Stock Market Index Prices Research Articles

Cointegration of Asian Developed and Frontier Stock Markets: An investigation of Diversification Opportunities

This study examines the cointegration relationship of the Colombo Stock Exchange (CSE) with developed Asian markets (Hong Kong, Japan, Singapore). The main objective of the study is to identify potential diversification benefits that exist in Sri Lankan stock market. The stock market indices are Hang Seng Index (HSI), Nikkei 225(N225), Straits Times Index (STI) and All Share Price Index (ASPI). Daily closing prices of stock market indices were selected for the period of 2013- 2019. The main theoretical base forthe stock market integration was the Law of One Price. The Autoregressive-Distributed Lag (ARDL) bound test used toanalyze data, revealed an insignificant cointegration relationship between CSE and developed stock markets. The ASPI has statistically significant short-run relationships with the STI. HSI and N225 did not provide any evidence of a long-run relationship with the ASPI. A negative insignificant correlation between ASPI and HSI indicates a good combination to have a well-balancedportfolio gain both long and short-run diversification benefits. In conclusion, the non-existence of cointegration among the above stock markets provides opportunities for international diversification of portfolios and possibilities for risk hedging.
 Keywords: Cointegration, Frontier and Developed, Stock Markets, Portfolio Diversification

Forecasting Stock Market Indices Using the Recurrent Neural Network Based Hybrid Models: CNN-LSTM, GRU-CNN, and Ensemble Models

Various deep learning techniques have recently been developed in many fields due to the rapid advancement of technology and computing power. These techniques have been widely applied in finance for stock market prediction, portfolio optimization, risk management, and trading strategies. Forecasting stock indices with noisy data is a complex and challenging task, but it plays an important role in the appropriate timing of buying or selling stocks, which is one of the most popular and valuable areas in finance. In this work, we propose novel hybrid models for forecasting the one-time-step and multi-time-step close prices of DAX, DOW, and S&P500 indices by utilizing recurrent neural network (RNN)–based models; convolutional neural network-long short-term memory (CNN-LSTM), gated recurrent unit (GRU)-CNN, and ensemble models. We propose the averaging of the high and low prices of stock market indices as a novel feature. The experimental results confirmed that our models outperformed the traditional machine-learning models in 48.1% and 40.7% of the cases in terms of the mean squared error (MSE) and mean absolute error (MAE), respectively, in the case of one-time-step forecasting and 81.5% of the cases in terms of the MSE and MAE in the case of multi-time-step forecasting.

Equity market integration in emerging economies: a network visualization approach

PurposeThe authors examine network features such as connectivity, centrality, adjacency matrices, closeness and betweenness measures through a variety of indicators. The results of the study indicate that over time there is a tendency for markets to integrate and segment due to various factors such as pandemics, financial crises, global trade relations and international investments.Design/methodology/approachThis paper employs a visualized network technique to study the dynamics of integration and comovements in global equity markets of emerging economies. Daily closing prices of stock market indices of 24 countries from January 2013 to July 2020 are used to construct a minimum spanning tree network (MSTN) and graph network (GN).FindingsThe authors identify India and China as global power hubs and clusters among the emerging economies. India and Bangladesh serve as bridging countries connecting to various other clusters. Bosnia serves as a center in the European region owing to Bosnia's trade relations with neighboring countries. Although Brazil has witnessed the worst recession in the early years of the decade, Brazil has risen to be a central cluster among the Latin American countries. Finally, the authors find that African countries tend to form links with the rest of the world rather than with economies within the Africa continent.Originality/valueThis is the pioneering study that uses network models such as MSTN and GN supplemented with measures of centrality and connectivity to study financial market integration in emerging countries. Against this backdrop, this paper aims to work on a network visualization strategy to examine global stock market integration. The authors also try to use graphs and the spanning trees instead of the correlation models to understand the association between the markets, avoiding the downsides of the existing models. The authors' approach tries to visualize the network integration to examine the interconnectedness in the global stock market.

A Study on the Relationship between CDS Premiums and Stock Market Indices: A Case of the Fragile Five Countries

International investors should have a pioneering knowledge of the country’s risk level before investing their savings in a country. For this purpose, Credit Default Swap (CDS) Agreements that serve as insurance against investor’s risk of not collecting their receivables have been developed. These contract premiums are called CDS premiums. The relationship between the Fragile Five countries’ CDS premiums and the stock market index prices has been examined by various researchers. The present study is unique because it is one of the pioneering studies examining the relationship between the CDS premiums of the Fragile Five countries and their Stock Market Indices. First, augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests were performed for this purpose. Then, the Granger Causality test, Johansen Cointegration, and Pearson Correlation analyses were conducted to reveal the relationship between two variables. The results obtained in the study indicated that for India and Turkey, among the Fragile Five, there was a causality relationship between the stock market indices and the CDS premiums, a short-term relationship. In addition, there was a long-term cointegration relationship between the CDS premiums and the stock market indices of Turkey.

Examining stock markets and societal mood using Internet memes

Using Internet memes collected from Reddit, we extract variables to represent societal mood and examine their relationships with various stock market index prices. Through this, we analyse the level of sentimentality information incorporated in stock markets. The results show that societal mood variables representing sentiment intensity and user engagement are most important and significant in explaining index prices. Large-cap U.S. indices show more prominent relationships with societal mood than small-cap international indices, with variabilities in sentimentality information incorporation in different timeframes. The results also show that common overall societal sentimentality information is present in both memes and index prices.

A Wavelet Coherence Analysis: Contagion in Emerging Countries Stock Markets

This study aims to investigate the financial contagion during and after Greek Crisis to observe the impact on global economy. Financial contagion may affect the portfolio risk management, the formulation of monetary, fiscal policy, strategic asset allocation and pricing. To analyse the contagion after Greek Crisis, the co-movements of six stock exchange markets have been studied for an 8-year term. For this study between countries’ time series, bivariate wavelet technique called wavelet coherence is employed, and Matlab 2016a wavelet tool is used for the analysis. Daily closing prices of stock market indices of six countries, Greece (ASE), UK (FTSE100), Germany (DAX), Hungary (BUX), Poland (WIG) and Turkey (BIST100) are used in this analysis between 06 March 2009 and 28 February 2017.This paper targets to show if there is a certain sign for a co-movement between markets during and after Greek Debt Crisis. Therefore, it eventually sets out the benefits or harm of integration in the financial markets by using Wavelet Method. This study contributes the literature by analyzing the effects of contagion among stock markets by using wavelet method. This analysis has been filling the gap in the literature by employing a new technique to explain leverage effect with financial time series data. As wavelet tool displays the leverage effect by comparing crisis and non-crisis periods, the study supports the idea that convergence is adversely affecting the connected markets. According to the results of the study, the contagion is high especially during crisis within European financial markets. whereas positive improvements have less impact on markets.

Predicting the Turkish Stock Market BIST 30 Index using Deep Learning

The non-linearity and high change rates of stock market index prices make prediction a challenging problem for traders and data scientists. Data modeling and machine learning have been extensively utilized for proposing solutions to this difficult problem. In recent years, deep learning has proved itself in solving such complex problems. In this paper, we tackle the problem of forecasting the Turkish Stock Market BIST 30 index movements and prices. We propose a deep learning model fed with technical indicators and oscillators calculated from historical index price data. Experiments conducted by applying our model on a dataset gathered for a period of 27 months on www.investing.com demonstrate that our solution outperforms other similar proposals and attains good accuracy, achieving 0.0332, 0.109, 0.09, 0.1069 and 0.2581 as mean squared error in predicting BIST 30 index prices for the next five trading days. Based on these results, we argue that using deep neural networks is advisable for stock market index prediction.

A network visualization approach and global stock market integration

AbstractThis paper applies a visualized network approach to analyse the degree of integration or comovement among global equity markets. We utilize daily prices of stock market indices of 57 countries from January 1997 to August 2012 to establish both a minimum spanning tree network and graph network. We examine network features including connectivity and centrality through a variety of indicators. Our results clearly show that there has been a tendency over time for markets to become more integrated globally even during periods of market stress. The centrality results suggest that the U.S. and Hong Kong markets have been the most dominant markets in their geographic region. For Europe, we find 3 dominant centres, the United Kingdom, France, and Germany in contrast to previous literature suggesting that the United Kingdom was the dominant country. We further identify that Japan and Australia, instead of acting as dominant countries in their region, serve as bridging countries between the region and the rest of the world. Finally, we find that Africa does not form a cluster, and individual African countries tend to connect to other developed markets.

Effect Of Macroeconomic Variables On Stock Market Returns For Four Emerging Economies: Brazil, Russia, India, And China

The relationship between share prices and macroeconomic variables is well documented for the United States and other major economies. However, what is the relationship between share prices and economic activity in emerging economies? The goal of this study is to investigate the time-series relationship between stock market index prices and the macroeconomic variables of exchange rate and oil price for Brazil, Russia, India, and China (BRIC) using the Box-Jenkins ARIMA model. Although no significant relationship was found between respective exchange rate and oil price on the stock market index prices of either BRIC country, this may be due to the influence other domestic and international macroeconomic factors on stock market returns, warranting further research. Also, there was no significant relationship found between present and past stock market returns, suggesting the markets of Brazil, Russia, India, and China exhibit the weak-form of market efficiency.

Do Sovereign Re‐Ratings Destabilize Equity Markets during Financial Crises? New Evidence from Higher Return Moments

AbstractThis study investigates the effects of S&P's sovereign re‐ratings on the higher moments of equity market returns over recent financial crises. Using a set of intraday stock market index prices and sovereign credit ratings for a sample of 36 countries that experienced sovereign rating changes over the period from 1996 to 2013, we find that the higher moments of stock market returns are significantly more responsive to sovereign re‐ratings during financial crises, but the effects on stock markets are not the same across different financial crises. The effects during crises are, however, magnified for large downgrades and those that are associated with a loss of investment grade status. We find that there are asymmetric effects during financial crises in that downgrades are consistently more significant than upgrades in increasing realized volatility and realized kurtosis. Both upgrades and downgrades affect realized skewness in times of crises in the expected direction.

Do Sovereign Credit Assessments Destabilize Equity Markets during Periods of Financial Crisis?

We investigate the effects of S&P’s sovereign re-ratings on the higher moments of equity market returns over recent financial crises. Using a set of intraday stock market index prices and sovereign credit ratings for a sample of 36 countries which experienced sovereign rating changes over the period from 1996 - 2013, we find that the higher moments of stock market returns are significantly more responsive to sovereign re-ratings during financial crises but the effects on stock markets are not the same across different financial crises. The effects during crises are however magnified for large downgrades and those that are associated with a loss of investment grade status. We find that there are asymmetric effects during financial crises in that downgrades are consistently more significant than upgrades in increasing realized volatility and realized kurtosis. Both upgrades and downgrades affect realized skewness in times of crises in the expected direction.

Can CDS indexes signal future turmoils in the stock market? A Markov switching perspective

Single-name Credit Default Swaps (CDS) are considered the main providers of direct information related with a reference entity’s creditworthiness and, for this reason, they have often been the core of news on the current financial crisis. The academic research has focused mainly on the capacity of CDS in anticipating agencies’ official rating changes and—in this respect—on their superior signalling power, compared to bond and stock markets. The aim of this work is, instead, to investigate the ability of fluctuations in CDS indexes in anticipating the occurrence of stock market crises. Our goal is to show that CDS indexes may provide investors and institutions with early warning signals of financial distresses in the stock market. We make use of a Markov switching model with states characterized by increasing levels of volatility and compare the times in which the first switch in a high volatility state occurs, respectively, in CDS and stock market index quotes. The data set consists of daily closing quotes for 5 years CDS and stock market index prices, covering the time period from 2004 to 2010. In order to capture possible geographic differences in CDS index capacity of foreseeing stock market distresses, data referring to two different regions, Europe and United States, are analyzed.

Effect Of Macroeconomic Variables On Stock Market Returns For Four Emerging Economies: Brazil, Russia, India, And China

The relationship between share prices and macroeconomic variables is well documented for the United States and other major economies. However, what is the relationship between share prices and economic activity in emerging economies? The goal of this study is to investigate the time-series relationship between stock market index prices and the macroeconomic variables of exchange rate and oil price for Brazil, Russia, India, and China (BRIC) using the Box-Jenkins ARIMA model. Although no significant relationship was found between respective exchange rate and oil price on the stock market index prices of either BRIC country, this may be due to the influence other domestic and international macroeconomic factors on stock market returns, warranting further research. Also, there was no significant relationship found between present and past stock market returns, suggesting the markets of Brazil, Russia, India, and China exhibit the weak-form of market efficiency.

Stationarity of Time Series and the Problem of Spurious Regression

The goal of this paper was to introduce some general issues of non-stationarity for practitioners, students and beginning researchers. Using elementary techniques we examined the effect of non-stationary data on the results of regression analysis. We further shoved the effect of larger sample sizes on the spuriousness of regressions and we also examined the well known “rule of thumb” of how to identify spurious regressions. We also demonstrated the problem of spurious regression on a practical example, using closing prices of stock market indices from CEE markets.

NEURAL NETWORKS IN FINANCE AND ECONOMICS FORECASTING

Artificial neural networks (ANNs) have been widely applied to finance and economic forecasting as a powerful modeling technique. By reviewing the related literature, we discuss the input variables, type of neural network models, performance comparisons for the prediction of foreign exchange rates, stock market index and economic growth. Economic fundamentals are important in driving exchange rates, stock market index price and economic growth. Most neural network inputs for exchange rate prediction are univariate, while those for stock market index prices and economic growth predictions are multivariate in most cases. There are mixed comparison results of forecasting performance between neural networks and other models. The reasons may be the difference of data, forecasting horizons, types of neural network models and so on. Prediction performance of neural networks can be improved by being integrated with other technologies. Nonlinear combining forecasting by neural networks also provides encouraging results.

The Effect of the Asian Financial Crisis on Stock Returns, Volatility and Market Integration in the Region

This paper investigates the change in informational spillover between nine Asian capital markets and the United States as a result of the 1997–98 financial meltdown in Asia. Our study period extends from about three years prior to the start of the crises on July 2, 1997 to one year after this date. We conduct spillover studies on daily stock market index prices and returns to determine the changes in market interdependence. Our results indicate a considerable increase in cross‐border cointegration during the crisis. Dramatic shifts in predictability and volatility spillovers are observed in most Asian countries as a result of the Asian financial crisis, providing evidence of an increase of interdependence between Asian countries, and thus suggesting contagion. We observe a strong interdependence with the US markets before the crisis, which persists during the crisis. We also show that Hong Kong and Korea have emerged as the most dominant influences in the region during the Asian financial crisis.