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This study uses the foreign exchange rate to investigate how the Indonesia Stock Exchange's Composite Stock Price Index is affected by growing oil prices and economic growth. The study's population consists of all data on the Indonesia Stock currency's Composite Stock Price Index (JCI), as well as all data on foreign exchange rates, GDP, and worldwide oil prices from Bank Indonesia for the quarters ending in 2010 and 2020. The analysis technique uses path analysis which is an extension of multiple linear analysis, or path analysis is the use of regression analysis to estimate the causal relationship between variables (causal model), the population used is all composite stock price index data and the sample used is a saturated sample of all combined stock price index data. The results of the study show that the Composite Stock Price Index is influenced by the World Oil Price. There is no contribution from Economic Growth to the Composite Stock Price Index. Foreign exchange rates have an effect on the Composite Stock Price Index based on the price of oil globally. Through foreign exchange rates, economic progress affects the composite stock price index. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose -- The purpose of this study is to examine the influence of global stock exchanges indices--the Dow Jones Industrial Average (DJIA) index, the Shanghai Stock Exchange (SSE) index, and the Nikkei 225 (N225) index--and macroeconomic variables (exchange rate and inflation) on the movement of the Jakarta Composite Index (JCI). Method -- The data analysis method used in this study is quantitative causality with the GARCH model technique. The samples include the JCI, global stock exchanges (DJIA, SSE, N225) indices, and macroeconomic variables (exchange rate, inflation) based on monthly secondary data from 2017 to 2022, encompassing 72 months of observation. Result -- The results showed that the global stock market variables of the DJIA and the SSE indices had a significant positive effect, while the N225 index had a significant effect on the JCI. Regarding macroeconomic variables, the exchange rate had a significant negative effect, whereas inflation did not affect the JCI. Novelty -- The novelty of this research, compared to previous studies, lies in the more recent research period, which spans from 2017 to 2022, and the inclusion of inflation variables, which have not been explored in prior studies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Selcuk University Social Sciences Institute / Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Journal of Selcuk University Social Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When activist shareholders file Schedule 13D filings, the average stock-price volatility drops by approximately 10%. Prior to filing days, volatility information is reflected in option prices. Using a comprehensive sample of trades by Schedule 13D filers that reveals on what days and in what markets they trade, we show that on days when activists accumulate shares, option-implied volatility decreases, implied volatility skew increases, and implied volatility time slope increases. The evidence is consistent with a theoretical model where it is common knowledge that informed trading occurs only in the stock market and market makers update option prices based on stock-price and order-flow dynamics. [ABSTRACT FROM AUTHOR]

We show that the prices of risk for factors that are nonlinear in the market return can be obtained using index option prices. The price of coskewness risk corresponds to the market variance risk premium, and the price of cokurtosis risk corresponds to the market skewness risk premium. Option-based estimates of the prices of risk lead to reasonable values of the associated risk premia. An analysis of factor models with coskewness risk indicates that the new estimates of the price of risk improve the models' performance compared with regression-based estimates. [ABSTRACT FROM AUTHOR]

In this article, we study the contagion effects of accounting restatements issued by foreign firms traded in the United States. Specifically, we predict and find that accounting restatements that negatively affect the share prices of the restating foreign firms raise investor concerns that nonrestating foreign firms from the same home countries have similar accounting issues, and therefore induce a negative stock market reaction to nonrestating home country peer firms. We refer to this as a restatement-induced home country contagion effect. On average, nonrestating home country peer firms experience a negative stock market return of approximately −0.69% over a 3-day window around the restatement announcement. Moreover, we hypothesize and show that the strength of the home market rule of law (ROL) affects investor assessment of the likelihood that peer firms have similar accounting issues, and therefore affects the magnitude of the contagion. Specifically, nonrestating home country peer firms from countries with weak ROL experience an average stock price decline of approximately −1.32%, whereas peer firms from strong ROL countries experience an average negative return of only −0.26% over the 3-day window around the restatement. These results suggest that restatements filed by weak ROL firms are perceived to be more "contagious" than those filed by strong ROL firms. [ABSTRACT FROM AUTHOR]

Purpose: This research will try to show that macroeconomic conditions are basically the main determinants of stock market performance and for this reason DAX Index will be analyzed. Design/Methodology/Approach: In particular, the macroeconomic factors that affect the performance of DAX will be used. The methodology to be followed is linear regression and in particular Ordinary Least Squares (OLS). Findings: The performance of a stock exchange is affected by the constantly continuous international political and economic conditions that have a strong influence on the operation of stock exchanges around the world. States remain the main actors in the international economy and this means that macroeconomic data act as catalysts for stock market performance. Practical Implications: Stock exchange is actually the “mirror” of an economy. There is much literature that has dealt with the determinants of stock market performance around the world. Today's era is highly globalized. The current era has made it clear that there are many parameters that are exogenous and have a serious impact on the economy and therefore on the performance of the stock market. Originality/Value: Since the economy is the field in which every stock market operates, it should be borne in mind that macroeconomic factors are of great importance. [ABSTRACT FROM AUTHOR]

The main objective of this analysis is to investigate how varying the forecast horizon and the input window length for calculating technical indicators affects the predictive performance of different machine learning algorithms on forecasting the direction of change of chosen stock market indices. Ten indices from CEE (Central and Eastern European) and SEE (Southern and Eastern European) countries are chosen for research in an attempt to investigate their behaviour in the light of the behaviour of bigger and more researched markets. In respect to similar research conducted on S&P 500 Index stocks, this analysis does not find the same pattern of highest system performance for each forecast horizon value when the input window length is approximately equal to the forecasting horizon. Instead, the forecasts seem to be better using shorter input window lengths for technical indicators in general. Also, on average, there is a notable deterioration in the performance with the increase of forecasting horizon. Furthermore, some algorithms perform very well for short horizons and then deteriorate substantially as the forecasting horizon increases, while others seem to have more consistent performance over different horizons. [ABSTRACT FROM AUTHOR]

This study aims to examine the impact of the world pandemic uncertainty index on the German stock market index (DAX index) for the 1996Q1 to 2020Q3 period while controlling real effective exchange rate, industrial production index, and consumer price index. The present study performs the Fourier Augmented Dickey-Fulle Unit Root, Fourier Engle-Granger Cointegration, Bayer-Hanck Cointegration, and Markov switching regression tests. The outcomes disclose that there is a long-run cointegration association between the stock market index and world pandemic uncertainty index, real effective exchange rate, industrial production index, and consumer price index in Germany, indicating that the combination of these factors significantly affects the German stock market index in the long-run. Moreover, in both high and low volatile regimes, the world pandemic uncertainty index and real effective exchange rate negatively affect the German stock market index while industrial production and consumer price indices impact positively. [ABSTRACT FROM AUTHOR]

This study aims to examine the influence of global stock indices and macroeconomic conditions in Indonesia on the Jakarta Composite Index (JCI). The global stock indices analyzed in this study are the Dow Jones Industrial Average (DJIA), Nikkei 225 (N225), Shanghai Stock Exchange Composite (SSE), Strait Singapore index. The macroeconomic indicators that analyzed in this study are the inflation rate, the US dollar exchange rate against the Indonesian rupiah, and the BI rate. This study was conducted using secondary data. The research period was 3 years for 36 months from January 2018 to December 2020. The data used in this study are secondary data with a sample collection method using Purposive Sampling against JCI. Data analysis in this study was carried out using Microsoft Office Excel 2016, as well as hypothesis testing using panel data regression analysis with the Eviews program version 10.0 with a significance level of 5%. Research Results The results showed that in the short term, Index DJIA, Nikkei Index 225, Interest rates have a positive effect on the movement of JCI. This suggests that an increase in the DJIA index, Nikkei 225 Index, interest rates will result in an increase in the JCI, While, Shanghai Index, STI, Inflation has no effect on the JCI. [ABSTRACT FROM AUTHOR]

This research investigates the impact of stock market indices, economic strength, and research and development expenditures on environmental deterioration in the EU-27 countries for the period 2000–2020. This study utilized linear and non-linear panel ARDL to estimate the short- and long-run effect. According to the results, the stock market indices have negative effect on environmental deterioration in the symmetric form. However, the asymmetric evidence shows that in the long run the positive shocks of stock market indices contribute positively to the environmental deterioration and negative shocks decrease the environmental deterioration. This effect is reversed in the short run. Linear effect of economic strength on environmental deterioration is positive. For non-linear effect, the long-run shocks show no difference. However, the negative shock of economic strength in the short run causes an increase in the environmental deterioration. Symmetric evidence for research and development increases environmental deterioration. However, asymmetric results show weak evidence. The study has policy implications in context of achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

Stock Market movement is highly volatile, complex, and non-linear. Several researchers have proposed innovative approaches to predict stock price movement using traditional data analytics, machine learning, or deep learning. Data scientists have proved that if effective mathematical models are deployed, stock prices can be predicted with very high accuracy. Deep learning is the most popular technique used for stock price prediction due to its effective results in time-series based and non-linear patterns. In the Year 2020, stock prices variations are too high to be analyzed by traditional approaches. Very few research works have been carried out to predict high variations in stock prices during this time. The main motive of this research is to investigate whether deep learning can predict so high variations in stock prices in the Year 2020 and build proposed neural network model. In this paper, Long Short-Term Memory (LSTM) is used with adam optimizer and sigmoid activation function to train and test the model. Various stock indexes data are extracted using Yahoo Finance API. Window size of 60 days is used as stock prices are dependent on the previous day's prices. Experiment analysis has proved that LSTM using our layers set up was able to predict stock prices with adequate accuracy. Mean Absolute Percentage Error (MAPE) values are better than traditional data analytics techniques. The values of MAPE score calculated using our proposed approach are 3.89, 1.21, 3.01, 1.19, 2.03, and 0.86 for NSE, BSE, NASDAQ, NYSE, Dow Jones, and Nikkei 225 respectively for duration Jan 2010 to March 2020. [ABSTRACT FROM AUTHOR]

This paper examines whether the Covid-19 pandemic has had a homogeneous or heterogeneous effect on stock returns in India. We consider panel data by using 1,318 companies that are listed on the National Stock Exchange of India. We find that the daily growth rate in Covid-19 cases and Covid-19 deaths are negatively associated with stock returns. Further, we observe that the average stock returns during Lockdown 2 are positive and highly significant, while the returns during Lockdowns 3 and 4 are negative. Moreover, our results show that the chemical, technology, and food and beverage industries earn higher returns. In contrast, the banking and finance, automotive, services, and cement and construction industries yield lower returns for the overall period. Interestingly, all industry groupings in this study earn a positive return during the lockdown period. In particular, the chemical, technology, automotive, metals and mining, and food and beverage industries provide higher returns during the lockdown period. Finally, this study supports the claim that the Covid-19 pandemic has had a heterogeneous effect in the Indian stock markets. [ABSTRACT FROM AUTHOR]

This study re-examined the causal relationship between economic policy uncertainty (EPU) and stock indices for Organization for Economic Co-operation and Development (OECD) and non-OECD countries. To this aim, it applied the lag-augmented vector autoregression model with a time-varying Granger causality test. Based on the standard Granger causality test results, only afew countries demonstrated acausal relationship between two variables. However, the empirical findings using of time-varying Granger causality test indicate that the causal relationship between the two variables is not present for the entire period, but it is present for some sub-periods in many countries. Additionally, the results found that causality running from stock indices to EPU (stock price-leading hypothesis) for many periods and countries. The results indicate that the relationship between two variables is not constant, meaning that it changes from one period to the next in many countries. [ABSTRACT FROM AUTHOR]

The macro policy of the stock market is an important market information. The implementation goal of the macro policy of the stock market is mainly to improve the effectiveness of the stock market. However, whether this effectiveness has achieved the goal is worth verifying through empirical data. The exertion of this information utility is closely related to the effectiveness of the stock market. Use the run test method in statistics to collect and sort out the daily data of stock price index in recent 30 years, the linkage between 75 macro policy events and 35 trading days of market efficiencies before and after the macro event are tested since 1992 to 2022. The results show that 50.66% of the macro policies are positively linked to the effectiveness of the stock market, while 49.34% of the macro policies have reduced the effectiveness of the market operation. This shows that the effectiveness of China's stock market is not high, and the nonlinear characteristics are obvious, so the policy formulation of the stock market needs further improvement. [ABSTRACT FROM AUTHOR]

The potential financial benefits of stock market forecasting have drawn a lot of interest. Due to the various interconnected aspects, predicting these markets is a difficult endeavour that necessitates a thorough as well as efficient feature selection procedure to discover the highest useful aspects. Stock price changes are also influenced by previous trading days' movements, which is a time series problem. In stock forecasting, feature selection techniques are commonly used, although most known systems use a single feature selection methodology that probably can neglect some key notions about the regression function that is at the root of the problem relating the variables for input and output. This study employs an artificial neural network (ANN) based generative model to forecast pricing changes in the future by combining features preferred by different feature picking strategies to build an ideal optimal feature group. We begin by calculating an expanded set of 83 technical indicators using day-to-day stock data of six stock indices, and then we normalize them using the Hybrid-Normalization (HN) technique. The important features are selected using various types of feature selection techniques and then considering the common features for the stock movement prediction. For stock trend predictions, we used a variety of classifiers such as Support Vector Machine, K Nearest Neighbour and Artificial Neural Network and. The system was given a performance review after simulations were done on 6 stock indices from various portions of the international market. The outcomes show that joining highlighted features got by various feature choice calculations and taking care of them into a profound generative model beats best in class techniques. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Economics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Stock Exchanges are safe and organized markets for stock and bond trading, which aim to expand the capital of both publicly traded companies and investors. Since 2009, cryptocurrencies have been created, mostly decentralized, registered through blockchain and encryption, which can be a new form of investment and trading. The main purpose of this research is to determine the short and long-term relationships of the main indicators of twenty-one Stock Exchanges and three cryptocurrencies and verify their effects on the financial market. The defined methodology was the VAR/VEC Model that obtained as a result the causality between all the series, which are stationary in the first difference, the VAR indicated the short-term relationship, the cointegration indicated the order for the VEC application, which was explained with the help of IRF and the variance decomposition that indicated the long-term relationship between the series and the possibility of cryptocurrencies' influence in the Stock Exchange series. The Bitcoin cryptocurrency, after the shock's application, indicated to influence the Stock Exchanges for an average time of 16 periods, Litecoin for 15 periods, and Ripple for 16 periods. The time evaluated was 24 periods and proved to be sufficient for the study's definition. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine whether referendums affect stock price risks and returns, using an event study approach. Daily end period data for the Swiss stock market index, the STOXX European market index, and the Swiss/US exchange rate running from the beginning of 2004 to June 2021, along with the EGARCH model, were applied to determine the effects on both the market's return and volatility. The results suggest that the day after the referendum, there was little evidence of a positive effect on stock returns. However, using a longer window of three days before and after the referendum, there was evidence of a positive effect from the referendum on the market's returns and a negative effect on its volatility. Analysing the effects of referendums on both asset returns and risks allows for a more comprehensive assessment of how they impact on the economy, with these results supporting previous studies that found a positive effect on economic returns, and also showing they can reduce risks. [ABSTRACT FROM AUTHOR]

Stock market indices are among the important figures that are followed in the trading rooms: on the movements of the Dow Jones or the S&P 500 depends the allocation of billions of dollars. At the same time, for the companies that produce and publish them, but also for those whose shares are included in them, they represent a significant source of visibility – and therefore of income. Based on an ethnographic survey at the Brussels stock exchange, this article documents how the different financial actors – aware of the performativity of stock market indices – try to impact their shape. The divergent opinions and interests of these actors are the source of methodological tensions at the heart of these numbers. Through the methods of calculation and selection chosen, the engineers of the indices make perilous trade-offs with far-reaching consequences. By exposing the cognitive and political debates that these indicators crystallise, this article also highlights the unequal representation of the various stakeholders within these statistics. This type of inequality, veiled by the numerical formatting, often goes under the radar of social scientists. [ABSTRACT FROM AUTHOR]

In the past, investors used their own or others' experiences to achieve their goals. With the development of financial management, investors' choices became more scientific. They could select the optimal choice by using different models and combining the results with their experiences. In portfolio optimization, the main issue is the optimal selection of the assets and securities that can be provided with a certain amount of capital. In the present study, the problem of optimization, i.e., maximizing stock portfolio returns and minimizing risk, has been studied. Therefore, this study discussed comprehensive modeling for the optimal selection of stock portfolios using multi-criteria decision-making methods in companies listed on the Tehran Stock Exchange. A sample of 79 companies listed on the Tehran Stock Exchange was used to conduct this research. After simulating the data and programming them with MATLAB software, the cumulative data analysis model was performed, and 24 companies were selected. This research data were collected from the financial statements of companies listed on the Tehran Stock Exchange in 2020. The primary purpose of this study was a comprehensive modeling for the optimal selection of stock portfolios using multi-criteria decision-making methods in companies listed on the Tehran Stock Exchange. The index in the Tehran Stock Exchange can be used to provide a comprehensive and optimal model for the stock portfolio; different multi-index decision-making methods (TOPSIS method), the taxonomy method (Taxonomy), ARAS method, VIKOR method, The COPRAS method and the WASPAS method can all identify the optimal stock portfolio and the best stock portfolio for the highest return. [ABSTRACT FROM AUTHOR]

This paper examines the interconnectedness between the COVID-19 uncertainty index and stock returns in selected ASEAN countries. Results from the study, which uses the dynamic connectedness approach, show that on average, 47.06% of a shock on one index spills over to all other indices. This indicates that stock market returns are highly interconnected to the COVID-19 uncertainty index in ASEAN countries. However, the COVID-19 uncertainty index is not a predictor of stock returns in the ASEAN countries chosen for the study. [ABSTRACT FROM AUTHOR]

This paper examines the European credit default swap spreads and their relationship to other financial assets such as stock indices. This study aims at better investigating the European sovereign debt crisis from the beginning of 2010 to the end of 2013. For this purpose, using daily data covering the period from January 2004 to December 2018, first we examine the impact of the sovereign crisis on spread sovereign credit default swaps, using both an asymmetric DCC-MGARCH model and a Copula-based Multivariate GARCH model (C-MGARCH). Then, we estimate the relations between spread sovereign credit default swaps and equity indices. We analyse in particular the lead–lag relationship between these two financial indicators by using a vector autoregressive model. We note a negative correlation between the spreads and stock market indices. There is a significant unidirectional causality from CDS spread changes to stock market indices returns. Our findings show that the CDS spreads have played a leading role in most European countries during this crisis. These results are potentially important because the current pandemic is threatening the economies of European countries, especially Greece. [ABSTRACT FROM AUTHOR]

This paper addresses a method to solve a multi-period portfolio selection on the stock market. The portfolio problem seeks an investor to trade stocks with a finite budget and a given integer number of stocks to hold in a portfolio. The trade must be performed through a stockbroker that charges its respective transaction cost and has its minimum required trade amount. A mathematical model has been proposed to deal with the constrained problem. The objective function is to find the best risk-return rate; thus, Sharpe Ratio and Treynor Ratio are used as objective functions. The returns are the same for these ratios, but the risks are not Sharpe considering covariance and Treynor systematical risk. The returns are predicted using a Neural Net with Long-Short-Term Memory (LSTM). This neural net is compared with simple forecasting methods through Mean Absolute Percentage Error (MAPE). Computational experiments show the quality prediction performed by LSTM. The heteroskedastic risk is estimated by Generalized Autoregressive Conditional Heteroskedasticity (GARCH), adjusting the variance for every period; this risk measure is used in Sharpe Ratio. The experiment contemplates a weekly portfolio selection with 5 and 10 stocks in 122 weekly periods for each Chilean market ratio. The best portfolio is Sharpe Ratio with ten stocks, performing a 62.28% real return beating the market, represented by the Selective Stock Price Index (IPSA). Even the worst portfolio, Treynor Ratio, overcomes the IPSA cumulative yield with ten stocks. [ABSTRACT FROM AUTHOR]

In econophysics, the analysis of the return distribution of a financial asset using statistical physics methods is a long-standing and important issue. This paper systematically conducts an analysis of composite index 1 min datasets over a 17-year period (2005–2021) for both the Shanghai and Shenzhen stock exchanges. To reveal the differences between Chinese and mature stock markets, we precisely measure the property of the return distribution of the composite index over the time scale Δ t , which ranges from 1 min to almost 4000 min. The main findings are as follows: (1) The return distribution presents a leptokurtic, fat-tailed, and almost symmetrical shape that is similar to that of mature markets. (2) The central part of the return distribution is described by the symmetrical Lévy α -stable process, with a stability parameter comparable with a value of about 1.4, which was extracted for the U.S. stock market. (3) The return distribution can be described well by Student's t-distribution within a wider return range than the Lévy α -stable distribution. (4) Distinctively, the stability parameter shows a potential change when Δ t increases, and thus a crossover region at 15 < Δ t < 60 min is observed. This is different from the finding in the U.S. stock market that a single value of about 1.4 holds over 1 ≤ Δ t ≤ 1000 min. (5) The tail distribution of returns at small Δ t decays as an asymptotic power law with an exponent of about 3, which is a widely observed value in mature markets. However, it decays exponentially when Δ t ≥ 240 min, which is not observed in mature markets. (6) Return distributions gradually converge to a normal distribution as Δ t increases. This observation is different from the finding of a critical Δ t = 4 days in the U.S. stock market. [ABSTRACT FROM AUTHOR]

The article is about the dynamics of the development of the capital market in Bulgaria. The period covered in this research is 2001 – 2021. The study of the established trends is based on the analysis of basic stock market indicators such as market capitalization, market capitalization to national GDP ratio, annual turnover, number of concluded transactions, number of issues of financial instruments listed for trading, basic stock exchange indices. The key factors that impact the dynamics of the capital market in this country have been extrapolated. Based on that, the main particularities of the Bulgarian capital market have been outlined. [ABSTRACT FROM AUTHOR]

Copyright of Contaduría y Administración is the property of Facultad de Contaduria y Administracion-Universidad Nacional Autonoma de Mexico and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The research purpose of this paper is to obtain an algorithm model with high prediction accuracy for the price of Bitcoin on the next day through random forest regression and LSTM, and to explain which variables have influence on the price of Bitcoin. There is much prior literature on Bitcoin price prediction research, and the research methods mainly revolve around the ARMA model of time series and the LSTM algorithm of deep learning. Although it cannot be proved by the Diebold–Mariano test that the prediction accuracy of random forest regression is significantly better than that of LSTM, the prediction errors RMSE and MAPE of random forest regression are better than those of LSTM. The changes in the variables that determine the price of Bitcoin in each period are also obtained through random forest regression. From 2015 to 2018, three US stock market indexes, NASDAQ, DJI, and S&P500 and oil price, and ETH price have impact on Bitcoin prices. Since 2018, the important variables have become ETH price and Japanese stock market index JP225. The relationship between accuracy and the number of periods of explanatory variables brought into the model shows that for predicting the price of Bitcoin for the next day, the model with only one lag of the explanatory variables has the best prediction accuracy. [ABSTRACT FROM AUTHOR]

This paper studies the impact of a change in promoter shareholding on small-cap-value stocks. NIFTY Small Cap 250 index stocks within the top 20th percentile of the book-to-market (B/M) ratio of the same universe have been considered for this study. The paper uses regression analysis for understanding the impact of independent variables on returns. The universe is further narrowed down to stocks with a positive change in promoter shareholding, which is found to be negatively related to stock returns. In addition, although the book-to-market ratio does not play any role in the prediction of returns while within this narrowed-down universe, the size effect is present. The results are discussed with reference to some relevant past research literature, and the scope for further research is also discussed. [ABSTRACT FROM AUTHOR]

This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish–Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model's results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets' sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR's highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH). [ABSTRACT FROM AUTHOR]

Market capitalisation plays a significant role in the performance of emerging stock markets. This study, therefore, analysed the trend of Dar es Salaam Stock Exchange's (DSE) indicators on its domestic capitalisation. Specifically, the study analysed trends of stock market indicators, namely, individual participation, share volume, share turnover, and DSE All Share Index. Time series monthly data from 2014 to 2020 were analysed using the ARMA model. The study further examined how the stock market indicators influence the domestic market capitalisation (DMC) of DSE using the ARMAX model. Results indicated that individual participation, turnover, and the DSE All Share Index significantly influenced DMC. However, the volume of shares traded did not influence DSE's domestic market capitalisation. Hence, DSE's performance is associated with share turnover, price, and public participation. Thus, the study urges stock brokers to reduce transaction costs to attract individual investors. It also recommends that DSE encourage new listings to trigger trading and increase the market share index. This study's results can help the government improve policies and plans to increase public participation, reduce income inequality and improve the economy. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to investigate the dynamic relationship between 19 pandemic and government actions, such as governmental response index and economic support packages. Design/methodology/approach: The authors use a panel dataset of 10 American and Latin countries for the period spanning from January 2020 to April 2021 to analyze the effect of government actions on stock market returns. The authors provide robust test results that improve the understanding of the impact of the pandemic on stock market indices through the break-up structure method and the new measure of Covid-19 extracted from Narayan et al. (2021) study. Findings: Empirical results show the harmful effect of the corona virus on stock prices, hence the risk adverse behavior of investors. On the other hand, the quantitative approach reveals that the positive impact of government actions is degraded during Covid-19. Originality/value: This article highlight that government actions may be effective in reducing new infections but could generate perverse economic impact through increasing uncertainty. The authors conclude that the adjustment of macroeconomic factors and the integration of financial news improve the forecasting performance of the model based on health news. [ABSTRACT FROM AUTHOR]

The paper aims to address a topic of interest, namely: the influence and effect of the major disruptions from recent years on one of the largest important stock markets. The purpose of the paper is to show the influence of these disruptions on the US stock market, considering market efficiency and measuring the estimated Bid-Ask spread. Using daily and weekly data sets over a period of 13 years, based on the closing stock prices of 10 companies listed in the category of the NASDAQ and NYSE stock indexes and calculating the return at (t) and (t+1) for each stock, the covariance of the two returns at (t) and (t+1) and using at t and (t+1) a "rolling window" of 21 days, which represents the trading days, as well as using the weekly data series in the same way, we obtained the relationship between the spread measurement and its size, a strong negative cross-sectional relationship, for which we performed a series of statistical tests summarized in the paper. Later, we split the data for each year separately so that we’d be able to use for each year a cross-sectional regression of the spread over the logarithmic values of the size and we noticed that there is a strong negative relationship between the two of them. According to the results obtained, it can be observed that the strongest negative correlations are in 2019 and 2021 in the case of data with daily frequency and 2020, and 2021 in the case of data with weekly frequency, for an informationally efficient market, where transaction costs are zero and in which the market price contains all the relevant information. The strongly negative correlations recorded can be explained by the fact that strong negative influences took place during these periods, which contributed to the disruption of the stock market and not only. At the same time, these negative correlations on the stock market analyzed in the last period also show a wider spread increase which theoretically shows low liquidity. [ABSTRACT FROM AUTHOR]

In this paper, we contribute to the old debate on the dynamic correlation between gold and stock markets by considering a spectral approach within the framework of portfolio hedging. Specifically, we consider eight MENA stock markets (Tunisia, Egypt, Morocco, Jordan, UAE, Saudi Arabia, Qatar, and Oman) and examine the optimal composition between gold and the stock market index, with a minimum portfolio risk and a high expected return. Based on the spectral approach, we propose seven portfolio structures and evaluate them through a comparison with the conventional DCC-GARCH method and the most best 10 portfolios constructed by using wavelet approach. The main results show that the spectral-based approach outperforms the DCC-GARCH and the wavelet methods. In fact, the optimal gold-stock composition depends on the spectral density of each stock market index, where a stock market index with a stable spectral density requires more investments in gold than a stock market index with an unstable spectral density. [ABSTRACT FROM AUTHOR]

With the economic globalization, it is important to accurately estimate and predict the correlation between global stock markets for the efficient asset allocation around the globe. While practitioners may resort to various GARCH-type models for correlation forecasting, the forecasting accuracy is still not very satisfactory. For the first time, this paper introduces a deep learning method into the research of stock market correlation. A hybrid DCDNN model is developed based on the RDNN (recurrent deep neural network) and DCC-GARCH models. Deep learning is then devised to forecast the prediction error of the DCC-GARCH model in order to improve the prediction accuracy of stock market correlation. An autoencoder is also introduced in the empirical study to extract useful features of the stock index data. Then, the correlations among the stock markets in China, Hong Kong, the United States and Europe are predicted and tested. We show that the accuracy of the DCDNN model is significantly higher than that of the DCC-GARCH model. The results indicate that the introduction of deep learning can help improve the efficacy of existing correlation forecasting methods. [ABSTRACT FROM AUTHOR]

The objective of this study is to examine the short‐run and long‐run impact of macroeconomic variables on E7 stock indices across bullish, bearish and normal states of the stock markets. For this purpose, this study uses both autoregressive distributed lag (ARDL) and quantile ARDL (QARDL) models. The findings based on the ARDL model indicate that, in the long‐run, foreign direct investment (FDI), trade balance and industrial production index (IPI) significantly affect emerging stock indices. In addition, the findings based on the QARDL model indicate that the short‐run effect of FDI, consumer price index, interest rate and exchange rate varies across bullish, bearish and normal states of the emerging stock markets, whereas the long‐run effect varies for all macroeconomic variables except IPI. These findings indicate that the results change when QARDL model is used; however, these findings remain same across seven emerging stock indices. Finally, this study proposes important policy recommendations based on the findings of this study. [ABSTRACT FROM AUTHOR]

This research investigates whether the US stock volatility index (S&P 500 index) has the forecasting ability to predict the volatility of CAC index (France), DAX index (Germany), and FTSE index (the UK) by employing a multivariate heterogeneous autoregressive realized volatility jump (MHAR-RV-CJ) model. Our empirical results provide consolidated comparisons using univariate and multivariate models. The in-sample results show us the US volatility will improve the long-term volatility regression coefficient. Moreover, our proposed model, the MHAR-RV-CJ model, nearly surpasses all competing models at out-of-sample forecasting, indicating that considering the multivariate DCC-GARCH information between US-France, US-Germany, and US-UK stock markets and jump component structures can help to predict individual European stock market volatility. Unsurprisingly, several forecasting evaluation tests and further analysis (high/low volatility) confirm the robustness of our results. [ABSTRACT FROM AUTHOR]

We analyze whether oil price uncertainty and U.S. stock uncertainty can simultaneously provide additional information to volatility forecast of six major stock indexes. For model settings, we find not only the uncertainty information of previous day, but that of previous week and month will also provide incremental predictive power for the stock market volatility. Based on that, from in-sample and out-of-sample perspective, the empirical evidences imply separately incorporating oil price uncertainty into the model can significantly improve the stock market volatility forecasting performance, but the improvements vanish after controlling the effects of volatility spillover from U.S. stock market while the effect of U.S. stock uncertainty is nonnegligible and sustainable for stock volatility forecasting. We confirm this finding from average and dynamic perspective. We further proceed the process in longer-horizon volatility forecasting, the evidences cannot overturn our conclusion. This conclusion implies that we should be cautious about the stock volatility predictability based on the oil price uncertainty, which further provide some important implications for researchers, regulators and investors. [ABSTRACT FROM AUTHOR]

The efficient market hypothesis dominates in the studies on the effectiveness of stock market performance, one illustration of which is the presence of calendar anomalies of different nature. The advent of the Adaptive Market Hypothesis calls into question both the presence of such anomalies and the effectiveness of the stock market. To confirm the effective market hypothesis, the time series behaviour of the rates of return of the most significant global stock indices and a local Ukrainian PFTS Stock Index has been investigated in the work. According to the study results, the efficient market hypothesis has not been confirmed; the results partially confirm the adaptive market hypothesis. To confirm the hypothesis that global stock markets have an impact on local stock exchanges, a pre-selected sample of time series of stock index rates of return was used. The Granger causality test was used for this purpose. To determine whether the time series of the dynamics of the stock index rates of return are stationary, the advanced Dickie-Fuller test was used since it takes into account the possible autocorrelation in residuals. The Phillips-Perron test was used as well. [ABSTRACT FROM AUTHOR]

In this study, we propose a predictive model TabLSTM that combines machine learning methods such as TabNet and Long Short-Term Memory Neural Network (LSTM) with a complete factor library for stock index trend prediction. Our motivation is based on the notion that there are numerous interrelated factors in the stock market, and the factors that affect each stock are different. Therefore, a complete factor library and an efficient feature selection technique are necessary to predict stock index. In this paper, we first build a factor database that includes macro, micro and technical indicators. Successively, we calculate the factor importance through TabNet and rank them. Based on a prespecified threshold, the optimal factors set will include only the highest-ranked factors. Finally, using the optimal factors set as input information, LSTM is employed to predict the future trend of 4 stock indices. Empirical validation of the model shows that the combination of TabNet for factors selection and LSTM outperforms existing methods. Moreover, constructing a factor database is necessary for stock index prediction. The application of our method does not only show the feasibility to predict stock indices across different financial markets, yet it also provides an complete factor database and a comprehensive architecture for stock index trend prediction, which may provide some references for stock forecasting and quantitative investments. [ABSTRACT FROM AUTHOR]

This paper proposes a method for detecting bubble phases and the timing of bursts in global stock markets. The study identifies 27 bubbles in 29 global stock market indices over the last century. Using transformations and change-point detection on index returns, we discover that every major bubble has the same two-phase pattern indeed. Although the mechanisms or causes of each bubble are complex and unique, they all follow the same pattern. Thus, our findings suggest that history has a tendency to repeat itself. [ABSTRACT FROM AUTHOR]

In the present work, a study has been made over the prime stock indices of some fiscally prominent countries impacted by COVID-19. The countries are separated in two ways: (1) considering gross total number of infected cases—here seven mostly impacted countries with certain global economic influence are selected; (2) considering the concentration of the infected cases—here six major impacted countries with considerable influence are selected. This sort of categorization is itself a novel strategy which is capable of including some less populated, but severely impacted countries of economic importance. The objective of the present analysis is to comprehend the impact of COVID-19 on these markets and to recognize the effect of COVID-19 on mutual association and dependence between these markets. To add more flavour of reliability, we have taken a new and fresh strategy of fixing the time frames under consideration before and during COVID-19 pandemic as uniform. We have used both linear and nonlinear Granger causality analysis and employed generalized forecast error variance decomposition analysis to review the exogeneity and endogeneity of the individual markets. The present study shows that this pandemic has changed the underlying relationship: some exogenous stock markets have become endogenous and vice versa in the pandemic. Linear relationship has been reduced radically, whereas nonlinear relationship has been improved during the COVID-affected period. TASE, the highest returned and significantly uncorrelated index, emerged as the most exogenous market in the pre-COVID period, though it is nonlinearly endogenous in the long term, in the COVD-affected period. CAC 40 is the most endogenous market for the short term in both pre-COVID and COVID-affected period. B3 and NYSE, exogenous in the pre-COVID period, turned out to be linearly endogenous in the COVID-affected duration, whereas BIST 100 and BSE SENSEX are found to be exogenous markets in the COVID-affected period according to both linear and nonlinear causal analysis. They were also exogenous in the pre-COVID era for the short-term period, with BSE SENSEX exhibiting exogeneity anti-persistently for the COVID-affected period too. Association among the markets is more in long term rather than short term. A possible conclusion is also that the markets may regain long-term association once the effect of COVID would fade away. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the risk-return trade-off in the Indian equity market at both the aggregate equity market level and in the cross-sections of stock return using alternative risk measures. Design/methodology/approach: The study uses weekly and monthly data of 3,085 Bombay Stock Exchange-listed stocks spanning over 20 years from January 2000 to December 2019. The study evaluates the risk-return trade-off at the aggregate equity market level using the value-weighted and the equal-weighted broader portfolios. Eight different risk proxies belonging to the conventional, downside and extreme risk categories are considered to analyse the cross-sectional risk-return relationship. Findings: The results show a positive equity premium on the value-weighted portfolio; however, the equal-weighted portfolio of these stocks shows an average return lower than the return on the 91-day Treasury Bills. The inverted size premium mainly causes this anomaly in the Indian equity market as the small stocks have lower returns than big stocks. The study presents a strong negative risk-return relationship across different risk proxies. However, under the subsample of more liquid stocks, the low-risk anomaly regarding other risk proxies becomes moderate except the beta-anomaly. This anomalous relationship seems to be caused by small and less liquid stocks having low institutional ownership and higher short-selling constraints. Practical implications: The findings have important implications for investors, managers and practitioners. Investors can incorporate the effects of different highlighted anomalies in their investment strategies to fetch higher returns. Managers can also use these findings in their capital budgeting decisions, resource allocations and other diverse range of direct and indirect decisions, particularly in emerging markets such as India. The findings provide insights to practitioners while valuing the firms. Originality/value: The study is among the earlier attempts to examine the risk-return trade-off in an emerging equity market at both the aggregate equity market level and in the cross-sections of stock returns using alternative measures of risk and expected returns. [ABSTRACT FROM AUTHOR]

Periods of economic turmoil distort the ability of stock prices to reflect the available information. In the last three decades, emerging markets experienced numerous crises. The major three of them are the Asian Financial Crisis (1997–1998), Global Financial Crisis (2007–2009) and Global Pandemic Crisis (2020–2022). The nature, intensity and duration of these crises differ significantly. This study investigates the impact of these varying natures of crises on the level of informational efficiency. The empirical evidence is based on the emerging stock market of Pakistan. Index-level data are collected from Pakistan Stock Exchange-100 Index for the period 1995–2022. The rebalancing is done each year to ensure that the final sample is composed of only 100 stocks with the highest market capitalization. The results based on the Variance Ratio (VR) test show that informational efficiency is time-varying. Among all the three crises, informational efficiency deters more in the COVID-19 pandemic, albeit the market efficiency recovers soon. This implies that the arbitrage opportunity is marginal in crisis periods, while investors prefer to invest in post-crisis periods. Finally, our results reveal that among all the crises, investors were more informed in the Global Financial Crisis. Investors must keep a close eye on market regimes for designing investment solutions. [ABSTRACT FROM AUTHOR]

Although the prediction of stock prices and analyses of their returns and risks have always played integral roles in the stock market, accurate predictions are notoriously difficult to make, and mistakes may be devastatingly costly. This study attempts to resolve this difficulty by proposing and applying a two-stage long short-term memory (LSTM) model based on multi-scale nonlinear integration that considers a diverse array of factors. Initially, variational mode decomposition (VMD) is used to decompose an employed stock index to identify the different characteristics of the stock index sequence. Then, an LSTM model based on the multi-factor nonlinear integration of overnight information is established in a second stage. Finally, the joint VMD-LSTM model is used to predict the stock index. To validate the model, the Shanghai Composite, Nikkei 225, and Hong Kong Hang Seng indices were analyzed. Experiments show that, by comparison, the prediction effect of the mixed model is better than that of a single LSTM. For example, RMSE, MAE and MAPE of the mixed model of the Shanghai Composite Index are 4.22, 4.25 and 0.2 lower than the single model respectively. The RMSE, MAE and MAPE of the mixed model of the Nikkei 225 Index are 47.74, 37.21 and 0.17 lower than the single model respectively, and the RMSE, MAE and MAPE of the mixed model of the Hong Kong Hang Seng Index are 37.88, 25.06 and 0.08 lower than the single model respectively. [ABSTRACT FROM AUTHOR]