An Efficient Stock Recommendation Model Based on Big Order Net Inflow

Abstract

In general, the stock trend is mainly driven by the big order transactions. Believing that the stock rise with a large volume is closely associated with the big order net inflow, we propose an efficient stock recommendation model based on big order net inflow in the paper. In order to compute the big order net inflow of stock, we use the M/G/1 queue system to measure all tick-by-tick transaction data. Based on an indicator of the big order net inflow of stock, we select some stocks with the higher value of the net inflow to constitute the prerecommended stock set for the target investor user. In order to recommend some stocks with which this style is familiar them to the target users, we divide lots of investors into several categories using fuzzy clustering method and we should do our best to choose stocks from the stock set once operated by those investors who are in the same category with the target user. The experiment results show that the recommended stocks have better gains during the several days after the recommended stock day and the proposed model can provide reliable investment guidance for the target investors and let them get more stock returns.