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This study focuses on listed and over-the-counter (OTC) companies in the Taiwan Stock Exchange. It found that an increase in the ownership proportion of institutional investors (INs), including foreign investors, investment trusts, and dealers can enhance the informativeness of stock prices. The relationship between these factors follows an inverted U-shaped pattern, indicating that excessively high ownership ratios can actually lead to a decrease in the informativeness of stock prices. Additionally, increasing the ownership proportions of foreign investors and investment trusts can reduce the risk of stock price collapse, while dealers show no significant relationship in this regard. This study also reveals that the technical variable of the price deviation rate is an important explanatory factor for post-collapse returns. It is positively correlated with the magnitude of the price decline after a collapse, meaning that stocks with weaker pre-collapse performance experience larger post-collapse declines. When the data during the 2020 pandemic period are excluded, changes in foreign ownership ratios show a significant positive correlation with postcrash returns in both the long and short term. The significant correlation in the short term may be due to a high proportion of foreign ownership. Any reduction in this could put pressure on stock prices, and retail investors may follow suit and sell-off, using foreign investors as a reference. The significant correlation in the long term might be due to foreign investors themselves possibly also trying to avoid the pressure that their own short-term sell-offs could exert on stock prices. The changes in the ownership ratios of investment trusts and dealers indicate that medium and long-term changes have a significant impact on postcrash returns, while the changes in the major players' ownership show no significant correlation. When data from 2020 are included in the analysis, the significance of all INs decreases.

This study aims to find the relationship of stock market returns and selected financial market variables (market capitalization, earnings per share, price-earnings multiples, dividend yield and trading volume) of Malaysia grounded by the arbitrage pricing theories.

This study empirically examines the effects of selected financial market variables on stock market returns using 64 companies listed in Malaysia's stock market with data spanning from 2005 to 2018. A systematic empirical study based on the Generalized Method of Moments following Arellano and Bond (1991) has been taken to estimate the effect.

The regression result of the financial market variables and stock market return shows that, except for trading volume, all selected financial market variables play significant roles in the stock market returns. Furthermore, market capitalization, earnings per share, price-earnings ratio, dividend yield and trading volume have a positive impact on stock market returns.

The outcome of this study can contribute by helping domestic and global investors devise strategies to minimize their risks. Also, policy administrators can use the outcomes of this study to inform the micro- and macro-level policy formulation.

This study will contribute to filling the gap in knowledge concerning the new release of factors affecting the stock market returns of Malaysia.

The purpose of this study, the nonlinear relationship between the real estate market and the stock market was investigated in Iran. For this intent, the monthly data from 2012:4 to 2022:5 is used.

In this study, the quantile-on-quantile estimation method is used, which is a combination of the nonparametric estimation methods and the quantile regression.

The research results show that, in the low quantiles, the effect of stock market return on the housing market return is negative or zero. In fact, in this situation, the increasing returns in the stock market will shift part of the financial resources of the economy to the market and create stagnation or even negative returns in the housing market. This situation is seen more strongly in some other quantiles, including the 0.25 and 0.75 quantiles; in contrast, the effect of high quantiles of stock market returns is positive on the housing market.

It seems that the demand in the housing market increase in a situation where the returns of the stock market are growing, and the market is in a bullish condition, and this causes an increase in the price and returns in this market. In addition, the results show that the effect of stock market returns on capital market returns is asymmetric and nonlinear.

This study aims to investigate the relationship between oil prices and stock returns of renewable energy firms in India under different market conditions.

The authors use the panel quantile framework with Fama–French–Carhart’s (1997) four-factor asset pricing model. All renewable energy firms listed in the National Stock Exchange of India are considered in this study. Three oil prices, such as West Texas Intermediate spot price, Europe Brent oil price and Indian basket oil price, are used in the regression. The analysis is done for the whole sample and its subgroups.

In the whole sample, stock returns of renewable energy firms respond positively to oil price changes in extreme market conditions only. In the subgroups of the renewable energy firms, the relationship between stock returns and oil price is positive and more robust in higher quantiles across all subgroup firms.

The contribution of the study is explained as follows. First, this study helps to explore the relationship between oil and stock returns of the renewable energy sector under different market conditions in the Indian context. Second, existing studies explore the effect of oil prices on stock returns of the renewable energy sector at the industry level, and most of the studies are in developed countries. To the best of the authors’ knowledge, this is the first study in the context of India. Third, this is a firm-level study

The purpose of this study was to examine stock price abnormal returns following title sponsorship announcement and event date of NASCAR, the PGA Tour, and the LPGA Tour. For this purpose, the authors used event study analysis where the analysis measures the impact that a specific event has on stock prices by comparing actual stock returns to estimated returns (Spais & Filis, 2008). An event study analysis demonstrated that title sponsors for the LPGA Tour and NASCAR garnered significant stock price increases on both the announcement date and the event date. The moderator tests suggested that high image congruence and high-technology related sponsorships assumed a key role in stock price increases.

The purpose of this study is to investigate the interactive relationships between oil price shocks and the Nigeria stock market.

The paper applied the multivariate vector auto‐regression that employed the generalized impulse response function and the forecast variance decomposition error.

Empirical evidence reveals that stock market returns exhibit insignificant positive response to oil price shocks but reverts to negative effects after a period of time depending on the nature of the oil price shocks. The results are similar even with the inclusion of other variables. Also, the asymmetric effect of oil price shocks on the Nigerian stock returns indices is not supported by statistical evidences.

This is the first study to examine the dynamic linkages between stock market behaviour and oil price shocks in Nigeria.

The purpose of this paper is to empirically investigate the volatility spillover between the Chinese stock market, investor’s sentiment and oil market, specifically during the 2014‒2016 turmoil period.

This study used the daily and monthly China market price index, oil-price index and composite index of Chinese investor’s sentiment. The authors first use the DCC GARCH model in order to study the correlation between variables. Second, the authors use a continuous wavelet decomposition technique so as to capture both time- and frequency-varying features of co-movement variables. Finally, the authors examine the spillover effects by estimating the BEKK GARCH model.

The wavelet coherency results indicate a substantial co-movement between oil and Chinese stock markets in the periods of high volatility. BEKK GARCH model outcomes confirm this relation and report the noteworthy bidirectional transmission of volatility between oil market shocks and the Chinese investor’s sentiment, chiefly in the crisis period. These results support the behavioral theory of contagion and highlight that the Chinese investor’s sentiment is a channel through which shocks are transmitted between the oil and Chinese equity markets. Thus, these results are important for Chinese authorities that should monitor the investor’s sentiment to better control the interaction between financial and real markets.

This study makes three major contributions to the existing literature. First, it pays attention to the recent 2015 Chinese stock market bumble. Second, it has gone some way toward enhancing our understanding of the volatility spillover between the investor’s sentiment, investor’s sentiment variation, oil prices and stock market returns (variables of interest) during oil and stock market crises. Third, it uses the continuous wavelet decomposition technique since it reveals the linkage between variables of interest at different time horizons.

The purpose of this paper is to evaluate the profitability of investment strategies based on past price changes and trading volumes.

Data are employed from January 1998 to December 2011 for select emerging markets. Portfolios are formed on the basis of past information on prices and/or volumes. Unrestricted and risk adjusted returns for sample portfolios are analyzed. The risk models employed in study are Capital Asset Pricing Model (CAPM), Fama-French (F-F) Model and Fama-French augmented models.

Price momentum patterns are observed for Brazil, India, South Africa and South Korea, while there are reversals in Indonesia and China. Low-volume stocks outperform high-volume stocks for all sample countries except China. Further, volume and price based bivariate strategies do a better job than univariate strategies in case of India, South Africa and South Korea. The past price and volume patterns in stock returns are not fully explained by CAPM as well as the F-F Model. Price and volume momentum factors do play a role in explaining some of these return patterns. Finally, the unexplained returns seem to be an outcome of investor under or overreaction to past information. The sources of price and volume momentum seem to be partly risk based and partly behavioral.

The study analyzes combined role of price and volume in portfolio formation with post holding analysis. The work is useful for global portfolio managers, policy makers, market regulators and the academic community. The study contributes to asset pricing and behavioral finance literature for emerging markets.

In this paper, we examine dynamic relationships among three housing market variables and a stock market index in a multivariate vector autoregressive error correction (VAREC) model. It is first found that, in the USA, sales and the median sales price of the existing single‐family homes and the 30‐year mortgage rate have unit roots, while the New York Stock Exchange (NYSE) value‐weighted portfolio returns appear random. Moreover, it is found that not only are three real estate variables cointegrated with one another but that they are also cointegrated with the stock index returns. After controlling for the unit root problem and cointegration, a multivariate VAREC model is further developed to examine dynamic relationships among the four variables using Johansen’s approach. It is found that the price, mortgage rate, and stock returns affect sales. It is found that the mortgage rate and stock returns affect the price. The 30‐year mortgage rate is affected by sales and the stock returns. Except for the mortgage rate which is negatively correlated with the stock returns, significant evidence is not found that sales and the median sales price affect the stock returns directly.

It has been said that oil price shocks affect stock market returns. However, empirical studies remain inconclusive regarding the nexus between oil price shocks and stock market returns. Consequently, the purpose of this study is to investigate the asymmetric impact of oil price shocks on stock returns in Nigeria.

A two-stage Markov regime-switching approach is used to examine the asymmetric effects of three different structural oil shocks on stock returns. The oil shocks, which include oil supply shock, aggregate demand shock and oil-specific demand shock, are derived using structural vector autoregressive. Monthly data that spans the period between January 1990 and December 2018 are deployed for estimation.

The linear estimation results show that only oil demand shock negatively and significantly affects the stock market returns. The Markov-switching regime results reveal that oil supply shock has a significant positive impact on the stock returns in a low-volatility state, whereas oil-specific demand shock negatively impacts the stock returns in a high-volatility state.

There is a need for policymakers and investors to take cognizance of not only the positive outcomes of a relatively stable state of oil price but also the negative consequences of a high-volatility state when formulating policy and making investment decisions, respectively.

This study differs from other similar studies in Nigeria that have examined the asymmetric relationship between oil price shocks and stock market return by using a two-stage Markov regime-switching approach. To the best of the authors’ knowledge, this is the first attempt at using this methodology.