This study brings to light the new empirical fact that flows into US domestic equity mutual funds depend less on past fund returns when the risk-free rate declines. A one-percent drop in interest rates is associated with a decrease in the slope of the flow-performance relationship of around 10%....

For various organizational reasons, large investors typically split their portfolio decision into two stages - asset allocation and stock selection. We hypothesise that mean-variance models are superior to equal weighting for asset allocation, while the reverse applies for stock selection, as...

We use long-distance running as a quasi-natural experiment and study whether endurance activities affect fund managers' trading behavior. We find that funds with a larger share of marathon runner managers are less prone to the disposition effect. A higher representation of runner managers also...

We integrate fundamental analysis with mean-variance portfolio optimization to form fully optimized fundamental portfolios. We find that fully optimized fundamental portfolios produce large out-of-sample factor alphas with high Sharpe ratios. They substantially outperform equal-weighted and...

What predicts returns on assets with "hard-to-value" fundamentals, such as Bitcoin and stocks in new industries? We propose an equilibrium model that shows how rational learning enables return predictability through technical analysis. We document that ratios of prices to their moving averages...

We introduce a new measure of stock misevaluation, 𝑄, which is consistent with the Gordon growth model for firm valuation. In our empirical application, we use 𝑄 to relate analyst forecasts to stock returns and measure the profitability of investment strategies that rely on information in...

Popular conceptions of long–short investing are distorted by a number of myths, many of which appear to result from viewing long–short from a conventional investment perspective. Long–short portfolios differ fundamentally from long-only portfolios in construction, in the measurement of...

We combine insights from machine learning and finance research to build machine learning algorithms for stock selection. Our study builds on weekly data for the historical constituents of the S&P 500 over the period from 1999 to 2019 and includes typical equity factors as well as additional...

Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can uncover subtle, contextual and non-linear relationships,...

We study the portfolio performance of investment strategies that jointly apply both fundamental analysis and technical analysis. Compared with strategies that rely on one-dimensional fundamental or technical information, the integrated approach to fundamental and technical investing...