Stock Market Indices Research Articles

Examining the Relationship Between Implied Volatility, Index Returns, and Trading Volume in the Indian Stock Market

Examining the Relationship Between Implied Volatility, Index Returns, and Trading Volume in the Indian Stock Market

Investment Decision Using Technical Analysis: A Study on Selected Stocks in Indian Stock Market

Technical analysis is a methodology in financial markets that asserts its capability to predict future stock directions by scrutinizing historical market data, patterns in price movements, and correlations between price and volume. Traders widely utilize these data in their decision- making processes. The approach also takes into account the real-time behavior of market prices and volumes, operating under the assumption that these are the most influential factors in determining a stock’s or market’s future trajectory. Investors often grapple with decisions regarding which stocks to buy, the optimal purchase quantities, and when to execute selling or holding actions to maximize profits in the trading period. This research delves into these considerations, offering insights for short-term traders by analyzing the stock price trends of twelve companies across three distinct sectors over the 2020-2022 period.

Hedging rainfall risk: An illustrative analysis of rainfall index-based futures contracts

Various studies have attempted to explore rainfall index (RI)-based derivatives as a tool to hedge rainfall risk. But no study delved into the issue to discuss the potential deployment of those tools for hedging rainfall risk on a model based on RI-based futures (RIBFs). An illustrative analysis of RIBFs contracts as hedging instruments has been undertaken in this study. The study is fundamentally based on empirical data. A brief account of the rainfall risk profile is presented to indicate the risk diversification opportunity. Besides, the results of an analysis of the correlation between the RI and the stock market index are projected to establish the RI as a unique asset class. The monsoon rainfall data for 50 years, i.e., from 1971 to 2020 have been considered. The Sensex and Nifty stock market indices data covered a period of 30 years, from 1991 to 2020. The study used correlation, the GARCH (1,1) model, and ratio analysis to test the hypotheses. A hypothetical insurance company representing major stakeholders in need of rainfall risk hedging has been taken as an example for evaluating the utility of RIBFs contracts as hedging tools. The results reveal that the party buying RIBFs contracts can significantly reduce the loss of revenue due to adverse monsoon outcomes.

The causality measure of partial mutual information from mixed embedding (PMIME) revisited.

The measure of partial mutual information from mixed embedding (PMIME) is an information theory-based measure to accurately identify the direct and directional coupling, termed Granger causality or simply causality, between the observed variables or subsystems of a high-dimensional dynamical and complex system, without any a priori assumptions about the nature of the coupling relationship. In its core, it is a forward selection procedure that aims to iteratively identify the lag-dependence structure of a given observed variable (response) to all the other observed variables (candidate drivers). This model-free approach is capable of detecting nonlinear interactions, abundantly present in real-world complex systems, and it was shown to perform well on multivariate time series of moderately high dimension. However, the PMIME presents some inefficiencies in its performance mainly when applied on strongly stochastic (linear or nonlinear) systems as it may falsely detect non-existent relationships. Moreover, and by construction, the measure cannot extract purely synergetic relationships present in a system. In the current work, the issue of false detections is addressed by introducing an improved resampling significance test and a procedure of rechecking the identified drivers (backward revision). Regarding the inability to detect synergetic relationships, the PMIME is further enhanced by checking pairs as candidate drivers for the response variable after having considered all drivers individually. The effects of these modifications are investigated in a systematic simulation study on properly designed systems involving strong stochasticity, regressor terms with synergetic effects, and a system dimension ranging from 3 to 30. The overall results of the simulations indicate that these modifications indeed improve the performance of PMIME and alleviate to a significant degree the issues of the original algorithm. Guidelines for balancing between accuracy and computational efficiency are also given, particularly relevant for real-world applications. Finally, the measure performance is investigated in the study of futures of various government bonds and stock market indices in the period around COVID-19 pandemic.

Pricing of Climate Risks in the Capital Market of South Africa

ABSTRACT: Climate risk represents an increasingly vital issue to countries, companies, and institutional investors, making it a reality but not a distant threat to humanity. Considering the effects of climate risks on firms' financial indices and financing options, the study investigates whether climate risk is priced by the capital markets of South Africa. The study used reported carbon emissions as a measure of climate risk of 81 listed companies in the Johannesburg Stock Exchange from 2011 to 2020 to examine whether climate risk is considered and priced by the South Africa capital market. Data was sourced from DataStream database- a global financial and macroeconomic time-series database providing data on equities, stock market indices, currencies, company fundamentals, fixed income securities, and key economic indicators. We used the two-step system Generalized Method of Moments estimation technique that copes with endogeneity concerns to corroborate the effects of climate risk on cost of capital and capital structure. We find that climate risk is priced in both cost of debt capital and cost of equity capital. Specifically, we find that an increase in a firm's exposure to climate risk increases the cost associated with issuing debt and equity capital. We also find that climate risk exposure decreases the debt-equity ratio. Additionally, the study showed that firm size, leverage ratio, capitalization, profitability, and turnover affect both cost of capital and capital structure of listed firms. The study concludes that climate risk is priced in cost of financing in the capital market of South Africa. The study recommends that firms should invest in installing eco-friendly machinery that aligns with changing market expectations in order to reduce their carbon emissions. The study therefore highlights the need for companies to proactively assess and manage climate risks, incorporate climate considerations into their strategic decision-making, and enhance their resilience to climate-related challenges.

Predicting the equity premium with financial ratios: A comprehensive look over a long period in Korea

This study investigates whether financial ratios, such as the dividend-price, price-to-book, or price-earnings ratios, reliably predict the equity premium in Korea. We construct the longest possible time series data for the stock market index excess returns including dividends and associated financial ratios by collecting data from various sources including data only available in hard copy publications from 1968 to 2021. We also construct the cyclically adjusted price-earnings (CAPE) ratio as a predictor which has not been used in previous studies on the Korean stock market. We estimate the predictive regressions using the IVX method, which is robust to having a persistent independent variable and overlapping data as a dependent variable and can accommodate multiple independent variables. We also assess the economic gains of the predictability by examining the certainty equivalent return for a mean-variance investor. We find that the CAPE ratio reliably predicts the equity premium for horizons ranging from one month to two years, and the results survive various robustness tests.

Parallel multivariate deep learning models for time-series prediction: A comparative analysis in Asian stock markets

<span lang="EN-US">This study investigates deep learning models for financial data prediction and examines whether the architecture of a deep learning model and time-series data properties affect prediction accuracy. Comparing the performance of convolutional neural network (CNN), long short-term memory (LSTM), Stacked-LSTM, CNN-LSTM, and convolutional LSTM (ConvLSTM) when used as a prediction approach to a collection of financial time-series data is the main methodology of this study. In this instance, only those deep learning architectures that can predict multivariate time-series data sets in parallel are considered. This research uses the daily movements of 4 (four) Asian stock market indices from 1 January 2020 to 31 December 2020. Using data from the early phase of the spread of the Covid-19 pandemic that has created worldwide economic turmoil is intended to validate the performance of the analyzed deep learning models. Experiment results and analytical findings indicate that there is no superior deep learning model that consistently makes the most accurate predictions for all states' financial data. In addition, a single deep learning model tends to provide more accurate predictions for more stable time-series data, but the hybrid model is preferred for more chaotic time-series data.</span>

Navigating Inflation Challenges: AI-Based Portfolio Management Insights

After 2010, the consumer price index fell to a low level in the EU. In the euro area, it remained low between 2010 and 2020. The European Central Bank has even had to take action against the emergence of deflation. The situation changed significantly in 2021. Inflation jumped to levels not seen for 40 years in the EU. Our study aims to use artificial intelligence to forecast inflation. We also use artificial intelligence to forecast stock index changes. Based on the forecasts, we propose portfolio reallocation decisions to protect against inflation. The forecasting literature does not address the importance of structural breaks in the time series, which, among other things, can affect both the pattern recognition and prediction capabilities of various machine learning models. The novelty of our study is that we used the Zivot–Andrews unit root test to determine the breakpoints and partitioned the time series into training and testing datasets along these points. We then examined which database partition gives the most accurate prediction. This information can be used to re-balance the portfolio. Two different AI-based prediction algorithms were used (GRU and LSTM), and a hybrid model (LSTM–GRU) was also included to investigate the predictability of inflation. Our results suggest that the average error of the inflation forecast is a quarter of that of the stock market index forecast. Inflation developments have a fundamental impact on equity and government bond returns. If we obtain a reliable estimate of the inflation forecast, we have time to rebalance the portfolio until the inflation shock is incorporated into government bond returns. Our results not only support investment decisions at the national economy level but are also useful in the process of rebalancing international portfolios.

Predicting Shariah Stock Market Indices with Machine Learning: A Cross-Country Case Study

ABSTRACT Stock prices are influenced by numerous factors, including policy adjustments, economic conditions, and international developments. Consequently, forecasting stock price trends accurately has posed a significant challenge for economists to study. The Islamic financial industry experiences fewer shocks compared to the traditional financial sector, allowing investors to anticipate the performance of Islamic indices. This study aims to predict the Islamic stock market indices in six countries, including Indonesia, Thailand, Malaysia, Pakistan, the United Arab Emirates, and Qatar, using the Autoregressive Integrated Moving Average (ARIMA) model. Monthly data from 2013 to 2023 sourced from investing.com and Yahoo Finance are analyzed using R machine learning. The objective of this study is to provide accurate predictions for the next 25 months and offer insights into potential price movements. Overall, this research also sheds light on the dynamics of the Islamic market in Indonesia, Thailand, Malaysia, Pakistan, the United Arab Emirates, and Qatar, which adhere to the Efficient Market Hypothesis (EMH) due to the predictability of index prices by historical data. Keywords: forecating, R-Studio, ARIMA, Islamic Stock Market, Machine Learning, R-Programming ABSTRAK Harga saham dipengaruhi oleh banyak faktor, termasuk penyesuaian kebijakan, kondisi ekonomi, dan perkembangan internasional. Oleh karena itu, memprediksi tren harga saham dengan akurat telah menjadi tantangan signifikan bagi para ekonom untuk mempelajarinya. Industri keuangan Islam mengalami lebih sedikit goncangan dibandingkan dengan sektor keuangan tradisional, yang memungkinkan investor untuk memperkirakan kinerja indeks Islam. Studi ini bertujuan untuk memprediksi indeks pasar saham Islam di enam negara, termasuk Indonesia, Thailand, Malaysia, Pakistan, Uni Emirat Arab, dan Qatar, menggunakan model Autoregressive Integrated Moving Average (ARIMA). Data bulanan dari tahun 2013 hingga 2023 yang berasal dari investing.com dan Yahoo Finance dianalisis menggunakan pembelajaran mesin R. Tujuan dari studi ini adalah untuk memberikan prediksi yang akurat untuk 25 bulan mendatang dan menawarkan wawasan tentang pergerakan harga yang potensial. Secara keseluruhan, penelitian ini juga memberikan cahaya tentang dinamika pasar Islam di Indonesia, Thailand, Malaysia, Pakistan, Uni Emirat Arab, dan Qatar, yang mengikuti Hipotesis Pasar Efisien (EMH) karena dapat diprediksi oleh data historis.. Kata Kunci: Prediksi, R-Studio, ARIMA, Indeks Pasar Modal Syariah, Machine Learning, R-Programming

EFFECT OF HEALTH SYSTEM PERFORMANCE ON VOLATILITY DURING THE COVID-19 PANDEMIC: A NEURAL NETWORK APPROACH

The study proposes an assessment of the link between the performance of national health systems and volatility during the COVID-19 pandemic. Data from the World Health Organization was accessed regarding the Global Health Security Index of the states considered in the analysis as well as the categories based on which it is determined. To characterise volatility, a representative stock market index was considered for each of the 60 states analysed. Data processing was carried out using an artificial neural network. The main results show that: i) before the pandemic, the link between market volatility and the performance of national health systems was weak; ii) during the pandemic, the connection between the two variables is much stronger; iii) between the six categories that define the Global Health Security Index, norms, health, and prevention had the greatest influence on volatility.

Stock market indices and interest rates in the US and Europe: persistence and long-run linkages

PurposeThis paper aims to analyse the persistence of the S&P500 and DAX 30 stock indices as well as of the Fed’s Effective Federal Funds rate and of the European Central Bank’s Marginal Lending Facility rate, and the long-run linkages between stock prices and interest rates in the USA and Europe, respectively.Design/methodology/approachThe methodology is based on the concepts of fractional integration and cointegration.FindingsUsing monthly data from January 1999 to December 2022, the results can be summarised as follows. All series examined are non-stationary: stock prices are found to be I(1) while interest rates display orders of integration substantially above 1, which implies a rejection of the hypothesis of mean reversion in all cases examined.Originality/valueThis paper uses an appropriate econometric framework to obtain new, reliable empirical evidence. All four series are highly persistent, and mean reversion does not occur in any single case. Moreover, the fractional cointegration analysis suggests that stock prices and interest rates are not linked in the long run.

Stock market and inequality distributions – Evidence from the BRICS and G7 countries

By examining the effects of three stock market indicators (market accessibility, efficiency, and stability) on income and wealth inequality in the BRICS and G7 countries, this study enriches lacking literature on income and wealth inequality, particularly for the BRICS countries. We apply the Autoregressive Distributed Lag–Mixed Data Sampling (ADL-MIDAS) model. We find that only enhancements in market stability reduce income inequality in the BRICS and G7 countries. Additionally, we find that while expansions of market accessibility contribute to narrowing wealth inequality, improvements in market stability widen the wealth disparity in the BRICS countries. Limited effects of the stock market indicators on wealth distribution are observed in the G7 countries.

Revisiting Stock Market Index for the Helsinki Stock Exchange 1912–1981

Stock market indices play a central role in portfolio and risk management and performance evaluation, as well as academic research. This paper presents a fully updated and extended stock market index for the Finnish stock market using new and updated historical databases that cover the period from the establishment of the Helsinki Stock Exchange in October 1912 to the end of 1981. In addition to the all-share market index, four industry indices are presented for the first time. The observed geometric mean market return is 1.034 percent per month (13.14% p.a.). Of the industry indices, the banking sector performed the worst as it was found to have clearly lagged behind in the market, whereas the paper and forest and the metal and manufacturing industries performed the best during the sample period. The results also highlight the importance of taking into account corporate capital actions—which are, historically, often the hardest information to obtain—as they can have a material effect on the index performance.

A new denoising approach based on mode decomposition applied to the stock market time series: 2LE-CEEMDAN.

Time series, including noise, non-linearity, and non-stationary properties, are frequently used in prediction problems. Due to these inherent characteristics of time series data, forecasting based on this data type is a highly challenging problem. In many studies within the literature, high-frequency components are commonly excluded from time series data. However, these high-frequency components can contain valuable information, and their removal may adversely impact the prediction performance of models. In this study, a novel method called Two-Level Entropy Ratio-Based Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (2LE-CEEMDAN) is proposed for the first time to effectively denoise time series data. Financial time series with high noise levels are utilized to validate the effectiveness of the proposed method. The 2LE-CEEMDAN-LSTM-SVR model is introduced to predict the next day's closing value of stock market indices within the scope of financial time series. This model comprises two main components: denoising and forecasting. In the denoising section, the proposed 2LE-CEEMDAN method eliminates noise in financial time series, resulting in denoised intrinsic mode functions (IMFs). In the forecasting part, the next-day value of the indices is estimated by training on the denoised IMFs obtained. Two different artificial intelligence methods, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), are utilized during the training process. The IMF, characterized by more linear characteristics than the denoised IMFs, is trained using the SVR, while the others are trained using the LSTM method. The final prediction result of the 2LE-CEEMDAN-LSTM-SVR model is obtained by integrating the prediction results of each IMF. Experimental results demonstrate that the proposed 2LE-CEEMDAN denoising method positively influences the model's prediction performance, and the 2LE-CEEMDAN-LSTM-SVR model outperforms other prediction models in the existing literature.

Multivariate Stochastic Volatility Modeling via Integrated Nested Laplace Approximations: A Multifactor Extension

This study introduces a multivariate extension to the class of stochastic volatility models, employing integrated nested Laplace approximations (INLA) for estimation. Bayesian methods for estimating stochastic volatility models through Markov Chain Monte Carlo (MCMC) can become computationally burdensome or inefficient as the dataset size and problem complexity increase. Furthermore, issues related to chain convergence can also arise. In light of these challenges, this research aims to establish a computationally efficient approach for estimating multivariate stochastic volatility models. We propose a multifactor formulation estimated using the INLA methodology, enabling an approach that leverages sparse linear algebra and parallelization techniques. To evaluate the effectiveness of our proposed model, we conduct in-sample and out-of-sample empirical analyses of stock market index return series. Furthermore, we provide a comparative analysis with models estimated using MCMC, demonstrating the computational efficiency and goodness of fit improvements achieved with our approach.

Interconnectedness and systemic risk: Evidence from global stock markets

The study aims to examine systemically important stock markets in the global financial system within the scope of portfolio theory. For this purpose, we use daily stock market indices from 46 countries (23 developed and 23 developing stock markets) in North America, Latin America, the Middle East and Africa, Asia, the Pacific, Eastern Europe, and Europe between 1995 and 2021. Based on the Component Expected Shortfall (CES), we identify systemically important stock markets and use the quantile spillover analysis to examine the financial contagion and directional spillovers emanating from downside risks among stock markets. Overall, we observe stock markets of developed countries figured prominently in terms of systemic risk until the Global Financial Crisis (2007–2009; henceforth GFC), while developing country stock markets particularly those of China and India gained traction after the GFC. Moreover, we observe a shift in terms of systemic risk in recent years from the West to the East geographically. To increase global financial market resilience and improve stability, supervision, and macroprudential policies can be formulated to limit risk spillovers in global stock markets. Additionally, it is critical to diversify investments outside equity markets, such as currency, bond, gold, and oil asset classes. When considering overseas portfolio choices for diversity, investors should keep the financial spillover effects in mind.

The War between Russia and Ukraine and its Effects on the Indian Stock Market

The world economies are still not over the impact of COVID 19 pandemic when Russia started war on Ukraine on February 24, 2022. The global economies sided for and against Russia, while few countries took a neutral stand like India. This study analyses the impact on the war on Indian stock market. We analyse BSE sectoral indices which includes Metals, Oil and Gas, Raw materials, Auto, Energy, FMCG, Industrial, Infrastructure Manufacturing for a short window period from the start to the partial end of the war which includes all sanctions imposed on Russia by the global economies. Using technical stock market indicators like MA, SMA and EMACD, this study finds that Indian stock market was not affected by Russia Ukraine war. The sectoral indices depicted an uptrend and the volatility index showed a downtrend. To further confirm our findings, the study analyses the abnormal returns (AR) and Cumulative Abnormal Returns (CAAR) of the sectoral indices through Event Study Analysis for 21 days around the date of declaration of war. The fin dings show that there was no value destruction in any of the indices during the observed period which confirm with the previous findings. The findings of this study are interesting and has implications from a practical standpoint considering that all the sectors in Indian stock market are doing well irrespective of the rising oil prices and materials shortage because of the war

Size, Value and Momentum Returns in Indian Stock Market

The study investigates the applicability of factor-based investing in the Indian stock market, particularly focusing on the size, value, and momentum factors. Using data from 2005 to 2022 and employing the Fama-French methodology, the research examines the performance of these factors and their impact on investment returns. The findings reveal that while the size factor yielded negative returns, both the value and momentum factors exhibited positive performance. Notably, momentum emerged as the most effective strategy, delivering substantial returns and outperforming other factors. Additionally, the study analyzes the Equity Risk Premium (ERP), indicating insignificant real returns in the market during the study period. Drawdowns and volatility are assessed for each factor, highlighting the inherent risks and fluctuations associated with factor based investing. The research emphasizes the significance of integrating factor analysis in investment strategies and provides insights into the dynamics of the equity segment of Indian capital market.

STUDY ON THE RELATIONSHIP BETWEEN FIRMS’ PERFORMANCE AND STOCK MARKET RETURNS IN SELECT NSE LISTED COMPANIES

Purpose: An investor considers lot of factors while investing his money into the Stocks. Money is majorly invested in BSE or NSE in India. BSE is established in the year 1875 and NSE established in the year 1992. Apart from this some stocks are traded in local exchange market in each city. Earlier we had manual trading. However, it is more organized and digitalized now. Every investor does some survey prior to investing their hard-earned money. The reason behind investment is to earn profits over the invested money. The returns over the stock market are dependent on various micro and macro-economic factors. Panel data analysis was conducted using data from the top 50 companies listed on the National Stock Exchange (NSE) over a 10-year period. The analysis explored the impact of various financial ratios on stock market returns, considering individual-specific effects and time variations. The study found that certain financial ratios, including Earnings per Share (EPS), Earning Yield, Debt to Equity ratio, and Market Capitalization to Sales ratio, significantly influenced stock market returns.
 
 Method: Panel data analysis, also known as longitudinal data analysis or panel regression, is a statistical method used to analyze data collected from a group of individuals, entities, or subjects over time. In panel data analysis, observations are made on the same subjects or entities repeatedly at multiple time points, creating a panel or panel dataset. Panel data analysis is particularly useful in social sciences, economics, and other fields where researchers are interested in studying the dynamics of individual units over time and accounting for both cross-sectional and time-series variations.
 
 Results and Conclusion: The findings from this study provide valuable guidance for individual investors, financial advisors, and policymakers in optimizing investment strategies. However, it is essential to acknowledge the limitations of this research, including the static nature of data collection and the focus on the Indian stock market. Future research should explore longitudinal effects and consider macroeconomic factors to deepen our understanding of the complex relationship between financial ratios, risks, and stock returns. The random effects model shows that the independent variables have a significant relationship with the dependent variable, meaning they are related to each other. The regression models confirm that certain financial ratios, like EPS, Price_To_Cash_Flow, and Cash_Flow_TO_Assets, have a significant impact on stock returns. The model considers the influence of time-specific factors, and the fixed effects model better captures the differences between entities, suggesting that each entity behaves differently in the analysis. In conclusion, this study highlights the multifaceted nature of the Indian stock market and the diverse factors that influence investors' decision-making processes. Enhancing financial literacy, promoting risk management, and understanding investor sentiment are crucial for building a robust and informed investor community. By addressing these aspects, investors and policymakers can contribute to the overall growth, stability, and efficiency of the Indian stock market.
 
 Implications of research: The key advantage of panel data analysis is that it allows researchers to control for individual heterogeneity and unobserved time-invariant factors, which can lead to more accurate and efficient estimates of the relationships between variables. By incorporating both within-unit and between-unit variations, panel data analysis provides a more comprehensive understanding of the data and can help identify causal relationships. Originality/value: the current paper aims to study the relationship between firms’ performance and stock market returns in select NSE listed companies.

The reaction of the Egyptian stock market to recurring devaluations: an event study approach

PurposeThis paper examines the reaction of the Egyptian stock market to two substantial devaluations of the Egyptian pound (EGP) in 2022 and tests the informational efficiency of the Egyptian market.Design/methodology/approachThe paper uses the event study framework to analyze the significance and direction of abnormal returns of the leading index of the Egyptian stock market (EGX30) on and around the devaluation days. It employs both the constant mean model and the market model to estimate the normal returns of the EGX30. Additionally, the paper uses data on two equity indices, one global and one for emerging markets, as benchmarks for normal returns.FindingsThe paper finds that the Egyptian stock market experienced significant positive abnormal returns on the devaluation days of the EGP in March and October of 2022, indicating a positive market reaction to the devaluation. Furthermore, evidence suggests that the Egyptian market may not be informationally efficient as significant positive abnormal returns were observed two weeks before and two weeks after the devaluation day, suggesting news leaks and delayed reactions, respectively.Originality/valueThis study is the first to examine the impact of the recent two devaluations of the EGP in 2022 on the Egyptian stock market. It complements existing literature by analyzing the immediate market reaction to two consecutive devaluations in an African country. Furthermore, the paper evaluates the efficiency of the Egyptian market in processing information related to exchange rates.