Predicting Time-Lag Stock Return Using Tactical Asset Allocation Trading Strategies Across Global Stock Indices

Adaptive stock trading with dynamic asset allocation using reinforcement learning

Stock trading is an important decision-making problem that involves both stock selection and asset management. Though many promising results have been reported for predicting prices, selecting stocks, and managing assets using machine-learning techniques, considering all of them is challenging because of their complexity. In this paper, we present a new stock trading method that incorporates dy...

Dynamic Asset Allocation for Stock Trading Optimized by Evolutionary Computation

Effective tradingwith given pattern-based multi-predictors of stock price needs an intelligent asset allocation strategy. In this paper, we study a method of dynamic asset allocation, called the meta policy, which decides how much the proportion of asset should be allocated to each recommendation for trade. The meta policy makes a decision considering both the recommending information of multi-...

Does Stock Return Predictability Imply Improved Asset Allocation and Performance?

Predicting stock market indices movements

This paper examines the extent to which the daily movements of three large emerging markets stock indices are predictable. Lagged technical indicators are used as explanatory variables. In the analysis we employed seven classification techniques and assessed the discriminatory power of the classifiers through the area under the receiver operating characteristic (ROC) curve. The results show tha...

Stock-Return Predictability and Asset Pricing Models

The regression of stock returns on predictive variables, such as dividend yield, has proven useful in optimal portfolio selection when investment opportunities are timevarying. Conditional versions of factor models impose a restriction on that regression, thereby implying a particular portfolio choice. The study examines several pricing models from a perspective of conditional mean-variance opt...