This study predicts stock market volatility and applies them to the standard problem in finance, namely, asset allocation. Based on machine learning and model averaging approaches, we integrate the drivers’ predictive information to forecast market volatilities. Using various evaluation...

Since Liu, Nissim and Thomas (LNT, 2002), researchers have been perplexed by how simple earnings forecasts using multiples, apparently outperform the theory-based residual income model in terms of pricing error. This paper explains mathematically how LNT (2002) find this curious result and...

Previous empirical studies derive the standard equity valuation models (i.e., DDM, RIM, and DCF model) while assuming that ideal conditions, such as infinite payoffs and clean surplus accounting, exist. Because these conditions are rarely met, we extend the standard models by following the...

This paper revisits two valuation models based on accounting figures: the Residual Income Valuation (RIV) and Abnormal Earnings Growth (AEG). Our research design has two approaches: i) we demonstrate theoretical integration of both models; and ii) we show in a practical manner that models...

Standard equity valuation approaches (i.e., DDM, RIM, and DCF model) are derived under the assumption of ideal conditions, such as infinite payoffs and clean surplus accounting. Because these conditions are hardly ever met, we extend the standard approaches, based on the fundamental principle of...

The Baker and Wurgler (2006) sentiment index purports to measure irrational investor sentiment, while the University of Michigan Consumer Sentiment Index is designed to largely reflect fundamentals. Removing this fundamental component from the Baker and Wurgler index creates an index of investor...

This paper aims to forecast the Market Risk premium (MRP) in the US stock market by applying machine learning techniques, namely the Multilayer Perceptron Network (MLP), the Elman Network (EN) and the Higher Order Neural Network (HONN). Furthermore, Univariate ARMA and Exponential Smoothing...

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)...

Is univariate or multivariate modelling more effective when forecasting the market risk of stock portfolios? We examine this question in the context of forecasting the one-week-ahead Expected Shortfall of a portfolio invested in the Fama-French and momentum factors. Apply ingextensive tests and...