Stock Market Variables Research Articles

Sentiment and Stock Characteristics: Comprehensive Study of Individual Investor Influence on Returns, Volatility, and Trading Volumes

Abstract Background Traditional asset pricing models face challenges from financial anomalies, prompting exploration through behavioural finance theory. This study analyses the nuanced relationship between individual investor sentiment and key stock market variables. Objectives To assess the impact of individual investor sentiment on stock returns, volatilities, and trading volumes using the American Association of Individual Investors (AAII) sentiment index. Methods/Approach Using regression models, we examine the relationship between individual investor sentiment and various stock characteristics across 480 components of the Standard & Poor's 500 index. Results We find a positive relationship between the AAII sentiment index and stock returns and a negative relationship with volatility and trading volume. Conclusions Our study contributes to understanding the intricate role of individual investor sentiment in financial markets.

Analysis of Capital Market Development and Economic Growth in Nigeria

This study examined the analysis of capital market development and economic growth in Nigeria between 1986 and 2022. As a result, the real gross domestic product was used as the dependent variable while independent variables in the study included market capitalisation, all share index, value of transactions, total number of new issues and exchange rate. The Auto-Regressive Distributed Lag Modelling (ARDL) technique as well as the Engle Granger causality test were used to analyse the data obtained from the CBN statistical bulletin. The results emanating from the analysis reported that the total number of new issues had a positive impact on economic growth while other variables had insignificant effects on economic growth. Furthermore, the causality results revealed that all the stock market variables had a uni-directional causality running from each of them to economic growth. Therefore, it is recommended that new issues in the capital market should be seriously regulated as they can affect economic growth in a significant manner.

COVID-19 uncertainty index in Japan: Newspaper-based measures and economic activities

This paper measures economic uncertainty induced by the COVID-19 using a newspaper-based approach in Japan and examines its economic impact in the VAR model. We specify two types of uncertainty indices and structural shocks: epidemiological and policy-related uncertainties. The constructed indices can trace well the events related to COVID-19. We then find that stock market variables respond significantly to a policy-related uncertainty shock, while real variables such as mobility and consumption are significantly affected by an epidemiological one. These findings highlight the importance of considering different types of uncertainty to assess the impact of COVID-19 induced uncertainty on economic activity.

Do corporate credit spreads predict the real economy?

We evaluate whether corporate credit-spreads measures contain predictive information about the real U.S. economy in a comprehensive specification that includes financial sectors’ profitability, stock and bond market. We find that corporate credit spreads contain no predictive information about U.S. real GDP or consumption and that corporate credit spreads have minimal information about forthcoming recessions. In comparison, the financial sector’s profitability, the Treasury bond and stock market variables contain leading information about recessions, consumption and real GDP.

A bibliometric visualization of behavioral biases in investment decision-making

PurposeThis study aims to conduct a bibliometric analysis to provide a comprehensive picture and identify future research directions to enrich the existing literature on behavioral biases.Design/methodology/approachThe data set comprises 518 articles from the Web of Science database. Performance analysis is used to highlight the significant contributors (authors, institutions, countries and journals) and contributions (highly influential articles) in the field of behavioral biases. In addition, network analysis is used to delve into the conceptual and social structure of the research domain.FindingsThe current review has identified four major themes: “Influence of behavioral biases on investment decisions,” “Determinants of home bias,” “Impact of biases on stock market variables” and “Investors’ decision-making under uncertainty.” These themes reveal that a majority of studies have focused on equity markets, and research on other asset classes remains underexplored.Research limitations/implicationsThis study extracted data from a single database (Web of Science) to ensure standardization of results. Consequently, future research could broaden the scope of the bibliometric review by incorporating multiple databases.Originality/valueThe novelty of this research is to provide valuable guidance by evaluating the existing literature and advancing the knowledge base on the conceptual and social structure of behavioral biases.

The impact of the covid-19 pandemic on the stock markets of some countries in the MENA region: An assessment with GARCH modeling

This study examines the impact of the COVID-19 pandemic on stock market volatility for stock indices of a few MENA countries. The analysis consists of several elements: first, we assess the impact of this infectious disease on the standard deviations of stock market variables; second, we analyze the influence of the number of new daily cases and the number of new daily deaths on the standard deviations of these returns; and third, we assess the impact of the coronavirus on stock market volatility for these market variables using GARCH models. We find that the minimum value for most indices occurred in March 2020. Additionally, the results of the regression analysis reveal that the COVID-19 dummy variable, the number of daily new cases, and the number of new daily deaths have all had a significant negative impact on stock market volatility. Finally, the GARCH model reveals that the coefficients of COVID-19 pandemic in the conditional variance equation had a significant positive impact on the conditional variance for stock indices, further indicating that the COVID-19 outbreak increased market volatility.

Generalized Methods on Access Bank Share Price Changes and Variations in Nigeria Stock Market

Purpose: Stock market performance and operation have been widely recognized as a viable investment field in financial markets. This paper studied the share price movement of Access Bank, PLC.Methodology:The share price data was subjected to a 3-step transition matrix for independent share prices, which enabled us to proffer precise conditions for obtaining future share price changes and placed criteria that enabled us to come up with a 2x2 matrix solution of Principal Component Analysis (PCA) on the share price of Access bank.Findings: The solution matrix of the stochastic analysis showed that Access Bank PLC has the best probability of price increasing shortly by 12%, the best probability of reducing in the future by 21%, and the best probability of no change shortly by 20%.Implication:More so, an increase in the trading days also increases the value of future share price changes, and other statistical variables were also found. Finally, the influences of the relevant parameters of stock market variables were effectively discussed in this paper.

Drivers of Realized Volatility for Emerging Countries with a Focus on South Africa: Fundamentals versus Sentiment

We use a quantile machine learning (random forests) approach to analyse the predictive ability of newspapers-based macroeconomic attention indexes (MAIs) on eight major fundamentals of the United States on the realized volatility of a major commodity-exporting emerging stock market, namely South Africa. We compare the performance of the MAIs with the performance of a news sentiment index (NSI) of the US. We find that both fundamentals and sentiment improve predictive performance, but the relative impact of the former is stronger. We document how the impact of fundamentals and sentiment on predictive performance varies across the quantiles of the conditional distribution of realized volatility, and across different prediction horizons. Specifically, fundamentals matter more at the extreme quantiles at short horizons, and at the median in the long-run. In addition, we report several robustness checks (involving sample period and alternative definitions of realized volatility), and indicate that the obtained results for South Africa also tend to carry over to other emerging countries such as, Brazil, China, India, and Russia. Our results have important implications for investors with volatility being an input for portfolio allocation decisions. In addition, with stock market variability also capturing financial uncertainty, its accurate prediction based on US fundamentals and sentiment also has a role in policy design to prevent possible collapse.

Forecasting real activity using cross-sectoral stock market information

Stock prices declined abruptly in the wake of the Covid-19, reflecting both the deterioration of investors’ expectations of profitability as well as the surge in risk aversion. In the following months however, economic activity remained sluggish while equity markets bounced back. This disconnect between equity values and macro-variables can be partially explained by other factors, namely the decline in risk-free interest rates, and -for the US- the strong earnings of the IT sector. As a result, an econometrician forecasting economic activity with aggregate stock market variables during the Covid-crisis is likely to get poor results. Our main contribution is thus to rely on sectorally disaggregated equity variables within a factor model in order to predict US economic activity. We find, first, that the factor model better predicts future economic activity compared to aggregate equity variables, or to conventional benchmarks used in the literature, both in-sample and out-of-sample. Second, we show that the strong performance of the factor model comes from the fact that it filters out the “expected returns” component of the sectoral equity variables as well as the foreign component of aggregate future cash flows. The constructed factor overweights upstream and “value” sectors that are found to be closely linked to the future state of the business cycle.

IFRS Convergence and Value Relevance of Indian Accounting Information: The Earnings-Returns Association Analysis

Manuscript type: Research paper Research aims: India has recently joined the accounting revolution by implementing the International Financial Reporting Standards (IFRS) through the convergence mode. This paper aims to examine the value relevance of Indian accounting information by finding an association between stock returns and the levels of earnings and changes in earnings. Design/Methodology/Approach: The study follows both relative and incremental association approaches to investigate changes in the value relevance of accounting information prepared using the IFRS converged Indian Accounting Standards (Ind-AS), while also examining the impact of their voluntary use. The study employs the Easton and Harris (1991) model on accounting data collected from 2012-13 to 2019-20. The panel data of 7,064 firm-year observations covers 883 firms listed on the National Stock Exchange (NSE) and uses relevant econometric tests and multivariate panel regressions to test the hypotheses. Research findings: The findings reveal a decline in the returns value relevance following both mandatory and voluntary IFRS convergence. Theoretical contribution/Originality: The study is the first to document the changes in value relevance based on stock returns since the IFRS convergence process began in India. Practitioner/Policy implication: This line of research is significant in Indian capital markets to unravel the effects of the new standards on accounting as well as stock market variables. It has managerial implications for firm and standard-setters. Research limitation: The value relevance results are based on the returns model alone and the study does not analyse the price model.

Stock Market Valuation and Output Growth: Panel Data Evidence from the Indian Public Limited Manufacturing Firms

The paper examines the relationship between stock market valuation and output growth at the firm level. Specifically, it aims at understanding the impact of firms’ stock market valuation and stock liquidity on the growth of real output. The sample for the study includes panel data of Indian public limited manufacturing firms. The study covers the period from March 2005 to March 2020. Firms with at least two consecutive years of data have been included in the sample. The full sample includes firm-year observations of 877 firms. The findings show that both stock market valuation and turnover ratio have a significant positive impact on the growth of output, even after controlling for other important determinants of output. Further, both stock market variables and bank credit significantly influence the growth of output. This suggests that banks and stock market provide complementary financial services required for the growth and the development of the stock market will not undermine the role of the institution based financial system.
 Keywords: Stock market, market Valuation, stock liquidity and output growth
 JEL Classification: G1

Strategic approach to analyze the effect of Covid-19 on the stock market volatility and uncertainty: a first and second wave perspective

PurposeThis paper aims to analyze the impact of Covid-19 on the stock market volatility and uncertainty during the first and second waves.Design/methodology/approachThis study has applied event study and autoregressive integrated moving average models using daily data of confirmed and death cases of Covid-19, US S&P 500, volatility index, economic policy uncertainty and S&P 500 of Bombay Stock Exchange to attain the purpose.FindingsIt is observed that, during the first wave, the confirmed cases and the fiscal measure have a significant impact, while the vaccination initiative and the abnormal hike of confirmed cases have a significant impact on the US stock returns during the second wave. It is further observed that the volatility of Indian and US stock markets spillovers during the sample period. Moreover, a perpetual correlation between the Covid-19 and the stock market variables has been noticed.Research limitations/implicationsAt present, the world is experiencing the third wave of Covid-19. This paper has considered the first and second waves.Practical implicationsIt is expected that business leaders, stock market regulators and the policymakers will be highly benefitted from the research outcomes of this study.Originality/valueThis paper briefly highlights the drawbacks of existing policies and suggests appropriate guidelines to successfully implement the forthcoming initiatives to reduce the catastrophic impact of Covid-19 on the stock market volatility and uncertainty.

The Methodology Matters: What Influences Market Reaction, and Post-Issue Returns in Seasoned Equity Offerings?

Using a large database of U.S. seasoned equity offering (SEO) announcements from 2010 to 2015, we examine the effects of several explanatory variables—firm specific, macroeconomic, fixed income, and stock market variables—on the announcement period abnormal stock returns and on the longer-run post-issue abnormal returns. We use five different statistical methods—multivariate linear regression, regression on a reduced model using principal components analysis, year-by-year regression on a reduced model using principal components analysis, random forest regression on the whole sample, and year-by-year random forest regression. In general, across the methods, we find that firm’s profitability in the recent past is an important explanatory factor in both short-term and long-term abnormal stock returns, but several other significant explanatory factors change based on the statistical method used. Therefore, the statistical method used affects the results reported.

Co-Integration among COVID-19, Investor Sentiment, and the Stock Market

This study focuses on stock market performance during the COVID-19 pandemic, aiming to research the co-integration among COVID-19 cases, investor sentiment, and the stock market. The data for the study comprised index returns, trading volume, turnover rate, and volatility from CSI300 index from January 2020 to December 2021. The paper planned to introduce methods that used the autoregressive distributed lag model, co-integration test, and error correction model to achieve the aims. The results show that there is a long-term co-integration relationship among these variables. However, when we consider the long-term association between COVID-19 and investor sentiment and individual stock market variables separately, we find no association among COVID-19 and market volatility, trading volume, or turnover rates. From the perspective of investor sentiment, there is no long-term relationship between investor sentiment and market volatility. Therefore, the model results show that there is a long-term relationship among the variables only when the data are integrated, but this relationship does not always persist when considering individual variables. As the COVID19 still continues, the study results would have the implications for the investment decision-making and risk avoidance to face the pandemic.

Prevalence of money laundering and terrorism financing through stock market: a comprehensive conceptual review paper

PurposeThis paper aims to provide a comprehensive conceptual framework and strong arguments with an intent to examine the stock market variables (predictors) indicating the money laundering (ML) and terrorism financing (FT) proceeds.Design/methodology/approachThis paper provides a comprehensive review of ML/FT through the stock market across developed, developing and emerging jurisdictions, sheds light on the existing literature and critically evaluates the gap in the relevant studies. Moving forward, this paper develops the conceptual framework and formulates hypotheses to explore the empirical relationship.FindingsThis paper advocates and finds a basis to carry out much-needed empirical research between the ML/FT and stock market keeping in view the growing criminal cases in the developing countries. This paper suggests mining proxies from the publically available stock market data and the results of existing seminal research as variables of the study. These data and results carry information about the ML determinants. After developing hypothetical research providing concepts, this paper also finds that using a suitable methodology, preferable Bayesian logistic and linear regression models, it is possible to find the typologies and factors that can indicate and endorse the use of the stock market for ML/FT. Broadly, it is found that the significance of this study will be two-pronged: empirical development and policy implications.Research limitations/implicationsThis paper mainly focuses on the developing region, a newly emerging market and, peculiarly, a grey-listed region by the Financial Action Task Force (FATF).Practical implicationsIn light of the existing literature and to the best of the researchers’ knowledge, this study will bring into focus the new age of the action research on the ML regime in the securities markets of the developing countries, hence, the emerging markets. Moreover, this research shall have a sheer significance for the policy measures on FATF recommendations on ML and FT, especially for the countries listed as “grey”.Social implicationsThe research based on comprehensive review will help in controlling the social behaviours aiding the proceeds of ML.Originality/valueThis research is extremely novel to the best of the researcher's knowledge.

Venture capital as innovative source of financing equity capital after the financial crisis in Spain

The global financial crisis affected the supply of funds to finance equity capital, thus calling for innovative risk capital financing methods. The paper explores the sources of venture capital (VC henceforth) fundraising and determines the relationship between VC fundraising, stock market returns, and market capitalization in Spain. The study uses time-series data of VC fundraising and stock market variables from 1989 to 2020 in a vector error correction model analysis after performing cointegration. The paper reports short-run and long-run causal relations between VC fundraising, stock market returns, and market capitalization when VC fundraising and stock market returns are used as dependent variables. However, such relations do not exist when the model is dependent on market capitalization. Our results show that the VC market raises funds from diversified (geographical and institutional) sources. Our findings support the risk diversification theory of VC financing. The paper provides implications for using alternative innovative ways of financing equity risk capital to spur economic growth.

Evaluating the interactive effect of domestic and global stock market variables on the depth of the financial system in Sub-Saharan African countries: a panel ARDL approach

PurposeThis study investigated the interactive impact of global and domestic stock market variables on the depth of the financial system in Sub-Saharan African (SSA) countries from 1990 to 2018.Design/methodology/approachThe study used the mean group and pooled mean group estimators for the dynamic heterogeneous panel.FindingsThe results provide strong statistical evidence that the depth of the financial system in SSA countries is influenced by a combination of local and international stock market indicators. While the local variables exert a positive influence, the global indicator tends to negatively affect the depth of the system, particularly the monetization ratio.Practical implicationsWhile the tendency of portfolio adjustments and reversal can be inferred, the study stresses the need for a more globalized approach to financial policy formulation and implementation even as the trend of global financializaton gets more robust and more profound.Originality/valueThis study is unique in that, unlike prior ones, it has extended the debate on the role of the stock market in financial deepening from a domestic to an international dimension. Financial policy making can be aided by the authors' findings through looking at the financial deepening-stock market linkage from both domestic and globalized perspectives.

Investigating the Effect of Stock Market Variables on GDP in Iran Using Autoregressive Distributed Lag (ARDL) Model

Investigating the Effect of Stock Market Variables on GDP in Iran Using Autoregressive Distributed Lag (ARDL) Model

Does the level of financial development affect renewable energy? Evidence from developed countries with system generalized method of moments (System‐GMM) and cross‐sectionally augmented autoregressive distributed lag (CS‐ARDL)

AbstractThe relationship between financial development and energy consumption is the most frequently research subject in economy and finance. The main objective of conducting this paper is to answer does financial development have an impact on renewable energy in developed countries? Many papers performed in energy literature, the findings were pointing to the existence of this kind of relationship. In order to examine the relationship between financial development and renewable energy consumption, a total of 23 developed countries, benefited from annual frequency data between 1990 and 2015. In addition to the widely used system generalized method of moments estimation method, models are also estimated with modern estimators using the cross‐sectionally augmented autoregressive distributed lag. As a result of the analysis performed indicates that there is a strong relationship between financial development and renewable energy consumption in developed countries when financial development is measured using banking variables. Otherwise, the estimated coefficients on stock market variables are negative and none of them found statistically significant in all models. These findings are consistent for both estimators.

Jumps and stock market variance during the COVID-19 pandemic: Evidence from international stock markets

Based on the work of Buncic and Gisler (2017), this paper investigates whether the roles of jump components will change in forecasting the volatility of international equity markets during the COVID-19 pandemic. Interestingly, in contrast to the conclusions of Buncic and Gisler (2017), we find jump components of the international equity indices are useful to predict the international stock markets’ volatility during the COVID-19 pandemic. Our study tries to provide new evidence of jump components in stock markets.