development gain traction, the ESG performance of companies is increasingly attracting investor and public attention. ESG ratings help identify responsible firms, enabling assessment of their contributions to sustainable growth.This article examines Shanghai and Shenzhen A-share data, focusing on the impact of ESG performance on stock price volatility. Key findings include:A significant negative correlation exists between stock price volatility and ESG performance; higher ESG ratings correspond to lower volatility;he nature of property rights affects this relationship, with non-state-owned enterprises benefiting more from ESG performance in reducing volatility than state-owned enterprises;Policy uncertainty in a company's location significantly moderates the relationship, with greater uncertainty intensifying the negative impact of ESG performance on stock price volatility.This research enhances understanding of the economic consequences of ESG performance and helps investors recognize its value, improving capital allocation efficiency.

News-Driven Volatility: A Deep Dive into Leading Nifty Healthcare Stocks

This study conducts a comprehensive analysis of stock price volatility within the healthcare sector, concentrating on five prominent companies: Sun Pharma, Dr. Reddy, Cipla, Apollo Hospitals, and Divis Laboratories. Leveraging annual daily data from the Nifty Healthcare Index for 2023, the research delves into the intricate relationship between stock price fluctuations and market-sensitive news events. Employing a robust methodology that integrates slope calculations, ANOVA, Tukey’s HSD tests, moving averages, and standard deviation metrics, the study evaluates the differential impact of these events on stock volatility. The findings reveal significant volatility, with Apollo Hospitals demonstrating the highest levels of price variability. The research highlights the profound influence of regulatory shifts, financial performance metrics, and macroeconomic conditions on stock behavior. These insights are pivotal for investors and financial analysts seeking to navigate the complex dynamics of the healthcare market. The application of moving averages further refines the analysis, offering a sophisticated lens through which to assess and interpret market trends, ultimately enhancing the strategic decision-making process within this critical sector.

The Impact Of Stock Price On Investment Decision: Theory & Evidence

Purpose – The purpose of this paper is to present a behavioral explanation of excess stock price volatility relative to present value theory. Design/methodology/approach – The conceptual basis is the impact of effect on investor decisions. The empirical tests involve survey data collected from a sample of semi-professional Investors and investment advisors. Findings – It is suggested that affect causes investors to perceive an inverse ex ante relationship Between risk perceptions and expected returns. Thus, new good or bad information has an amplified effect on stock valuations. In addition, investors tend to extrapolate recent short-term market movements into the future. Practical implications – The primary implications are that ex ante perceptions of risk and return Vary inversely and that affect has a strong influence on valuation. This means that simple statistical Measures of risk are unlikely to fully capture risk perceptions and that market volatility can be Expected to be greater than a simple present value model would imply. Originality/value – This paper is unique as to the conclusion that risk and return perceptions vary Inversely ex ante and that affect can amplify stock price volatility

The impact of ESG rating changes on fund shareholding

This study examines the impact of environmental, social and governance (ESG) rating changes on fund shareholding based on rating data from five agencies. Using a sample of Shanghai and Shenzhen A-share listed companies in China spanning from 2019 to 2021, we find robust evidence that the mean value of ESG ratings by agencies positively impact the number of fund holdings, while the standard deviation of ESG ratings by agencies have no impact on the number of fund holdings. Additionally, the mean value of ESG ratings by agencies have a greater impact on the number of fund holdings among state-owned and industrial companies, and the mean value of ESG ratings by agencies has a larger impact on the number of fund holdings in a one-year window than in a six-month window. Moreover, we find that the mean value of ESG ratings by agencies negatively impact the stock price volatility. The results show that ESG rating changes play a positive role in the decision-making of fund shareholding and in promoting the sustainable development of firms.

Investor Sentiment and Corporate Governance: The Role of Behavioral Biases in Stock Price Volatility and Managerial Decisions

Behavioural biases influence investor sentiment and behaviour, which in turn influence stock returns, stock price volatility and corporate governance structures. This paper focuses on the role of three common behavioural biases (overconfidence, herding and loss aversion) on investor behaviour, and how these biases drive inefficiencies in financial markets. Using worldwide market data from 1970 to 2019 and focusing on key events such as the 2008 financial crisis and the dot-com bubble, we investigate how these behavioural biases cause market volatility. This analysis further reinforces the importance of strong corporate governance structures such as independent boards, executive pay structures and risk management protocols that help reduce the impact of investor sentiment. The role of speculative bubbles, market overreaction and volatility clustering is also examined further, and corporate governance mechanisms are proposed to help mitigate the effects of behavioural biases on stock prices. The findings demonstrate the importance of adopting long-term corporate goals, transparency and strategies to help better manage market sentiment and safeguard shareholder value.

The Link Effect of ESG Score, Stock Price Volatility, and Tax Payment: Doing Well while Doing Good

This study aims to identify how environmental, social and governance (ESG) performance influences stock price volatility, explicitly focusing on the moderating role of tax engagement. ESG performance is measured by an ESG Score calculated from the weighting of three dimensions: environmental, social and governance. Stock price volatility is measured by the degree of price variations over 12 months based on the last 52 weeks’ prices. A sample of Indonesia-listed firms is used, with 770 observations from 2023. The results show that the ESG Score negatively impacts stock price volatility, which is more significant in the social dimension than in the environmental and governance dimensions. In addition, the tax payment variable moderates the relationship and increases the effect of the ESG Score on stock price volatility. These findings suggest that ESG practices and tax transparency are ethical elements and critical components for financial stability, promoting the high-quality development of listed firms. This study is significant for firms, regulators, policymakers and investors. Overall, it underrates the importance of firms adopting ESG activities and engaging in tax management to mitigate risks and maintain viability in the contemporary business environment. This study provides new empirical evidence regarding the factors driving corporate stock price volatility. In addition, it offers pertinent policy recommendations for businesses and governments regarding the significance of ESG investments.

Beyond GARCH: Intraday Insights Into the Exchange Rate and Stock PriceVolatility Dynamics in Borsa Istanbul Sectors

This study investigated the impact of exchange rate volatility on sectoral stockvolatility by employing the intraday volatility measure directly calculated fromthe original data, using daily data from 27 Borsa Istanbul sectors between April 29, 2003, and April 25, 2023. In the literature, GARCH models are commonly usedtostudy the volatility spillovers between exchange rates and stock prices, typicallyusing aggregate data. However, the GARCH family models provide inefficient andbiased estimates if they are misspecified. Moreover, using aggregate-level data maylead to biased and misleading conclusions. The research used intraday volatilitymeasures to overcome the shortcomings of GARCH models. The ordinary least squares (OLS), GARCH (1,1) methods, and Garman and Klass (1980) volatilityestimator are used. The empirical results showed that the estimates fromeachmethod vary significantly, and these disparities in the results might be due tomisspecification in GARCH (1,1) models. The intraday volatility model estimationresults showed that although stock price volatilities in all sectors are positively andsignificantly affected by exchange rate volatility, their magnitudes varysignificantly. Taken together, this implies the presence of vast heterogeneities inthe responses of sectoral stock price volatilities to exchange rate volatility. The results encourage policymakers to pay special attention to these heterogeneities toprevent capital flights and underinvestment. Additionally, the findings assist investors in making more effective decisions by helping themadapt their investment strategies to factor in exchange rate fluctuations and mitigate the impact of unexpected events in the exchange rate market

Does Dividend Policy Affect Stock Price Volatility? Evidence from Egypt

Does Dividend Policy Affect Stock Price Volatility? Evidence from Egypt

Integrating LSTM Networks with Mean-Variance Optimization for Enhanced Portfolio Construction: An Empirical Study on UK Stock Market

Portfolio optimization has long been a central theme in finance. With ongoing advancements in machine learning, there is a significant opportunity to integrate predictive methods into portfolio optimization. This paper proposes leveraging Long Short-Term Memory (LSTM) networks alongside the established Mean-Variance (MV) optimization framework to construct optimal portfolios. These portfolios aim to help financial investors effectively manage and mitigate risk while maximizing returns. The study meticulously screened the leading stocks of 12 prominent UK companies listed on the London Stock Exchange (LSE), known for their influence and visibility. Initially, the study applies the LSTM networks to predict stock price volatility and integrates these predictions into the MV model to allocate portfolio weights effectively. To underscore the superiority of the proposed approach, the study compares cumulative returns from portfolios optimized for maximum and minimum variance Sharpe ratios with real data against the FTSE100 index over the same period. The results demonstrate that the portfolio optimized with the maximum Sharpe ratio significantly outperforms both the conservative minimum variance portfolio and the FTSE100 index. This empirical evidence underscores the practical value of integrating advanced machine learning techniques with established financial theory, enabling more innovative and efficient investment strategies to meet the evolving needs of financial investors.

Pengaruh Volume Penjualan Saham, Profitabilitas, Solvabilitas, Earning Volatility dan Tranding Volume terhadap Volatilitas Harga Saham dengan Kebijakan Deviden sebagai Variabel Mediasi

This research aims to determine the effect of share sales volume, profitability, solvency, earnings volatility and trading volume on share price volatility with dividend policy as a mediating variable in food and beverage companies listed on the Indonesia Stock Exchange for the 2017-2021 period. The sample for this research consisted of 55 companies, using a purposive sampling method with the criteria stating dividend payments for 5 consecutive years. The research results show that debt ratio, return on assets, trading volume have an influence on stock price volatility. However, earnings volatility and share sales volume have no effect on share price volatility. Dividend policy is able to mediate share sales volume on stock price volatility, dividend policy is able to mediate profitability on stock price volatility, dividend policy is able to mediate solvency on stock price volatility, dividend policy is able to mediate earnings volatility on stock price volatility and dividend policy is able to mediate volume trending on stock price volatility in food and beverage companies listed on the Indonesia Stock Exchange for the 2017-2021 period.

DETERMINAN VOLATILITAS HARGA SAHAM PADA INDEKS IDX30 (STUDI PADA TAHUN 2018-2021)

The purpose of this study is to determine the factors that influence the volatility of the IDX30 company’s stock prices. Independent variable used are Stock Trading Volume, dividend policy, leverage, firms size, and asset growth. This study using the report data from companies listed in IDX in the Index IDX30 between 2018-2021. Data analyzed using data panel regression analysis. The result from the analysis found out if variable dividend yield, firm size, and growth asset has no significant effects on Stock Price Volatility. Meanwhile, trading volume and leverage have a significant effect on stock price volatility.