Selected Stock Market Indices Research Articles

Comovement of stock markets pre- and post-COVID-19 pandemic: a study of Asian markets

PurposeThe study aims is to explore the cointegration level among major Asian stock indices from pre- COVID-19 to post COVID-19 times.Design/methodology/approachJohansen cointegration test is employed to know the long run relationship among the stock market indices of Hong Kong, Indonesia, Malaysia, Korea, India, Japan, China, Taiwan, Israel and South Korea. The empirical testing was done to analyze whether any significant change has been induced by the COVID-19 pandemic on the cointegrating relationship of the selected markets or not. Through statistics of trace test and maximum eigen value, total number of cointegrating equations present among all the indices during different study periods were analyzed.FindingsThe presence of cointegration was found during all the sample periods and the findings suggests that the selected stock markets are associated with each other in general. During COVID-19 crisis period the cointegration level was reduced and again it regained its original level in the next year and again reduced in the subsequent next year. So, the cointegrating relationship among selected stock market indices remains dynamic and no evidence of impact of COVID-19 on this dynamism was found.Originality/valueThe study has explored the level of cointegration among the major stock indices of Asian nations in the pre, during, post-crisis and the most recent periods. The interconnectedness of the stock markets during the COVID-19 times has been compared with similar periods in different years immediately preceding and succeeding the COVID-19 times which has not been done in any of the existing study.

Time-varying effects of fuel prices on stock market returns during COVID-19 outbreak

This article explores the impact of fuel price movements on the stock market return of 2020 during the COVID-19 disruptions. In doing so, a monthly data of seven selected stock market indices representing developed and emerging economies globally was used for analysis. The study used a time-varying parameter VAR model to examine a time-varying causal association between oil prices and stock market returns and a novel quantile-causality approach to capture the fluctuations of these markets under COVID-19's varying market conditions. The study further utilises the entropy transfer approach to capture the Granger-causal relationship in the presence of nonlinearities of the data series. The results indicate a high information flow from fuel prices to the FTSE-100, Pacific, and European stock indicies, but not the other way round. The results show that, for the FTSE-100 and the European region, there is a two-way information flow between equities and natural gas, and vice-versa. However, a one-way information flow was established from the stock market to the Pacific and emerging economies.

Economic policy uncertainty and stock markets’ co‐movements

AbstractHere, we investigate the direction of the relationship between economic policy uncertainty (EPU) and stock markets. We focus on time‐variant co‐movements between the EPU index and selected stock market indices (S&P500, UK100, Nikkei225 and DAX30) at different investment horizons. We show that the EPU index lags stock markets at longer investment horizons during global financial turmoil, especially in the United States, Japan and Germany. The identified lag between the changes of the EPU index and selected stock market indices ranges between 2 and 6 months at investment horizons exceeding 32 months. In addition, we confirm the existence of the short‐term effects of EPU on stock markets.

The causality relationship between energy prices and developed countries indices

The aim of this study is to determine the causality relationship between energy prices, which are among the most important inputs of the economy, and selected stock market indices of developed countries. Crude oil and natural gas are used as energy variables. G7 countries were selected to represent developed countries. Stock indices used in the study are Dow & Jones (USA), DAX (Germany), CAC40 (France), FTSE250 (England), FTSE Italia All Share (Italy), NIKKEI225 (Japan), and S&P/TSX (Canada). In the study, Johansen (1988) cointegration test and Granger (1969) causality test were used to analyse the causality relationship between energy prices and selected stock market indices. The research could not find a long-term balance relationship between energy prices and developed country indices. Also, while the causality relationship was determined between crude oil prices and NIKKEI225, DAX, and CAC40 indices, a causal relationship between natural gas prices and Dow & Jones and FTSE250 indices was determined. In the study, it was found that energy prices can be used for diversification in investments to be made with stock market indices of developed countries. This study is one of the most comprehensive studies in the literature that examines the relationship between energy prices and the stock market indices of G7 countries. It is expected to contribute to the literature in this way.

First to React Is Last to Forgive: Evidence from the Stock Market Impact of COVID 19

COVID 19 has had parallel and uneven economic shocks across countries since its outbreak in December 2019. Stock markets as usual were the first to react, with drop rates as much as the Global Financial crises of 2008. This study uses daily data to model the dynamic impact of COVID 19 pandemic on returns of selected stock market indices and globally-traded commodities. The overall panel least squares VAR estimation results indicate a negative short termed impact of 2.3% on the performances of the stock markets when the spread rate of corona-virus increases by 1% across countries ceteris paribus. While The COVID 19 contamination rate is not statistically significant to explain the changes in the exchange rate and gold prices in the countries of analysis, yet the virus spread rate is found to be significant in steering prices of platinum, silver, WTI, and Brent crude oil.

The Causality Relationship between Energy Prices and Developed Countries Indices

The aim of this study is to determine the causality relationship between energy prices, which are among the most important inputs of the economy, and selected stock market indices of developed countries. Crude oil and natural gas are used as energy variables. G7 countries were selected to represent developed countries. Stock indices used in the study are Dow&Jones (USA), DAX (Germany), CAC40 (France), FTSE250 (England), FTSE Italia All-Share (Italy), NIKKEI225 (Japan), and S&P/TSX (Canada). In the study, Johansen (1988) cointegration test and Granger (1969) causality test were used to analyze the causality relationship between energy prices and selected stock market indices. The research could not find a long-term balance relationship between energy prices and developed country indices. Also, while the causality relationship was determined between crude oil prices and NIKKEI225, DAX, and CAC40 indices, a causal relationship between natural gas prices and Dow&Jones and FTSE250 indices was determined. In the study, it was found that energy prices can be used for diversification in investments to be made with stock market indices of developed countries. This study is one of the most comprehensive studies in the literature that examines the relationship between energy prices and the stock market indices of G7 countries. It is expected to contribute to the literature in this way.

The comparative analysis of investor sentiment effect on two major earthquakes and tsunami incidents

Purpose- Natural catastrophes such as earthquakes and tsunamis have been a great concern since the beginning of humanity and even before. Apart from the severe property damage they cause, these unforeseen incidents leave thousands of injured and dead. Events like the Indian Ocean and Japan earthquakes and tsunamis even lead to substantial impacts over the global economies. Through this interest, this study aims to determine the impacts of both incidents, over several stock market indices. Methodology- In order to capture the influence of these events on various stock markets, an investor sentiment methodology is adopted. Trading volume is selected as an investor sentiment proxy and it is regressed against multiple macroeconomic data. The residuals of these analyses are assigned as investor sentiment indices for both cases respectively.Furthermore, an EGARCH model is developed to observe the impacts of investor sentiment associated with both incidents, on the considered stock markets. Findings- The stock returns of Japan and Vietnam are negatively influenced by investor sentiment associated with the Indian Ocean Earthquake and Tsunami while the stock returns of Indonesia, South Korea, China and Singapore are positively influenced. Thus, the conditional volatilities of Sri Lanka, Indonesia, Thailand, Taiwan and Vietnam are negatively affected. Moreover, the stock returns of Indonesia, South Korea and Thailand are impacted negatively by investor sentiment related to the Japan Earthquake and Tsunami. Hence, the conditional volatility of Indonesia is positively influenced while it is negatively affected for Vietnam. Conclusion- In contrast with the previous studies, there is strong evidence that most of the selected stock market indices are influenced by both events while the shocks brought by these events are long-lived through the selected stock markets. Moreover, Indian Ocean Earthquake and Tsunami incident seems to generate higher investor sentiment effects on the stock markets than Japan Earthquake and Tsunami incident does.Consequently, Indonesia and Vietnam are considered to be highly impacted by the investor sentiment related to both events. However, Hong Kong, Malaysia and India do not happen to have any investor sentiment effects regarding both earthquakes and tsunami incidents.

Modelling and Forecasting Four Market Indices: Autoregressive Integrated Moving Average Model versus Artificial Neural Network Model

This study model and forecast the stock prices of 4 stock market indices: the FTSE KLCI, Dow Jones Industrial Average, NASDAQ Composite and S&P 500. We utilize two distinct approaches which are time series analysis and artificial intelligence system. We model the data with Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) applying the time series data of the 4 selected stock market indices. The time frame was five years, starting on 3 January 2012 to 29 December 2017. After the modelling, the outputs are subsequently compared and contrasted in terms of forecast accuracy, such as MAE, MAPE, RMSE and MSE, and the model with the lowest forecast error is sought to be the best-fitted model. We found ANN model to be superior as it outperformed the time series model by generating the lowest forecast error across four datasets.

Causality analysis between stock market indices

The paper examines relationships between selected stock market indices in Western Europe, Central Europe, and the United States. The study focuses on two periods, from January 1998 to August 2006 and from September 2006 to December 2016. The first one includes stock quotes from before the financial crisis while the second one covers the crisis and changes in the economic situation in post-crisis years. Relationships between stock market indices in developed economies were more frequent and durable than in Central Europe, although they were subject to changes. In our investigation into Granger causality relationships we observed changes in these relationships and in their direction for stock markets in Central Europe, while bidirectional relationships between indices in developed economies remained stable over time. Changes in relationships between indices, in particular long-term interdependences, may result from the impact of the 2008 financial crisis. The increased number of causality relationships for the markets in Central Europe may testify to the advancing integration of the EU common market.

Hybrid Models Combining Technical and Fractal Analysis with ANN for Short-Term Prediction of Close Values on the Warsaw Stock Exchange

This paper presents new methods and models for forecasting stock prices and computing hybrid models, combining analytical and neural approaches. First, technical and fractal analyses are conducted and selected stock market indices calculated, such as moving averages and oscillators. Next, on the basis of these indices, an artificial neural network (ANN) provides predictions one day ahead of the closing prices of the assets. New technical analysis indicators using fractal modeling are also proposed. Three kinds of hybrid model with different degrees of fractal analysis were considered. The new hybrid modeling approach was compared to previous ANN-based prediction methods. The results showed that the hybrid model with fractal analysis outperforms other models and is more robust over longer periods of time.

Testing Random Walk and Market Efficiency: A Cross-Stock Market Analysis

The study seeks to examine the Random Walk Hypothesis (RWH) and market efficiency of the selected stock market indices particularly London Stock Exchange, EuroStoxx 50, Nihon Keizai Shimbum (NIKKI), Shanghai Composite Stock Exchange and Bombay Stock Exchange. Daily closing index value is considered and transformed into logarithm return. Various tests like serial independence test, unit root test and multiple variance tests are applied. It is observed that the null hypotheses (presence of random walks) of the daily returns of the indices are rejected and in few cases are accepted based on various test statistics. JEL Classification: G00, G01, G02

The Multivariate DCC-GARCH Model with Interdependence among Markets in Conditional Variances’ Equations

The article seeks to investigate the issue of interdependence that during crisis periods in the capital markets is of particular importance due to the likelihood of causing a crisis in the real economy. The research objective of the article is to identify this interdependence in volatility. Therefore, first we propose our own modification of the DCC-GARCH model which is so designed as to test for interdependence in conditional variance. Then, the DCC-GARCH-In model was used to study interdependence in volatility of selected stock market indices. The results of the research confirmed the presence of interdependence among the selected markets.

Sharia Stock Index and Economic Activity in Malaysia : Is There a Connection ?

The purpose of this study is to investigate whether stock market can predict economic activity in Malaysia, with the Industrial production index as proxy for real economic output and 3month Malaysian Treasury-bill rate as a proxy for the interest rate and monetary policy. The underlining motivation is to ascertain the levels of influence, Shariah index has on real sector growth and to see at what level Conventional index has influential power, greater or smaller than Shariah index to policy makers who are concerned with real sector growth. The data used are the monthly closing of the selected stock market indices for the period of 31 October 2006 to 30 March 2012. This study employs a time series technique, in particular, co integration, error correction modeling, and variance decomposition. Result show that Shariah Index is a better predictor of the real economic activity as compared to the Conventional index. Therefore, policy makers should be more cautious about the stock market dynamics in Malaysia, especially the Shariah index, which could be vital to the improvement and state of the economy.

MERGERS AND ACQUISITIONS IN THE GLOBAL AGRO-FOOD SECTOR AND FLUCTUATIONS IN ECONOMIC ACTIVITY

The intensity of business merges and acquisitions changes over time. Theoretically, competitive companies constantly seek for ways to grow, mainly due to expected economies of scale and scope. In the paper we discuss various aspects of business mergers and acquisitions in the context of market concentration. We also highlight the issue of symptoms and causes of fluctuations in economic activity. Using data for the period of 2000-2012 we analyse the value of the overall business mergers and acquisitions in connection with the global economic growth and selected stock market indices. Next, we focus on the global agro-food sector and test the hypothesis that the number and value of mergers and acquisitions in this sector were related to the business cycle fluctuations observed in the period of 2000-2010. Firstly, the results of our research show that both merger and acquisition transactions carried out in the agro-food sector had largely inter- -branch or, at the most, inter-sectorial character. Secondly, a positive relationship between fluctuations in economic activity in the global agro- -food sector and the intensity of mergers and acquisitions was found, especially regarding the value of those transactions, what means that economic recovery facilitates concentration processes. Thirdly, the observed connection between the directions of changes in the merger and acquisition activities and the changes in stock market indices seems to be determined by the situation on the capital markets.

Long memory in return structures from developed markets

The present study aimed at investigating the existence of long memory properties in ten developed stock markets across the globe. When return series exhibit long memory, the series realizations are not independent over time and past returns can help predict future returns, thus violating the market efficiency hypothesis. It poses a serious challenge to the supporters of random walk behavior of the stock returns indicating a potentially predictable component in the series dynamics. We computed Hurst-Mandelbrot's Classical R/S statistic, Lo's statistic and semi parametric GPH statistic using spectral regression. The findings suggest existence of long memory in volatility and random walk for logarithmic return series in general for all the selected stock market indices. Findings are in line with the stylized facts of financial time series.

Long Memory in Stock Returns: A Study of Emerging Markets

The present study aimed at investigating the existence of long memory properties in ten emerging stock markets across the globe. When return series exhibit long memory, it indicates that observed returns are not independent over time. If returns are not independent, past returns can help predict future returns, thereby violating the market efficiency hypothesis. It poses a serious challenge to the supporters of random walk behavior of the stock returns. Hurst-Mandelbrot's Classical R/S statistic, Lo’s statistic and semi parametric GPH statistic were computed as well as modified GPH statistic of Robinson (1995). The findings suggest existence of long memory in volatility as well as in absolute returns and random walk for asset return series in general for all the selected stock market indices. The study did not support existence of Taylor’s effect in the selected emerging markets.