Stock Market Indices Research Articles

Comparing the hedge and safe haven properties of individual commodities for China and United States equity sectors

PurposeThe purpose of this paper is to scrutinize the safe haven benefits of 13 individual commodities for the USA and Chinese equity sectors during the financial turmoil period. Therefore, sectoral investors in the USA and China could invest in those specific commodities that provide stable returns during the health crisis and financial turmoil periods.Design/methodology/approachThe daily data spans from February 1, 2015, to July 28, 2022. The present study applies several different approaches to analyzing the data set. The author apply the cross-quantilogram (C.Q) methodology to capture the lead-lag bivariate quantile interdependence between two stationary time series variables during the bearish, bullish and normal periods. Then the study used the hedging effectiveness (HE) and conditional diversification benefits (CDB) approaches to capture the hedging and diversification benefits of commodity classes and individual commodities.FindingsThe noteworthy findings of the quantilogram methodology reveal that livestock and agriculture commodities serve as better refuges as compared to the precious metals and energy index in both countries. On average, precious metals failed to serve as safe haven investments for the USA and Chinese equity market sectors. All energy commodities except soybean oil had strong comovements with China and the US equity sectors during bearish, bullish and normal periods. Lean hogs, fiddler cattle and live cattle are perfect hedging assets for both countries due to the presence of blue color at normal and bullish periods in all C.Q heat-maps. The HE table depicts that commodity indices and individual commodities failed to serve as hedging assets for the Chinese equity sectors. But commodities are semistrong hedging assets for the US equity sectors and the S&P 500 due to the average HE values being 0.7 and above. The CDB values depict that precious metals provide diversification benefits in both equity markets.Practical implicationsThe present study results have important implications for equity sector investors of the USA and China in suggesting particular commodity during the financial turmoil period. During the bearish market condition, risk averse equity sector investors can invest in livestock commodities and agriculture commodities, due to their relatively stable returns. In addition, policymakers can use the analysis insights to formulate policy tools and monitoring mechanisms, effectively mitigating the unfavorable effects arising from asymmetric dependence between commodities and equity sectors during the upper tail, middle and lower tail. Policymakers can suggest equity investors to invest in which commodity during extreme conditions.Originality/valueThe current study has the following points of originality. First, to the best of the author’s knowledge, this is the first study to investigate the individual commodities’ roles as safe havens taken from all four major commodity classes. More importantly, it is also noticeable that the safe haven abilities of commodities are usually tested for the stock market, but the equity sectors are ignored. Therefore, the present study used both stock market and sectoral indices data.

Novel design of a sentiment based stock market index forecasting system

Novel design of a sentiment based stock market index forecasting system

Parsing the Performance of Stock Market Indices Amidst COVID-19 and Russia-Ukraine War: A Comparative Study of DSEX, Nifty-50, and KSE-100

The primary objective of this investigation is to assess the performance of the benchmark indices in the stock markets of Bangladesh, India, and Pakistan, denoted by DSEX (Dhaka Stock Exchange Broad Index), Nifty-50 (National Stock Exchange Fifty), and KSE-100 (Karachi Stock Exchange 100 Index), respectively. Data is meticulously gathered across distinct periods, namely the pre-COVID, the COVID-19, and the concurrent COVID-19 era along with the Russia-Ukraine War period, and amid the consequent global ongoing financial crises. The study encompasses information spanning from July 2, 2013 to May 9, 2023, employing descriptive statistics, where both mean returns and risk-adjusted returns were calculated consecutively. The evaluation of diversification among the indices during the specified periods is conducted through descriptive statistics and Ordinary Least Squares (OLS) estimation. The findings of this study indicate that all the indices exhibited negative mean returns during the COVID-19 phase. Furthermore, both mean returns and risk-adjusted returns of the indices witnessed substantial increments amid the inflationary environment concurrent with the COVID-19 and Russia-Ukraine War periods. OLS estimation results suggest an absence of a discernible diversification relationship among the indices. In summary, our results underscore the dynamic nature of index prices across diverse temporal and global crisis scenarios. These implications offer valuable insights for investors and stockholders, enabling them to discern hedging opportunities and identify diversifiable indices within the global stock markets across disparate periods and crises.

ASEAN-5 Stock Indexes Predictions using Geometric Brownian Motion

Even though the ASEAN-5 region has recently experienced enormous economic growth and advancement, this expansion has been associated with increased market volatility. Analysing historical data to assess stock market performance is crucial to comprehending these markets' dynamics and spotting potential hazards and opportunities. This study seeks to determine the mean and volatility parameters of the Geometric Brownian Motion (GBM) model for stock indexes to see trends and patterns in the ASEAN-5 stock market from 2017 to 2022. Once these parameters are determined, they are used in the GBM model to forecast the stock market indexes. Consequently, this research intends to highlight the value of incorporating GBM into stock indexes and assist investors in making short-term price predictions. The geometric Brownian motion involving randomness, volatility, and drift that might aid investors in making sensible investment decisions will be elaborated on in this research.

ANALYSIS OF THE DEPENDENCIES COMMODITY PRICES AND STOCK MARKET INDEXES USING COPULA

Indonesia is rich in natural resources and occupies an important position in the global raw materials market. The country's rich resources such as oil, coal, nickel, and crude palm oil (CPO) have a significant impact on the economic situation. As one of the world's leading producers and exporters of these raw materials, Indonesia's economic fate is closely linked to price fluctuations. This study uses the copula method to model the dependence between stock and commodity returns and calculates the dependence between commodity prices (oil, coal, nickel, CPO) and Indonesian stock market index (IHSG) The data used for this analysis was sourced from Bloomberg.com, covering the period from 29 September 2021 to 29 September 2023. This study investigates the dynamic dependencies between commodity price returns and the Indonesian stock market index. The results show that the correlations between oil prices and the Indonesian stock index, and between CPO prices and the stock index are generally weak. However, there are exceptions to stock index returns, such as their relatively high dependence on coal and nickel. This diverse research provides valuable insight into the complex interdependencies in Indonesia's financial landscape. Understanding dependence between commodity prices and stock indexes is of great value to investors and policymakers, as it is the basis for making informed decisions to navigate the complex global economy.

Safety assessment of cryptocurrencies as risky assets during the COVID-19 pandemic

This study examines the safe-haven properties of cryptocurrencies during the COVID-19 pandemic by exploring various cryptocurrency allocation strategies and their impact on downside risk compared to holding only the stock market index. Spanning from 2019 to 2021, the analysis includes seven cryptocurrencies—Bitcoin, Ethereum, Litecoin, Dash, Maker, Monero, and NEO—and six stock market indexes—SP500, FTSE, CAC40, IBEX, AEX, and DJIM. The research introduces a portfolio optimization approach focused on mitigating significant losses during crises, employing Value at Risk (VaR), Modified Value at Risk (MVaR), and Conditional Value at Risk (CVaR) metrics across different portfolio compositions. Findings highlight several key insights: higher cryptocurrency allocations correlate with increased downside risk, with none of the studied cryptocurrencies acting as a safe haven for international equity indexes during the COVID-19 pandemic. Relative MVaR exceeds CVaR across most combinations, and Maker demonstrates the highest downside risk, while Bitcoin and Litecoin exhibit the lowest risk levels based on CVaR and MVaR, respectively. Integration of the FTSE index is associated with peak risk levels, whereas incorporating DJIM and AEX shows reduced risk exposure. The study contributes novel insights into cryptocurrency risk dynamics during the COVID-19 pandemic, offering valuable implications for risk management strategies in investment portfolios.

VADER SENTIMENT ANALYSIS ON TWITTER: PREDICTING PRICE TRENDS AND DAILY RETURNS IN INDIA’S STOCK MARKET

The study explores the effectiveness of sentiment analysis in predicting stock price movements, specifically focusing on the Indian Stock Market. The study investigates the reliability of social media sentiment analysis in financial markets and its implications for investors and traders. The research utilizes a sample of Twitter data comprising tweets containing hashtags related to the State Bank of India (SBI), used as a representative sample of the broader Indian Stock Market, collected from January 2021 to February 2024. The Valence Aware Dictionary for Sentiment Reasoning (VADER) algorithm was employed to analyse the sentiment of the Twitter data. Machine learning methods, including Random Forest, XGBoost, and AdaBoost, were used to integrate sentiment scores with technical indicators for predicting stock price trends. The results reveal that using only sentiment analysis achieved an accuracy of around 60% in predicting stock price direction. However, this accuracy increased to 70% with the AdaBoost method, 79% with the XGBoost method, and 82% with the Random Forest method combined with technical indicators while increasing the F1 scores from 0.4 to 0.8 in all three methods. Integrating sentiment analysis with technical indicators enhances financial market predictions by combining real-time investor sentiment with empirical historical data, leading to more accurate and adaptive trading strategies. Sentiment score was found to have a strong positive correlation with positive daily returns compared to negative daily returns, indicating that higher positive sentiment is associated with increased returns. Although negative sentiment exhibits a statistically significant correlation with daily returns, it shows a weaker positive association.

Linkages between financial and macroeconomic indicators in emerging markets and developing economies

This paper provides empirical evidence on the finance-growth nexus, making key contributions by focusing on previously understudied Emerging Markets and Developing Economies (EMDEs) and employing mixed-frequency data. Utilizing panel forecasting models for 50 countries from 1990 to 2019, we examine the empirical link between macroeconomic indicators (e.g., aggregate production) and financial indicators (e.g., stock market indexes). Our results support the notion that financial indicators can indeed serve as robust predictors of macroeconomic indicators. Further, the use of mixed data sampling (MIDAS) models enhances the results, confirming the presence of valuable predictive information in higher-frequency data, even for lower-income countries. These findings bear particular significance for policymakers and investors, given the persistent challenge of accessing timely and reliable data on real indicators in EMDEs.

The Impact of the COVID-19 Pandemic on metal, mining, and allied sectors in the Indian Stock Market

The paper investigates the effect of the first and second waves of COVID-19 on metal, mining, and its associated sectors stocks in India. Furthermore, the paper explores how the share price of the Indian metal, mining, and allied sectors is impacted and how it differs from the rest of the market. We also compare the effect of the pandemic waves on share prices related to metal, mining, and associated sectors in India during the study period, which ranges from January 2020 to December 2021. The share prices are collected from CMIE prowess for the sample firms. We use event study methodology to explore the impact of the different waves on the above-mentioned sectors. The finding exhibits that the first wave of the pandemic was more extreme in terms of the adverse price reaction than the second wave of the pandemic in the short term in the first five days. In the first wave, a more negative impact was on the Nifty50 index than the metal index. The firms in the metal and allied sectors experienced more negative price reactions than the metal index on the announcement of the lockdown event in the first wave. In the second wave, the impact was less than experienced by the firms in the first wave. We present the comparative empirical findings in daily and period-wise frequency to understand the effect of the 1st and 2nd waves of the pandemic on metal, mining, and its associated sectors in India. Our paper will contribute to the literature on extreme events and assist different stakeholders.

Assessing the Impact of the Turkey-Syria Earthquake on Global Stock Markets

Using the event study and cross-sectional analysis, we show that the impact of the Turkey-Syria earthquake on the leading stock market indices of 53 nations was not uniform. While the global, European, and developed markets exhibit negative returns on t+1 and t+4, positive returns were observed on t+2 and t+3, indicating that market participants later adjusted their perceptions and expectations of the event. The country-wise analysis suggests that investors' optimistic view leads to positive returns. We show that trade dependence and proximity to the event zone negatively impact returns, while past returns just before the event can predict returns during the event windows.

Exploring the psychology of price barriers in Baltic stock markets

PurposeThe purpose of this study is to investigate the presence of psychological barriers both in the main stock market indices of the Baltic states and the most actively traded individual stocks. A psychological barrier refers to a specific price point, often at round numbers (i.e. powers of 10), that investors believe is challenging to breach, influencing their behavior and trading decisions.Design/methodology/approachWe conduct uniformity tests and barrier tests, such as barrier proximity tests and barrier hump tests, to evaluate the presence of psychological barriers. Additionally, we explore variations in means and variances near these potential barriers using regression and GARCH analysis.FindingsThe findings reveal that psychological barriers do exist in the Baltic stock markets, particularly within market indices. The Estonian market index stands out with the most pronounced indications of psychological barriers. Individual stocks also display significant changes in means and variances related to potential barriers, albeit with less uniformity.Practical implicationsCollectively, our findings challenge the traditional assumption of random returns within the Baltic stock markets. For practitioners, the finding that psychological barriers exist opens up opportunities for investment strategies that can capitalize on them.Originality/valueThis study is the first to comprehensively investigate psychological barriers in the Baltic stock markets. Our results provide a valuable contribution to understanding the impact of that phenomenon on pricing dynamics, which is particularly pertinent in less-researched frontier markets like the Baltic states.

Emerging Sectors in the Indian Stock Market: Key Opportunities and Trends for Future Growth

This paper focuses on the growth prospects and market dynamics of emerging sectors in the Indian stock market. It analyses key drivers and trends, evaluates economic and regulatory influences, and identifies specific investment opportunities. The study also examines the reasons behind sectoral plays and offers strategic implementation plans for investors. The aim is to provide insights for effectively capitalising on these sectors to maximise returns.

A Markov‐switching spatio‐temporal ARCH model

Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models neglect structural breaks triggered by sudden shocks that may lead to an overestimation of persistence, causing an upward bias in the estimates. Different regime‐switching models that have abrupt regime‐switching governed by a Markov chain were developed to model volatility in financial time series data. Volatility modelling was also extended to spatially interconnected time series, resulting in spatial variants of ARCH models. This inspired us to propose a Markov switching framework of the spatio‐temporal log‐ARCH model. In this article, we discuss the Markov‐switching extension of the model, the estimation procedure and the smooth inferences of the regimes. The Monte Carlo simulation studies show that the maximum likelihood estimation method for our proposed model has good finite sample properties. The proposed model was applied to 28 stock indices' data that were presumably affected by the 2015–2016 Chinese stock market crash. The results showed that our model is a better fit compared to that of the one‐regime counterpart. Furthermore, the smoothed inference of the data indicated the approximate periods where structural breaks occurred. This model can capture structural breaks that simultaneously occur in nearby locations.

Stock Reverberation Due to International Trade Related Economic Factors

The economic activities of a nation are measured through various macroeconomic factors such as GDP, EXIM rate, unemployment level, Wholesale Price Index (WPI), and Consumer Price Index (CPI). Numerous studies have focused on identifying the association between inflation and stock prices, with some also highlighting the relationship between gold prices and stock price volatility. This study uses exchange rate and balance of trade as indicators to predict stock price volatility in China. The stock price volatility is measured using the Shanghai Stock Exchange Composite Index (SSE Composite Index). An analytical method is employed to evaluate the involved variables, with Johansen integration used to determine the relationships between them. The Vector Error Correction Model (VECM) is utilized to model these relationships. The Granger causality test and Johansen co-integration test measure the causal relationships of economic variables on the stock market indices. The findings indicate that the exchange rate positively influences stock prices, whereas the balance of trade shows a negative relationship. The exchange rate Granger-causes stock prices, but the reverse is not true. A similar Granger-causing relationship exists between the balance of trade and stock prices. The study also reveals that while the exchange rate of RMB to USD may not have a strong impact on stock prices, the exchange rates of other currencies may significantly influence China's stock prices.

On testing the changes in trends of stock market index and rates

Calibration of interest rate models benefits from grouping data to homogenous classes. Such an approach is typical in many financial time series. Preliminaries have been developed for Cox–Ingersoll–Ross models but this issue remains an open problem for many more realistic interest rate models. Here we develop such a strategy for general class interest rate and classes are based on p-value thresholds for testing for normality and gamma distributions. We use as the benchmark financial series of Chilean stock market index IPSA (Indice de precios selectivo de acciones) and its log-returns. We also study the relationship between interest rate and the market returns represented by the IPSA indicator, with positive correlation in some lags which reveals some interesting facts in the contrary to the conventional theory.

Role Of Foreign Direct Investment In Indian Stock Market: A Critical Appraisal

Role Of Foreign Direct Investment In Indian Stock Market: A Critical Appraisal

Optimization Algorithms in Deep Learning Models for Improving the Forecasting Accuracy in Sequential Datasets with Application in the South African Stock Market Index: A Review

Optimization Algorithms in Deep Learning Models for Improving the Forecasting Accuracy in Sequential Datasets with Application in the South African Stock Market Index: A Review

On joint marginal expected shortfall and associated contribution risk measures

Systemic risk is the risk that a company- or industry-level risk could trigger a huge collapse of another or even the whole institution. Various systemic risk measures have been proposed in the literature to quantify the domino and (relative) spillover effects induced by systemic risks such as the well-known CoVaR, CoES, MES and CoD risk measures, and associated contribution measures. This paper proposes another new type of systemic risk measure, called the joint marginal expected shortfall (JMES), to measure whether the MES of one entity's risk-taking adds to another one or the overall risk conditioned on the event that the entity is already in some specified distress level. We further introduce two useful systemic risk contribution measures based on the difference function or relative ratio function of the JMES and the conventional ES, respectively. Some basic properties of these proposed measures are studied such as monotonicity, comonotonic additivity, non-identifiability and non-elicitability. For both risk measures and two different vectors of bivariate risks, we establish sufficient conditions imposed on copula structure, stress levels, and stochastic orders to compare these new measures. We further provide some numerical examples to illustrate our main findings. A real application in analyzing the risk contagion among several stock market indices is implemented to show the performances of our proposed measures compared with other commonly used measures including CoVaR, CoES, MES, and their associated contribution measures.

Oil Price Dynamics and Sectoral Indices in India – Pre, Post and during COVID Pandemic: A Comparative Evidence from Wavelet-based Causality and NARDL

Due to the COVID pandemic, the stock market has been affected adversely around the globe and investment decisions are now more challenging and riskier. Hence, in this paper, we aim to investigate the impact of oil prices on the Indian stock market and eight sectoral indices for the period of pre, post, and during the COVID pandemic. The maximal overlap discrete wavelet transform (MODWT) is used to decompose and to denoise the original time series data as oil price and market return are found to be noisy. We employ the wavelet-based Granger causality (WGC) and non-linear, autoregressive distributed lag model (NARDL) to investigate the causality in the frequency domain as well as the short-run and long-run asymmetry of oil price impact. Our analysis shows a feedback relation between low frequency (higher investment horizon) and the long-run asymmetric impact of oil prices on all sectors during all three periods. We discuss the dynamic time-varying relationship between the oil price and sectoral return along with the investment implications in detail.

Economic policy uncertainty and Indian equity sectors: a quantile regression approach

PurposeThis paper aims to investigate the effect of economic policy uncertainty (EPU) shocks on Indian equity market sectors. The effect of domestic (Indian) and foreign (USA) EPU shocks is examined on ten major Bombay Stock Exchange sectors.Design/methodology/approachThe study uses data covering the period from September 2005 to July 2023 and uses the methodology of quantile regression to investigate the heterogenous response of stock market sectors under diverse market conditions explained through the analysis of conditional quantiles distribution.FindingsThe results demonstrate that domestic and foreign EPU shocks negatively affect most of the sectors in bearish market conditions. Industrials, commodities, utilities, consumer discretionary and financial services are the most affected sectors by domestic EPU. However, the information technology sector is found to be immune to domestic EPU shocks but negatively affected by foreign EPU shocks. On the other hand, energy, financial services and fast-moving consumer goods sectors are found to be immune to foreign EPU shocks but are negatively affected by domestic EPU shocks.Practical implicationsUnderstanding the heterogeneous response of different sectors to EPU shocks could help investors and portfolio managers identify portfolio diversification opportunities.Originality/valueThis study makes an inaugural attempt to examine the responses of Indian stock market sectors to domestic and foreign EPU shocks using the approach of quantile regression and unveils the previously unexamined diverse reactions of Indian stock market sectors to EPU shocks originating from both India and USA.