This paper examines the effect of formal financial intermediation (inclusion) on informal financial intermediation and the use of cash for economic activities. Using data from the Global Findex 2014, we examine whether the use of formal financial intermediaries reduces cash preference and the use of informal financial intermediaries. Our empirical results show that informal financial intermediation is positively associated with formal financial inclusion. This indicates that the relationship between informal and formal financial intermediation is complementary rather than a trade-off, which demonstrates the importance of informal finance plays in the financial system of Africa. Moreover, the use of formal financial intermediaries significantly reduces the preference for holding cash, implying that a robust financial system infrastructure has the potential of mobilizing excess liquidity in the informal economy of Africa for growth and development.

Ownership structure plays a vital role in stock market liquidity. We analyze the impact of ownership concentration, institutional ownership and earnings management on stock market liquidity. We select 114 firms from manufacturing sector of Pakistan, India, Australia and Singapore. We extract data from DataStream from 2010 to 2018 of selected countries. We apply Generalized Method of Moments (GMM) to analyze the data. We find that ownership concentration, institutional ownership and earnings management significantly affect the stock market liquidity.

Purpose: The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory.Design/methodology/approach: This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory.Findings: Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts.Practical implications: The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management.Social implications: It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks.Originality/value: This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.

Default prediction has commanded the attention of researchers for at least 50 years. This paper addresses several testable hypotheses regarding the relations between corporate governance and default prediction. We employ the conventional logistic regression to provide empirical evidence from U.S. default data over the period of 2000 to 2015. Empirical results are consistent with the following notions: First, default firms are associated with high ownership concentration, low shareholder rights, low financial transparency and disclosures, and less board effectiveness. Second, in-sample and out-of-sample tests support the incremental contribution of corporate governance information on default prediction, when compared with the models involving just financial information.