Quantitative Factor Exploration Based on Insider Trading Detection

Abstract

Factor research has always been the focus of financial quantitative forecasting research. In the existing multi-factor strategies, we have noticed that the combination modes of factors are different and arbitrary. We hope to develop a more accurate and effective multi-factor model by selecting the most common and most interpretable multi-factors and combining them with equal weight method and assigned weights developed by Hidden Markov Model after some optimizations applied to selected multi-factors. At the same time, we noticed that in the existing data information, there are reports or information that reveal the insider trading of related companies. The existing reports show that the abnormal data volume caused by insider trading will make the prediction of the model inaccurate. Therefore, we added insider trading as a factor into our model training through the order imbalance algorithm to obtain more accurate prediction results. The results show that the multi-factor model is interpretable and effective, and its effect is better than the predicted value than that of the single factor model. After adding the related factors of insider trading into the forecast, it has a certain normalization effect on the original predicted value with large deviation, but has little influence on the effect of the original value with small deviation, which proves the effectiveness of our factor based on insider trading.