Stock Price Volatility Research Articles

The effect of macro variables on Indonesian stock exchanges in pandemic COVID-19

During the COVID-19 pandemic, it is crucial for stock traders inside a specific nation to conscientiously examine and assess the current monetary conditions and macroeconomic data. This study examined the influence of macroeconomic factors, namely unemployment, inflation, interest rates, and exchange rates, on the Indonesian stock market index within the framework of the COVID-19 pandemic. This research employs quantitative approaches, notably autoregressive distributed lag (ARDL) analysis, to evaluate the dynamics of time series data. The current analysis shows that a variety of factors, such as inflation, interest rates, and exchange rates, have an impact on the stock price index. Furthermore, the results of this study indicate that the deviation from the long-term equilibrium in the short term is rectified at a monthly rate of 33.47%. As a result of completing an exhaustive analysis of this study, it is of the utmost importance for the Indonesian government to concentrate its attention on the issues of unemployment, inflation, interest rates, and currency rates during the COVID-19 epidemic. This is done with the intention of achieving stability on the stock exchange. In addition, it is strongly suggested that investors do a comprehensive analysis of the volatility of stock prices by incorporating a variety of macroeconomic elements.

The Effect of Gambling Preference on Stock Volatility: From the Evidence of Looser Price Limits Regulation

ABSTRACT This article explores the intricate relationship between gambling preferences and stock market dynamics, with a focus on the repercussions of a more lenient price limit regulation. Specifically, we examine the effects of expanding the price limit range from 10% to 20% for stocks listed on the second board market of the Shenzhen Stock Exchange. Employing a difference-in-differences methodology for causal inference, our research reveals a notable surge in price volatility, particularly evident among lottery stocks. To validate these findings, we employ various statistical techniques, including propensity score matching tests, parallel trend hypothesis tests, placebo tests, and alternative volatility measurements. Our study unveils that looser price limits contribute to heightened stock price volatility through two primary channels: the inherent lottery characteristics of certain stocks and the increased attention from retail investors. Moreover, we identify that the amplified volatility is more pronounced within non-state-owned enterprises and small-cap entities. In summary, our results indicate that price limits play a crucial role in stabilizing emerging stock markets by mitigating the gambling tendencies of retail investors. This research sheds light on the positive aspects of price limits regulation, offering valuable insights for market policymakers and participants alike.

Optimizing Investment Strategies: A Case Study on JPMorgan Chase & Co. Stock Options Using the Black-Scholes Model and What-If Analysis in Excel

This research focuses on applying the Black-Scholes Model to evaluate European options on JPMorgan Chase & Co. stocks. This model has been a critical foundation in evaluating financial instruments, especially options, since its development in 1973 by Fisher Black, Myron Scholes, and Robert Merton. The study utilizes secondary data from some sources to obtain current information regarding stock prices, strike prices, expiration time, volatility, and relevant risk-free interest rates for option valuation as of December 19, 2023. Through this approach, our aim is to gain a better understanding of how the Black-Scholes Model is used as a framework in determining option prices for these stocks. The research methodology involves What-If analysis, exploring variations in key variables such as current stock price, strike price, expiration time, stock price volatility, and risk-free interest rates to assess how these changes affect the prices of both call and put options. Additionally, the study presents graphs representing stock prices, strike prices, interest rates, time, and volatility to visually support the research findings. The analysis results reveal that the prices of both call and put options are highly responsive to changing market conditions. An increase in the current stock price tends to raise the call option price while reducing the put option price. Conversely, an increase in the strike price has the opposite effect. Moreover, variations in the risk-free interest rates influence the option prices, with rising rates increasing the call option price and decreasing the put option price. Furthermore, as the expiration time approaches or stock price volatility increases, both call and put option prices tend to rise. These findings provide a comprehensive understanding of the dynamics of JPMorgan Chase & Co.'s stock option pricing, offering a foundation for investors to make informed and adaptable investment decisions amid constantly evolving financial markets. Sensitivity to changes in key variables is an essential aspect to consider in designing effective investment strategies in the face of ever-changing financial markets.

Harnessing Cognitively Inspired Predictive Models to Improve Investment Decision-Making

In the last years, researchers and practitioners have focused on defining portfolio optimization approaches. This task aims to identify a suitable distribution of assets for maximizing profits and minimizing risks, also offering protection against unexpected market behaviors. Nevertheless, the state-of-the-art approaches encounter significant limitations due to the complex nature of the task: (1) forecasting of non-stationary, non-linearity and volatile stock price; (2) budget allocation over different stocks satisfying multi-objective objective function; (3) risk costs can significantly affect the effectiveness of the designed approaches. In this paper, we propose a cognitively inspired framework for portfolio optimization by integrating deep learning-based stock forecasting for maximizing the revenue and portfolio diversification and Shape Ratio for minimizing the risk. Furthermore, the cognitively inspired forecasting module relies on the LSTM-based approach which combines historical financial data and technical indicators. Hence, this approach addresses the portfolio optimization task with the aim of designing more and more cognitive agents that perform autonomous actions for supporting decision-making. To make these agents cognitive, we further integrate stock forecasting into the portfolio optimization model, also investigating the main factors affecting both stock forecasting and portfolio optimization tasks. The proposed framework has been evaluated in two stages on a real-world dataset, composed of four years of information about stocks from six different areas. Firstly, we compare the proposed forecasting models based on LSTM and GRU, pointing out that the former achieves higher effectiveness results although the latter has a shorter training time. Finally, the proposed framework has been compared with different baselines, obtaining a net difference of $168 at the maximum. Finally, we compare the proposed approach w.r.t. several baselines in terms of total revenue, also providing an ablation analysis to investigate how stock prediction might support investors in dealing with portfolio optimization task.

So Right It’s Wrong? Right Governments, Far Right Populism, and Investment Risk

Political economy literature documents how financial investors are more partial to right executives than left ones. Right cabinets face lower interest rates, less volatile stock prices and exchange rates, and higher credit ratings than left cabinets, even after accounting for fiscal differences. But does this advantage persist if right governments accommodate far right parties or ideas? I hypothesize that because far right populism can introduce political instability, markets’ evaluation of right executives might deteriorate if they enter coalition with far right parties or adopt their positions. Employing a panel analysis of bond spread data, and a comparative case study of the Netherlands and Sweden, I find that right executives enjoy significantly lower spreads than their left-wing counter-parts, but this advantage disappears if they rule in coalition with the far right or produce overly right-wing manifestos. These findings highlight that right parties may encounter tangible borrowing costs and market rebuke if they accommodate far right populism.

Debt To Equity Ratio Moderating Dividend Payout Ratio on Stock Price Volatility

This research aims to analyze the influence of the dividend payout ratio on stock price volatility, moderated by the debt-to-equity ratio. The research adopts a quantitative approach, with the population consisting of companies listed in the LQ45 index on the Indonesia Stock Exchange. Sample selection is carried out using purposive sampling, with the criteria that companies must consistently be listed in the LQ45 index during the study period from 2019 to 2022. The data used in this study are secondary, and data analysis is conducted through the technique of panel data regression analysis. The empirical findings of this research indicate that the most appropriate model is the fixed effect. Based on the analysis results, the first hypothesis states that the dividend payout ratio does not have a significant direct effect on stock price volatility. Conversely, the second hypothesis states that the debt-to-equity ratio has a direct positive and significant effect on stock price volatility. Furthermore, the findings suggest that the third hypothesis states that the debt-to-equity ratio can strengthen the positive and significant impact of the dividend payout ratio on stock price volatility

When Vegas Comes to Wall Street: Associations Between Stock Price Volatility and Trading Frequency Amongst Gamblers

AbstractBoth gambling and trading involve risk-taking in exchange for potential financial gains. In particular, speculative high-risk high-frequency trading closely resembles disordered gambling behaviour by attracting the same individuals who tend to be overconfident, sensation-seekers, and attracted to quick large potential payoffs. We build on these studies via an incentivised experiment, in which we examine how manipulated levels of market volatility affected trading frequency. Gamblers (N=604) were screened based on the existence of household investments and recruited across the four categories of the Problem Gambling Severity Index. The volatility of stocks was manipulated between-participants (high vs. low). Participants traded fictitious stocks and were provided bonuses based on the results of their trading activity (M=US$4.77, range=[0, 16.99]). Participants traded more often in the high-volatility market, and this finding remained robust after controlling for financial literacy, overconfidence, age, and gender. Many investors trade more frequently than personal finance guides advise, and these results suggest that individuals are more likely to commit this error in more volatile markets. Exploratory analyses suggest that the effect of the volatility manipulation was strongest amongst gamblers who were at low-risk of experiencing gambling harms. As they might be otherwise considered low-risk, these individuals could be overlooked by protective gambling interventions yet nonetheless suffer unmitigated financial harms due to unchecked excessive trading.

PENGARUH KEBIJAKAN DIVIDEN, LEVERAGE, DAN FIRM SIZE TERHADAP VOLATILITAS HARGA SAHAM

The purpose of this study was to determine and analyze the effect of dividend policy, Leverage, and Firm Size on stock price volatility. The research method used was purposive sampling with a total sample of 18 companies listed on the LQ-45 index listed on the Indonesia Stock Exchange (IDX). This research was conducted using secondary data on observations in 2018-2020. Data processing in this research using Microsoft Office 365 software and IBM SPSS Statistics V25.0. The results showed that Dividend Yield, Dividend Payout Ratio and Firm Size did not have a significant effect on Stock Price Volatility, while Leverage had a significant and negative effect on Stock Price Volatility. The implication of this research is the need to know what are the factors that can affect the stock price volatility which will give a good or bad signal to investors in carrying out their investment activities.

Recession-proof marketing? Unraveling the impact of advertising efficiency on stock volatility

The current study examines how corporate advertising efficiency (estimated using data envelopment analysis) is impacted during economic recessions. The study then investigates how advertising efficiency affects stock price volatility in recession and non-recession times. Drawing on the resource-based view and contingency theory, this study utilizes generalized method of moments (GMM) estimation to analyze a sample of US listed firms. The findings reveal that advertising efficiency tends to decline during economic downturns. Moreover, the study demonstrates that firms with higher advertising efficiency experience more stable stock prices during normal economic conditions. However, an interesting observation emerges during recessions, as firms with strong advertising efficiency face higher stock price volatility. This suggests that while consumers are attracted to efficient advertisements, investors do not assign the same value to advertising spending during economic contractions. Importantly, these results are robust to endogeneity issues and various measurements of variables.

Pengaruh Dividen, Pertumbuhan Aset, dan Volume Perdagangan terhadap Volalitas Harga Saham JII70

Purpose: The study is intended to empirically examine the fundamental factors of micro companies in influencing stock price volatility in the Jakarta Islamic Index 70 From the period 2019-2023. The form of Islamic stock market reaction in the Jakarta Islamic Index 70 is shown by fluctuations resulting in stock price volatility events in the 2019-2020 period. Through this research, the fundamental factors taken, namely the distribution of company dividends, the growth of company assets, and the volume of stock trading in stocks in JII70 for the 2019-2020 period. Research Methodology: With data from the Indonesian stock exchange from 2019 to 2022, comparative descriptive approaches and quantitative methodologies were employed in this study. Purposive sampling was used to sample, and it revealed 18 businesses that fit the bill. Results: The findings results of the study, which was conducted on 18 companies listed on the Jakarta Islamic Index 70 during the 2019-2022 period, showed that stock price volatility was influenced by variable dividend payout ratios with a significant value of 0.048, stock trading volume was also influential with a significant value of 0.041, and was not influenced by asset growth variables. Limitation: Some scientific limitations in this study, that this analysis only includes 3 independent variables whose capacity to explain the dependent variable is still somewhat limited. The companies listed in the Jakarta Islamic Index are 70, producing a small research sample and the research period is still relatively short. Contribution: The results of this study will likely be applied in investment decision-making. Businesses with concerns about fluctuations, and stock price volatility can also utilize this research's findings as knowledge.

Research on the impact of digital transformation of energy enterprises on stock price fluctuation

As an important choice of strategic transformation of energy enterprises, digital transformation has a profound impact on the stock price fluctuation of enterprises. From the perspective of dynamic capacity and environmental regulation, analyzes influences of digital transformation upon energy companies’ share movement volatility, constructs a theoretical model that considers digital transformation and stock price volatility as the primary effects, dynamic capabilities as the mediator, and environmental regulation as the moderator. In addition, the study employs data from China’s A-share listed energy enterprises from 2013 to 2020, utilizing a fixed-effect model to perform an empirical test. The findings demonstrate a significant positive correlation between the digital transformation of energy enterprises and the volatility of stock prices, indicating that the greater the extent of digital transformation, the higher the volatility of enterprise stock prices. Among the dimensions of dynamic capability, only adaptability and innovation ability appears to mediate the relation between digital transformation and stock price fluctuation. Moreover, environmental regulation positively moderates the relationship between digital transformation and the learning ability dimension. Finally, from the macro and micro levels, this study puts forward the policies and supportive measures to stabilize the stock price of energy enterprises, and suggestions on how to implement the digital transformation strategy reasonably according to their own development status and characteristics to provide valuable insights for encouraging the digital transformation among energy firms.

Identifying the nexus between financial stability and economic growth: the role of stability indicators

PurposeThis study aims to examine the interrelationship between financial stability and economic growth with a comprehensive analysis.Design/methodology/approachThe panel Granger causality testing approach is carried out to the panels of the Fragile Five (F5) and the Group of Seven (G7) countries for the period 1998–2020. To capture the different aspects of financial stability the authors use eight different indicators.FindingsThe findings reveal some important implications: the relationship between financial stability and economic growth is sensitive to the financial stability indicators for both the F5 and G7 countries. The stability indicators related to the credit market contain much more causality relationship with economic growth than the indicators related to the stock market. Z-score and provisions to nonperforming loans (NPLs) are among the two variables with the highest causality relationship with economic growth. The least number of causality link is found for the Regulatory Capital Ratio and Stock Price Volatility in F5 countries and Credit Ratio, NPLs and Stock Price Volatility in G7 countries. Economic growth affects financial stability through credit market stability indicators and mostly for the F5 countries. No causal relationship is found for any of the financial stability indicators of Canada, the UK and the USA from economic growth to financial stability.Originality/valueSince the linkages between financial stability and economic growth may vary due to country/group specific differences, apart from the previous studies, the authors select two different groups of countries in terms of financial stability and economic size.

Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction.

Stock price data often exhibit nonlinear patterns and dynamics in nature. The parameter selection in generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA) models is challenging due to stock price volatility. Most studies examined the manual method for parameter selection in GARCH and ARIMA models. These procedures are time-consuming and based on trial and error. To overcome this, we considered a GWO method for finding the optimal parameters in GARCH and ARIMA models. The motivation behind considering the grey wolf optimizer (GWO) is one of the popular methods for parameter optimization. The novel GWO-based parameters selection approach for GARCH and ARIMA models aims to improve stock price prediction accuracy by optimizing the parameters of ARIMA and GARCH models. The hierarchical structure of GWO comprises four distinct categories: alpha (α), beta (β), delta (δ) and omega (ω). The predatory conduct of wolves primarily encompasses the act of pursuing and closing in on the prey, tracing the movements of the prey, and ultimately launching an attack on the prey. In the proposed context, attacking prey is a selection of the best parameters for GARCH and ARIMA models. The GWO algorithm iteratively updates the positions of wolves to provide potential solutions in the search space in GARCH and ARIMA models. The proposed model is evaluated using root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE). The GWO-based parameter selection for GARCH and ARIMA improves the performance of the model by 5% to 8% compared to existing traditional GARCH and ARIMA models.

A study of the impact of financial news media coverage patterns on stock price fluctuations

Abstract This paper examines the impact of media communication on the stock market in the context of the digital age, particularly its role in stock price volatility. With the significant increase in the speed, breadth and depth of media communication, the efficiency of stock market information transmission has been dramatically improved, significantly impacting stock price volatility. The article first explores the specific impact of media coverage characteristics and methods on stock price volatility by quantitatively analyzing media coverage’s breadth, manner and content. Then, the article utilizes multiple linear regression models to deeply explore the impact of various news texts on stock price volatility in different reporting modes (e.g., live reporting, television, and text reporting) in the Shanghai and Shenzhen stock markets. The findings show a significant association between the breadth of media coverage and stock prices at the 1% significance level. In addition, the regression coefficients of content and manner of coverage on stock price changes are 0.096 and 0.229, respectively, which are both significant at the 5% significance level. The research results of this paper not only reveal the close relationship between media communication and stock market volatility and provide theoretical support for predicting dramatic stock market volatility, which helps to promote the construction of a healthy and orderly capital market order.

Markov Prediction Models and Its Applications in the Analysis on the Volatility of Stock Prices

Markov Prediction Models and Its Applications in the Analysis on the Volatility of Stock Prices

ESG ratings and stock price volatility: An empirical analysis amidst the COVID-19 pandemic

This study, grounded in the framework of environmental, social, and governance (ESG) integration theory, systematically explores the relationship between ESG scores and stock price volatility of Chinese enterprises during the COVID-19 pandemic. Utilizing a multivariate linear regression model, it explores how ESG ratings influence stock price dynamics across different sectors. Findings suggest a negative correlation between higher ESG ratings and stock price volatility, indicating ESG as a mitigating factor. Additionally, the study examines the moderating effects of company size and industry variations on this relationship. Contributions include providing insights into the role of ESG in risk management and guiding policy formulations to enhance corporate ESG performance amidst market uncertainties.

Tác động lan truyền giữa tăng trưởng tín dụng và giá tài sản tài chính: Bằng chứng từ mô hình TVP-VAR

The article assesses the spillover effects among credit growth, stock, and bond returns through a dynamic network model with time-varying parameters, TVP-VAR, from 2010 to 2023Q2. The findings reveal a dynamic interconnection among credit growth, stock price volatility, and bond prices over time. Notably, credit growth emerges as the primary shock spillover to the system. However, credit growth spillover effects are mainly toward stock returns dynamics, while the connection between credit growth and bond returns is much less clear. Conversely, stock return exhibits spillover effects on bond return, indicating changes in risk appetite among market participants during various financial market cycles. Based on the empirical results, the authors propose some policy implications

Exploring dividend policy, stock price volatility, and the impact of ownership structure: a study of non-financial companies

Exploring dividend policy, stock price volatility, and the impact of ownership structure: a study of non-financial companies

A Hybrid GARCH and Deep Learning Method for Volatility Prediction

Volatility prediction plays a vital role in financial data. The time series movements of stock prices are commonly characterized as highly nonlinear and volatile. This study is aimed at enhancing the accuracy of return volatility forecasts for stock prices by investigating the prediction of their price volatility through the integration of diverse models. Thus, the study integrated four powerful methods: seasonal autoregressive (AR) integrated moving average (MA), generalized AR conditional heteroskedasticity (ARCH) family models, convolutional neural network (CNN), and bidirectional long short‐term memory (LSTM) network. The hybrid model was developed using the residuals generated by the seasonal AR integrated MA model as input for the generalized ARCH model. Following this, the estimated volatility obtained was utilized as an input feature for both the hybrid CNNs and bidirectional LSTM models. The model’s forecasting performance was assessed using key evaluation metrics, including mean absolute error (MAE) and root mean squared error (RMSE). Compared to other hybrid models, our new proposed hybrid model demonstrates an average reduction in MAE and RMSE of 60.35% and 60.61%, respectively. The experimental results show that the model proposed in this study has good performance and accuracy in predicting the volatility of stock prices. These findings offer valuable insights for financial data analysis and risk management strategies.

Decisions regarding the role of bad news and asymmetric effects in the Middle East stock markets

This paper aims to study the impact of the COVID-19 pandemic, the Russian invasion of Ukraine, and the Turkey-Syria earthquake on the Middle East’s developed, emerging, and frontier markets. For this purpose, panel data of nine Middle East financial markets listed in Morgan Stanley from January 2, 2018, to July 27, 2023, were analyzed using multi criteria. In the event study, two approaches were deducted to analyze the price impact: 1) a standard event study and 2) an independent sample, following Brown and Warner (1985). The generalized autoregressive conditional heteroskedasticity (GARCH) group captures asymmetric and leverage effects. The results show volatility in financial market index returns and the impact of bad news and leverage in all markets. However, this effect is asymmetric across markets, indicating a low integration. Moreover, the negative impact of COVID-19 was more pronounced than that of both the Russian invasion of Ukraine and the Turkey-Syria earthquake. This study’s findings can help investors make informed investment decisions and select optimal portfolios. It will also add to the existing body of knowledge by shedding new light on the factors that influence stock price volatility and risk management in Middle Eastern international fiscal issues.