Stock Market Index Values Research Articles

Relationship between consumer confidence and stock market index in Vietnam: Research impacts of the passion of Covid-19

The article, studies the relationship between consumer confidence and the stock market index in Vietnam's stock market in the context of the Covid-19 pandemic shock, data collected from 2012Q1 to 2021Q4 from Nielsen data sources. Consumer confidence is measured by the consumer confidence index (CCI), and the stock market index (SMI). The authors use the P.VAR model to solve the set goals. Research results show that there is a strong positive relationship between consumer confidence and the stock market index in Vietnam in the period from 2012 to 2021. In addition, shocks of the past CCI and SMI value also affect current CCI and SMI values. This shows that when consumer confidence is in a positive direction, the SMI will also be in a positive direction and vice versa. In addition, the results showed that Covid 19 did not significantly affect the above relationship. From the research results, the authors propose governance implications to attract more investors to participate in the stock market, contributing to promoting capital in the circulating economy more effectively

WHY INVEST GLOBALLY IN FAMILY FIRMS

Purpose- Family firms have a significant economic role in many countries around the world. Family firms make a significant contribution to World GDP and employ a significant part of the global workforce. The scope of this study covers the top 25 largest and publicly owned family firms announced by Ernst & Young’s 2021 Report for Family Businesses. These 25 family firms generated more than 2 trillion USD and employed 6.5 million in 2021. This empirical study aims to investigate the excess stock returns of family firms over the related country stock market indexes and the risks (betas) for the period between 2002 and 2021. Therefore, this study explores the question of “why invest globally in family firms and whether this investment pays off with higher returns and less risk”. Methodology- The World's Largest Family Companies" list is published every other two years by Ernst & Young and the last issue was published in 2021. The world’s largest family companies list includes both private and publicly owned family firms. This study employs 25 world’s largest family firms after the exclusion of privately held family firms. The monthly stock prices of family firms, related country stock market index values, and global stock market index values are obtained from Refinitiv Eikon (Reuters) database for the period between 2002 and 2021 (20 years). Therefore, a total of 9120 observations are extracted for this empirical study. Eviews-10 is utilized for all econometric analysis. Findings- This study investigates whether an individual or intuitional investor can earn more than the average return of the stock markets by investing in publicly traded family firms meanwhile exposing less risk. The empricial results reveal that Maersek shows 354% (beta of 1.18) excess return over the 20 year period and followed by Hanwha with a 335% (beta of 0.69) excess return. Later, all family firms are grouped based on country of headquartered and 7 country portfolios are formed. The highest excess returns are provided by South Koean portfolio (an excess return of 189% with a beta of 0.83) and it is followed by Indian portfolio (an excess return of 174% with a beta of 1.0). Finaly, a best performer portfolio is formed by the 10 family firms with highest excess returns. This portfolio provides a 131% excess return with a beta of 1.18 over 20-year peirod. Conclusion- The empirical results show that the individual firm returns and portfolio returns of family firms are higher than the returns of the stock market indexes. Those who invest in family businesses get higher returns with less risk. Investments in publicly traded family firms pay off. Keywords: Family firms, global investment, return, risk, stock markets. JEL Codes: G10, G11, G15

Geometric Brownian Motion (GBM) of Stock Indexes and Financial Market Uncertainty in the Context of Non-Crisis and Financial Crisis Scenarios

The present article proposes a methodology for modeling the evolution of stock market indexes for 2020 using geometric Brownian motion (GBM), but in which drift and diffusion are determined considering two states of economic conjunctures (states of the economy), i.e., non-crisis and financial crisis. Based on this approach, we have found that the GBM proved to be a suitable model for making forecasts of stock market index values, as it describes quite well their future evolution. However, the model proposed by us, modified geometric Brownian motion (mGBM), brings some contributions that better describe the future evolution of stock indexes. Evidence in this regard was provided by analyzing the DAX, S&P 500, and SHANGHAI Composite stock indexes. Throughout the research, it was also found that the entropy of these markets, analyzed in the periods of non-crisis and financial crisis, does not differ significantly for DAX—German Stock Exchange (EU) and S&P 500—New York Stock Exchange (US), and insignificant differences for SHANGHAI Composite—Shanghai Stock Exchange (Asia). Given the fact that there is a direct link between market efficiency and their entropy (high entropy—high efficiency; low entropy—low efficiency), it can be deduced that the analyzed markets are information-efficient in both economic conjunctures, and, in this case, the use of GBM for forecasting is justified, as the prices have a random evolution (random walk).

STOCK MARKET PREDICTION IN BRICS COUNTRIES USING LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK HYBRID MODELS

The BRICS (Brazil, Russia, India, China and South Africa) acronym was created by the International Monetary Foundation (IMF)–Group of Seven (G7) to represent the bloc of developing economies which crucially impact on the global economy by their potential economic growth. Most of the foreign direct investment are considering the stock markets of BRICS as the most attractive destination for foreign portfolio investment. This study aims to identify the relationship between macroeconomic variables and the stock market index values of BRICS and generate accurate predictions for index values by performing linear regression and artificial neural network hybrid models. Monthly data from January 2003 to December 2019 are used for the empirical study. The results indicate that a strong correlation exists between the stock market and macroeconomic variables in BRICS over time. The hybrid model is observed very accurate for index value prediction where the mean absolute percentage error (MAPE) value is 0.714% for the whole data set covering all BRICS countries data during the study period. Additionally, MAPE values for each of the BRICS countries are, respectively, obtained as 0.083%, 2.316%, 0.116%, 0.962% and 0.092%. Thus, the main findings of this study show that while neural network-integrated models have high performances for volatile stock market prediction, macroeconomic stabilization should be the priority of monetary policy to prevent the high volatility of stock markets.

Analysis of financial time series with model hybridization

Purpose- The aim of this study is to obtain better estimation results by hybridizing models that reveal linear and nonlinear relationships used in the intended financial time series. Methodology- ARIMA and Artificial Neural Networks (ANN) models were used in estimating NASDAQ stock market index values between 03.01.2012 and 30.06.2017 comparison of hybrid model results with different ways of error determination in literature. Findings- ARIMA residues have been tested different models where only residues are used with basic indications, only residues and basic. The calculation of residues was done separately with the addition and multiplication function. These residues were modeled with ANN, and the obtained results are collected and established hybrid model with ARIMA forecasts. When the results obtained at the end of the operations are compared, it is seen that the product function of some of the addition functions gives better results in some models. Conclusion- The hybridization of the ANN and NASDAQ index estimates with the ARIMA method resulted in processing for both addition and multiplication functions. Residues calculated with the addition model showed better results in ANN hybrid. What variables are used to calculate residuals is that the hybrid model gives better estimation results than single models.

The Leading Indicators of the Economic Cycles in Lithuania

Identification of leading indicators that precede economic events and predict the next phase of business cycle is undoubtedly important issue seeking to protect the country against the recession or other negative economic events. The paper analyses potential leading indicators and identifies the best predictors of the business cycles in Lithuania. Various economic, industrial, financial, real estate market indicators as well as consumer and business expectations are analysed in order to find which of them cause the changes in growth rate of GDP. The analysis is based on Granger causality test and autoregressive distributed lag model. The research shows that considered economic indicators are weak predictors of the growth rate of GDP. Volume index of intermediate goods production is the best predictor in the group of industry data as it holds predictive attributes even three years before the changes in economics. That can be also said about two financial indicators, i.e. short-term interest rate and the value of stock market index. Real estate market data such as residential buildings permits and growth rate in house price index can also warn about the changes in the growth rate of GDP two years before. Nevertheless, consumer and business expectations are the most important for prediction of the changes in the growth rate of GDP.DOI: http://dx.doi.org/10.5755/j01.ee.28.3.16705

Forecasting of future stock prices using neural networks and genetic algorithms

Neural networks are a well established and widely used class of machine learning tools for classification and clustering that have been successfully applied to time-series analysis and prediction. On the other hand, genetic algorithms have been used in the literature for a vast range of optimisation problems ranging from electromagnetic optimisation to mechanical design, industrial control and genetic engineering. In this work, we propose to use the former in predicting future values of a time-series of particular interest, i.e., the future values of stock market indices. Based on a large body of work that is present in the literature, we develop, test and present a set of neural networks for predicting future stock market index values. Furthermore, we evaluate the use of modified GAs as a stand-alone tool for prediction, but also the use of GAs as neural network training and optimising tools. We also test two benchmark time-series extrapolation techniques based on linear regression. The proposed stock market prediction tools are fine-tuned and applied to a number of stock market index time-series and numerical results are presented demonstrating their superiority compared to standard benchmark techniques.

Impact of economic indicators on development of capital market

The capital market is one of the most important elements of any healthy, well-functioning economy. The volume and value of executed capital market transactions are affected not only by the number of issuers and investor’s willingness to buy, but also by economic development in the broadest sense. This article discusses the relationship between macroeconomic indicators and development of stock market index in different countries. It assesses the direction and intensity of relationships between macroeconomic indicators and stock market index and explains possible causes of these conditions. Analysis of the relationship is carried out in different countries with different levels of economic development, both in Europe and overseas. Emphasis was put on the selection of those countries, which were significantly affected by the economic recession. The indicators that affect value of stock market index were selected based on economic theory. Among them belong: gross domestic product, inflation, interest rate, export, and import and unemployment rate. After determining the degree of dependence between macroeconomic indicators and value of stock market index, countries were grouped together in clusters. Clustering was therefore not conducted on the basis of the values of macroeconomic indicators of a country, but on the basis of these indicators having a similar effect on the value of the stock market index. Before cluster analysis, input matrix of variables was subjected to factor analysis to reduce the original number of variables.

Sovereign credit rating change in emerging markets and its impact on their financial markets

Credit rating revision has become rampant in recent years for sovereign borrowers. Downgrading has economic and social changes leading to hardship. This study examines the effect of sovereign credit rating change announcement in a number of bond, stock, and future markets for a sample of 94 rating change announcements from nine developing countries. Our findings indicate significant effect on the sovereign bond index as well as national stock market index values. Both markets show indication of information leakage prior to announcement day. The stock future market only shows reaction to downgrade announcement. Hence, future market is not sensitive to sovereign rating upgrade which is consistent with spot market behaviour as well. The results add new findings especially on flow-on-effect to shares and futures securities.

An experimental analysis of forecasting the high frequency data of matured and emerging economies stock index using data mining techniques

In this paper we study the applicability of three data mining techniques, viz. backpropagation neural network (BPNN), support vector regression (SVR) and multivariate adaptive regression splines (MARSplines) for one step ahead forecast of stock index using intraday data. High frequency data of S&P CNX Nifty (Nifty) from India and Nasdaq Composite Index (NCI) from the USA are used for the analysis to check the efficiency of these techniques in predicting stock market in emerging and mature economies. To the best of our knowledge, there have been no prior studies on the use of MARSplines to forecast high frequency data in mature and emerging markets. This paper provides new useful information to intraday traders about the use of these techniques in forecasting stock index or stocks in emerging economies. Our study shows that MARSPlines and SVR perform better than BPNN in predicting the stock market index values, the former being the best of the three for the prediction of NCI and the latter being best suited for Nifty.

A comparison of four different block bootstrap methods

The paper contains a description of four different block bootstrap methods, i.e., non-overlapping block bootstrap, overlapping block bootstrap (moving block bootstrap), stationary block bootstrap and subsampling. Furthermore, the basic goal of this paper is to quantify relative efficiency of each mentioned block bootstrap procedure and then to compare those methods. To achieve the goal, we measure mean square errors of estimation variance returns. The returns are calculated from 1250 daily observations of Serbian stock market index values BELEX15 from April 2009 to April 2014. Thereby, considering the effects of potential changes in decisions according to variations in the sample length and purposes of the use, this paper introduces stability analysis which contains robustness testing of the different sample size and the different block length. Testing results indicate some changes in bootstrap method efficiencies when altering the sample size or the block length.

Forecasting IBOVESPA Index with Fuzzy Logic

Much research has been done aiming the forecasting of stock market index values. However, very few researches focus on the predictability of the direction of stock market movements. This paper fills this gap through the estimation of a model, using fuzzy logic to forecast the direction of the movements of the Sao Paulo Stock Exchange index (IBOVESPA). To establish the rules of the model it was used an estimation period based on 1,000 daily data sets, corresponding to the period of January 8, 1997 to January 22, 2001. The test period was from January 23, 2001 to February 2, 2005. A software called FuzzyTECH® was used. Despite the estimated model produces an inexact answer, with a probabilistic output, it was possible to implement an investment strategy, using the IBOVESPA index as a proxy for an investment fund, which outperformed a buy-and-hold strategy.

On non-Gaussianity and dependence in financial time series: a nonextensive approach

In this article a probability density function and dependence degree analysis of financial time series, namely the Dow Jones and NYSE, is presented. The present study, which aims to give theoretical support to some stylized empirical evidence, is performed under the present non-extensive framework for which the probability distributions that optimize its fundamental information measure form, , are also the (stationary) solutions of a nonlinear Fokker–Plank equation. One determines the rescaled coefficient of the drift force and diffusion coefficient for both market indices and various aggregated times. Using a generalized form of Kullback–Leibler mutual information, Iq , one analyses the non-Gaussianity of returns using the dependence between stock market index values. The same mutual information form is used to determine the degree of dependence between returns. The analysis shows that this dependence can be considered independent from the time distance τ result that is connected with the long-range correlation in volatility.