Date Published: February 8, 2018
Publisher: Public Library of Science
Author(s): Hongjun Guan, Zongli Dai, Aiwu Zhao, Jie He, Zhaohong Deng.
In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day’s value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.
Forecasting is an important means of reducing risk and increase revenue in financial sector. Stock price prediction models can be divided into two categories: statistical model and artificial intelligence model. The former models include ANFIS , ARIMA , ARCH , GARCH , and so on. In such models, the variables must strictly obey the restrictive assumptions of linear or normal distribution. However, because of the uncertainty and complexity of the stock market, it is difficult to make out a strict normal assumption for a linear prediction model. Wang  studied the relationship between stock price and the changing of investors’ social network. He established a mathematical model based on fuzzy method. However, such models based on external factors are varying from different stock markets. What is more, other external factors, such as economic environment, policy changing and so on also have great relationship with the fluctuation of a stock market. In fact, historical data can somewhat reflect the internal rule for the evolution of a stock market. Artificial intelligence models can reveal the internal rule and therefore achieve the desired results without any strict assumptions. Such models have better nonlinear processing capabilities, so many researchers have applied it to the prediction of various fields [6–8], such as Mishra use it forecast the PM 2.5 during haze episodes. Raza proposed artificial intelligence method to forecast the load demand of smart grid.
In this paper, we propose a novel forecasting model based on High-Order Fuzzy-FluctuationTrends and BP Neural NetworkMachine Learning. In order to compare the forecasting results with other researchers’ work, the authentic TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) is employed to illustrate the forecasting process. The data from January 1999 to October 1999 are used as training time series and the data from November 1999 to December 1999 are used as testing dataset. The basic steps of the proposed model are shown(Fig 2).
This paper presents a prediction model based on high order fuzzy fluctuation and BP neural network. This method is based on the high order fuzzy logic relation of time series and then uses the self-learning of BPNN to automatically find the optimal prediction rules to predict the fluctuation trend. The greatest advantage of this approach is that the fuzzy theory, stock market fluctuation model and neural network algorithm are combined to construct a new model, which solves the problem of overfitting and over-fuzzy existing models. Experiments show that the parameters generated from the training data set can also be used for future data sets. To compare the performance of other methods, we take TAIEX1999 as an example. We also predicted the validity and universality of TAIEX 1997-2005and Shanghai Stock Exchange Composite Index (SHSECI) from 2007 to 2015. The model presented in this paper has a significant advantage in universality, flexibility and comprehensibility. However, because of the influence of changing external factors, the accuracy of the forecasting results is just acceptable comparing with other models. In further research, we will take more consideration of the influence of external factors to improve the accuracy. Moreover, we will consider other factors that may affect the volatility of the stock market, such as trading volume, starting value, final value, etc. We will also consider the impact of other stock markets, such as the Dow Jones, the NASDAQ, and so on.