Stock Index Research Articles

Connectedness Structure and Volatility Dynamics Between BRICS Markets and International Volatility Indices: An Investigation

ABSTRACTThis research aims to explore and understand the dynamic nature of volatility connectedness between BRICS stock markets and various asset price implied volatility indices through a TVP‐VAR broadened connectedness approach. Results display nontrivial dynamic connectedness in the BRICS stock markets and uncertainties in different markets during the period 31 March 2019–31 August 2023. They also report heterogeneous patterns in the connectedness between stock indices and volatility indices. The time‐varying spillover effect seems to be strong during the black‐swan events. The variations of volatility connectedness among each volatility index and stock market increasingly depend on unusual stress caused by the outbreak of unexpected events. These finding provide significant guidance for investors seeking to enhance their risk management practices. By leveraging the insights into volatility transmission mechanisms and the roles of different volatility indices, investors can make informed decisions to protect and grow their investments in an increasingly volatile global market.

Do Financial Community’s Tweet Sentiments Affect the Stock Market? Evidence from an Emerging Economy

In the era of social media and internet, Twitter (presently ‘X’) has been recognized as the prominent factor for financial markets by multiple corporates and experts. The motivation of present research hinges on burgeoning popularity of Twitter in the financial community for sharing information and impact of their hidden sentiments on financial markets. Research on tweet-based sentiments and their influence on financial markets in India is still in the infancy stage. To cover this gap, the present study empirically investigates the predictive impact of financial tweet sentiments on the Indian stock Index’s returns over a short period. The paper scraped 22,816 tweets and Nifty 50 index’s intraday hour end values for January 2023. Using Natural Processing Technique, the overall sentiments of the financial community are found as highly positive for each trading hour. Next, it presents a low positive correlation between positive sentiments, sentiment polarity, and hourly returns. Lagged negative sentiments reflect the better ability to impact current period returns than positive sentiments and polarity. Additionally, sentiment polarity and returns reflect bi-directional causation for the future period impact. Investors, stock market traders, and other practitioners can employ the proven concept of linkage between tweet sentiments and returns for constructing effective investment and trading strategies. JEL Classifications: G41, D53, G17, G02, G19

Extreme downside risk connectedness between green energy and stock markets

This study addresses the need to understand the transmission of tail risk across financial markets, especially in the context of green energy and stock markets. Utilizing the Time-Varying Parameter Vector Autoregressive (TVP-VAR) methodology combined with the Conditional Autoregressive Value-at-Risk (CAViaR) framework, the model analyzes data from September 30, 2013, to October 11, 2023. This study focuses on five major stock indices (S&P500, CSI300, Nikkei225, STOXX50, and FTSE100), West Texas Intermediate (WTI) crude oil, and three sectors within green energy markets (Green bond, Global Clean Energy, and Renewable Energy and Clean Technology), highlighting the significant role of these sectors in risk propagation. The model can capture dynamic changes and asymmetries in financial returns, thus providing a precise estimation of extreme downside risks. Key findings show that three stock indices (S&P500, STOXX50, and FTSE100) and one green energy sector (Renewable Energy and Clean Technology sectors) are the predominant sources of risk, with significant connectedness around events, such as the COVID-19 pandemic. These insights are crucial for developing effective risk-management strategies and supporting the transition to a sustainable energy sector. This study concludes that understanding these risk dynamics is essential for strategic planning and market stability.

Predicting the volatility of Chinese stock indices based on realized recurrent conditional heteroskedasticity.

The realized recurrent conditional heteroscedasticity (RealRECH) model improves volatility prediction by integrating long short-term memory (LSTM), a recurrent neural network unit, into the realized generalized autoregressive conditional heteroskedasticity (RealGARCH) model. However, at present, there is no literature on the ability of the RealRECH model to fit and predict volatility in the Chinese market. In this paper, a study is conducted to test the in-sample explainability and out-of-sample prediction ability of the RealRECH model for the SSE50, CSI300, CSI500 and CSI1000 indices in the Chinese market and to determine whether it performs better than the RealGARCH model. The results of the in-sample analysis show that the RealRECH model not only provides better in-sample interpretability for all four indices but also captures the complex dynamics of time series volatility that the RealGARCH model cannot capture, such as long-term dependence and nonlinearity. The results of out-of-sample volatility prediction show that the RealRECH model better predicts the volatility of the CSI500 and CSI1000 indices but yields worse predictions for the SSE50 and CSI300 indices. Thus, the RealRECH model can be used for CSI500 and CSI1000 prediction.

Volatility spillovers and time-varying correlations across stock and commodity markets: An assessment during the epidemic crisis era

The financial and economic stability of the business world is greatly damaged by a number of crises over the years. These crises also change and affect stock markets around the globe. This study examines the nonlinear behavior of well-known stock markets in the US, Europe, Canada, and Asia (S&P 500, Nikkei 225, CAC40, DAX30, Bombay Stock Index, Hang Seng Index, and Canadian Stock Index). We also investigate the commodity indexes (Gold, Bitcoin, Brent Crude Oil, and West Texas Crude Oil). The transmission of returns and the volatility propagation pattern across stock-commodity markets during the COVID-19 era is analyzed using the VAR DCC MEGARCH model. The results demonstrate that correlations intensify and become more complicated during the COVID-19 period, especially between financial market indices and oil commodity indices. However, the volatility spillover statistics during the pandemic period show a definite rise in the uncertainty of financial market indices due to index instability. The analysis also shows that financial markets experience a substantial leverage effect during the pandemic crisis. According to the research, Bitcoin is crucial for reducing portfolio risk during the health crisis, and gold proves to be the best option for investors to diversify their financial risk.

The Affect of Firm Size, Debt Policy, Profitability on Stock Returns: Moderating Role Dividend Policy

This research aims to examine the influence of Firm Size, Debt Policy and Profitability on Stock Returns with Dividend Policy as a moderating variable in non-financial Firms included in the LQ45 stock index on the Indonesia Stock Exchange (BEI) for the 2017-2022 period. The sampling technique in this research is the purposive sampling technique. This research uses secondary data, namely the firm's annual financial report (annual report) obtained from the official website of the Indonesian Stock Exchange (IDX). The analytical method used is multiple linear regression techniques and moderated regression analysis (MRA) with testing using the SPSS version 29.0 application. The research results show that Firm size has a positive and significant effect on stock returns. Debt policy and profitability have a negative and insignificant effect on stock returns. Dividend policy can moderate Firm size on stock returns. However, dividend policy does not moderate debt policy and profitability on stock returns; however, dividend policy can moderate the influence of Firm size, debt policy, and profitability simultaneously on stock returns. The managerial implications of this research show that Firm size, debt policy, and profitability have a significant influence on stock returns, while dividend policy can moderate this relationship.

A Comparative Study of NIFTY-50 Prediction Using Linear Regression and Regression Tree Models

It is important for investors to predict the tendency of stock. However, the stock market fluctuation is too hard to predict. A single stock may be inflected by many elements, so it would be much more feasible to study a stock index. A stock index may be decided by many stocks, which means that it would not be effected by some only one company. In this passage, NIFTY-50 would be analyzed to make a prediction. The linear regression and regression tree are used to predict the close price of NIFTY-50. To make sure the data size is big enough, the data includes the price from 2000 to 2021. By comparing two models, a proper model to predict stock index would be found. After the comparison of the MSE and R square index, it is easy to find that the regression tree is the model much more reliable. However, there is still long way to go before researchers find way to predict stock price precisely.

Assessing Carbon Stocks and Ecosystem Functioning in Char (Buchanania lanzan) Forests of Central India

The present study assessment of plant community structure, carbon stock and CO2 sequestration of char (Buchanania lanzan) dominant forest sites in Central India during 2020-22. The forest vegetation was analysed using 20 quadrats (each 10 x 10 m in size for tree layers, 5 x 5 m in size for sapling layers and 1 x 1 m size for seedling layers) within the representative one-hectare plot on each site. The biomass, carbon stock, and carbon dioxide sequestration from three district (Mahasamund, Gariaband, and Kabirdham) char dominated forest sites of dry deciduous forests in Central India were estimated using the non-destructive allometric equation approach. The results revealed that the density of tree, sapling and seedling were ranged from 430-605 stems ha-1, 120-600 stems ha-1 and 28000-34500 stems ha-1, respectively. The basal area of tree and sapling layers were varied from 18.15-29.68 m2 ha-1 and 0.33-1.04 m2 ha-1, respectively. The diversity indices of tree layer viz; Shannon index, Simpson index, Evenness, species richness and beta diversity were ranged from 2.40-2.72, 0.17-0.22, 1.08-1.18, 2.26-3.11 and 5.0-6.0, respectively on different forest site in Central India. The total biomass, carbon stock and CO2 sequestration potential of tree layers were varied from 105.72-216.96 Mg ha-1, 50.22-103.06 Mg ha-1 and 184.29-378.22 Mg ha-1, respectively on char dominant various forest sites in Central India. The correlation coefficients were statistically significant performed between basal area and biomass, carbon stock and CO2 sequestration potential with R2 values of 0.988 at p<0.01 levels. After doing this study, it can be concluded that estimating the biomass and carbon stock in Central India will be useful for managing forests sustainably.

The impact of stock index futures margin levels on market quality

We examine the impact of margin levels on market quality in the China Shanghai–Shenzhen 300 (CSI 300) Stock Index Futures market. Utilizing microstructure theory and the OPOK model, we specifically focus on volatility, liquidity, and effectiveness. Our findings reveal that lowering the margin level for stock index futures reduces capital constraints for both informational and non-informational traders. Furthermore, we show that a reduction in margin levels enhances market liquidity and effectiveness. Improvements in liquidity and effectiveness subsequently reduce market volatility, indicating that a decrease in margin levels does not necessarily increase market risk. Based on these results, we recommend setting margins for stock index futures with comprehensive consideration of their combined effects on volatility, liquidity, and effectiveness.

The Fluctuation Characteristics and Risk Investigation of China's Gold Future Market

This paper deeply explores the fluctuation characteristics and risks of China's gold futures market. Through the collection of data on macroeconomic factors, stock index interest rates, and energy futures, the paper employs multiple linear regression models and intermediary variable models to quantitatively analyze the impact of these factors on the volatility of gold futures prices. The research finds that there is a significant positive correlation between the gold futures price and the growth of the U.S. GDP and the silver futures price index, while the impact of other macroeconomic indicators such as China's gold reserves and the inflation rate is not significant. Policy support and market regulation play a key role in the healthy development of the market, and the broad participation of market participants and the innovation of financial products have driven the diversification of the market. The intermediary effect analysis shows that the impact of macroeconomic factors on the gold futures price is partially indirect through the intermediary variable of stock index interest rates.

ANALYSIS OF PORTFOLIO FORMATION ON THE LQ45 STOCKS INDEX, USING THE MARKOWITZ AND SINGLE INDEX MODELS

In an investment, there will always be a return and risk, especially in the capital market in the form of stocks. The risk in an investment can be minimized by diversifying assets into several stocks to form a portfolio formation. Several models, such as the Single Index and Markowitz, can evaluate optimal portfolio formation. In this study we provide additional information and discourse on capital market studies and as the input for investors in making investment decisions in the form of stocks. The study shows that based on 25 companies, the Markowitz model gives 12 companies as the optimal portfolio with the largest proportion of funds owned by PT Bank Central Asia Tbk (BBCA), 82.22%. The portfolio of those 12 stocks can provide an expected return of 44.8% where its risk is about 13.77%. The Single Index model provides a formation based on 9 companies as the optimal portfolio with the largest proportion of funds owned by -again- PT Bank Central Asia Tbk (BBCA) which is 66.23%. The portfolio of these nine stocks can provide the expected return of 1.68% and its risk is 0.43%. The ratio of risk and return from each model justifies that the Single Index model gives better portfolio formation. This result should be further compared with other stock indexes, nationally and globally, and also needs to be compared with the period after the pandemic.

Embracing market dynamics in the post-COVID era: A data-driven analysis of investor sentiment and behavioral characteristics in stock index futures returns

This paper aims to enhance the understanding and prediction of stock market behavior during unexpected events like the COVID-19 pandemic, with a specific focus on the role of market attention, social media sentiment indicators, and the development and evolution of unexpected events. We highlight that the common trading and technical indicators used in forecasting the stock index futures prices often overlook investor sentiment and pandemic-related data, which can be instrumental in predicting stock market behavior during significant emergencies. In response, we propose a multi-faceted approach that incorporates these overlooked factors. First, we enhance the predictive index system by integrating investor sentiment, derived from stock message board commentary, and investor behavior influenced by the development and evolution of the pandemic. This innovative approach refines our model's predictive capabilities and is validated through comparative analysis. Second, we introduce a hybrid framework for predicting stock index futures closing prices. By decomposing the closing price series into long-term trends, cyclical variations, and random fluctuations, we create a more nuanced forecast. Each component is predicted separately using appropriate time-series algorithms, improving the overall predictive accuracy and offering generalizability and scalability. Third, we devise a dynamic trading strategy that recognizes pandemic-related data, evolving over time, as a pivotal factor. This strategy is adaptable to evolving market conditions, and our experimental evidence demonstrates its effectiveness in yielding higher returns and reducing associated risks. Our findings underline the importance of incorporating investor sentiment and pandemic-related data into stock market predictions, thus offering a more comprehensive and accurate approach to market forecasting and risk management.

PM10 AIR QUALITY INDEX MODELING USING ARFIMA-GARCH METHOD: BUNDARAN HI AREA OF DKI JAKARTA PROVINCE

Air quality is an essential factor in urban life, and its’ assessment often relies on the concentration of measurable air pollution parameters. One critical parameter is Particulate Matter (PM), particularly PM10, which comprises solid or liquid particles dispersed in the air from various sources. One of the methods employed for predicting stock index prices is ARFIMA. ARFIMA is used to model long memory data characterized by a slowly decreasing Autocorrelation Function (ACF) plot (hyperbolic) or a difference value in the fractional from. This method is widely used due to its ability to handle nonstationarity issues in time series. However, the time series data often contain heteroskedasticity problems. Data with heteroscedasticity are then further addressed using the GARCH model, because it can model volatility changes occurring over longer periods and capture the persistence of volatility. The ARFIMA-GARCH model can explain long-memory patterns in time series data and address heteroscedasticity issues. The data are sourced from the Jakarta open data web, which is integrated with DLH DKI Jakarta Province. The aim of this research was to forecast the PM10 air quality index at the Bundaran HI Area in the Province of DKI Jakarta for the next 14 days, from January 1st to January 14th, 2021, using an ARFIMA model enhanced with GARCH. The analysis reveals that the best model is ARFIMA ([17], d, [1])-GARCH (1,1). Forecasting using this model resulted in a MAPE of 3.47%, indicating that the model is highly capable of forecasting several periods.

THE IMPACT OF THE PRESIDENTIAL ELECTION ON IDX COMPOSITE PREDICTIONS USING LONG SHORT TERM MEMORY

An analysis of the performance of Indonesia's capital market, or Indonesia Stock Exchange (IDX), shows significant growth in recent years, with market capitalization increasing dramatically from IDR 679.95 trillion in 2004 to IDR 11,674.06 trillion by 2023. The IDX plays an important role in the Indonesian economy by facilitating capital formation and providing opportunities for investors to diversify their portfolios. However, the capital market is vulnerable to political events, such as presidential elections, which can affect national stability and economic performance. An analysis of the stock index performance before the presidential election showed a significant bullish trend. Still, given the considerable impact of political events, such as presidential elections, on financial markets, this study aims to analyze and forecast the performance of the IDX Composite by examining historical data from past election years, we provide insights and predictions in highlighting how the LSTM model accommodates these political factors in its forecasts. IDX Composite closing price forecasting was conducted using the bidirectional LSTM model to anticipate the impact. The analysis results show that this model can predict the weekly closing price of the IDX Composite with an error of 1.04%, with estimated stock price fluctuations in the next 8 weeks in the range of 6619.755 to 6812.722

COMPARISON OF VECTOR AUTOREGRESSIVE (VAR) AND VECTOR ERROR CORRECTION MODELS (VECM) FOR THE COMPOSITE STOCK PRICE INDEX (JCI) IN INDONESIA

The capital market has a major role for economic development which is able to encourage capital formation and sustain economic growth. Financial liberalisation between countries in the world will make capital markets in each country interconnected. If there is a large movement in the global stock index, it will also have an impact on the JCI in Indonesia. In addition to global indices, macroeconomics is also seen as capable of influencing capital markets. Inflation and interest rates are macroeconomic components that can be used. The purpose of this research is to be able to see the relationship between one variable and another, namely the Composite Stock Price Index (JCI), Dow Jones Industrial Average (DJIA), inflation rate, interest rate. The VAR/VECM analysis approach was used in this study. Data was taken from 2017 to 2023. The data used for modelling is 84 observations. Based on the results, Inflation and DJIA have a long-term influence on the increase of JCI, while interest rates have no long term influence on JCI in Indonesia. With the model projection for the next 10 years. The projection results state that the variability of JCI in Indonesia is most dominantly explained by the JCI variable itself with a proportion of 95.21619%, while inflation contributes 0.193202%, interest rates 0.249170%, and DJIA index 4.341438%. The results of this analysis can be used as a consideration of strategies in increasing the JCI in Indonesia to maintain the health and stability of the economy.

MACROECONOMIC ASPECTS IN INFLUENCING STOCK PRICE MOVEMENT ON THE INDONESIAN SHARIA STOCK INDEX 2018-2022

This study aims to analyze the influence of macroeconomic variables and world oil prices in influencing stock price movements on the Indonesian Sharia Stock Index (ISSI) in 2018-2022. In this case, stock price movements can be influenced by several factors, one of which is macroeconomics. The research methodology used in this study is to use a quantitative research approach and secondary data type, with quarterly samples from each year 2018 to 2022 which amounted to. The research model carried out is log linear regression panel data analysis conducted through the chow test and classical assumptions using Eviews 10 tools. Based on the results of this study, it states that partially shows the variables of Inflation, BI Interest Rate Economic Growth (GDP) and Money Supply. While simultaneously all variables did not have a significant effect on the Indonesian Sharia Stock Index (ISSI).

DETERMINAN KINERJA KEUANGAN PERUSAHAAN SEKTOR PROPERTI & REAL ESTATE PADA INDEKS SAHAM SYARIAH INDONESIA

This research aims to identify and analyze factors that influence the financial performance of companies operating in the property and real estate sectors listed in the Indonesian Sharia Stock Index. Specifically, this research evaluates the influence of internal company factors, such as leverage, profitability, and operational efficiency. Apart from that, this research also analyzes the impact of compliance with sharia principles. This research uses quantitative research, the data source used in this research is secondary data in the form of annual and quarterly financial reports. The analysis technique used is panel data regression, with the Chow test and Hausman test showing that the best model results are the fixed effect model. The results of the research show that return on assets (ROA) with a probability value of 0.0308 partially has a significant effect on share prices, earnings per share (EPS) with a probability value of 0.0081 partially has a significant effect on share prices, but the debt to equity ratio (DER) with a value of probability 0.5249 partially has no significant effect on stock prices. Simultaneously, with a probability F value of 0.000000, return on assets (ROA), earnings per share (EPS), debt to equity ratio (DER) together have a significant effect on share prices in property and real estate companies.

PERBANDINGAN RETURN PENGGUNAAN MOVING AVERAGE CONVERGENCE DIVERGENCE DAN RELATIVE STRANGE INDEX PADA INDEKS SAHAM SYARIAH INDONESIA

This study compares the return levels from the use of the technical indicators Moving Average Convergence Divergence (MACD) and Relative Strength Index (RSI) applied to the simulation of the movement of the Indonesia Sharia Stock Index (ISSI). This research is a population study using panel data in the form of daily closing values over 12 years of the Indonesia Sharia Stock Index movement. The data was processed using a technical analysis approach with two momentum indicators, MACD and RSI, to obtain buy and sell signals based on the principles of each indicator, applied to the ISSI. The use of the MACD and RSI technical indicators on the ISSI shows that MACD yielded a profit of 207% with a success rate (win rate) of 72%, while RSI provided a profit of 125% with a win rate of 55%.

Pengaruh Fraud Hexagon terhadap Kecurangan Laporan Keuangan pada Perusahaan Sektor Energi yang Terdaftar di ISSI Tahun 2019-2022

This study aims to determine the effect of the fraud hexagon model, namely stimulus (pressure) proxied by financial stability, opportunity (opportunity) proxied by ineffective monitoring, rationalization (rationalization) proxied by change in auditor, capability (ability) proxied by change in director, ego ( arrogance) is proxied by the frequent number of CEO's picture, and collusion is proxied by state-owned enterprises in detecting potential fraud in financial statement. The sample of this study is energy sector companies listed on the Indonesian Sharia Stock Index (ISSI) in 2019-2022. This study uses secondary data, namely financial reports and annual reports. Based on the purposive sampling method, the number of companies sampled in this study was 27 companies out of a total of 34 companies registered and analyzed using multiple linear regression analysis using the SPSS 25 program. The results of this study found that the stimulus element proxied by financial stability had a significant effect on fraudulent financial statements. Meanwhile, ineffective monitoring, change in auditor, change in director, frequent number of CEO's picture, and state-owned enterprises have no significant effect on fraudulent financial statement in energy sector companies registered with ISSI in 2019-2022.

Multi-scale contrast approach for stock index prediction with adaptive stock fusion

The stock index summarizes the overall movement of a group of stocks, typically calculated as a weighted average of constituent stock prices. Stock Index Prediction (SIP) aids investors in assessing economic performance and making informed decisions. Traditional SIP methods rely heavily on historical index data, neglecting valuable insights from macro-financial indicators and stock prices. However, challenges arise when incorporating these data: (1) Including numerous unfiltered macro-financial indicators introduces noise. (2) Using non-correlated stock prices can be counterproductive. (3) Supervised training on stochastic time series can lead to overfitting. To overcome these issues, we propose a multi-scale contrast approach for stock index prediction with adaptive stock fusion (MCSIP). This method identifies highly correlated macro-financial indicators and stocks, optimizing the model through self-supervised multi-scale contrastive learning. MCSIP relies on contrastive learning in the time series domain to extract robust contextual information from stock index time series, mitigating stochastic data backpropagation and ensuring reliable representation. Experiments on US and Chinese stock datasets demonstrate superior performance compared to existing methods.