Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems.     

基于神经网络的股票中期预测

本文给出了一种基于BP神经网络的股票市场建模、预测以及决策方法.应用神经网络进行股票中期预测,输入数据的复杂性给网络训练效率和预测精度造成了显著的负面影响.我们应用模糊曲线分析法进行了输入变量的筛选,该方法主要是用来压缩输入数据的维度,发现影响产出变量的重要因素.它通过求相关度,贡献弹性,根据样本点拟合样本曲线,最后选取出影响变量的重要因素.结果表明,经该方法处理后的数据输入神经网络不仅减少了输入数据量,使训练时间减少,运算速度提高,而且预测精度有了明显的改善.     

SDAE-LSTM模型在金融时间序列预测中的应用

针对金融时间序列预测的复杂性和长期依赖性，提出了一种基于深度学习的LSTM神经网络预测模型。利用堆叠去噪自编码从金融时间序列的基本行情数据和技术指标中提取特征，将其作为LSTM神经网络的输入对金融时间序列进行预测；通过LSTM神经网络的长期依赖特性来提高金融时间序列的预测精度。利用股价指数数据，与传统的神经网络的预测结果进行比较，结果表明基于深度学习的LSTM神经网络具有比较高的预测精度。     

基于遗传算法的模糊神经网络股市建模与预测

提出一种基于模糊神经网络的股票市场建模与预测方法，并采用遗传算法训练网络权值及模糊子集的划分，对于上证指数及个股的建模与预测结果表明，该方法具有很强的学习与泛化能力，在处理诸如股票市场上这种具有一定程度不确定性的非互性的建模与预测方面有很发的价值。     

A combination of hidden Markov model and fuzzy model for stock market forecasting

This paper presents a novel combination of the hidden Markov model (HMM) and the fuzzy models for forecasting stock market data. In a previous study we used an HMM to identify similar data patterns from the historical data and then used a weighted average to generate a ‘one-day-ahead’ forecast. This paper uses a similar approach to identify data patterns by using the HMM and then uses fuzzy logic to obtain a forecast value. The HMM's log-likelihood for each of the input data vectors is used to partition the dataspace. Each of the divided dataspaces is then used to generate a fuzzy rule. The fuzzy model developed from this approach is tested on stock market data drawn from different sectors. Experimental results clearly show an improved forecasting accuracy compared to other forecasting models such as, ARIMA, artificial neural network (ANN) and another HMM-based forecasting model.     

模糊组合神经网络智能选股模型的建立

针对传统的股票市场预测模型,为了准确地预测股票价格趋势、为广大投资者规避风险,应用模糊逻辑和组合神经网络,利用贝叶斯统计学与组合理论使二者有机结合,提出一种股票市场建模及预测方法。组合神经网络结合BP网络和径向基函数网络(RBF),神经元模糊系统有更强的学习和推理机制,能避免黑箱问题。实证研究结果表明,该方法有较高的预测精度和更好的稳定性。     

人工神经网络在证券价格预测中的应用

证券市场中成功的交易模式是可以模仿及学习的.证券价格走势实质是一种复杂时序函数.人工神经网络是在模仿人脑处理问题过程中发展起来的新型智能信息处理系统,人工神经网络可以通过调节连接权值以任意精度逼近任何连续函数,因此也可以逼近证券价格随时间变换这种函数.文中采用基于BP模型的神经网络,用BP算法和遗传算法来训练网络权值,同时也采用了动量法和学习率自适应调整相结合的策略,对证券市场的价格进行建模和预测,结果表明,此模型具有较好的学习、泛化能力,对股票市场或其他类似的非线性经济系统的走势预测决策具有较好的效果.     

Predicting stock index increments by neural networks: The role of trading volume under different horizons

Recent studies show that there is a significant bidirectional nonlinear causality between stock return and trading volume. In this research, we reinforce this statement and the results presented in some earlier literatures and further investigate whether trading volume can significantly improve the prediction performance of neural networks under short-, medium-and long-term forecasting horizons. An application of component-based neural networks is used in forecasting one-step ahead stock index increments. The models are also augmented by the addition of different combinations of indices’ and component stocks’ trading volumes as inputs to form more general ex-ante forecasting models. Neural networks are trained with the data of stock returns and volumes from NASDAQ, DJIA and STI indices. Results indicate that augmented neural network models with trading volumes lead to improvements, at different extents, in forecasting performance under different terms of forecasting horizon. Empirical results indicate that trading volumes lead to modest improvements on the performance of stock index increments prediction under medium-and long-term horizons.     

基于遗传神经网络的股指预测

股票市场是一项集合许多市场复杂因素的活动,股票分析的方法非常多。将遗传算法用于BP神经网络的训练过程对股票价格的预测,设计一个三层的BP神经网络,优化网络输入,在传统BP神经网络的基础上加入遗传算法。通过实例分析及实际结果表明这种BP神经网络的准确性和科学性。     

Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes

With the economic successes of several Asian economies and their increasingly important roles in the global financial market, the prediction of Asian stock markets has becoming a hot research area. As Asian stock markets are highly dynamic and exhibit wide variation, it may more realistic and practical that assumed the stock indexes of Asian stock markets are nonlinear mixture data. In this research, a time series prediction model by combining nonlinear independent component analysis (NLICA) and neural network is proposed to forecast Asian stock markets. NLICA is a novel feature extraction technique to find independent sources from observed nonlinear mixture data where no relevant data mixing mechanisms are available. In the proposed method, we first use NLICA to transform the input space composed of original time series data into the feature space consisting of independent components representing underlying information of the original data. Then, the ICs are served as the input variables of the neural network to build prediction model. Among the Asian stock markets, Japanese and China’s stock markets are the biggest two in Asia and they respectively represent the two types of stock markets. Therefore, in order to evaluate the performance of the proposed approach, the Nikkei 225 closing index and Shanghai B-share closing index are used as illustrative examples. Experimental results show that the proposed forecasting model not only improves the prediction accuracy of the neural network approach but also outperforms the three comparison methods. The proposed stock index prediction model can be therefore a good alternative for Asian stock market indexes.     

A GA-weighted ANFIS model based on multiple stock market volatility causality for TAIEX forecasting

Stock market forecasting is important and interesting, because the successful prediction of stock prices may promise attractive benefits. The economy of Taiwan relies on international trade deeply, and the fluctuations of international stock markets will impact Taiwan stock market. For this reason, it is a practical way to use the fluctuations of other stock markets as forecasting factors for forecasting the Taiwan stock market. In this paper, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs a genetic algorithm (GA) to refine the weights of rules joining in an ANFIS model to forecast the Taiwan stock index. To evaluate the forecasting performances, the proposed model is compared with four different models: Chen's model, Yu's model, Huarng's model, and the ANFIS model. The results indicate that the proposed model is superior to the listing methods in terms of the root mean squared error (RMSE).     

A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting

In the stock market, technical analysis is a useful method for predicting stock prices. Although, professional stock analysts and fund managers usually make subjective judgments, based on objective technical indicators, it is difficult for non-professionals to apply this forecasting technique because there are too many complex technical indicators to be considered. Moreover, two drawbacks have been found in many of the past forecasting models: (1) statistical assumptions about variables are required for time series models, such as the autoregressive moving average model (ARMA) and the autoregressive conditional heteroscedasticity (ARCH), to produce forecasting models of mathematical equations, and these are not easily understood by stock investors; and (2) the rules mined from some artificial intelligence (AI) algorithms, such as neural networks (NN), are not easily realized.In order to overcome these drawbacks, this paper proposes a hybrid forecasting model, using multi-technical indicators to predict stock price trends. Further, it includes four proposed procedures in the hybrid model to provide efficient rules for forecasting, which are evolved from the extracted rules with high support value, by using the toolset based on rough sets theory (RST): (1) select the essential technical indicators, which are highly related to the future stock price, from the popular indicators based on a correlation matrix; (2) use the cumulative probability distribution approach (CDPA) and minimize the entropy principle approach (MEPA) to partition technical indicator value and daily price fluctuation into linguistic values, based on the characteristics of the data distribution; (3) employ a RST algorithm to extract linguistic rules from the linguistic technical indicator dataset; and (4) utilize genetic algorithms (GAs) to refine the extracted rules to get better forecasting accuracy and stock return. The effectiveness of the proposed model is verified with two types of performance evaluations, accuracy and stock return, and by using a six-year period of the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) as the experiment dataset. The experimental results show that the proposed model is superior to the two listed forecasting models (RST and GAs) in terms of accuracy, and the stock return evaluations have revealed that the profits produced by the proposed model are higher than the three listed models (Buy-and-Hold, RST and GAs).     

基于改进的Elman神经网络的股价预测模型

Elman神经网络是一种典型的回归神经网络,比前向神经网络具有更强的计算能力,具有适应时变特性的能力,因而非常适用于对股市这一类极其复杂的非线性动力学系统进行预测。文中以深市A股中的个股中集集团(股票代号:000039)的共180天的实际收盘价的时间序列作为预测对象,提出基于改进的Elman神经网络的个股价格预测模型,实验结果取得较高的预测精度、较为稳定的预测效果和较快的收敛速度。这表明该预测模型对于个股价格的短期预测是可行和有效的。     

AN ADAPTIVE EXPECTATION GENETIC ALGORITHM BASED ON ANFIS AND MULTINATIONAL STOCK MARKET VOLATILITY CAUSALITY FOR TAIEX FORECASTING

The stock market is a highly complex and dynamic system, and forecasting stock is complicated and difficult. Successful prediction of stock prices may promise attractive benefits; therefore, stock market forecasting is important and of great interest. The economy of Taiwan relies on international trade deeply and the fluctuations of international stock markets impact Taiwan's stock market to certain degree. It is practical to use the fluctuations of other stock markets as forecasting factors for forecasting on the Taiwan stock market. Further, stock market investors usually make short-term decisions based on recent price fluctuations, but most time series models use only the last period of stock price in forecasting. In this article, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs an expectation equation method whose parameters are optimized by a genetic algorithm (GA) joined with an adaptive network–based fuzzy inference system (ANFIS) model to forecast the Taiwan stock index. To evaluate the forecasting performance, the proposed model is compared with Chen's model and Yu's model. The experimental results indicate that the proposed model is superior to the listing methods (Chen's model and Yu's model) in terms of root mean squared error (RMSE).     

基于股票市场灵敏度分析的神经网络预测模型

股票市场是非线性系统,具有内部结构复杂性和外部因素多变性,建立基于股票市场灵敏度分析的神经网络预测模型。针对神经网络结构设计问题,计算网络输入层与隐层神经元的灵敏度,并修剪网络中不敏感的神经元,在保证模型泛化能力的同时,实现网络结构精简;针对神经网络黑箱问题,根据输入层神经元灵敏度解决各输入变量对股票市场的重要性和反馈机制。以上证指数为例,在不同的时间跨度下对股票市场运行规律进行学习,并分析不同结构修剪模型的适用性和市场意义。最后,通过与其他神经网络预测模型比较,验证本文模型的有效性。     

Forecasting stock market short-term trends using a neuro-fuzzy based methodology

A neuro-fuzzy system composed of an Adaptive Neuro Fuzzy Inference System (ANFIS) controller used to control the stock market process model, also identified using an adaptive neuro-fuzzy technique, is derived and evaluated for a variety of stocks. Obtained results challenge the weak form of the Efficient Market Hypothesis (EMH) by demonstrating much improved and better predictions, compared to other approaches, of short-term stock market trends, and in particular the next day’s trend of chosen stocks. The ANFIS controller and the stock market process model inputs are chosen based on a comparative study of fifteen different combinations of past stock prices performed to determine the stock market process model inputs that return the best stock trend prediction for the next day in terms of the minimum Root Mean Square Error (RMSE). Gaussian-2 shaped membership functions are chosen over bell shaped Gaussian and triangular ones to fuzzify the system inputs due to the lowest RMSE. Real case studies using data from emerging and well developed stock markets – the Athens and the New York Stock Exchange (NYSE) – to train and evaluate the proposed system illustrate that compared to the “buy and hold” strategy and several other reported methods, the proposed approach and the forecasting trade accuracy are by far superior.     

Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine

Stock trend prediction is regarded as one of the most challenging tasks of financial time series prediction. Conventional statistical modeling techniques are not adequate for stock trend forecasting because of the non-stationarity and non-linearity of the stock market. With this regard, many machine learning approaches are used to improve the prediction results. These approaches mainly focus on two aspects: regression problem of the stock price and prediction problem of the turning points of stock price. In this paper, we concentrate on the evaluation of the current trend of stock price and the prediction of the change orientation of the stock price in future. Then, a new approach named status box method is proposed. Different from the prediction issue of the turning points, the status box method packages some stock points into three categories of boxes which indicate different stock status. And then, some machine learning techniques are used to classify these boxes so as to measure whether the states of each box coincides with the stock price trend and forecast the stock price trend based on the states of the box. These results would support us to make buying or selling strategies. Comparing with the turning points prediction that only considered the features of one day, each status box contains a certain amount of points which represent the stock price trend in a certain period of time. So, the status box reflects more information of stock market. To solve the classification problem of the status box, a special features construction approach is presented. Moreover, a new ensemble method integrated with the AdaBoost algorithm, probabilistic support vector machine (PSVM), and genetic algorithm (GA) is constructed to perform the status boxes classification. To verify the applicability and superiority of the proposed methods, 20 shares chosen from Shenzhen Stock Exchange (SZSE) and 16 shares from National Association of Securities Dealers Automated Quotations (NASDAQ) are applied to perform stock trend prediction. The results show that the status box method not only have the better classification accuracy but also effectively solve the unbalance problem of the stock turning points classification. In addition, the new ensemble classifier achieves preferable profitability in simulation of stock investment and remarkably improves the classification performance compared with the approach that only uses the PSVM or back-propagation artificial neural network (BPN).     

基于遗传算法和神经网络的新股上市价格预测法

新股上市价格预测方法的研究具有重要的理论和实践意义。鉴于此,该文提出了一种基于遗传算法和神经网络的新股上市价格预测方法。将影响股票价格的因素抽象出来,部分纳斯达克新股上市价格的历史数据作为训练样本,确定该文新股上市价格预测方法中的相关参数。并以另外9家公司的上市股票为测试样本验证了该方法的有效性。     

灰色理论和神经元网络在证券市场中的应用

将灰色系统和神经网络模型分别应用于证券市场中股票价格的预测;同时提出将灰色模型与神经网络模型进行有机组合,建立一种新的灰色神经网络组合预测模型,并以股票市场上证指数为例进行模拟预测。分析结果表明：组合预测模型的模拟预测精度比单一模型更为精确。     

Surveying stock market forecasting techniques – Part II: Soft computing methods

The key to successful stock market forecasting is achieving best results with minimum required input data. Given stock market model uncertainty, soft computing techniques are viable candidates to capture stock market nonlinear relations returning significant forecasting results with not necessarily prior knowledge of input data statistical distributions. This paper surveys more than 100 related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets. Classifications are made in terms of input data, forecasting methodology, performance evaluation and performance measures used. Through the surveyed papers, it is shown that soft computing techniques are widely accepted to studying and evaluating stock market behavior.