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...

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...

While common machine learning algorithms focus on minimizing the mean-square errors of model fit, we show that genetic programming, GP, is well-suited to maximize an economic objective, the Sharpe ratio of the usual spread portfolio in the cross-section of expected stock returns. In contrast to...

On 9 October 2007, the Dow Jones Industrial Average reached a high of 14,164.53; by 9 March 2009, it had dropped about 54 percent, to a low of 6,547.05. Former Fed chairman Alan Greenspan called this a “once-in-a-century” crisis. The authors show that the probability of a stock market drop of...

In the recent financial crisis, the Dow Jones stock market index dropped about 54% from a high of 14164.53 on October 9, 2007 to a low of 6547.05 on March 9, 2009. Alan Greenspan calls this a 'once-in-a century' crisis. While we do not know how he drew his conclusion, we show that the...

In practice, traders, such as high-frequency and day traders, rely in part or primarily on moving averages to predict market directions, but their equilibrium impact is unknown. This paper presents a model to analyze how such technical traders compete trading with informed investors and how they...

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...

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...