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Volatility is a statistical measure of stock price fluctuation. This study aims to investigate the effect of trading volume, firm size, inflation, and exchange rate on stock price volatility of the companies included in the Jakarta Islamic Index (JII) from 2014 to 2018. By using a purposive sampling technique, the research sample is sixteen compani...
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... Further, (2020) investigates Indonesian firms, showing that trading volume and firm size significantly affect volatility, while inflation and exchange rates do not. 18 This highlights that different markets may have unique volatility determinants. Add another layer by exploring how the COVID-19 pandemic impacted stock price volatility, identifying firm size, dividend payments, and trading volume as significant determinants. ...
... The reviewed literature highlights consistent determinants of stock price volatility, such as firm size, dividend policy, leverage, and trading volume, analyzed through methods like GARCH and linear regression. 15,[17][18][19] However, a gap exists in applying machine learning techniques, such as PCA and K-means clustering, to analyze volatility in emerging markets like Turkey's BIST100 index. Additionally, few studies combine machine learning clustering with panel regression to explore financial ratios' impact on volatility within distinct firm groups. ...
This study conducts a comprehensive volatility analysis among firms listed on the BIST100 index using machine learning techniques and panel regression models. Focusing on the period from 2006 to 2023, the study excludes financial firms, resulting in a dataset of 46 companies. The methodology follows a two-step process: First, firms are clustered into low and high-volatility groups using Principal Component Analysis (PCA) and the K-means algorithm; second, panel regression models are applied to determine the financial ratios influencing stock price volatility. The Parkinson Volatility measure is used as the dependent variable, while independent variables include Return on Assets (ROA), Return on Equity (ROE), liquidity ratios, firm beta, and leverage ratios. Results indicate that firm beta has a statistically significant positive impact on volatility across all models, while the current ratio negatively affects volatility in the model 1. These findings provide valuable insights for investors and policymakers regarding risk management in the Turkish stock market. Applying machine learning and advanced econometric techniques adds to the literature on volatility forecasting and financial decision-making.
... The OLS regression model presented in Table 6 evaluates the impact of various factors on stock market volatility. The analysis identifies Trade Volume and Market Return as significant predictors, corroborating the findings of prior studies such as Chan and Fong (2000) [22] , Hugida (2011) [43] , Damiran, et al. (2022) [27] , and Sutrisno, (2020) [73] . In contrast, variables like Stock Market Capitalization and Trade Openness do not show significant effects. ...
... Previous work has explored macroeconomic drivers such as inflation (Thampanya et al. 2020;Aliyu 2012), business cycles (Corradi et al. 2013;Officer 1973), and exchange rates (Olweny and Omondi 2011;Kennedy and Nourzad 2016). Concurrently, the literature has also examined microeconomic factors influencing stock volatility, including stock trading volume (Sutrisno 2020;Narayan et al. 2013), firm sizes (Mazzucato and Semmler 2002), dividend policies (Baskin 1989;Hashemijoo et al. 2012;Hooi et al. 2015), and capital structure (Christie 1982). The current research enriches this domain by rigorously analyzing the cross-market impacts of carbon ETS markets on stock volatility. ...
The carbon emission trading markets represent an emerging domain within China. The primary objective of this study is to explore whether carbon price volatility influences stock market volatility among companies subject to these emission trading regulations. Employing daily returns data from 293 publicly traded companies regulated by these emission trading markets, this study encompasses the national carbon market and eight pilot regional carbon markets spanning from August 2013 to October 2023. The results demonstrate that volatility in regional carbon prices positively impacts the stock volatility of companies in the corresponding emission trading region, indicating a volatility spillover effect. Moreover, this spillover effect is more pronounced in sectors marked by lesser carbon intensity than those with greater carbon intensity. The volatility transmission is more pronounced in coastal areas than in inland regions. However, no notable distinctions in volatility transmission are discerned between the periods before and throughout the COVID-19 pandemic. Vector autoregression analyses substantiate that lagged carbon price fluctuations possess limited predictive capacity for contemporaneous equity market volatility and vice versa. The robustness of these outcomes is fortified by applying the E-GARCH model, which accounts for the volatility clustering phenomenon. As the first investigation into the volatility spillover effect between China’s emission trading market and corresponding stock markets, this study offers valuable insights into the investment strategies of retail investors, the formulation of carbon regulations by policymakers, and the carbon emission strategies of corporate managers.
... Research observing the stock price index volatility has been related to economic activity. Changes in several macroeconomic factors can cause increases and decrease in the stock price index, which is observed by measuring the volatility of the stock price index (Khalid & Khan, 2017;Setiawan, 2020;Sutrisno, 2020;Handika et al., 2021). Macroeconomic factors are claimed to influence the occurrence of stock market volatility and provide helpful information in estimating the volatility of the stock price index to assist investors in managing their portfolios by enabling the correct forecast of stock price movements. ...
... Inflation in Indonesia during the study period was not included in the very high inflation rate category. An inflation rate of less than 10% and no drastic changes in the inflation rate can still be accepted by the market so that changes are considered not to significantly affect stock market conditions (Yunita & Robiyanto, 2018;Sutrisno, 2020). In addition, Kumalasari (2016) revealed that inflation could be caused by pressure on the supply side or cost-push inflation, where its emergence is caused by increased product prices. ...
Stock price changes within a certain period can be observed in the volatility of the stock price index. Changes in stock prices can be influenced by a country's macroeconomic conditions and global economic conditions. Macroeconomic factors include interest rates, inflation, exchange rates, and economic growth, while global economic uncertainty is observed using the Global Economic Policy Uncertainty (GEPU) index. This study examines the effect of macroeconomic variables and the GEPU index on the volatility of the stock price index on the Indonesia Stock Exchange (IDX). A descriptive analysis method was used using time series data between Q1 2016 and Q4 2021. The results indicate that the interest rate has a negative and significant effect on the volatility of the stock price index. In contrast, the value of each GEPU index and exchange rate have a positive and significant effect on stock price index volatility. However, inflation and economic growth have a positive relationship and do not significantly affect the volatility of the stock price index.
... Volatilitas keuangan biasanya heteroskedastis atau berubah terhadap waktu. Sehingga semakin tinggi volatilitas, maka semakin tinggi ketidakpastian dari returns aset keuangan (Sutrisno, 2020;Virginia, Ginting, & Elfaki, 2018). GARCH (Generalized Autoregressive Conditional Heteroskedasticity) adalah model volatilitas yang sering digunakan untuk pemodelan dan peramalan volatilitas dari aset keuangan (Gulay & Emec, 2019;Nugroho, Susanto, & Rosely, 2018). ...
This study applies the exponential transformation to the exogenous variables in the GARCH-X(1,1) model. The model assumes that the returns error is Normally and Student-t distributed. The data type used as an exogenous variable in the GARCH-X(1,1) model is Realized Volatility 10 minutes. Empirical analysis was carried out based on stock price index data FTSE100 and SP500 daily period from January 2000 to December 2021. Metropolis Adaptive Random Walk method was implemented in the Markov Chain Monte Carlo algorithm to estimate the model parameters. The High Posterior Density interval at the 95% level indicates the significance of the exponential transformation for the exogenous variable in both cases assuming a distribution for the returns error. Moreover, the Akaike Information Criterion (AIC) value indicates that the proposed model outperforms the basic GARCH-X(1,1) model, where the best fit model is given by the Student-t distributed model.
... According to (Sutrisno, 2020;Mehmood et al., 2019), the corporation's size positively impacts stock volatility. The higher the diversification of activities. ...
This study explores the association between ERP adoption and stock price volatility (SPV). An ERP software reshapes information collected, processed, and disseminated. We examine whether ERP usage influences the stock price volatility of Egyptian firms. Our investigation is among the first to examine how ERP usage affects SPV, an overall outcome measure of a firm’s information environment. The Findings document that ERP has a significant and negative association with SPV. Stock price volatility (SPV) relationships have expanded the literature on ERP facts. ERP is essential for both strategic and operational logistics decisions. This study also gives strong evidence that ERP-adopted firms help to reduce the risk of stock price volatility and build a good relationship with the shareholders. Overall, it showed the financial effect of ERP in the emerging economy context of Egypt. OLS Regression Analysis 115 organizations in Egypt were edited in 1150 observations registered from 2011 to 2020 on the Egyptian Stock Exchange (ESE). The main results of tests are robust after analysis with a cluster of the standard errors by companies' change of critical variables.
... Sutrisno [75] extended the work done in Indonesia on the determinants of stock price volatility to include the effects of firm size on the volatility of stock price within the Jakarta Islamic Index Companies. Data were collected from sixteen companies across a period of five years from 2014 to 2018 and analysed using panel regression methods. ...
... The findings of this study also conformed to that of Chiang and Chan (2017) when they examined the impact of foreign ownership on the firm's stock return volatility between the period of 1994 to 2014 within the Taiwan capital market. The conclusion of this study negates the findings of Sutrisno [75] within the Indonesian market when they examined the determinants of stock price volatility within the Jakarta Islamic Index Companies from 2014 to 2018. Also, it does not align with the conclusion of Phan and Tran [66] when they concluded that both foreign and local state ownership has no impact on stock price volatility. ...
Shareholders wealth volatility has exhibited different patterns in different global exchange markets including the Nigerian exchange. Unravelling attempts of the possible causes of this volatility have been made, as well as how the aforementioned are mitigated. These attempts are due to their implications on share valuation as well as the need to reduce market manipulations. Studies have shown that dividend decision has been one of the major puzzles yet unresolved regarding shareholders wealth volatility and there have been fewer studies in this regard, especially in developing economies like Nigeria. This study, therefore, examined the effect of dividend policy on shareholders wealth volatility of selected companies listed on the Nigerian Exchange. The study adopted ex-post facto research design. The population of the study is 162 companies listed in the Nigerian Exchange as at 31 December 2020. The study sample consisted of 49 companies randomly selected. Data for the period 2010-2020 were collected from the NSE, and Original Research Article Koleosho et al.; AJEBA, 22(7): 1-26, 2022; Article no.AJEBA.84337 2 companies' data on the Bloomberg Terminals and their official websites. Descriptive and inferential statistics were used to analyze the data. Inferential statistics resulted from Regression and Correlation analysis. The study found that the dividend policy exerted a statistically significant effect on Shareholders wealth Volatility (Adj.R 2 = 0.303, W (3, 2156) = 95.82, p = 0.000). Firm Size, Number of Shares Outstanding and Ownership Structure jointly and significantly controlled the effect of Dividend Policy on Shareholders Wealth Volatility (∆Adj.R 2 = 0.114, W (6, 2156) = 320.41, p = 0.000). The study concluded that dividend policy affects shareholders' wealth volatility. The study recommended that the companies should focus more on the payout ratio while investors should go for entities with constant dividend payout ratio. In addition, it further recommended that policy owners should enforce adherence to the minimum free float requirements of the Nigerian Exchange.
... Table 5 shows that stock price volatility is statistically positively affected by trading volume, implying that the greater the trading volume, the higher the stock price volatility, and vice versa. Investors in the stock market mainly rely based on information from the market when making their investment decisions, when they receive good and accurate information from the market, they will buy and sell stocks, which leads to an increase in trading volume, and then price risk and volatility will increase and vice versa (Sutrisno, 2020). This finding is consistent with Ngugi (2017) and Sutrisno (2020). ...
... Investors in the stock market mainly rely based on information from the market when making their investment decisions, when they receive good and accurate information from the market, they will buy and sell stocks, which leads to an increase in trading volume, and then price risk and volatility will increase and vice versa (Sutrisno, 2020). This finding is consistent with Ngugi (2017) and Sutrisno (2020). Dividend yield is statistically positively affected by price risk, implying that the higher the dividend yield, the higher the price risk, and then the higher the stock price volatility. ...
Internal (firm-specific) and external (macroeconomic) determinants of stock price fluctuations are vital for investors seeking to invest their money in a firm’s stocks. Thus, the main aim of this study is to explore macroeconomic and firm-specific factors that influence stock price fluctuations for all conventional banks in Jordan in 2010–2019. Ordinary least squares multiple regression (panel data) is applied for data analysis. The results report that trading volume (TV), dividend yield (DY), and Gross Domestic Product (GDP) have a positive effect on stock price volatility, while stock price volatility is statistically negatively affected by return on assets (ROA), dividend payout ratio (DPR), and price-earnings ratio (PE). On the other hand, money supply (MS) does not affect stock price volatility. Paying more dividends can reduce stock risk and, in turn, reduce stock price volatility. The findings can benefit current and potential investors, firm managers, brokers, dealers, portfolio managers, regulatory bodies, policy makers, and researchers.
... Different researchers have attempted to establish different factors affecting the fluctuations in stock prices. Sutrisno [14], examined the determinants of stock price volatility in Indonesia. From the period of 2014-2018, the author analyzed variables such as trade value, business size, inflation, and stock volatility exchange rate for Jakarta Islamic Index firms. ...
This research purposely looks into earnings management and how it relates to firm performance. By attempt of gaining empirical results, we proxy firm performance by return on assets (ROA)and return on equity (ROE) as dependent variables. We respectively model discretionary accruals and abnormal cash flow from operations as independent variables supported by firm size, leverage, and liquidity as control variables. Panel analysis was adopted to test a sample of 14 non-financial listed on the Ghana Stock Exchange from 2008 to 2019. We find that firms use both accrual earnings and real earnings methods to manage earnings. Results further indicate firms employ the efficient concept of earnings management to facilitate positive firm performance. Our analysis shows that companies with future acquisition prospects can use earnings management to boost financial efficiency. Although we find evidence of a positive relationship between EM and firm performance, we encourage authorities and facilitators to implement rules requiring transparent financial information to mitigate misleading results and reduce managers discretion. Prospective investors must also perform an in depth review of financial records prior to investing. Finally, our research has added to the body of expertise on earnings management, which positively impacts firm performance. KEYWORDS: Earnings Management, Sales Manipulation, Ghana Stock Exchange, Firm Performance.
... Different researchers have attempted to establish different factors affecting the fluctuations in stock prices. Sutrisno [14], examined the determinants of stock price volatility in Indonesia. From the period of 2014-2018, the author analyzed variables such as trade value, business size, inflation, and stock volatility exchange rate for Jakarta Islamic Index firms. ...
