Sectoral Indices Research Articles

Analysing the Impact of Major Financial Crisis on Macroeconomic Variables and Stock Prices: An Empirical Study of Stock Market of United Kingdom

The research paper empirically tests the impact of major financial crisis of 2008 by testing the causality between five macroeconomic variables (interest rate, inflation, money supply, GDP and exchange rate), the FTSE All-Share index and its sectoral indices. Furthermore, it analyses whether the global financial crisis of 2008 affected the direction of the causality or not. For this purpose the causality tests were performed in two sections one on the data for the pre financial crisis period from 1999 to 2007 and another on the data for post financial crisis period from 2008 to 2022. The causality was tested using the quantile Granger causality test, the research findings reveal the complex causality between the UK stock market and the most prevalent macroeconomic factors. The findings of the research are suggestive that the causality between macroeconomic factors changed from pre-crisis period to post-crisis period. This is indicative of the event sensitivity of the stock market of UK. It was found that the direction of this causality varies according to the circumstances of the economy and across different sectors involved, and particular economic conditions at various times. The findings of this research provide important insights for policymakers, investors, and economic analysts, as they shed light on the dynamic and complex relationship between the indicators, both on an aggregate and sectoral level. The research findings also emphasise the importance of considering the variables' distribution when analysing the issue of causality.

Testing event-based day of the week anomaly and trading opportunities: Evidence from Indian sectoral indices

The study is an attempt to examine the day-of-the-week anomaly of fourteen Indian sectoral indices and identify profitable opportunities, considering multiple positive and negative events. The aim of this study is to analyze the day-of-the-week effect on fourteen Indian sectoral indices and find profitable opportunities while considering multiple events that have positive and negative impacts. The study takes into consideration event-based anomalies, both national and global, and provides timing for trading to generate abnormal returns from the market. At first, dummy variable regression analysis was used to understand the initial anomalies. Later, time-varying symmetrical and asymmetrical volatility models, such as Generalized Autoregressive Conditional Heteroscedasticity (1, 1) and Exponential Generalized Autoregressive Conditional Heteroscedasticity (1, 1) were applied to determine the short-term and long-term volatility persistence. These models capture the leverage effect from various events that occurred during the study. The results showed mixed outcomes during multiple positive and negative shocks. After the recession, anomalies were observed across all sectoral indices, except for commodities, energy, and information technology. During the scam period, anomalies occurred in all sectors, except for consumer durables, financial services, and information technology. However, after the new government took over, anomalies persisted in all sectors. During the pandemic, anomalies persisted in all sectors except for finance, IT, pharmaceuticals, and services. Hence, national and global events have shown varied impacts on the Indian markets. The study provides investors with implications on strategies and timing techniques for planning their investments in different sectors of the Indian economy.

Practical Analysis of Stock Market Under ESG

In today's era, climate change brought about by the greenhouse effect is gradually bringing more and more economic losses and natural disasters. In order to mitigate the negative impact of climate change, many countries have jointly formulated a 1.5°C climate scenario, which has brought a lot of impact on the global economy and industry development (Henderson et al., 2020). At the same time, my client commissioned me to build a portfolio that complies with the 1.5°C climate scenario, and the shares must be included in the S&P 500 Net Zero 2050 Climate Transition ESG Index as of 30/09/2022. The total portfolio is $100,000, and the benchmark designates the S&P 500 Net Zero 2050 Climate Transition ESG Index. Because the investment horizon is short and only six weeks, I chose semi-active management in my portfolio management process, and the rebalancing method was selected as percentage rebalancing. Finally, based on macro and industry analysis and relative valuation and technical analysis of the corresponding stocks, I selected ten stocks: Starbucks Corporation, Tesla, Inc., Amgen Inc., Cisco Systems, Inc., Eversource Energy, The Coca-Cola Company, and Expeditors International of Washington, Inc., Trans Digm Group Incorporated， Netflix, Inc., S&P Global Inc. I calculated their respective shares using the Markowitz model and bought and rebalanced, but unfortunately, my portfolio did not work as expected. The sector index fell by 4.87% in the week of the investment term, but my portfolio fell by 7.45% and performed poorly. Among them, the collapse of Tesla due to special events and my previous judgment of Tesla were greatly affected, and although its share price was only 9.94%, its share price fell by 33.74%. Although the late-stage rebalancing gave me hope for the better, the short investment horizon became a significant obstacle to the improvement of my portfolio.

Empirical Evidence on the Validity of the Conditional Higher Moment Capm in the Bombay Stock Exchange

Various researchers criticized the assumption of a normal distribution of returns in the traditional Capital Asset Pricing Model (CAPM), leading to the recognition of the non-normal distribution of returns and the mean and variance were insufficient to characterize the distribution of returns completely which led the researchers to look for higher order moments. The present study investigated empirical validity of the conditional higher order moment Capital Asset Pricing Model (CAPM) in the context of the Indian stock market, specifically the Bombay Stock Exchange utilizing the data from sectoral indices for the period from April 2011 to March 2021. To test the four moment CAPM empirically, the specification derived from Bollerslev et al. (1988) has been used in the study. The study's findings reveals that the higher moment i.e. conditional coskewness and cokurtosis hold significant pricing implications and do have an impact on returns in the Indian stock market. The explanatory power of the model showed an increase as compared to the unconditional CAPM as the R-square value obtained was greater than the latter model. The results of the study were found to be mixed and inconclusive. Hence, the conditional higher moment CAPM cannot be validated in the Indian context.

Empirical Evidence on the Validity of the Unconditional Higher Moment CAPM in the Bombay Stock Exchange

The traditional Capital Asset Pricing Model (CAPM) assumed a normal distribution of returns, which was criticized by various researchers who recognized the non-normal distribution of returns in the Sharpe-Lintner CAPM. The introduction of unconditional higher moments, namely co-skewness and co-kurtosis, as additional measures of systematic risk may enhance the model's explanatory power, especially when the distribution function of stock returns is asymmetric. The present study empirically investigates the applicability of unconditional higher order moment CAPM and the impact of higher moments in the Indian stock market i.e. Bombay Stock Exchange using the data of sectoral indices for the period from April 2011 to March 2021. To test the four moment CAPM empirically, the specification given by Fang and Lai has been used in the study. The findings of the present study revealed that the higher moments (coskewness and cokurtosis) are significantly priced and have impact on the returns in the Indian stock market. The market risk premium for covariance was found to be insignificant. Further the hypothesis related to intercept term was accepted and market risk premiums were rejected. The results showed an increase in the explanatory power of the model as compared to the unconditional CAPM as the R-square value of the model was obtained better than the latter model. The mixed and inconclusive findings contradicted the model in the Indian context. The alternate models like the Fama-French three factor and five factor model should be exploited in the Indian context as those models have been rarely used in the Indian context.

Sectoral–regional resilience and productive specialization: a comparison among the last three crises

The objective of this paper is to compare the economic resilience of industries and regions along the last four decades, which include the three last major crises for the advanced countries (90s, Great Recession in 2007 and recent COVID-19 pandemic). Productive structure and sectoral specialization are introduced as explaining factor for the different regional behaviors. For this purpose, we estimate both resilience and specialization indices based on employment data. To analyze the relationship between specialization and resilience, a cross-sectional descriptive analysis is complemented by a panel data model that includes some controls. Finally, a regional shift-share analysis contributes to exert some other regional effects affecting economic resilience. The results of this research show some continuities and other significant changes in the regional impact of crises from one to the next. Although there are other effects to have played a role, results show economic specialization is found to have exerted influence on the recovery of certain regions during this period. Recovery periods differ from each crisis and the results might be affected for this dimensionality limitation. Due to the characteristics of the database, the study includes only six sectors. However, general results are generalizable to other regions as the response of employment growth to crises is similar in many advanced regions and specialization patterns does not significantly differ among regions. The paper carries a structural long-term comparison between the last major crises for Spanish regions, which clearly differentiates one from another in their causes and effects. We introduce specialization to explain the different regional resilience patterns. Methodologically, we estimate both sectoral and regional resilience indices for these three crises and relate this resilience to specialization and other explaining factors.

The analysis of performance due to impact of bullwhip effect in Covid: select Indian sector perspective

Purpose The purpose of the study is to analyze and measure the impact of disruption in demand which causes the bullwhip effect. The bullwhip effect impacts the performance of firm. Just like everything else, covid has had an impact on the disruption of supply chain too leading to the need of measuring the bullwhip effect of select Indian sectors. The comparison on bullwhip effect is drawn in pre- and during covid era in major sectors. The study helps to understand, analyze and measure the impact of covid and its challenges to supply chain. Design/methodology/approach The empirical study is carried out on five major select Indian sectors which have the largest market capitalization in Indian economy, namely, FMCG (fast-moving consumer goods), automobile, utility, consumer durable and IT (information technology). The disruption in the supply chain is measured in terms of bullwhip effect. The novel metric ratio of bullwhip effect is computed which is based on demand–supply mismatch and analyzed based on 10 years of observations. The data is analyzed twice, first from 2011 to 2019 (pre-covid era) and second from 2019 to 2021 (during covid era). Each time, Bombay Stock Exchange (BSE) sectoral indices are used to compute the bullwhip ratio, and empirical data is collected using Prowess. The firms listed in BSE represent most of the sector. Such panel data helps us to analyze inter- and intraindustry bullwhip effect. The changes in the bullwhip effect for various BSE listed firms are analyzed pre- and during covid era. These changes are specifically studied at the manufacturer end of the supply chain. Later regression analysis is performed to study the changes required in production based on the demand. The various strategies that cause or mitigate the impact of covid in intraindustry can be derived from the study. The disruption in production is analyzed based on the disruption in demand and profit before interest and tax (PBIT). Findings In pre-covid era, the percentage of demand disruption was low in select sectors but not exactly zero. Covid caused the disruptions in supply chain across the globe which resulted in bullwhip effect in Indian sectors too. Yet some of the sectors were able to cope better with the situation as compared to others. In the present study, same is analyzed statistically, and results are derived for practical significance. Research limitations/implications The empirical data is having the observations of past 10 years to analyze the pattern of demand disruption in the firms and hence the sectors. The impact of covid is studied on performance, which is analyzed in terms of PBIT. The impact of other factors (political, social, marketing policies, etc.) that may cause disruption in the supply chain of a firm is not considered in the study. Originality/value Study is unique, as it measures disruption and provides a peerless way to study the inter- and intrasectors. To analyze the impact of bullwhip effect on sector performance, it is very much required to first measure the bullwhip; this measure of bullwhip as a ratio of the slopes of demand and supply is a novel approach. The study emphasizes that the impact of covid is not the same among the firms, and hence among the sectors. Also, it is found that the impact of such adversities can be mitigated, and performance of firm can remain intact in turbulent times too.

Examining the dynamics of risk, performance, and volatility during COVID-19: Evidence from Moroccan stock market

This study delves into the repercussions of the COVID-19 pandemic on the Moroccan stock market, with a specific focus on the MASI index and sectoral indices. The examination en-compasses distinct pre-COVID and during-COVID periods, shedding light on the market’s evolution, marked by unique phases and fluctuations. Notably, the MASI index experienced a significant downturn in March 2020, indicative of the pandemic’s disruptive impact on investor behavior. Despite this setback, the market showcased remarkable resilience, staging a swift recovery and surpassing pre-crisis levels by the close of 2020. This rebound can be attributed to various factors, including historically low bond yields, the initiation of vaccination campaigns, and the resumption of dividend payouts by the banking sector. Our findings bring forth a nuanced understanding of performance and risk dynamics across individual sectors. Moreover, there is a noteworthy surge in correlations between sectoral returns during the COVID-19 period, limiting diversification options for investors and exposing them to heightened risks. The volatility patterns, analyzed using GARCH models, underscore the dynamic nature of the MASI index, exhibiting stability in the pre-pandemic phase and a transient disturbance during the initial pandemic shock. This study contributes to the existing body of literature on the global financial impact of COVID-19, providing valuable insights into the Moroccan context. The results emphasize the significance of comprehending sector-specific vulnerabilities and market dynamics for both investors and policymakers. In navigating the uncertainties of the post-pandemic era, these insights offer crucial perspectives for market participants to make informed decisions and adapt optimal strategies.

An Improved Index Price/Movement Prediction by using Ensemble CNN and DNN Deep Learning Technique

As it knows prediction is always a challenging task in all terms. The goal of any stock prediction method is to develop a robust method for predicting trading price that can be used to improve investment decisions and accurate models. The paper proposes a hybrid model that combines the strengths of deep learning models CNN and DNN, and to develop a comprehensive methodology for the prediction of stock/index prices on Banknifty (NSE Bank), a highly volatile Indian sectorial Index that represents 12 major banks of the country. The hybrid model consists of two main components a CNN for feature extraction and a DNN for regression or classification tasks. In the context of stock price prediction, CNN layers can be used to extract features from input data (such as stock prices and indicators) related to an estimated future value. DNN layer can be used to combine features learned from the CNN layers. Model performance will be evaluated using various metrics including Accuracy, Precision, Recall, Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared. The method also analyses the impact of various factors on stock prices, including market volatility, economic indicators, and geopolitical events. The achieved accuracy of 97.48% indicates that the model was successful in accurately predicting the stock prices of Bank Nifty. The proposed method is expected to provide investors and financial analysts with a valuable tool for making informed investment decisions.

Risk synchronization in Australia stock market: A sector analysis

This research employs dynamic connectedness to assess the financial risk synchronization of 11 stock sector indices in Australia spanning May 10, 2001, to April 16, 2021. The results suggest that stock sectors in Australia co-move to a considerable degree with minimal expectations for own contribution to the system. The degree of connectedness rises during major crises. The study adds value to the specific strategies for portfolio allocation, rebalancing, and redeployment of stock sectors amid crises. Evidence also reveals that, except for the persistent net transmitter (Financials) and intermediate geared toward net transmitting (Industrials and Consumer Discretionary), all the remaining sectors offered safe-haven benefits during the global financial crisis and the COVID-19 pandemic. The pairwise dynamic connectedness shows that asset allocation between most pairs sometimes offers hedge, diversification, or safe-haven benefits.

Tail risk spillovers among Chinese stock market sectors

This study employs a combination of the CAViaR model and the TVP-VAR-based connectedness approach to investigate tail risk spillovers among Chinese stock sectors. Using daily data on ten sector indices from January 5, 2006, to September 28, 2023, the empirical findings reveal significant time-varying tail risk spillovers among sectors, with heightened spillovers observed during extreme events. Moreover, the industrials, materials, and consumer discretionary sectors are found to be the senders of tail risk spillovers, while the energy, estate, and finance sectors are receivers during the sample period. The findings carry substantial implications for policy shaping and investment decision-making.

Mutual information between Polish subindexes – the use of copula entropy around the time of the COVID-19 pandemic

In this paper, the copula theory is used to describe the dependence structure between variables, while the information theory provides the tools necessary to measure the uncertainty associated with these variables. What both theories have in common is copula entropy, which is strictly related to mutual information. The findings of this study, focusing on the dependence of the (sub)indexes of the Polish stock market during the pandemic period, may prove useful not only to investors from Poland, but also from other countries, especially Central European, in making investment decisions. The results of calculating the interdependencies between WIG, sectoral indexes and among sectoral indexes of the Polish economy using copula entropy and Pearson’s correlation are quite different. The source of the basic difference between copula entropy and Pearson’s correlation is that the former enables the measurement of nonlinear interdependencies, while the latter not. The interrelations on the stock markets are nonlinear and returns are not normally distributed in general. The use of copulas is also superior in terms of ranking correlation, as it is more general and allows the examination of the structure of dependencies between extreme values.

Relationship between COVID-19 waves and stock market: An event study analysis

This study makes an attempt to evaluate the impact of first and second waves of COVID-19 on the major global stock markets. Further, the study also analyzed the effect of COVID-19 on the sectoral indices of India during the first and second waves. In order to achieve the objective, the event study methodology has been used and abnormal returns are computed around the event dates. The findings of the study reveal that major stock markets in the world adversely affected during the first wave of COVID-19 and the highest abnormal loss was observed in Toronto Stock Exchange (7.55%) on the event date. The impact of second wave of COVID-19 was not as severe as first wave and highest abnormal loss (2.37%) was observed in Moscow Exchange of Russia. The Indian sectoral indices also showed high volatility during both the waves of COVID-19.

Risk quantification and validation for green energy markets: New insight from a credibility theory approach

We aimed at constructing the Credible VaR (Credible ES) forecast for a target green energy instrument by combining its VaR (ES) forecast and the expected VaR (expected ES) forecast for all green energy instruments. Using return data for eleven sectoral green energy indices, we revealed the tendency of their risks to decline following the Paris Agreement but then substantially increase during COVID-19 and the Russia–Ukraine conflict. Despite a much higher trust in the VaR (ES) forecast, the resulting Credible VaR (Credible ES) forecast performed relatively better, as validated through backtesting, thereby improving investment strategies and decision-making through risk sharing.

When to Hedge Downside Risk?

Hedging downside risk before substantial price corrections is vital for risk management and long-only active equity manager performance. This study proposes a novel methodology for crafting timing signals to hedge sectors’ downside risk. These signals can be integrated into existing strategies simply by purchasing sector index put options. Our methodology generates successful signals for price corrections in 2000 (dot-com bubble) and 2008 (global financial crisis). A key innovation involves utilizing sector correlations. Major price swings within six months are signaled when a sector exhibits high valuation alongside abnormal correlations with others. Utilizing the price-to-earnings ratio for identifying sectors’ high valuations is more beneficial than the bond–stock earnings yield differential. Our signals are also more efficient than those of standard technical analyses.

Comparison between the impact of the brazilian real estate crisis and the critical period of Covid 19 on the financial economic indicators of companies

Until 2013, the real estate sector experienced an excellent moment due to favorable economic conditions and the high housing deficit in the country, and it can be said that the real estate market went through a period of “euphoria”. But as of 2014, this growth in the sector was interrupted by a strong crisis due to the oversupply of real estate units, and the lack of bank credit from customers for the acquisition of real estate. This period of decline lasted, on average, until 2018, when companies in the sector resumed growth, but shortly thereafter, in early 2020, the Covid19 pandemic brought new challenges for companies, providing negative as well as positive impacts. This work aimed to study the economic and financial indicators of some important developers (Cyrela, Even, EZTEC, MRV and Direcional) in the national market, listed in the IMOB sectorial index of the Stock Exchange, to measure the evolution of these indicators during the period of 2013 to 2021, a period in which the construction sector experienced two crises of global proportions. As shown in the results, the main financial economic indicators (liquidity, indebtedness, profitability, and turnover or activity) had a behavior with only negative impacts in the 2014 real estate crisis. but in the Covid crisis these impacts were predominantly positive

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

Existence of Herding Behaviour in the Indian Stock Market: An Empirical Analysis During the COVID-19 Period

The current study focuses on herding behaviour that occurred in the “Indian Stock Market (ISM)” between 2011 and during the time of the Covid-19 pandemic. The study examines the closing price of the NSE benchmark Nifty and 33 companies from three sectoral indices selected out of fifteen available on a daily basis. The sectors are the Auto sector, Nifty Bank, and IT sector, from 1 January 2011 to 31 December 2020. Christie and Huang (1995) suggested the CSSD and Chang, et al. (2000) suggested the CSAD models to study the existence of herding behavior during the Covid-19 period. Findings based on the daily closing price reveal that for the ten-year data, no significant herding is present in the market, whereas Covid-19 had caused herding during the rising (up) and falling (down) market. The calculated herding period during the Covid-19 timeframe is from 11 March 2020 to 31 December 2020. The study found no herding between 1st January 2011 to 31 December 2019 in the ISM. The herding was evident during the Covid-19 time frame, possibly due to emotional biases, fear, and uncertainty of the pandemic. The study will be helpful to investors, decision-makers, and market regulators in decisionmaking in the capital market. It will also facilitate brokers and market participants to prepare different trading strategies to be successful in regular times and times of adversity.

Connectedness between oil price shocks and US sector returns: Evidence from TVP-VAR and wavelet decomposition

This paper examines the dynamic return and volatility connectedness between oil price shocks (demand, supply, and risk shocks) and US sector returns from October 2001 to January 2022. For this purpose, we combine the decomposition of the time series in time scales through the wavelet approach with the application of the TVP-VAR model proposed by Antonakakis et al. (2020). Our results show the high dynamic connectedness between markets and allow the identification of the role of all sector indices (except Communication Services, Utilities and Real Estate) and risk shocks as net contributors of shocks to the system, whereas demand and supply shocks are net receivers of spillovers. We further explore and document from a portfolio performance perspective the benefits of diversified portfolios comprised of all consider sector indices that include assets linked to the calculation of oil price shocks according to Ready (2018).

Real exchange rates and manufacturing exports in emerging economies: the role of sectoral heterogeneity and product complexity

The empirical evidence on the relationship between the real exchange rate and export performance in emerging economies is inconclusive. In this paper, we present evidence that one reason for this inconclusiveness is the use of real exchange rate (RER) measures that do not consider the heterogeneity between economic sectors. To this end, we calculate a unique sectoral bilateral RER index (SBRER) for 12 Latin American economies, which considers the variation of producer price differentials and bilateral nominal exchange rates across 21 manufacturing sectors and 38 trade partners between 2001–2018, and to estimate the effect of SBRER movements on manufacturing exports. The regression results show that the SBRER is a significant determinant of aggregate manufacturing exports, whereas the bilateral RER is not significant. Moreover, sectoral export elasticities indicate that mainly sectors with low levels of product complexity and, to a lesser extent, those of medium complexity are affected by RER movements. These findings show that it is important to consider sectoral heterogeneity when estimating RER export elasticities from a macroeconomic perspective.