Multi Factor Quantitative Stock and Transaction Timing Selection Model Based on Information Coefficient Mean Value Scoring

With the continuous development of automation and computer technology, information tools with high computational ability has been widely employed in stock transaction industry. Moreover, due to the subjective influence of people and the unstable return of traditional investment, more and more institutions gradually start to use the quantitative stock selection model in order to get higher return in pursuit of lower risk. As the most classic algorithm in quantitative stock selection, multi factor model is favored by many institutional investors. However, due to the difference of financial system between China and western countries, the existing quantitative model in western countries is not fully applicable in China. At the same time, the previous multi factor stock selection models have more subjective factors and less consideration of time series, so the earning rate is usually not so good. In order to pursue a higher earning rate, in this paper, by taking the impact of time series into consideration, a multi factor quantitative stock and transaction timing selection model has been proposed based on information coefficient (IC) mean value scoring. And by choosing constituent stocks in Shanghai and Shenzhen 300 (HS300) Stock Index have been chosen as the target, the effectiveness of proposed model has been analyzed and verified.