Hurst exponent behavior and assessment of the MENA stock markets efficiency

In this paper, we test the evolving efficiency of MENA stock markets. Our empirical approach is founded on the behavior of the Hurst exponent over time. We computed the Hurst exponent using a rolling sample with a time window of 4 years. The empirical investigation has been conducted on the major Middle East and North African stock markets. The sample data covers in daily frequency the period (January 1997 to December 2007). Our empirical results show that all MENA stock returns exhibit long-range memory and certain markets are becoming more efficient. Ranking MENA stock markets by efficiency with our measures of long-range dependence have shown that Israel's, Turkey's and Egypt's markets are the less inefficient markets in this region. Furthermore, we have founded evidence of statistically significant rank correlation between the measure of long-range dependence and average trading costs, market capitalization and anti-self-dealing index, which suggests that these variables play a role in explaining these differences in the stage of inefficiency.     

Nonlinear duration dependence in stock market cycles

We reexamine duration dependence in stock market cycles using a generalized Weibull model. Recent empirical work by Cochran and DeFina [Cochran, S. J., & DeFina, R. H. (1995). Duration dependence in the U.S. stock market cycle: A parametric approach. Applied Financial Economics, 5, 309-318.], who use a chronology of stock market cycles to estimate a Weibull hazard model, shows duration dependence in stock prices. They find evidence of duration dependence in prewar market expansions and postwar market contractions. We update their postwar sample, then use a more flexible model that finds evidence of duration dependence for all prewar and postwar samples. The generalized Weibull model is shown to be statistically superior to the conventional Weibull model for all samples except prewar expansions.     

Does the Stock Market Overreact?

Research in experimental psychology suggests that, in violation of Bayes' rule, most people tend to “overreact” to unexpected and dramatic news events. This study of market efficiency investigates whether such behavior affects stock prices. The empirical evidence, based on CRSP monthly return data, is consistent with the overreaction hypothesis. Substantial weak form market inefficiencies are discovered. The results also shed new light on the January returns earned by prior “winners” and “losers.” Portfolios of losers experience exceptionally large January returns as late as five years after portfolio formation.     

Estimation and pricing under long-memory stochastic volatility

We treat the problem of option pricing under a stochastic volatility model that exhibits long-range dependence. We model the price process as a Geometric Brownian Motion with volatility evolving as a fractional Ornstein–Uhlenbeck process. We assume that the model has long-memory, thus the memory parameter H in the volatility is greater than 0.5. Although the price process evolves in continuous time, the reality is that observations can only be collected in discrete time. Using historical stock price information we adapt an interacting particle stochastic filtering algorithm to estimate the stochastic volatility empirical distribution. In order to deal with the pricing problem we construct a multinomial recombining tree using sampled values of the volatility from the stochastic volatility empirical measure. Moreover, we describe how to estimate the parameters of our model, including the long-memory parameter of the fractional Brownian motion that drives the volatility process using an implied method. Finally, we compute option prices on the S&P 500 index and we compare our estimated prices with the market option prices.     

Why Do Price Limits Exist in Stock Markets? A Manipulation‐Based Explanation

Numerous stock market regulators around the world impose daily price limits on individual stock price movements. We derive a simple model that shows that price limits may deter stock market manipulators. Based on our model's implications, we predict that regulators impose price limit rules for markets where the likelihood of manipulation is high. We present empirical evidence consistent with this hypothesis. Our study is the first to formally propose a manipulation‐based rationale for the existence of price limits in stock markets.     

Nonparametric rank tests for event studies

Because stock prices are not normally distributed, the power of nonparametric rank tests dominate parametric tests in event study analyses of abnormal returns on a single day. However, problems arise in the application of nonparametric tests to multiple day analyses of cumulative abnormal returns (CARs) that have caused researchers to normally rely upon parametric tests. In an effort to overcome this shortfall, this paper proposes a generalized rank (GRANK) testing procedure that can be used on both single day and cumulative abnormal returns. Asymptotic distributions of the associated test statistics are derived, and their empirical properties are studied with simulations of CRSP returns. The results show that the proposed GRANK procedure outperforms previous rank tests of CARs and is robust to abnormal return serial correlation and event-induced volatility. Moreover, the GRANK procedure exhibits superior empirical power relative to popular parametric tests.     

Price informativeness and stock return synchronicity: Evidence from the pricing of seasoned equity offerings

We investigate what stock return synchronicity reflects in terms of price informativeness by examining its effect on the pricing of seasoned equity offerings (SEOs). Based on 5,087 SEOs from 1984 to 2007, we find a significantly negative relation between stock return synchronicity (estimated as the logit transformation of the R-squared statistic from a two-factor regression) and SEO discounts (the percentage differences between pre-offer day closing prices and offer prices). The negative relation is strongest when there is no analyst coverage, and it declines as analyst coverage increases. This shows that stock price is more informative when stock return synchronicity is higher and also that information asymmetry can be mitigated by analyst coverage. We further decompose stock return synchronicity into the market comovement and industry comovement components and find that both components are equally important in affecting SEO discounts.     

COMPARATIVE EFFICIENCY OF MARKET INDICES: AN EMPIRICAL STUDY

This study ranks eight widely used market indices using two mean-variance efficiency scores developed from Roll's geometric representation of the Shanken's CRS T² test statistic and the likelihood ratio test statistic of Kandel. The results indicate that, over the period of 1973–1982, the CRSP equally weighted, CRSP value weighted, and S&P 500 emerge consistently as the three most efficient indices under all market conditions.     

Do Islamic Bond (Sukuk) Prices Reflect Financial and Policy Uncertainty? A Quantile Regression Approach

We studied the relationship between Islamic bond (sukuk) prices and financial and policy uncertainty conditions using a quantile regression approach. Our empirical results for the period 2010–2014 show that US bond prices had a negative impact and causality effects on sukuk prices, whereas European Monetary Union bond prices only co-moved with sukuk prices. We also show that financial uncertainty had a negative effect that was limited to intermediate sukuk quantiles; moreover, sukuk prices were not affected by economic policy uncertainty or stock market returns. Therefore, although Islamic bonds are distinctive assets, their price dynamics are dependent on other bond-related asset prices and so incorporate financial market uncertainty.     

Testing weak form efficiency on the Toronto Stock Exchange

In order to test for weak form efficiency in the market a vast pool of individual stocks must be analyzed rather than a stock market index. In this paper, a model-based bootstrap is used to generate a series of simulated trials and a modified chart pattern recognition algorithm is applied to all stocks listed on the Toronto Stock Exchange (TSX). The number of patterns detected in the original price series is compared with the number of patterns found in the simulated series. By simulating the price path specific time dependencies present in real data are eliminated, making price changes purely random. Patterns, if consistently identified, carry information which adds value to the investment process, however, this informativeness does not guarantee profitability. Conclusions are drawn on the relative efficiency of some sectors of the economy. Although the null hypothesis of weak form efficiency on the TSX cannot be rejected, some sectors of the Canadian economy appear to be less efficient than others. In addition, pattern frequencies appear to be negatively dependent on the two moments of return distributions, variance and kurtosis.