Sectoral Indices Research Articles

FOURIER CAUSALITY AND VOLATILITY SPILLOVER PERSPECTIVES TO ANALYZE THE EFFECT OF TERRORISM ON TURKISH AND EUROPEAN STOCK MARKETS

Terrorism has become a curse for most of the underdeveloped economies. Even relatively developed regions like Europe and Turkey are struggling to combat it. Besides the economic and political consequences, terrorist activities also have psychological cost that may reflect in stock market. This study investigates the causal effects of systematic shocks on mean returns and volatility of the general and 14 sectoral indices in Europe and Turkey using contemporary causality tests like Fourier Toda-Yamamoto Causality test (Nazlioglu et al., 2016), Fourier Standard Granger Causality test (Enders and Jones, 2015), and Causality in Variance (Hafner and Herwartz, 2006). Tourism related sectors turn out to be fragile whereas financial sector have significant resilience to terrorist attacks. Moreover, the spillover analysis shows that the Turkish stock market turns out to be more fragile to terrorist attacks in Europe than the other way around.

A Comparative Study of BSE Listed Sectoral Indices of Real Estate and Banking Industry

Purpose: Shareholders are the real owners of the company. They provide capital to the business to get a better return on their investment. Maximizing shareholder wealth has become the primary goal of today's business world. No business entity can become profitable without producing a lot for various investors. There are some strategies to determine the organization productivity and valuation of investors. A cutting-edge form of top management, economic value added (EVA) is a way of valuing shareholder wealth. This helps ensure the true productivity of the organization at the expense of value and capital. The concept was first created by Stern Stewart and Co. The company proposed 164 changes in GAAP rules to calculate real economic profit and some changes in economic capital. MVA is another modern way of valuing shareholder wealth. It describes how efficiently a company uses shareholders' assets. The study made two corrections to the calculation of economic benefit and two corrections to the related population. Real estate and banking companies listed on the Bombay Stock Exchange were selected for the study. The purpose of the audit was to determine the value of investors in BSE-listed land and bank units based on EVA and MVA over a period of five years. Speculations were tested using ANOVA and T-TEST. The audit revealed that an organization like Legend Engine Corp expanded its EVA and MVA during this period. Design/Methodology/Approach: Five BSE listed industries are considered for the present research work and analysed by using EVA and MVA. Findings/Result: A profitable sector is identified. Opportunity is provided for the investors to buy, sell, or hold the stock to generate more profits in the selected sector. It has been observed that SBI has a high typical EVA unlike various banks but it has decreased in the long run. All financial organizations have a positive relationship between EVA and MVA. Originality/Value: Among the organizations in the selected countries, Godrej Properties, and DLF are positively associated with MVA and investment wealth creation every 5 years. Choosing stocks from an industry is a big problem for investors because the market is unpredictable and offers more opportunities for traders and investors to buy sell or hold stocks and earn potential profits in the market. This study aims to compare EVA and MVA to provide some recommendations for stock and industry selection to all investors. Paper Type: Comparative research

Does oil price volatility matter for the US transportation industry?

Although the US transport sector is one of the major users of fossil fuel (e.g., crude oil), the impact of energy price volatility on transport stock sector indexes remains under-researched. The present study addresses this research void by investigating the impact of energy implied volatility on transportation stock returns in the US. Using the crude oil volatility index (OVX), as a proxy of energy price volatility, and three Dow Jones indexes tracking the performance of the airlines, marine and trucking stock subsectors, we employ a GARCH-jump model. The main results show that the oil market sends volatility to the US transport subsector stock indexes, suggesting that oil implied volatility plays a role in pricing US transport stocks. The impact of OVX shocks is asymmetric, indicating that increases and decreases in oil implied volatility have a heterogeneous impact on the transport subsector stock markets. Jumps are significant in the three transport subsector stock indexes, and are time-dependent. Notably, the three transportation subsector stock indexes are more sensitive to OVX shocks than the S&P 500 index. These results have important implications for investors, policymakers, academics, and managers of the US transportation industry.

Clean energy stock price forecasting and response to macroeconomic variables: A novel framework using Facebook's Prophet, NeuralProphet and explainable AI

The clean energy market analysis has seen a strong surge in the post-Paris agreement owing to its undeniable environmental sustainability. The present work investigates the predictability of clean energy investment in the US market by selecting eight sectoral stock indices. The predictive exercise is carried out separately during pre-COVID-19 and COVID-19 timelines to draw key behavioral aspects of the underlying sectors. After scrutiny of previous research, we identify a set of technical and macroeconomic variables as explanatory constructs. We then utilize Facebook's Prophet and NeuralProphet to find future figures for the clean energy indices. Finally, Explainable Artificial Intelligence (XAI) is used to draw deeper insights into the contribution patterns of the constituent explanatory variables and obtain their relative importance. The findings suggest that the future movements of the sectoral clean energy assets can be predicted with a very high level of accuracy. Also, the predictability marginally improves during the COVID-19 pandemic despite the unprecedented uncertainty. Technical indicators appear to be the dominant features, while market sentiment and fear exert significant influence.

Hisse Senedi Piyasalarının Depremlere Karşı Kırılganlığı / Türkiye'de 2023 Yılında Meydana Gelen 7.7 ve 7.6 Ölçekli Depremler: Taş Toprak, Metal Ana, İnşaat ve Bayındırlık İşleri Sektörleri

This study aims to reveal whether stock markets are vulnerable to the effects of earthquakes and how they are affected by earthquakes. The 7.7 and 7.6 magnitude earthquakes in Turkey in February 2023 were explored. The impact of the earthquake on Borsa Istanbul was analyzed with the event study method, specific to non-metallic mineral products, basic metals, and construction & infrastructure sectors. Firstly, the paired-samples T-Test and the Wilcoxon Signed Ranks Test were used to evaluate whether there was a difference that was statistically significant in the cumulative abnormal returns (CARs) of the sector-specific indexes in the 5, 10, 15, 20, 25, 30, 35, 40, 45 and 50 hour time periods prior to and following the earthquake first occurred. The first results indicate that for all evaluated time intervals, the first occurred earthquake had positive effects on the CARs of the non-metallic mineral products and base metals sector indices and negative effects on the construction & infrastructure sector indices. Secondly, the effects of both the first and second earthquakes were analyzed with the Seemingly Unrelated Regressions (SUR) Model. According to the SUR Model analysis, the direction of the effects of the first earthquake is consistent with the first findings. In this model, the direction of the effects of the second earthquake is also compatible with the direction of the effects of the first earthquake, but the size and trend of the consequences of these earthquakes change over time. The findings are expected to contribute to the literature by shedding light on how sectors can respond to catastrophes like earthquakes occurrences at the micro level.

The effect of quantitative easing on US sector returns

In this study, we used event analysis to examine whether abnormal returns can be observed for US sector returns at the time when quantitative easing was announced. In our investigation, we sought to identify the sectors in which the amount of money introduced into the economy as a result of quantitative easing. In addition, since the programme is basically implemented by the central bank in order to stabilise the economy, an examination of its effect on the sectors may provide guidance on which segments have reacted negatively, thus possibly requiring additional central bank or public intervention. In our results, we found consistency for all four QE programme in some sectors In the examination of the S&P 500 sector indices, surprisingly no significant abnormal return was found in the financial sector, by contrast, for the healthcare sector, as well as discretionary and general consumer goods, the stocks included in the sector index reacted in the same way for all 4 announcements.

The Energy Commodities Price and The Energy Sector Index: Evidence from Indonesia Stock Exchange

Purpose: This study aims to determine the effect and correlation of daily and weekly returns on commodity prices on the IDX Energi index on the Indonesia Stock Exchange. Theoretical Framework: Commodities are goods in the form of raw materials or raw materials that have a selling value. Energy commodities are mining products in the form of extracted fossils which can be used as energy sources. Stock price indices can be divided into several categories, namely stock market indices, sectoral stock price indices and individual stock prices. The stock market index is a representation of the total listed stock prices based on certain criteria and methods and is evaluated periodically. Method: The research period used was January 25 2021 to July 30 2023. The analysis technique used was multiple regression and Dynamic Conditional Correlation Generalized AutoRegressive Conditional Heteroskedasticity. Results: The results showed that the daily and weekly returns from energy commodities had a significant positive effect on the return on the IDX Energy index. Weekly returns on commodity prices can predict returns on the IDX Energi index better than daily returns. Energy commodities are also correlated with the IDX Energi index, but the dynamic correlation pattern of natural gas price returns to the IDX Energi index is different from the dynamic correlation pattern between world oil and coal prices. In general, returns on oil, natural gas and coal prices have an effect and are correlated with returns on the IDX Energi index. Conclusions: The research results can be used as a decision making in investing, especially investing in companies that are included in the IDX Energi sector. The weekly return of the IDX Energi index can be predicted through returns on energy commodity prices, but the contribution is still small. Subsequent research can add new variables that can affect the movement or return of the IDX Energi index, besides that it can examine with a longer sample period.

Assessing systemic risk and connectedness among dirty and clean energy markets from the quantile and expectile perspectives

In response to environmental and climate change issues in recent decades, clean energy resources have been promoted as substitutes for fossil fuel-based dirty energy resources. While receiving great attention from environmentally conscious investors and policy-makers, the markets for the former energies could be affected by financial instability in the markets for the latter, which could lead to systemic risk and directional connectedness. In this paper, we aimed to assess potential systemic risk in such markets by employing conditional quantile- and conditional expectile-based Value-at-Risk (henceforth CoVaR and CoEVaR, respectively). Due to their definition through expected conditional asymmetric loss functions, we adopted Kabaila’s efficient method for the computation of conditional expectations and proposed a backtesting technique based on Diebold and Mariano’s test. Using Delta-CoVaR and Delta-CoEVaR, we constructed conditional tail risk networks to describe the directional tail risk connectedness. We revealed that the CoVaR and CoEVaR for almost all pairs of energy markets had the best forecast accuracy when computed using a copula exhibiting symmetric tail dependence with asymmetric and heavy-tailed margins. Compared to CoVaR, CoEVaR possessed a relatively better conditional coverage performance in several cases, complementing its advantage of being more sensitive to (extreme) loss magnitude. In addition, we found crude oil, heating oil, and sectoral clean energy indices to be highly interconnected, but the connectedness weakened following the 2015 Paris Climate Agreement. In particular, crude oil and the renewable energy sector played a consistent role as the most systemically important net receiver and net transmitter, respectively. These results provided important implications for policy-makers and investors aware of systemic risk and connectedness among dirty and clean energy markets.

Is It a Real COVID-19 Fear? A Cross-Industry Study of Fear on the Stock Market

Theoretical background: According to many behavioural studies, emotions (e.g. fear, greed, overenthusiasm) strongly influence investment decisions and are one of the main sources of irrational behaviour. Purpose of the article: The study aims at investigating the impact of fear related to the COVID-19 crisis on sector indices on the stock market, in the face of anticipatory restrictions imposed by the authorities. The paper illustrates the investors’ decisions affected by fear. Research methods: The empirical study includes statistical analyses, in particular an analysis of the coefficient of variation. Main findings: The highest increase in the implied volatility index, which is used as a proxy of fear, is noted for the period of uncertainty (before the decisive reactions of government), during which, investors’ reactions are the most strongly related to fear and the negative sentiment affects all the sectors.

International oil shocks and the volatility forecasting of Chinese stock market based on machine learning combination models

This paper aims to forecast the volatility of Chinese stock market under the effects of international crude oil shocks. Eight individual models, including multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), support vector regression (SVR), artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM) network, gated recurrent unit (GRU) and bidirectional gated recurrent unit (BiGRU) are constructed. The realized volatilities of the CSI 300 index and ten primary sector indices are taken as explained variables, respectively. Four oil shock indicators and the autoregressive terms of the realized volatilities are taken as explanatory variables. The SHAP method is used to analyze their effects on the stock indices. Based on eight individual models, four kinds of combination models, i.e., a mean combination (Mean), a median combination (Median), a trimmed mean combination (Trimmed Mean), and two discount mean squared forecasting error combinations (DMSPE (1) and DMSPE (0.9)) are proposed. We compare forecasting performance between combination and individual ones. Empirical results show that the effects of international crude oil shocks on Chinese stock market are significant and have strong predictability. The effects on the energy, industry, optional consumption, and public sectors are greater than those on the CSI 300 and other sectors. Most of the combination models can effectively improve forecasting accuracy. In addition, by changing the benchmark model, the lengths of the rolling window, and the historical lengths of oil shock indicators, we find that most of the combination models are robust in volatility forecasting. This study is of guiding significance for individual and institutional investors to understand the operating mechanism of Chinese stock markets.

The impact of news about COVID-19 on the dynamics of the Russian stock market

Subject. The article addresses the Russian stock market during the COVID-19 pandemic. Objectives. The study aims at empirical substantiation of the influence of tonality of news about COVID-19 published in official sources and social networks, on the Russian stock market. Methods. We employ a set of methods and procedures to assess the sentiment of downloaded news texts and tweets and to model the dynamics of general and sectoral stock indices (bag-of-words method, Markov-switching GARCH models, evaluation of text tonality using EcSentiThemeLex dictionary). Results. The paper reveals the influence of the tone of coverage of events related to COVID-19 pandemic in news sources and social networks on changes in stock prices of Russian companies. We substantiated the existence of industry specifics in terms of the degree of influence of the tone of news and tweets on stock price dynamics. The findings can be used by investors and issuers to model and predict changes in securities prices, and complement the theory, by underpinning the significance of the tonality of messages in the news and social networks for the dynamics of the Russian stock market. Conclusions. Changes in the emotional tonality of news and social media posts about COVID-19 impacted the stock market of the Russian Federation. The models enabled to prove that in a volatile economy, not only the information published about the coronavirus is significant for the stock market, but also the dynamics of the number of cases.

Capital Market Cointegration Based on ASEAN-4 Capital Market Financial Sector Stock Price Index

This research aims to test and analyze the ASEAN capital market integration plan, which has been declared since 2004, and whether the ASEAN capital market was integrated or segmented during the research sample period of 2008–2020. This research will test cointegration based on sectoral stock price indices, namely the financial sector index (FIN) from four ASEAN countries: Indonesia (IDX), Malaysia (KL), Thailand (SET), and the Philippines (PHS). Using a vector autoregressive (VAR) model and time series data, a series of tests have been carried out, especially stationarity, stability, and cointegration tests, to capture the existence of long-term relationships between variables in the VAR system. Empirical evidence shows that using the Granger Causality, Impulse Response Function (IRF), and Variance Decomposition (VD) test approaches, all three found relatively the same thing: the industrial and financial sector index of the ASEAN-4 Exchange tends to be segmented rather than integrated. This finding follows the results of the 2015 ASEAN Capital Market Forum (ACMF) study, where ASEAN capital market integration only showed limited progress because many obstacles still had not been resolved.

Extreme contributions of conventional investments vis-à-vis Islamic ones to renewables

The growing global awareness of social and environmental issues has led investors to prioritize sustainable investments. This study examines the relationship between ten Islamic and conventional sector indices, which include stocks screened for environmental, social, and governance (ESG) factors, across three energy-related fields: renewable energy, energy efficiency, and oil equipment and services. Using a quantile cross-spectral approach, this research finds a high level of long-term synchronization among all energy-related sectors and indices. However, as markets shift from bearish to bullish, the current relationship between energy-related sectors and Islamic and conventional indices weakens. To support the findings in this research and investigate potential financial implications, this investigation applies a causality-in-quantiles approach. The research in this study shows a bidirectional causal relationship between conventional and Islamic indices, particularly in the energy efficiency sector during bearish market conditions. At lower tails, ESG market indices are found to cause renewable energy products, suggesting a hedging mechanism. The findings of this study assert that investments that do not possess any ESG shortcomings are imperative for effectively bolstering renewable energy companies and fostering opportunities for energy efficiency. These conclusions hold considerable ramifications for energy policymakers as they strive to devise more robust financial mechanisms that can provide substantial support to the burgeoning green sector.

Macroeconomic Variables, Global Stock, and Financial Sector Stock Indices in Indonesia

This study aims to analyze the impact of macroeconomic variables and global stock indices on the financial sector stock index in Indonesia. The data used is secondary data in the form of monthly time series from January 2010 to December 2019, which comes from Bank Indonesia, the Central Bureau of Statistics (BPS), and Yahoo Finance. The variables of this study include the Jakarta Islamic Index (Indonesia), IDX Sectoral Index (Indonesia), Money Supply Inflation, Rupiah against USD Exchange Rate, Crude Palm Oil Price, Industrial Production Index, KL Financial Sectoral Index (Malaysia), SET Financial Sectoral Index (Thailand) and S&P 500 Stock Index (USA). The analysis method used is Autoregressive Distributed Lag (ARDL). The results concluded that the short-term estimation resulted in the exchange rate variable, the Industrial Production Index (IPI), and the Thailand SET financial sector stock index having a significant effect on the JII index, while the money supply variable, the Malaysia KL financial sector stock index, the Thailand SET financial sector stock index, and the US S&P 500 stock index had a significant effect on the IDX financial sector index. Then, the long-term estimation found that the Thailand SET financial sector stock index variable has a significant effect on the JII. Then, in the IDX financial sector index model, there are two significant variables, namely money supply and Industrial Production Index.

Dynamic spillover and connectedness in higher moments of European stock sector markets

This study examines the spillovers in high-order moments (realized volatility, jumps, skewness, and kurtosis) among European stock sectoral indices. Using 5-minute data from January 2, 2013 to January 7, 2022, we show that the four sources of systemic risk, namely, volatility, jumps, skewness, and kurtosis, are transmitted from four main sectors before and during COVID-19 pandemic. Further, volatilities and jumps (bad volatility) associated with activities of the European energy and chemicals sectors that spillover shocks to other European markets are the greatest sources of systemic risk. Whereas, skewness (asymmetry) and kurtosis (fat-tail) associated with activities of the European industrial and insurance sectors that spillover shocks to other European markets are the greatest sources of systemic risk.

The cascade effect: Are the U.S. economy and global stock markets vulnerable to the collapse of First Republic Bank?

Following the collapse of Silicon Valley Bank and Signature Bank, First Republic Bank collapsed and is considered the second-largest bank failure in U.S. history. These bank runs can have a cascading or contagion effect on other large banks, and U.S. banking crises can flare up again. We examine the effect of the First Republic bank run on top U.S. banks, U.S. stock indices, and global stock indices using standard event study methodology. We report abnormal returns and cumulative abnormal returns for the event day (t = May 01, 2023) and the 10-day event window (t-5 to t+5), respectively, using data from the 120-day estimation window. The results indicate that on the event day, only JP Morgan Bank's returns were negative, while other banks acted as safe havens for investors. No significant change in returns on the event day is observed for U.S. sector indices (except for the healthcare sector) and global stock exchanges, except for the European and Chinese markets. During the event window, the occurrence of the event significantly affects bank returns after the event date, but no significant effect is found before the event date. Similarly, the healthcare and transportation sectors are more affected than other sectors, while the U.S. and Canadian stock markets seem to be more susceptible to the bank run. Overall, the results suggest that the U.S. government should take decisive initiatives to stop the ripple effect and protect the entire financial system.

Economic Virus in Our Body Cells: To What Extent Are Gold Prices Immune Against Sectoral Stock Indexes?

The novel coronavirus (COVID-19) has revealed profound and disproportionate effects not only on global financial markets but also on commodity markets. Considering the potential safe-haven role of gold as a vital commodity against economic turmoils and uncertainties; in this study, it has been aimed to investigate the long run impacts and short-run dynamics of the COVID-19 related variables (daily new confirmed cases in the world and U.S., COVID-19 Containment & Health Index) and different macro-financial factors on daily COMEX gold futures contracts prices, especially presenting a comparative analysis with respect to various sectoral stock indexes associated with S&P 500 and Dow Jones -including Health Care, Technology, Medical Equipment, Financials, Industrials, Information Technology and Energy sectors- for the sample period 10.03.2020-22.03.2021 by utilizing from Autoregressive Distributed Lag (ARDL) approach. Analysis results are of great importance in forming crucial policy implications about by which factors and sectoral stocks gold prices are being influenced the most. Consequently; Dow-Jones Financials, S&P 500 Industrials and S&P 500 Energy stock indexes can be said to have a significant impact on gold prices in the long run at 10% significance level. In the models containing these indexes, CBOE volatility index affects gold prices inversely while WTI oil prices have no impact on gold prices. In the long term, the weakest findings in terms of coefficient significancies have been obtained for the model which includes S&P 500 Energy stock index returns. Furthermore, daily new cases in the world -contrary to the cases in U.S.- generally affect gold prices.

Analisis Fundamental Terhadap Harga Saham Pada Saat Pandemi

The COVID-19 pandemic caused the BEI sectoral index to experience declines and increases in share prices during the 2020 period. This research aims to provide fundamental analysis of share prices. Case Study of the 2020-2021 LQ45 Company Listed on the Indonesian Stock Exchange. The population in this research was 45 companies, and based on the sample selection criteria above, the research sample was 19 companies and the total amount of data examined was 38 data. The results of this research are to partially explain the variables Debt To Equity Ratio, Price To Book Value, Return On Equity, Price Earning Ratio, And Earnings per share which simultaneously influence Stock Prices. Meanwhile, partially the Debt To Equity Ratio and Price Earning Ratio have no effect on Stock Prices, while price to book value, return on equity, and Earnings Per Share partially have an effect on stock prices.

Impact of Macroeconomic Factors on Selected Sectors of BSE

The year 1991 will forever remain a landmark year in the history of stock markets in India. The stock markets have become a yardstick for measuring the performance of an economy and the macro economic variables have become important indicators of the health of the economy. Here, an attempt has been made to study the relationship between selected external macroeconomic variables and different sectoral indices at Bombay Stock Exchange (BSE). For the purpose of study seven macroeconomic variables such as Index of Industrial Production, Consumer price Index, Call Money Rate, Dollar Price, Foreign Institutional Investment, Crude Oil Prices and Gold Price are used to study the impact of macroeconomic variables on Sectoral Indices at Bombay Stock Exchange (BSE) viz. Auto, Bankex, FMCG, Consumer Durable and Metal. The monthly statistical data for above mentioned variables have been used for five years covering the period from April 2014 to March 2019. It was observed that all variables show high correlation amongst themselves and Call Money Rate is the only variable that affects all the Indices.

How do sectoral Islamic equity markets react to geopolitical risk, economic policy uncertainty, and oil price shocks?

This study explores the impact of three major risk and uncertainty indices (Geopolitical Risk (GPR), Economic Policy Uncertainty (EPU), and Oil Market Volatility (OVX)) on the Dow Jones Islamic Market (DJIM) World and the ten major sectoral Islamic equity indices. The investigation further evaluates whether the impact of these uncertainties varies between Islamic sectoral indices and the global benchmark. Our time-varying analysis shows that most Islamic equity indices are more resilient in hedging against GPR and EPU shocks compared to OVX shocks. Furthermore, the Quantile-on-Quantile results establish a positive correlation between DJIM World and most Islamic equity indices with GPR, demonstrating their robust capabilities to hedge against GPR shocks. These hedging abilities are more pronounced in the consumer goods, oil & gas, and financial sectors. The dependence structures between extreme EPU shocks and the DJIM world and consumer goods, financials, healthcare, and industrial sectors are positive only in bearish conditions, suggesting hedging benefits are predominantly limited to lower quantiles. Yet, the basic materials and oil & gas sectors can hedge EPU shocks better than the DJIM world and other sectors, depending on market states. Notably, no Islamic equity indices show resilience against OVX shocks. Our findings have prominent portfolio risk management and policy implications for investors and policymakers. These findings hold significant implications for portfolio risk management and policy strategies for investors and policymakers.