We establish two previously undocumented patterns in the purchase selections of individual investors and confirm a related pattern. These patterns hinge on investors’ previous experience with a stock. We demonstrate that investors prefer to: (1) repurchase stocks they previously sold for a gain rather than stocks they previously sold for a loss, (2) repurchase stocks that have lost value subseq...

It has been widely debated how much nonsynchronous trading drives asymmetric portfolio cross-autocorrelations: lagged returns on a portfolio of larger-capitalization stocks are far more heavily correlated with current returns on a portfolio of smallercapitalization stocks than the converse. This paper proposes a new method to generate precise estimates of the extent to which nonsynchronous trad...

We measure net arbitrage trading by the difference between abnormal hedge fund equity holdings and abnormal short interest on a stock. In the cross section, net arbitrage trading strongly predicts future stock returns. This predictability is not due to temporary price pressure, cannot be produced using total institutional holdings, but is consistent with information advantage and copycat tradin...

Stock data mining such as financial pairs mining is useful for trading supports and market surveillance. Financial pairs mining targets mining pair relationships between financial entities such as stocks and markets. This paper introduces a fuzzy genetic algorithm framework and strategies for discovering pair relationship in stock data such as in high dimensional trading data by considering use...

We find optimal trading strategies for an insider who is trading in two convergent stocks and is bound by margin constraints.

Utilizing a unique database of daily trading activity, this study examines the ability of active Australian equity managers to earn superior risk-adjusted returns. We find evidence of superior trade performance, where performance is a function of stock size. Our findings indicate that active equity managers are able to successfully exploit private information more readily in stocks ranked 101-1...

A novel algorithm for actively trading stocks is presented. While traditional expert advice and “universal” algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirical results on historical markets provide strong evidence that this type of tec...

A developmental co-evolutionary genetic programming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks across market sectors. Both implementations were found to be impressively robust to market fluctuations while reacting efficiently to opportunities for profit, where PAM DGP proved slightly more reactive to market changes than LGP...

Neurofuzzy approaches for predicting financial time series are investigated and shown to perform well in the context of various trading strategies involving stocks and options. The horizon of prediction is typically a few days and trading strategies are examined using historical data. Two methodologies are presented wherein neural predictors are used to anticipate the general behavior of financ...

The implementation of margin trading and securities lending mechanism offers us a unique circumstance to analyze the impact of short selling regulations in China. We define the addition events as the stocks are included to the designated securities list and therefore can be sold short. By focusing on the 30 trading days around the addition events, the results document statistically significant ...