Automated machine learning extends the search space to include hyperparameters and algorithm selection. We apply automated machine learning (AutoML) to cross sectional stock return prediction with factors. We formulate factor dimension reduction and hyperparameter tuning in conventional ML...

Automated machine learning extends the search space to include hyperparameters and algorithm selection. We apply automated machine learning (AutoML) to cross sectional stock return prediction with factors. We formulate factor dimension reduction and hyperparameter tuning in conventional ML...

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 propose a 4-factor model for the Chinese stock market by adding a trend factor into the market, size, and value of Liu, Stambaugh, and Yuan's (2019) 3-factor model. Because of up to 80% of individual trading, the trend factor captures salient relevant price and volume trends, and earns a...

We explore the effects of fundamental extrapolation on stock returns. Empirically, we propose a novel approach to extrapolate firms' fundamental information and find that a strategy based on fundamental extrapolation earns an average return of 0.80% per month. Theoretically, we show that...

In this paper, we propose a stop-loss strategy to limit the downside risk of the well-known momentum strategy. At a stop-level of 10%, we find, with data from January 1926 to December 2013, that the maximum monthly losses of the equal- and value-weighted momentum strategies go down from -49.79%...

In this paper, we provide a trend factor that captures simultaneously all three stock price trends: the short-, intermediate-, and long-term, by exploiting information in moving average prices of various time lengths whose predictive power is justified by a proposed general equilibrium model. It...

Using time-series trends of a set of firms' major fundamentals, we find that there is a fundamentalmomentum in the stock market. Buying stocks in the top quintile of fundamental trends and selling stocks in the bottom quintile earns a monthly average return of 0.88%, whose magnitude is...

Based on a rational option pricing framework that incorporates short-selling and margin-trading constraints in the stock market, we present evidence that Chinese warrant prices, which are regarded as bubbles in the previous literature, can be explained by a new option pricing model. Based on the...

In this paper, we analyze the usefulness of technical analysis, specifically the widely used moving average trading rule, from an asset allocation perspective. We show that when stock returns are predictable, technical analysis adds value to commonly used allocation rules that invest fixed...