A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming

Stock price prediction is a very important financial topic, and is considered a challenging task and worthy of the considerable attention received from both researchers and practitioners. Stock price series have properties of high volatility, complexity, dynamics and turbulence, thus the implicit relationship between the stock price and predictors is quite dynamic. Hence, it is difficult to tackle the stock price prediction problems effectively by using only single soft computing technique. This study hybridizes a self-organizing map (SOM) neural network and genetic programming (GP) to develop an integrated procedure, namely, the SOM-GP procedure, in order to resolve problems inherent in stock price predictions. The SOM neural network is utilized to divide the sample data into several clusters, in such a manner that the objects within each cluster possess similar properties to each other, but differ from the objects in other clusters. The GP technique is applied to construct a mathematical prediction model that describes the functional relationship between technical indicators and the closing price of each cluster formed in the SOM neural network. The feasibility and effectiveness of the proposed hybrid SOM-GP prediction procedure are demonstrated through experiments aimed at predicting the finance and insurance sub-index of TAIEX (Taiwan stock exchange capitalization weighted stock index). Experimental results show that the proposed SOM-GP prediction procedure can be considered a feasible and effective tool for stock price predictions, as based on the overall prediction performance indices. Furthermore, it is found that the frequent and alternating rise and fall, as well as the range of daily closing prices during the period, significantly increase the difficulties of predicting.     

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).     

Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction

Stock market price is one of the most important indicators of a country's economic growth. That's why determining the exact movements of stock market price is considerably regarded. However, complex and uncertain behaviors of stock market make exact determination impossible and hence strong forecasting models are deeply desirable for investors' financial decision making process. This study aims at evaluating the effectiveness of using technical indicators, such as simple moving average of close price, momentum close price, etc. in Turkish stock market. To capture the relationship between the technical indicators and the stock market for the period under investigation, hybrid Artificial Neural Network (ANN) models, which consist in exploiting capabilities of Harmony Search (HS) and Genetic Algorithm (GA), are used for selecting the most relevant technical indicators. In addition, this study simultaneously searches the most appropriate number of hidden neurons in hidden layer and in this respect; proposed models mitigate well-known problem of overfitting/underfitting of ANN. The comparison for each proposed model is done in four viewpoints: loss functions, return from investment analysis, buy and hold analysis, and graphical analysis. According to the statistical and financial performance of these models, HS based ANN model is found as a dominant model for stock market forecasting.     

A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock

Linear model is a general forecasting model and moving average technical index (MATI) is one of useful forecasting methods to predict the future stock prices in stock markets. Therefore, individual investors, stock fund managers, and financial analysts attempt to predict price fluctuation in stock markets by either linear model or MATI. From literatures, three major drawbacks are found in many existing forecasting models. First, forecasting rules mined from some AI algorithms, such as neural networks, could be very difficult to understand. Second, statistic assumptions about variables are required for time series to generate forecasting models, which are not easily understandable by stock investors. Third, stock market investors usually make short-term decisions based on recent price fluctuations, i.e., the last one or two periods, but most time series models use only the last period of stock price. In order to overcome these drawbacks, this study proposes a hybrid forecasting model using linear model and MATI to predict stock price trends with the following four steps: (1) test the lag period of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and calculate the last n-period moving average; (2) use subtractive clustering to partition technical indicator values into linguistic values based on data discretization method objectively; (3) employ fuzzy inference system (FIS) to build linguistic rules from the linguistic technical indicator dataset, and optimize the FIS parameters by adaptive network; and (4) refine the proposed model by adaptive expectation models. The proposed model is then verified by root mean squared error (RMSE), and a ten-year period of TAIEX is selected as experiment datasets. The results show that the proposed model is superior to the other forecasting models, namely Chen's model and Yu's model in terms of RMSE.     

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.     

Indicator selection with committee decision of filter methods for stock market price trend in ISE

Prediction of the stock market price direction is a challenging and important task of the financial time series. This study presents the prediction of the next day stock price direction by the optimal subset indicators selected with ensemble feature selection approach. The main focus is to obtain the final best feature subset which also yields good prediction of the next day price trend by removing irrelevant and redundant indicators from the dataset. For this purpose, filter methods are combined, support vector machines (SVM) has been carried out and finally voting scheme is applied. In order to conduct these processes, a real dataset obtained from Istanbul Stock Exchange (ISE) is used with technical and macroeconomic indicators. The result of this study shows that the prediction of the next day direction with reduced dataset has an improvement over the prediction of it with full dataset.     

A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting

Time series forecasting is an important and widely popular topic in the research of system modeling, and stock index forecasting is an important issue in time series forecasting. Accurate stock price forecasting is a challenging task in predicting financial time series. Time series methods have been applied successfully to forecasting models in many domains, including the stock market. Unfortunately, there are 3 major drawbacks of using time series methods for the stock market: (1) some models can not be applied to datasets that do not follow statistical assumptions; (2) most time series models that use stock data with a significant amount of noise involutedly (caused by changes in market conditions and environments) have worse forecasting performance; and (3) the rules that are mined from artificial neural networks (ANNs) are not easily understandable.To address these problems and improve the forecasting performance of time series models, this paper proposes a hybrid time series adaptive network-based fuzzy inference system (ANFIS) model that is centered around empirical mode decomposition (EMD) to forecast stock prices in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Hang Seng Stock Index (HSI). To measure its forecasting performance, the proposed model is compared with Chen's model, Yu's model, the autoregressive (AR) model, the ANFIS model, and the support vector regression (SVR) model. The results show that our model is superior to the other models, based on root mean squared error (RMSE) values.     

A Bayesian regularized artificial neural network for stock market forecasting

In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. The accurate prediction of stock price movements could play an important role in helping investors improve stock returns. The complexity in predicting these trends lies in the inherent noise and volatility in daily stock price movement. The Bayesian regularized network assigns a probabilistic nature to the network weights, allowing the network to automatically and optimally penalize excessively complex models. The proposed technique reduces the potential for overfitting and overtraining, improving the prediction quality and generalization of the network. Experiments were performed with Microsoft Corp. and Goldman Sachs Group Inc. stock to determine the effectiveness of the model. The results indicate that the proposed model performs as well as the more advanced models without the need for preprocessing of data, seasonality testing, or cycle analysis.     

一种改进的组合SOFM-SVR股票价格预测模型

股票市场价格预测一直以来都被认为是金融时序预测领域的一项具有挑战性的工作。综合回归支持向量机SVR和自组织特征函数(SOFM)技术,并引入基于过滤的特征选择算法确定重要的输入变量,在SVR核函数的参数选择上采用粒子群优化算法(PSO)。SOFM算法将训练样本聚类,然后分别应用SVR来预测股票价格走势。最后应用上海A股的浦发银行日数据来做股票价格日预测,实验结果表明,经过改进的SOFM-SVR模型与之前的SOFM-SVR模型相比,在预测精度和训练时间上都有了较大的提高。     

基于遗传算法和模糊决策树的时间序列预测模型

股票价格预测总是投资者和技术分析者感兴趣的一个主题.然而,决定买卖股票的最好时间仍然是困难的,因为有很多因素可能影响股票价格.通过改进模糊决策树建立了一个新型金融时间序列数据预测模型.该预测模型融合数据聚类技术,模糊决策树及遗传算法来构建基于历史数据和技术指标的一个决策系统.提出的GAFDT模型在与各种股票的其它方法相比较时有平均预测准确率为0.82的最好绩效.     

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。     

股票价格预测的建模与仿真研究

研究股票价格准确预测问题,由于股票价格数据具非线性、随机性等变化规律,同时股票市场与国内外经济政治变化有关,传统股票价格预测方法只能对其线性变化规律进行准确预测,无法反映股票价格非线性部分进行有效建模,导致股价预测精度不高。为了提高股票价格预测精度,提出了一种遗传优化BP神经网络的股票价格预测模型。充分利用BP神经网络良好的非线性映射能力,对股票价格变化规律进行建模,并通过遗传算法对BP神经网络模型参数进行优化,从而获最优股票价格最优预测模型。实验结果表明,相对于传统股票价格预测模型,遗传算法优化BP神经网络的股票价格预测模型拟合程度更好,预测精度更高,为股票价格预测提供了依据。     

Modeling fitting-function-based fuzzy time series patterns for evolving stock index forecasting

Fuzzy time series models that have been developed have been widely applied to many applications of forecasting future stock prices or weighted indexes in the financial field. Three interesting problems have been identified in relation to the associated time series methods, as follows: (1) conventional time series models that consider single variables on associated problems only, (2) fuzzy time series models that determine the interval length of the linguistic values subjectively, and (3) selected variables that depend on personal experience and opinion subjectively. In light of the above limitations, this study constitutes a hybrid seven-step procedure that proposes three integrated fuzzy time series models that are based on fitting functions to forecast weighted indexes of the stock market. First, the proposed models employ Pearson correlation coefficients to objectively select important technical indicators. Second, this study utilizes an objective algorithm to determine the lower bound and upper bound of the universe of discourse automatically. Third, the proposed models use the spread-partition algorithm to automatically determine linguistic intervals. Finally, they combine the transformed variables to build three fuzzy time series models using the criterion of the minimal root mean square error (RMSE). Furthermore, this study provides all of the necessary justifying information for using a linear process to select the inputs for the given non-linear data. To further evaluate the performance of the proposed models, the transaction records of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Hang Seng Indexes) from 1998/01/03 to 2006/12/31 are used to illustrate the methodology with two experimental data sets. Chen’s (Fuzzy Sets Syst. 81:311–319, 1996) model, Yu’s (Physica A 349:609–624, 2005) model, support vector regression (SVR), and partial least square regression (PLSR) are used as models to be compared with the proposed model when given the same data sets. The analytical results show that the proposed models outperform the listed models under the evaluation criteria of the RMSE (in contrast to the forecasting accuracy) for forecasting a weighted stock index in both the Taiwan and Hong Kong stock markets.     

The forecasting ability of Internet-based virtual futures market

Internet-based virtual futures markets (VFMs) have been used in predicting election results and movie ticket sales. We construct an Internet-based VFM to predict an underlying stock price. While the virtual futures market has received much attention, questions remain as to the ideal number of participants. Results of Granger causality tests and analysis of directional accuracy show that a VFM with only a small number of participants (75) is able to generate informative futures prices useful in the prediction of the underlying stock price. Moreover, the participants were not professional investors but merely undergraduate finance students with only a cursory introduction to futures trading. Our results provide additional evidence supporting the use of VFMs in forecasting and show that VFMs are powerful forecasting tools.     

Enhanced stock price variation prediction via DOE and BPNN-based optimization

Stock price variation predictions are at the core of many research issues, and neural networks (NNs) are widely applied and were proven to be more efficient than time series forecasting for stock price forecasting. However, this type of research always determines the parameter settings of the NNs rationally through a trial-and-error methodology. This paper integrates design of experiment (DOE), Taguchi method, and back-propagation NN (BPNN) to construct a robust engine to further optimize the prediction accuracy under a robust DOE-based predictor. Adopting data from Taiwan Stock Exchange (TWSE), the technical analytical indexes and β value of the listed stocks of TWSE were computed. The research results indicated that the proposed approach can effectively improve the forecasting rate of stock price variations.     

Fusion ANFIS models based on multi-stock volatility causality for TAIEX forecasting

Stock market investors value accurate forecasting of future stock price from trading systems because of the potential for large profits. Thus, investors use different forecasting models, such as the time-series model, to assemble a superior investment portfolio. Unfortunately, there are three major drawbacks to the time-series model: (1) most statistical methods rely on some assumptions about the variables; (2) most conventional time-series models use only one variable in forecasting; and (3) the rules mined from artificial neural networks are not easily understandable. To address these shortcomings, this study proposes a new model based on multi-stock volatility causality, a fusion adaptive-network-based fuzzy inference system (ANFIS) procedure, for forecasting stock price problems in Taiwan. Furthermore, to illustrate the proposed model, three practical, collected stock index datasets from the USA and Taiwan stock markets are used in the empirical experiment. The experimental results indicate that the proposed model is superior to the listing methods in terms of root mean squared error, and further evaluation reveals that the profits comparison results for the proposed model produce higher profits than the listing models.     

Incorporating feature selection method into support vector regression for stock index forecasting

Stock index forecasting is one of the most difficult tasks that financial organizations, firms and private investors have to face. Support vector regression (SVR) has become a popular alternative in stock index forecasting tasks due to its generalization capability in obtaining a unique solution. However, the major limitation of SVR is that it cannot capture the relative importance of independent variables to the dependent variable when many potential independent variables are considered. This study incorporates feature selection method and SVR for building stock index forecasting model. The proposed model uses multivariate adaptive regression splines (MARS), an effective nonlinear and nonparametric regression methodology, to identify important forecasting variables. The obtained significant predictor variables are then served as the inputs for the SVR model. Experimental results reveal that the obtained important variables from MARS can improve the forecasting performance of the SVR models. Moreover, the MARS results provide useful information about the relationship between the selected predictor variables and stock index through the obtained basis functions, important predictor variables and the MARS prediction function. Hence, the proposed stock index forecasting model can generate good forecasting performance and exhibits the capability of identifying significant predictor variables, which provide valuable information for further investment decisions/strategies.     

A study of ANFIS-based multi-factor time series models for forecasting stock index

Despite the widespread use of time series models in stock index forecasts, some of these models have encountered problems: (1) the selection of input factors may depend on personal experience or opinion; and (2) most conventional time series models consider only one variable. Furthermore, traditional forecasting models suffer from the following drawbacks: (1) models may rely on restrictive assumptions (such as linear separability or normality) about the variables being analyzed; and (2) it is hard to define and select applicable input factors for artificial neural networks (ANNs) in particular, and the rules generated from ANNs are not easily understood. To address these issues, we propose a multi-factor time series model based on an adaptive network-based fuzzy inference system (ANFIS) for stock index forecasting. In the proposed model, stepwise regression was first applied for the objective selection of technical indicators and then combined with ANFIS to construct the forecasting model. We evaluated the performance of our proposed model against three other models, with transaction data from the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the Hong Kong Hang Seng Index (HSI) stock markets from 1998 to 2006 as experimental data sets and the root mean square error (RMSE) as the evaluation criterion. The results show the superiority of the proposed combined model, which outperformed other models in terms of RMSE and profitability, with strategies for increasing long-term uses of stock index forecasts made on the TAIEX and the HSI.     

Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system

The increasing reliance on Computational Intelligence techniques like Artificial Neural Networks and Genetic Algorithms to formulate trading decisions have sparked off a chain of research into financial forecasting and trading trend identifications. Many research efforts focused on enhancing predictive capability and identifying turning points. Few actually presented empirical results using live data and actual technical trading rules. This paper proposed a novel RSPOP Intelligent Stock Trading System, that combines the superior predictive capability of RSPOP FNN and the use of widely accepted Moving Average and Relative Strength Indicator Trading Rules. The system is demonstrated empirically using real live stock data to achieve significantly higher Multiplicative Returns than a conventional technical rule trading system. It is able to outperform the buy-and-hold strategy and generate several folds of dollar returns over an investment horizon of four years. The Percentage of Winning Trades was increased significantly from an average of 70% to more than 92% using the system as compared to the conventional trading system; demonstrating the system’s ability to filter out erroneous trading signals generated by technical rules and to preempt any losing trades. The system is designed based on the premise that it is possible to capitalize on the swings in a stock counter’s price, without a need for predicting target prices.     

遗传程序设计分析股价移动平均及中长期走势*

将遗传程序设计应用到股票价格分析,在股票市场各种因素相互作用与影响很难厘清的情况下,只从个别因素（价格）入手,测试对单一因素预测所能达到的效果;提出了两种预测方法：对不同尺度的股票移动平均线进行预测和对股票价格数据进行平滑预处理之后所进行的中长期预测。通过遗传程序设计算法,寻找前几个时间单位的股票价格对本期股票价格影响的经验公式,以期反映价格变动的规律。计算机实验模拟表明,该方法对于平均线的预测和中长期预测有较好的效果。