This paper examines the time-series predictability of aggregate stock returns in 20 emerging markets. In contrast to the aggregate-level findings in US, earnings yield forecasts the time-series of aggregate stock returns in emerging markets. We consider aggregate earnings not as normalizing...

The most popular method of calculating asset prices is the capital asset pricing model (CAPM). What is the appropriate amount of years to use in the estimation and which variation of the capital asset pricing beta provides the best results? This research looks at the out of sample forecasting...

We examine whether the predictability and business-cycle dependence of excess returns in US Treasuries can be more naturally explained in terms of state-dependent risk premia or a specific cognitive bias (representativeness). We show that the extremely parsimonious cognitive-bias model in...

This paper provides global evidence supporting the hypothesis that expected return models are enhanced by the inclusion of variables that describe the evolution of book-to-market-changes in book value, changes in price, and net share issues. This conclusion is supported using data representing...

We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer...

Capital asset pricing model is a popular formula using to calculate asset prices. This research looks at the sample forecasting of three CAPM constant beta model from 2005 to 2014. This research is going to look at the capabilities of CAPM by using the past varying. Five US sectors, five ASEAN...

This paper examines the evidence regarding predictability in the market risk premium using artificial neural networks (ANNs), namely the Elman Network (EN) and the Higher Order Neural network (HONN), univariate ARMA and exponential smoothing techniques, such as Single Exponential Smoothing (SES)...

This paper examines the evidence regarding predictability in the market risk premium using artificial neural networks (ANNs), namely the Elman Network (EN) and the Higher Order Neural network (HONN), univariate ARMA and exponential smoothing techniques, such as Single Exponential Smoothing (SES)...

This paper studies the time-series predictability of currency carry trades, constructed by selecting currencies to be bought or sold against the U.S. dollar, based on forward discounts. Changes in a commodity index, currency volatility and, to a lesser extent, a measure of liquidity predict...