Mathematical Problems in Engineering

Volume 2016, Article ID 5725143, 15 pages

http://dx.doi.org/10.1155/2016/5725143

## An Efficient Stock Recommendation Model Based on Big Order Net Inflow

^{1}School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China^{2}Department of Computer Science and Technology, Huaihua University, Huaihua 418008, China^{3}Hunan Provincial Key Laboratory of Ecological Agriculture Intelligent Control Technology, Huaihua 418008, China

Received 14 August 2015; Revised 10 December 2015; Accepted 15 December 2015

Academic Editor: David Bigaud

Copyright © 2016 Yang Yujun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### 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.

#### 1. Introduction

In the area of stock recommendation method research [1], most of the research mainly focuses on the two areas: stock recommendation methods based on stock comment [2] and price forecasting [3]. The former is easy to understand and master for investors. However, in such a complicated stock market, the investors do not know which one to believe among the lots of stock comments with dubious authenticity and every choice has a great risk for them. The latter method [4] is difficulty for investors to understand and master since the application of the latter method is relatively complex and involves a lot of profound mathematical knowledge [5]. Given this situation, many scholars have done a lot of research on the stock recommendation [6].

Currently, the stock recommendation based on price forecasting relies [7] mainly on mathematical and statistical methods [8], time series model [9, 10], and machine learning model [11]. Sonsino and Shavit [12] have researched a stock prediction and selection method based on unidentified historical data. M.-Y. Chen and B.-T. Chen [13] have proposed a stock price forecasting method based on the hybrid fuzzy time series and granular computing. Xin et al. [14] have given a strategy for filtering out users with similar demand characteristics by using collaborative recommendation algorithm with fuzzy clustering method, which shows excellent recommendation effect.

In present financial field, how to integrate multiple technologies [15], such as data mining, machine learning and herd psychology [11], and other nontraditional technologies, into stock recommendation has become a hot topic. Few papers use money net inflow as stocks recommendation techniques. Given this situation, we proposed an efficient stock recommendation model based on big order net inflow in the paper. At first, we divide lots of users into several categories utilizing collaborative filtering algorithm based on user fuzzy clustering [16]. We get some stocks from the stocks once operated by those users in same category and form a prerecommended stock set. Then, we use a method based on M/G/1 [17] to compute the net inflow amount of big order for every stock in the prerecommended stock set. From the prerecommended stock set descending ordered by the value of big order net inflow, we choose some stocks with highest value in front of the set as the last recommendation stock set for the target user.

In general, we believe that the stock rise with a large trading volume is closely related to the purchase stock of big order. In order to analyze the big order net flow of stock, we need to observe the stock trading volume and turnover. The money net flow and the money flow [18] are different in concept. The big order refers to the amount of each transaction over one million yuan or the volume of each transaction over fifty thousand in a single transaction. So the big order net inflow refers to the amount of money of big order buy or sell of the same stocks within a day. Most of the time, the money flow is bigger than zero, and the money net flow is less than zero. In individual cases, the money net flow is bigger than zero, and the big order net flow is less than zero. Under this situation, we proposed an efficient stock recommendation model based on big order net inflow. The new recommendation model can measure the capital and the pulsation of the stock markets and consider investors preferences and behavior characteristics; it can improve the existing deficiencies of some current stock recommendation. In addition, the new recommendation model can analyze and filter the stock with less returns in the future and improve the investment gains of investors. 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 investment returns.

The rest of this paper is organized as follows. Section 2 briefly reviews the definitions and theorems of fuzzy clustering and the framework of the collaborative filtering algorithm based on user fuzzy clustering. Section 3 demonstrates the method for computing big order net inflow and the framework of the proposed model. Section 4 presents the simulation experiment and empirical analyses of the proposed model; finally some conclusions are given and some future works are pointed out in Section 5.

#### 2. Theoretical and Modeling Framework

The concept of fuzzy set [16] in fuzzy cluster is put forward by Zadeh in 1965. Gath and Bar-On earlier applied that theory to compute the scoring of poly graphic sleep recordings in their study [19]. In this section, we will briefly review the definitions and theoretic of fuzzy cluster.

##### 2.1. Fuzzy Clustering Theory

*Definition 1. *Defined set , a matrix with rows and columns; if , then is called fuzzy matrix . When is only 0 or 1, said is a Boolean matrix. When elements of the diagonal are all 1 in fuzzy matrix , said is reflexive fuzzy matrix.

*Definition 2. *If is -order square, defined , , and .

*Definition 3. *Defined set , and called is the transposed matrix of , where .

*Definition 4. *Defined set , for any , and said is the -cut matrix of the fuzzy matrix* R*. When , and , , said is a Boolean matrix, and said is a confidence level parameter or cut level parameter.

*Definition 5. *Suppose two limited discourse domains and ; then to fuzzy relation is an order fuzzy matrix, and set , where represents relevance on fuzzy relation .

Theorem 6. *If is fuzzy similar matrix, then, for any natural number , is fuzzy similarity matrix.*

Theorem 7. *If is an order fuzzy similar matrix, then there is minimum natural number , for all natural number ; constant , , namely, fuzzy equivalent matrix . At this point, said is the transitive closure of , denoted by .*

*Method 8. *If is fuzzy similar matrix, then the following method will solve the transitive closure , and the said method is the square self-synthesis method ().

##### 2.2. Fuzzy Clustering Analysis

The fuzzy clustering analysis is an analyzing clustering and classification method by establishing fuzzy similar relationship of objective things based on the objective characteristics, the degree of closeness, and similarity between objective things.

In Figure 1, the fuzzy clustering processing model can be divided into four stages, namely, the data preprocessing, the data standardization, the constructing fuzzy similar matrix (FSM), and the clustering and analysis.