Stock Market Index Returns Research Articles

Asymmetric Effect of Oil Prices on Kazakhstan’s Stock Market Index and Exchange Rate

In this study, the asymmetric effect of oil prices in Kazakhstan on the exchange rate (USD/KZT) and Kazakhstan stock market index (KASE) returns was analyzed using the non-linear ARDL cointegration (NARDL) method with monthly data for the period January 2010-February 2024. As a result of the findings of the study, both positive and negative changes in oil prices statistically affect stock returns and exchange rates in the long term. The estimated long-term positive (0.115982) and negative (0.111234) coefficients of oil prices on KASE returns were found to be statistically significant at the 5% significance level. Thus, in the examined period, the KASE variable gave different reactions to positive or negative shocks in the oil price. In the model where the exchange rate is the dependent variable, the estimated long-term positive (-0.093875) and negative (-0.092918) coefficients of oil prices were found to be statistically significant at the 95% reliability level.

The Impact of the Last Two Presidential Periods on the Performance of the Indonesia Stock Exchanges: An Approach of Arbitrage Pricing Theory

This study aims to examine the influence of economic and non-economic macro factors on the performance of the Indonesia Stock Exchange (IDX) and the differences in their effects on the last two presidential terms using the Arbitrage Pricing Theory (APT) approach. The macro factors studied are an aggregate of 41 indicators of stock market index returns, benchmark interest rates, exchange rates, economic stability indicators, commodity prices, and corruption perception index (CPI). For the process of aggregation and identification of macro factors, this study uses Principal Component Analysis (PCA). The population studied were publicly listed companies on the IDX since October 2004 (299 companies) with a sample size of 157 companies. The observation period is 205 months: 120 months of the presidency of Susilo Bambang Yudhoyono (SBY) and 85 months of the presidency of Joko Widodo (JKW). The research design used the explanatory study method. The research model was analyzed using panel data regression. The results identified seven macro factor aggregates from 41 indicators studied: global index, macro economy, world oil prices, China index, Arabia index, inflation, and competitive resources. There was a simultaneous effect of macro factors on stock performance during the presidency of SBY and JKW. Partially, stock returns during the SBY era were positively influenced by the global index, world oil prices, the China index, the Arabian index, and inflation and negatively influenced by the macroeconomy. Meanwhile, during the JKW period, returns were positively influenced by the global, China, and Arabia indexes and inflation.

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.

Uncertainty, stock and commodity prices during the Ukraine-Russia war

ABSTRACT This article mainly investigates whether the war in Ukraine-Russia induces uncertainties and how this affects the returns of world stock market indices and commodity prices. To do so, we use the Economic Policy Uncertainty index (EPU), 96 stock market indices and the price of 67 commodities worldwide over the period from January 2022 to April 2023. Our empirical results show that the war between Ukraine and Russia increases uncertainties, and which negatively affect the performance of global financial markets and lead to higher commodity prices. These negative shocks tend to be smaller the longer the war persists. We also observe that responses to war-induced uncertainties are stronger in Europe and America. Overall, we provide unique evidence of the impact of war on uncertainties and how this is transmitted to stock market performance and commodity prices.

Has COVID-19 changed the correlation between cryptocurrencies and stock markets?

The COVID-19 pandemic has challenged the notion that cryptocurrencies are uncorrelated with traditional asset markets. This study uses VAR-OLS techniques to investigate the time-varying correlation between Bitcoin and three major European stock market indices from January 4, 2016, to February 26, 2021. Our results show that cryptocurrencies and stock markets are dependent during crisis periods, but not during non-crisis periods. This confirms the time-varying correlation between cryptocurrencies and stock markets, which depends on the extent and persistence of responses to own and cross shocks. To improve the robustness of our results, we also test the impact of government measures on Bitcoin and stock market indices and find that they are both affected by these measures. Our study adds to the literature by examining the impacts of pandemics on the correlations between Bitcoin returns and the stock market, oil, and gold index returns, which have so far been unaddressed.

Sentiment investor, exchange rates, geopolitical risk and developing stock market: evidence of co-movements in the time-frequency domain during RussiaUkraine war

PurposeThe aim of this paper is threefold: (1) to develop a new measure of investor sentiment rational (ISR) of developing countries by applying principal component analysis (PCA), (2) to investigate co-movements between the ten developing stock markets, the sentiment investor's, exchange rates and geopolitical risk (GPR) during Russian invasion of Ukraine in 2022, (3) to explore the key factors that might affect exchange market and capital market before and mainly during Russia–Ukraine war period.Design/methodology/approachThe wavelet approach and the multivariate wavelet coherence (MWC) are applied to detect the co-movements on daily data from August 2019 to December 2022. Value-at-risk (VaR) and conditional value-at-risk (CVaR) are used to assess the systemic risks of exchange rate market and stock market return in the developing market.FindingsResults of this study reveal (1) strong interdependence between GPR, investor sentiment rational (ISR), stock market index and exchange rate in short- and long-terms in most countries, as inferred from (WTC) analysis. (2) There is evidence of strong short-term co-movements between ISR and exchange rates, with ISR leading. (3) Multivariate coherency shows strong contributions of ISR and GPR index to stock market index and exchange rate returns. The findings signal the attractiveness of the Vietnamese dong, Malaysian ringgits and Tunisian dinar as a hedge for currency portfolios against GPR. The authors detect a positive connectedness in the short term between all pairs of the variables analyzed in most countries. (4) Both foreign exchange and equity markets are exposed to higher levels of systemic risk in the period of the Russian invasion of Ukraine.Originality/valueThis study provides information that supports investors, regulators and executive managers in developing countries. The impact of sentiment investor with GPR intensified the co-movements of stocks market and exchange market during 2021–2022, which overlaps with period of the Russian invasion of Ukraine.

Multiple time scales investor sentiment impact the stock market index fluctuation: From margin trading business perspective

The stock market is viewed as a complex dynamic system, and investor sentiment has an important impact on index fluctuation. This study constructs investor sentiment from margin trading business perspective and investigates its impact on the Chinese stock market index fluctuation in multiple time scales. First, we utilize 12 indicators and two-stage PCA to construct a composite investor sentiment index of the margin trading business(ISMT). Second, based on TEI@I complex system theory, we use the VMD algorithm to decompose and reconstruct the ISMT, Shanghai Securities Composite Index(SSEC), and Shenzhen Securities Component Index(SZI), and obtain multiple time scale measurement sequences that reflect short-term, medium-term, and long-term fluctuation of each index, respectively. We provide evidence that the ISMT has an asymmetric impact on stock market index fluctuations. Specifically, for long-term trend, the ISMT has a significant positive impact on the SSEC, and a significant negative impact on the SZI. For medium and short-term trends, the ISMT has a significant positive impact on both the fluctuations of SSEC and SZI, and the impact degree on SSEC is greater than SZI. We also find that the impact degree of ISMT on SSEC and SZI decreases from short to long-term trend. In addition, we measure the fluctuation periodicity of ISMT in multiple time scales based on Fast Fourier Transformation, investigate the impact result during the COVID-19 pandemic, discuss the impact of ISMT on the other nine indexes commonly used in the Chinese stock market, and evaluate the predictive power of ISMT for 11 stock market index returns. This paper takes a new perspective and technology to investor sentiment research, and the results enrich relevant financial theories. The findings are crucial for investor decision-making and financial department regulation.

How do carbon emissions and eco taxation affect the equity market performance: an empirical evidence from 28 OECD economies.

This study examines the nexus between "stock returns," carbon emissions, and "environmental tax" in 28 OECD countries for the period 1994 to 2014. To this end, we employ second-generation econometric techniques that are robust to macroeconomic and financial datasets to eliminate the issues associated with heterogeneity and cross-sectional dependence. The "common correlated effects mean group (CCEMG)" and "augmented mean group (AMG)" estimators are a pioneering attempt to explore the association between the cross sections. These approaches take into account the complexities of real-world data and provide a more accurate understanding of the relationship between the variables. Several studies explored the relationship between CO2 emissions and economic growth. However, the literature lacks studies examining the impact of "environmental tax" implementation on "stock returns," which is a central policy instrument to curb emissions. The results demonstrate a significant and negative relationship between CO2 emissions and "stock market index returns" and a positive and significant relationship between "environmental tax" and "stock market index returns." Furthermore, both these relationships prevail in the world's biggest financial markets and second largest CO2 emitters, the USA. The empirical findings offer various useful implications for investors, policymakers, brokers, corporations, governmental pollution abatement institutions, and other stakeholders who wish to obtain a carbon risk premium.

Impact of public news sentiment on stock market index return and volatility

Impact of public news sentiment on stock market index return and volatility

New tourism financial condition index: extended with terrorist attacks

Purpose- In this study, a composite indicator that can be followed in the financial markets has been developed. The tourism financial conditions index has been developed in the literature for tourism based on MCI (Monetary Conditions Index) and FCI (Financial Conditions Index). The index is called the Tourism Financial Conditions Index in their studies. This study ensures that the calculation of the tourism index by adding the terror variable to the index can better reflect the expectations. Methodology- While constructing the composite indicator Z-score is used for standardization. With this standardization terrorist attacks variable is added to the composite indicator. The relationship between New Tourism Financial Composite Indicator and Tourism Stock Market Index Return is analyzed with the Threshold VAR method. This method is used because of the nonlinearity of variables. Findings: Monthly data for the period 2009:07 – 2017:12 were used in the study. The countries examined are Australia, Denmark, France, Italy, Spain, Türkiye and the UK. It is aimed to examine the relationship between the calculated NTFCI and the Tourism Stock Exchange data of each country. T-VAR results show the relationship between these variables. Conclusion - In this study, the new version of the TFCI index with terrorist incidents is presented. Chang (2015) developed the TFCI index for the tourism sector. We think that the countries should also consider the effect of terrorism in their economic and financial research in tourism area. Based on this idea, the index was expanded by adding the terror variable to the previously developed TFCI composite indicator and its relationship with the tourism stock market index as a financial indicator was examined. The TVAR model is applied which shows the short-term dynamic relationship. Models for different countries were estimated and similar results were obtained for different countries. Keywords: Tourism financial conditions index, tourism sector, monetary conditions index, financial conditions index JEL Codes: E44, E47, G32

INVESTIGATION OF SERIAL DEPENDENCE ASYMMETRY AND TIME IRREVERSIBILITY IN STOCK MARKET RETURNS OF MIST COUNTRIES USING THE QUANTILE PERIODOGRAM

The stock market indices of the countries are indicators that provide information about the countries' economies and financial stability. The aim of the study is to determine the similarities and differences in the stock market index return behaviors for Mexico, Indonesia, South Korea and Türkiye, which constitute the MIST country group. For this purpose, the spectral density kernel estimator "Quantile Periodogram" was used. The reason why this estimator is preferred is that it allows the investigation of serial dependence at different quantiles-frequencies and it is robust to outliers frequently encountered in return series, heavy-tailed distribution and changes in the distribution at high moments. The asymmetry of the serial dependence in different quantiles-frequencies and time-irreversibility which gives information about whether the financial series behavior is predictable or not, were analyzed with the quantile periodogram. According to the findings, Türkiye is the most preferred country by financial investors among MIST countries, while Mexico is the least preferred. Secondly, it is seen that the long-term behavior predictability of the returns has increased. This means that returns are more stable in the long run. When the findings are evaluated collectively, it is concluded that MIST countries are attractive for long-term financial investment.

Impact of Economic Policy Uncertainty on Indian Stock Market Returns: Evidence from Large -Cap, Mid-Cap and Small-Cap Stocks

Impact of Economic Policy Uncertainty on Indian Stock Market Returns: Evidence from Large -Cap, Mid-Cap and Small-Cap Stocks

The Impact of Government Response on the US Stock Market during the COVID-19 Pandemic

Although pandemics have occurred with some degree of frequency over the past century, the degree to which social and economic pandemic-related government responses have been used across the globe during the COVID-19 pandemic period has been unprecedented. Using the S&P 500 stock market index return over the period January 2020-June 2021as our dependent variable in a regression-based model, we measure, via a Government Response Index, the extent to which government response impacted stock market returns. We also include the VIX (“fear gauge”), Market Value, Trading Volume, and Price-to-Earnings Ratios to control for other factors that may have impacted the market during the pandemic. Although we found that the VIX was highly negatively correlated with stock market returns during this period, we found very limited evidence of a significant relationship between stock market returns and government response over this time period. Thus, we believe it would be prudent for governments to be more strategic in their approach regarding initiatives if or when a future pandemic should arise, as not all government-related policy initiatives have the intended effect.

EFFECTS OF GEOPOLITICAL RISKS AND POLITICAL UNCERTAINTIES ON STOCK MARKETS: COUNTRY SPECIFIC NEW GENERATION PANEL DATA ANALYSIS FOR DEVELOPING ASIAN COUNTRIES

In general, there are strong and meaningful relationships between risks and uncertainties, and economic activities. In this context, geopolitical risks (GPR) and global economic and political uncertainties (WUI) can significantly affect financial markets and investor behavior. In this study, the effects of GPR and WUI on stock markets for 10 developing Asian countries were investigated for 2001:M07-2020:M12 period. Cross-section dependency was examined with LM, and tests, series stationarity with Hadri and Kurozumi (2012) test, cointegration relations with Westerlund (2006) cointegration with multiple structural breaks method, and regression analyzes with Eberhardt and Bond (2009) method. It was determined that high GPR decreases the stock market index in Turkey, Korea, Russia, Indonesia and Malaysia, whereas it increases in India, Thailand and Philippines. It was found that high WUI decreases the stock market index in Korea, China, Indonesia and Thailand, whereas it increases in Turkey, India and Malaysia. On the other hand, while the stock market returns of the change in GPR decreases the stock market returns in Turkey, Korea, Russia, Indonesia and Malaysia, it increases in India, Thailand and Philippines. It has been determined that change in WUI decreases the stock return in Korea, China, Indonesia and Thailand, and it increases in Turkey, India and Malaysia. It was also observed that changes in GPR increases the volatility of the stock markets in India, Thailand and Philippines, and it decreases in Turkey, S. Korea, Russia, Indonesia and Malaysia. While the effects of change in WUI increases stock market volatility in Turkey and Malaysia, it decreases in Korea, China, Indonesia and Thailand. As the results achieved are heterogeneous, investors should avoid a basket trading strategy in these countries. Country-specific causality test was carried out with the Konya (2006) method, and it was observed that there are causal relationships between high WUI and stock market index, high WUI and stock market return in Turkey. Similarly, causal relationships were determined between high WUI and stock market index in India, between change in WUI and stock market index volatility in Philippines, between high GPR and stock market index, change in GPR and stock market return in Hong Kong. Causal relationships for all countries in the panel were examined using the Dumitrescu and Hurlin (2012) method, and causality was only found between high WUI and stock market index.

A Global Version of Samuelson's Dictum

Samuelson’s Dictum refers to the conjecture that there is more informational inefficiency at the aggregate stock market level than at the individual stock level. Our paper recasts it in a global setup: there should be more informational inefficiency at the global level than at the country level. We find that sovereign CDS spreads can predict future stock market index returns, GDP, and PMI of their underlying countries. Consistent with the global version of Samuelson’s Dictum, the predictive power for both stock returns and macro variables is almost entirely from the global, rather than country-specific, information from the sovereign CDS market. (JEL G12, G14, G17)

The Relationship Between Stock Market Index Returns and Gold Returns during the First Year of the Coronavirus Pandemic: An Asymmetric Causality Test

The Coronavirus is one of the most influential infectious diseases of the 21st century. This study investigates the Hatemi-J asymmetric causality relationship between stock market returns and gold market returns for five of the most affected countries between January 02, 2020 and December 31, 2020. The results show that because of the demand for liquidity, the atmosphere of panic, and the perception of gold as a safe haven, the causal relationship is not strong for each country.

MODELING AND FORECASTING VOLATILITY OF STOCK MARKET USING FAMILY OF GARCH MODELS: EVIDENCE FROM CPEC LINKED COUNTRIES

For economists and investors, it is necessary to understand the random and nonlinear pattern of the stock market volatility. High volatility directly affects the financial market that leads to unpredictability. China–Pakistan Economic Corridor attracts economists and investors worldwide. Therefore, predicting the volatility of the stock markets related to CPEC is important. In this study we consider the most important stock markets lying on the route of CPEC, namely KSE 100 (Pakistan), SSE 100 (China), TADAWUL (Kingdom of Saudi Arabia), KASE (Kazakhstan), KLSE (Malaysia), BIST (Turkey), MOEX (Russia), FTSE (United Kingdom) and CAC40 (France). The daily returns of stock market indices consist of 1706 observations from December 2014 to July 2021. After the confirmation from the ARCH effect test, family GARCH models are employed, among them, based on AIC and BIC criteria, GARCH (1,1), EGARCH (1,1), and GARCH-M (1,1) are found suitable to forecast the volatility. The empirical study also suggests that the out-of-sample forecast GARCH-M (1,1) model is more appropriate as it has a minimum value of MAE, MSE, RMSE, MAPE, TheilU1, and Theil U2 among all the studied GARCH models. Furthermore, it is also found that the KSE-100 and SSE-100 have moderate and slow market average returns even though both stock markets are found to be the least risk-returns markets.

Significant Insights, Value Orientation and Differences Between the Mutual Fund Investment Flow and Indian Stock Market Returns – A Theoretical Assimilation

Researchers and academicians from all over the world have become interested in the study of the causal relationship between mutual fund investment flow and stock market returns in recent years. But there is currently a contradictory body of empirical data on this matter. Additionally, there are a few studies that take the case of India into account. In order to better understand the dynamics of the relationship between mutual fund investment flow and stock market returns in India from January 2000 to May 2010, the following article will do just that. The Granger causality tests are applied using the Toda and Yamamoto approach, which yields evidence of a one-way causal relationship between stock market returns and mutual fund investment flow. This suggests that the expansion of stock market activity in India draws mutual funds to the stock market. Therefore, the government and monetary authorities should take the necessary actions to reduce the volatility and increase the efficiency of the capital market. By gathering money from households and investing it in the stock and debt markets, mutual funds enable portfolio diversification and relative risk aversion. In India, a specific type of mutual fund called fixed-income funds invests in debt securities that have been issued by businesses, banks, or the government. In India, fixed-income funds are also referred to as debt funds and income funds. The goal of the current study is to assess the performance of a few selected debt or income mutual fund schemes in India based on their daily NAV using various statistical measures. In the past ten years, income schemes have become more and more popular. The securities that were purchased are referred to as the fund's portfolio. There may have been restrictions on rival products, which led to the emergence of money market and (short-term) bond funds. In this study, the performance of several mutual fund types in India was compared and studied. The results showed that equities funds outperformed income funds. The study also found that institutional fund managers can time their investments and that equity fund managers have significant market timing ability, but broker operated funds did not demonstrate this ability. Additionally, empirical research has shown that fund managers possess significant timing ability and can time their investments to match market conditions.

The Effect of Four Events (NITI Aayog, Demonetization, GST & N-Covid-19) on Indian Stock Market with Special Reference to Sectorial Indices

Study seeks to determine the effects of NITI Aayog, Demonetization, GST and N-Covid-19 on Indian stock market return. Systematic literature review (SLR) method is used to assess the effect, considering 50 journal publications between 2017 to 2022. Automobile, Banking, FMCG, Information Technology, Oil & Gas sectors are cosnidered. Event Study Methodology is followed with pre (-21 days) and post-event window (+21days) analysis. Index values were observed and abnormal returns were calculated for the study period and the impact was evaluated. The findings revealed that indices were significantly impacted by events with negatively yielding in the short run. In long run, markets gradually recovered and stabilized. Further, the analysis highlights that, each of the events affect on various sector is not same. These occurrences may alter investor sentiment, which in turn affects stock prices. Study concludes with the effect and highlighting conclusive effect.

Examining the association between stock return and seasonally adjusted trade in India

This paper aims to examine the association between Indian stock market return and seasonally adjusted trade for Indian Banking sector shares. The objective of the study is to measure the association between stock return and seasonally adjusted trade in the Indian stock market and recommend strategies. Due to the Covid 19 outbreak banking sector was highly affected, the Government of India also announced a moratorium on all categories of loans, banking business majorly depending on the deposit and loan creation, so the Government decision threaten the banking sector. Many private sector banks terminated temporary staff because of cut cost policies. We divided the banking sector into three segments. Namely, Public, Private and Small Finance Banks. Utilizing daily data from February 2020 to July 2020, consisting of 2196 in numbers, we ran a panel Vector Autoregression model to analyze the association. It was found that the return of stocks is influencing the volume of trade during this period. Also, while measuring the short-run causality, it is found that the return of banking stocks specifically granger causes the volume of trade. The suggestions of the study lie in providing importance to framing policies on improving the financial health of the economy through different fiscal policies. Strategic policies are required to face post-Covid situations. The turnaround strategies to combat the effects of the pandemic are characterized by the availability of the sustainable resources of the particular sector in consideration.