This paper introduces a new algorithm for exploiting time-series predictability-based patterns to obtain an abnormal return, or alpha, with respect to a given benchmark asset pricing model. The algorithm proposes a deterministic daily market timing strategy that decides between being fully...

We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the "implementable efficient frontier." While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading...

In this paper, we document the importance of memory in machine learning (ML)-based models relying on firm characteristics for asset pricing. We find that predictive algorithms perform best when they are trained on long samples, with long-term returns as dependent variables. In addition, we...

Traditional portfolio optimization models specify placement of capital as rather irrevocably and fully at risk through investment horizon(s) or continuously. Under this constraint, asset class allocation typically serves as primary mode of diversification, pursuing risk moderation by seeking to...

This article derives a new formula for the yield elasticity of bond price. The formula provides accurate results without resorting to complex mathematics, and gives new meaning to the concept of duration in fixed-income analysis

Rebalancing alpha is the excess return of a fixed-weight portfolio, which is regularly rebalanced, over its buy-and-hold counterpart. Two kinds of effects, both results of portfolio rebalancing, contribute to rebalancing alpha. The first is a volatility effect that arises from randomness of...

We examine in this paper the training and test set performance of several equity factor models with a dataset of 20 years of data, 1,200 stocks and 100 factors. First, we examine several models to forecast expected returns, which can be used as baselines for more complex models: linear...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...