Abstract
Personalized recommended method is widely used to recommend commodities for target customers in e-commerce sector. The core idea of merchandise personalized recommendation can be applied to financial field, which can also achieve stock personalized recommendation. This paper proposes a new recommended method using collaborative filtering based on user fuzzy clustering and predicts the trend of those stocks based on money flow. We use M/G/1 queue system with multiple vacations and server close-down time to measure practical money flow. Based on the indicated results of money flow, we can select the more valued stock to recommend to investors. The experimental results show that the proposed method provides investors with reliable practical investment guidance and receiving more returns.
Highlights
The scale of the stock market is growing stronger; stock investment as a kind of high-risk and high-reward investment highlights people’s high attention
This paper proposes a new recommended method using collaborative filtering based on user fuzzy clustering and predicts the trend of those stocks based on money flow
We proposed a personalized stock recommended method based on money flow model
Summary
The scale of the stock market is growing stronger; stock investment as a kind of high-risk and high-reward investment highlights people’s high attention. Current stock recommended methods are mainly concentrated on two types, online stock recommended methods based on stock comment and price forecasting model based on mathematical analysis The former cannot meet the demands of investors personalized recommendations, and the application process of latter method is relatively complex; it has certain difficulty for investors to understand and master. Our goal in this paper is to propose a novel personalized stock recommended method based on money flow. The new personalized recommended method based on money flow using the indicators for investors to measure the capital and the pulsation of all market and considering investors’ preferences and behavior characteristics, which can improve the existing deficiencies of some current stock recommendation. The proposed method can analyze and filter the recommendation stock returns and improve the investment benefits of investors.
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