We empirically analyze equity premium predictions with “traditional” linear regression
models and tree-based machine learning approaches. Based on a commonly used dataset …

We propose an asset allocation strategy that engineers optimal weights before feeding them
to a supervised learning algorithm. In contrast to the traditional approaches, the machine is …

We propose a simple approach to bridge between portfolio theory and machine learning.
The outcome is an out-of-sample machine learning efficient frontier based on two assets …

One of the main challenges facing researchers and industry professionals for decades is the
successful prediction of asset returns. This paper enriches this endeavor by applying …

A wide body of academic literature suggests that macro factors can be significant drivers of
asset returns. And among practitioners, statements such as “stocks make money in …

Empirical evidence for the price-dividend ratio to be a predictor of the equity premium is
weak. We argue that changes in the economic conditions and market composition lead to a …

In the late 1990s, Samuelson offered a dictum suggesting that the equity market, although
micro efficient, is also macro inefficient. That is, at the aggregate level, the market does not …

We postulate that utilizing return prediction models with fundamental, macroeconomic, and
technical indicators instead of using historical averages should result in superior asset …

The fair value of an option is given by breakeven volatility, the value of implied volatility that
sets the profit and loss of a delta-hedged option to zero. We calculate breakeven volatility for …

We present novel evidence of factor timing in the Chinese stock market. Actively managed
Chinese stock mutual funds have larger exposure to the size factor when it performs well …