Do average higher moments predict aggregate returns in emerging stock markets?

PurposeIt has been demonstrated in the US market that expected market excess returns can be predicted using the average higher-order moments of all firms. This study aims to empirically test this theory in emerging markets.Design/methodology/approachTwo measures of average higher moments have been used (equal-weighted and value-weighted) along with the market moments to predict subsequent aggregate excess returns using the linear as well as the quantile regression model.FindingsThe authors report that both equal-weighted skewness and kurtosis significantly predict subsequent market returns in two countries, while value-weighted average skewness and kurtosis are significant in predicting returns in four out of nine sample markets. The results for quantile regression show that the relationship between the risk variable and aggregate returns varies along the spectrum of conditional quantiles.Originality/valueThis is the first study that investigates the impact of third and fourth higher-order average realized moments on the predictability of subsequent aggregate excess returns in the MSCI Asian emerging stock markets. This study is also the first to analyze the sensitivity of future market returns over various quantiles.

Performance comparison between Islamic and conventional stocks: evidence from Pakistan's equity market

PurposeThis paper aims to examine the performance of Islamic and conventional stocks listed at the Pakistan Stock Exchange by using both parametric and non-parametric approaches. The motivation is to do risk-return analysis of Islamic stock prices and conventional stock prices.Design/methodology/approachIt uses various measures of performance, e.g. Sharpe ratio, Treynor ratio, Jensen's alpha, beta, generalized auto-regressive conditional heteroskedasticity and stochastic dominance. Using the Karachi Meezan Index-30 (KMI-30) and the Karachi Stock Exchange Index-30 (KSE-30) as proxies for Islamic and conventional stock prices, respectively, it examines the performance of Islamic and conventional stocks. The daily data of KMI-30 and KSE-30, covering period from June 9, 2009 to June 20, 2020 are used.FindingsThe results show that the overall KMI-30 outperforms the KSE-30. The returns of the KMI-30 are greater than the KSE-30. However, the risk and volatility of the KMI-30 and KSE-30 are similar. Further, the KMI-30 has higher excess returns per unit of total risk than the KSE-30. But both indexes have similar excess returns per unit of systematic risk. Moreover, the KMI-30 returns have stochastically dominance over the KSE-30 returns. These results reveal that the Islamic index performs better than the conventional index.Practical implicationsThe findings provide several practical implications in financial and investment decisions making by investors, managers and policymakers such as strategies for asset allocation and investment. Further, in risk management, it provides guidance for allocating portfolios and managing risk. The investment in Islamic stocks may mitigate potential risk within asset portfolios.Originality/valueThis research is unique in its approach to the analysis of the performance comparison of conventional and Islamic stock by using comprehensive parametric and non-parametric estimation techniques. Such research has not been undertaken in the Pakistan's equity market since.

Political risks and financial markets: emerging vs developed economies

PurposeThe authors provide a comparative analysis between emerging and developed financial markets in terms of the effects of political risks on stock market returns and volatility. The authors also examine whether this impact depends on the nature of political risks. Therefore, this study aims to detect which financial markets are the most profitable and the riskiest in terms of political risks.Design/methodology/approachThe authors investigate the impact of political risks on the excess stock market return and its conditional volatility using the generalized ARCH model for a sample of 46 developed and emerging markets over a period ranging from 1995 to 2019. In order to test how the nature of political risks affects equity excess returns and volatility differently in different markets, the authors employ (1) a composite political risk score, (2) the four subgroups of political risks as defined by Bekaert et al. (2005, 2014) and (3) the individual dimensions of political risks.FindingsThe findings indicate that the composite political risk is priced into both stock markets. The effect of political risks is positive for excess returns and negative for volatility. The authors show that the political risk leads to more volatility in developed markets. Nevertheless, the effect of individual components varies according to the market category.Practical implicationsThe authors provide a framework for predicting market returns and volatility using changes in the political risk of the country. The findings help investors make investment decisions based on the political decisions of governments. In other words, investors should consider political uncertainty when determining their expected earnings.Originality/valueThe authors engage monthly panel data methodology in terms of the political risk stock market relationship. In addition, the authors consider recent and very long data covering the period 1995–2019. Furthermore, this study combines three various political risk measures, and both equity returns and volatility.

Monetary policy effects on equity returns: application of SVAR identified with high-frequency external instrument

The purpose of this study is to examine the effects of monetary policy on equity returns by applying an alternative econometric approach. Campbell and Ammer (1993) decomposed unexpected equity excess returns into three news components: risk premium news, real interest rate news and cash-flow news. The literature has determined the monetary policy (MP) effects on these news components. The authors propose an alternative MP shock identification approach to analyze the MP effects on the above-mentioned news components under a structural vector autoregression (SVAR) setup. Under this approach, one can apply an MP indicator in the SVAR, which helps forecast equity excess returns along with its external instruments for identification. Further, this study uses the various recently proposed measures of exogenous MP shocks and Fed information shocks as external instruments, and shows the different patterns of the news components' responses depending on the information in the applied instruments.

The Information Conveyed in a SPAC′s Offering

The popularity of SPACs (Special Purpose Acquisition Companies) has grown dramatically in recent years as a substitute for the traditional IPO (Initial Public Offer). We modeled the average annual return for SPAC investors and found that this financial tool produced an annual return of 17.3%. We then constructed an information model that examined a SPAC′s excess returns during the 60 days after a potential merger or acquisition had been announced. We found that the announcement had a major impact on the SPAC’s share price over the 60 days, delivering on average 0.69% daily excess returns over the IPO portfolio and 31.6% cumulative excess returns for the entire period. Relative to IPOs, the cumulative excess returns of SPACs rose dramatically in the next few days after the potential merger or acquisition announcement until the 26th day. They then declined but rose again until the 48th day after the announcement. Finally, the SPAC’s structure reduced the investors’ risk. Thus, if investors buy a SPAC stock immediately after a potential merger or acquisition has been announced and hold it for 48 days, they can reap substantial short-term returns.

Application of multi-criteria decision analysis for investment strategies in the Indian equity market

In the Indian equity market, the Systematic Investment Plan (SIP) is the most popular strategy due to its convenience for disciplined investing regardless of market conditions. This study analyzes the excess returns of an extensive dataset of listed Indian companies from 2010 to 2019, along with a value-based version of the Multi-Criteria Decision Analysis (MCDA), to identify top performing stocks, based on their sectors and market capitalization. The findings of the study provide empirical evidence of Value Averaging (VA) as a viable alternative strategy over SIP (also known as Dollar Cost Averaging or Rupee Cost Averaging) as 352 out of 359 companies yielded higher returns under VA. The superiority of the VA strategy over the SIP was particularly marked in the consumer goods, financial services and industrial manufacturing sectors, with a clear dominance of small cap companies. The results also show that risk factors for VA strategy play an important role and should be taken into account, rather than base investment decisions on excess returns alone. The efficiency scores of individual stocks provide important insights for mutual funds, financial brokers and individual investors in India.