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The last decades have experienced increasingly integrated global political and economic dynamics ranging especially from the influence of exchange rates and trade amid other sources of uncertainties. The purpose of this study is to examine the exchange rate dynamics of Brazil, Russia, India, China, and South Africa (BRICS) and the Republic of Turkey.

Given this perceived global dynamics, the current study examined the BRICS countries and the Republic of Turkey's exchange rate dynamics by using the United States (US) monthly dollar exchange rate data between January 2002 and August 2019. The price bubble which is expressed as exceeding the real value of assets' prices which is observably caused by speculative movements is investigated by using the Supremum Augmented Dickey-Fuller (SADF) and the Generalized Supremum Augmented Dickey-Fuller (GSADF) approaches.

Accordingly, the GSADF test results opined that there are price bubbles in the dollar exchange rate of other countries except for the United States Dollar (USD)/Indian Rupee (INR) exchange rate. As the related countries are classified as developing countries in terms of their structure, they are also expectedly the subject of speculative exchange rate movements. Speculative movements in exchange rates may cause serious problems in national economies.

Thus, the current study provides a policy framework to the BRICS countries and the Republic of Turkey.

The purpose of this paper is to consider the economic information content within several popular stock market factors and to the extent to which their movements are both explained by economic variables and can explain future output growth.

Using US stock portfolios from 1964 to 2019, the authors undertake three related exercises: whether a set of common factors contain independent predictive ability for stock returns, what economic and market variables explain movements in the factors and whether stock market factors have predictive power for future output growth.

The results show that several of the considered factors do not contain independent information for stock returns. Further, most of these factors are neither explained by economic conditions nor they provide any predictive power for future output growth. Thus, they appear to contain very little economic content. However, the results suggest that the impact of these factors is more prominent with higher macroeconomic risk (contractionary regime).

The stock market factors are more likely to reflect existing market conditions and exhibit a weaker relation with economic conditions and do not act as a window on future behavior.

Fama and French three-factor model still have better explanations for stock returns and economic information more than any other models.

This paper contributes to the literature by examining whether a selection of factors provides unique information when modelling stock returns data. It also investigates what variables can predict movements in the stock market factors. Third, it examines whether the factors exhibit a link with subsequent economic output. This should establish whether the stock market factors contain useful information for stock returns and the macroeconomy or whether the significance of the factor is a result of chance. The results in this paper should advance our understanding of asset price movement and the links between the macroeconomy and financial markets and, thus, be of interest to academics, investors and policy-makers.

The author investigates whether investors’ online information demand measured by Google search query and the changes in the numbers of Wikipedia page view can explain and predict stock return, trading volume and volatility dynamics of companies listed on the Nigerian Stock Exchange.

The multiple regression model which encompasses both the univariate and multivariate regression framework was employed as the research methodology. As part of our pre-analysis, we test for multicollinearity and applied the Wu/Hausman specification test to detect whether endogeneity exist in the regression model.

We provide novel and robust evidence that Google searches neither explain the contemporaneous nor predict stock return, trading volume and volatility dynamics. Similarly, results also indicate that trading volume and volatility dynamics have no relationship with changes in the numbers of Wikipedia pages view related to stock activities.

This study opens new strand of empirical literature of “investors' attention” in the context of African stock markets as empirical evidence. No evidence from previous studies on investors' attention exist, whether in Google search query or Wikipedia page view, with respect to African stock markets, particularly the Nigerian stock market. This study seeks to bridge these knowledge gaps by examining these relations.