This study exploits information contained in high frequency sample data by computing higher realized moments of individual firms in the emerging stock market of Pakistan. Furthermore, the relation of higher moments with future stock returns is examined by constructing decile portfolios based on...

Using a large hand-collected dataset, we provide novel evidence on the additional information embedded in the designs and graphs of financial reports. We find that firms that add graphic financial reports experience a positive 2.7% abnormal returns in the following 3 to 6 months. The finding...

Which market has leading informational advantage: stocks or options? Using large set of stock and option characteristics, and machine learning, we provide a comprehensive analysis of which characteristics are the first order importance predictors of options and stock returns. First, we find that...

This paper examines the main drawbacks of technical analysis. Although this is widely used by practitioners, from an academic perspective it can only be seen as a form of "voodoo finance". In particular, it runs into the following pitfalls: Subjectivity; Doubtful assumptions; Unjustified...

Using a novel equity lending dataset, this paper is the first to show that expected returns strongly and negatively predict future equity lending fees. In comparing two expected return measures, I find that a rational expected return has stronger predictive power of future short selling activity...

Financial markets are expected to predict macroeconomic conditions as their movements depends upon expectations of future performance. However, existing evidence is mixed. We argue that this is because the stock return and term structure series typically used in studies fail to capture investor...

The purpose of this study is to explore a model in which asset prices are endogenously determined by information acquisition when investors have different prediction abilities. The authors discuss how equilibrium price and investor's demand for information are affected by investors' risk...

This paper documents that factors extracted from a large set of macroeconomic variables bear useful information for predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression models improve upon both benchmark models that only...

We perform a comprehensive examination of the recursive, comparative predictive performance of a number of linear and non-linear models for UK stock and bond returns. We estimate Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STR) regime switching models,...

This paper is concerned with empirical and theoretical basis of the Efficient Market Hypothesis (EMH). The paper begins with an overview of the statistical properties of asset returns at different frequencies (daily, weekly and monthly), and considers the evidence on return predictability, risk...