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).     

基于改进回声状态神经网络的个股股价预测

当今社会股价预测是研究的热门问题,人们越来越关注对股价预测模型的建立,提高股价预测的精度对股票投资者有实际的应用价值.目前股价的预测方法层出不穷,其中较为典型的有传统的技术分析和ARMA模型等.为了提升预测的精度,同时考虑到股市的非线性,本文提出一种改进的回声状态神经网络的个股股价预测模型,针对回声状态神经网络(ESN)泛化能力不强的特点,应用改进的粒子群算法(GTPSO)对回声状态神经网络(ESN)的输出连接权进行搜索,最终得到最优解,即ESN的最优输出连接权,GTPSO算法概括来说就是在传统粒子群算法(PSO)的基础上引入禁忌搜索算法(TS)中禁忌的思想和遗传算法(GA)中变异的思想,从而降低PSO在学习过程中陷入局部最小值的状况,同时提高PSO搜寻全局的能力.将预测模型用于个股每日收盘价预测中,使用每10天的收盘价预测第11天的收盘价.通过实验验证了模型的正确性,实验证实,该模型拥有较好的预测效果.     

Support vector regression with chaos-based firefly algorithm for stock market price forecasting

Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box–Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).     

A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting

In order to effectively model crude oil spot price with inherently high complexity, a hybrid learning paradigm integrating least squares support vector regression (LSSVR) with a hybrid optimization searching approach for the parameters selection in the LSSVR [consisting of grid method and genetic algorithm (GA)], i.e., a hybrid grid-GA-based LSSVR model, is proposed in this study. In the proposed hybrid learning paradigm, the grid method, a simple but efficient searching method, is first applied to roughly but rapidly determine the proper boundaries of the parameters in the LSSVR; then, the GA, an effective and powerful intelligent searching algorithm, is further implemented to select the most suitable parameters. For illustration and verification, the proposed learning paradigm is used to predict the crude oil spot prices of the West Texas Intermediate and the Brent markets. The empirical results demonstrate that the proposed hybrid grid-GA-based LSSVR learning paradigm can outperform its benchmarking models (including some popular forecasting techniques and similar LSSVRs with other parameter searching algorithms) in terms of both prediction accuracy and time-savings, indicating that it can be utilized as one effective forecasting tool for crude oil price with high volatility and irregularity.     

Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting

This study considers real estate appraisal forecasting problem. While there is a great deal of literature about use of artificial intelligence and multiple linear regression for the problem, there has been always controversy about which one performs better. Noting that this controversy is due to difficulty finding proper predictor variables in real estate appraisal, we propose a modified version of ridge regression, i.e., ridge regression coupled with genetic algorithm (GA-Ridge). In order to examine the performance of the proposed method, experimental study is done for Korean real estate market, which verifies that GA-Ridge is effective in forecasting real estate appraisal. This study addresses two critical issues regarding the use of ridge regression, i.e., when to use it and how to improve it.     

Housing price forecasting based on genetic algorithm and support vector machine

Accurate forecasting for future housing price is very significant for socioeconomic development and national lives. In this study, a hybrid of genetic algorithm and support vector machines (G-SVM) approach is presented in housing price forecasting. Support vector machine (SVM) has been proven to be a robust and competent algorithm for both classification and regression in many applications. However, how to select the most appropriate the training parameter value is the important problem in the using of SVM. Compared to Grid algorithm, genetic algorithm (GA) method consumes less time and performs well. Thus, GA is applied to optimize the parameters of SVM simultaneously. The cases in China are applied to testify the housing price forecasting ability of G-SVM method. The experimental results indicate that forecasting accuracy of this G-SVM approach is more superior than GM.     

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.     

Automated generation of new knowledge to support managerial decision-making: case study in forecasting a stock market

Abstract: The deluge of data available to managers underscores the need to develop intelligent systems to generate new knowledge. Such tools are available in the form of learning systems from artificial intelligence. This paper explores how the novel tools can support decision‐making in the ubiquitous managerial task of forecasting. For concreteness, the methodology is examined in the context of predicting a financial index whose chaotic properties render the time series difficult to predict. The study investigates the circumstances under which enough new knowledge is extracted from temporal data to overturn the efficient markets hypothesis. The efficient markets hypothesis precludes the possibility of anticipating in financial markets. More precisely, the markets are deemed to be so efficient that the best forecast of a price level for the subsequent period is precisely the current price. Certain anomalies to the efficient market premise have been observed, such as calendar effects. Even so, forecasting techniques have been largely unable to outperform the random walk model which corresponds to the behavior of prices under the efficient markets hypothesis. This paper tests the validity of the efficient markets hypothesis by developing knowledge‐based tools to forecast a market index. The predictions are examined across several horizons: single‐period forecasts as well as multiple periods. For multiperiod forecasts, the predictive methodology takes two forms: a single jump from the current period to the end of the forecast horizon, and a multistage web of forecasts which progresses systematically from one period to the next. These models are first evaluated using neural networks and case‐based reasoning, and are then compared against a random walk model. The computational models are examined in the context of forecasting a composite for the Korean stock market.     

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。     

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.     

Applying option Greeks to directional forecasting of implied volatility in the options market: An intelligent approach

This paper examines movement in implied volatility with the goal of enhancing the methods of options investment in the derivatives market. Indeed, directional movement of implied volatility is forecasted by being modeled into a function of the option Greeks. The function is structured as a locally stationary model that employs a sliding window, which requires proper selection of window width and sliding width. An artificial neural network is employed for implementing and specifying our methodology. Empirical study in the Korean options market not only illustrates how our directional forecasting methodology is constructed but also shows that the methodology could yield a reasonably strong performance. Several interesting technical notes are discussed for directional forecasting.     

基于KNN-ANN算法的边际电价预测

在电力市场中,价格一直受到买卖双方的广泛关注。但是,电价影响因素的不确定性给电价的预测带来难度。针对该问题,提出一种通过结合人工神经网络和KNN算法来进行时间序列预测的模型,用KNN算法找出历史数据中相似的数据子序列集合(最近邻),并用人工神经网络来寻找这些最近邻的最优权重,得出预测的时间序列。以美国纽约州电力市场的电价数据进行实验分析,同时比较了利用ARIMA算法以及Naive I预测的结果,证明该方法简单、有效。     

一种基于小波变换和ARIMA的短期电价混合预测模型

为在实时电价情况下预测未来24小时电价, 提出一种基于小波变换和差分自回归移动平均(ARIMA)的短期电价混合预测模型。该模型分别根据是否受到需求量影响使用ARIMA模型对多尺度小波变换分解后的时间序列进行预测。同时提出一种电价突变点发现和处理算法。使用澳大利亚新南威尔士州2012年真实数据验证表明, 相对ARIMA预测, 改进后的混合模型在不考虑需求量影响时预测精度更高; 电价突变点发现和处理算法能够准确处理电价异常点, 提高预测精度。     

基于自适应动态规划的系统边际电价预测

针对目前电价预测算法的局限性,提出一种基于自适应动态规划方法的自学习、自适应智能算法。按照Bellman最优化基本原理,使用Agent逐步与环境的交互作用来寻求预测电价和实际电价的误差最小值,得到系统边际电价的最优解。采用美国加州电力市场的数据进行电价预测仿真。与常规方法相比,该方法的拟合精度和平均绝对百分误差均有很大提高。     

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).     

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。     

基于时间序列神经网络的鲜切花价格指数短期预测

鲜切花价格指数是反映鲜切花市场现状的风向标，研究鲜切花价格指数变化，掌握鲜花市场的动态和规律性具有重要意义。本文针对具有时序特点的鲜切花价格指数，基于BP模型中的L-M优化算法构建鲜切花价格指数短期预测模型，采用tansig和purelin作为各层之间的传递函数，利用时间序列分析方法确定输入层的神经元个数，通过实验数据对比来确定隐含层的神经元个数。采用平均绝对误差、平均相对误差和均方根误差这3个评价指标对模型的预测精度进行检验，实验结果表明所构建模型是有效的和具有实际应用价值的。     

Bridging the divide in financial market forecasting: machine learners vs. financial economists

Financial time series forecasting is a popular application of machine learning methods. Previous studies report that advanced forecasting methods predict price changes in financial markets with high accuracy and that profit can be made trading on these predictions. However, financial economists point to the informational efficiency of financial markets, which questions price predictability and opportunities for profitable trading. The objective of the paper is to resolve this contradiction. To this end, we undertake an extensive forecasting simulation, based on data from thirty-four financial indices over six years. These simulations confirm that the best machine learning methods produce more accurate forecasts than the best econometric methods. We also examine the methodological factors that impact the predictive accuracy of machine learning forecasting experiments. The results suggest that the predictability of a financial market and the feasibility of profitable model-based trading are significantly influenced by the maturity of the market, the forecasting method employed, the horizon for which it generates predictions and the methodology used to assess the model and simulate model-based trading. We also find evidence against the informational value of indicators from the field of technical analysis. Overall, we confirm that advanced forecasting methods can be used to predict price changes in some financial markets and we discuss whether these results question the prevailing view in the financial economics literature that financial markets are efficient.     

Feature selection for support vector machine in financial crisis prediction: a case study in China

Abstract: Corporate financial crisis forecasting plays an increasingly important role in the current intense and competitive commercial environment. It is an important phase in financial crisis forecasting to find the features that discriminate different financial conditions. In this paper, a genetic algorithm (GA) based approach and statistical filter approaches are applied to identify the best features for the support vector machine (SVM). The proposed GA‐based approach is carefully designed in order to have the capability of simultaneously optimizing the features and parameters of the SVM. Experimental results on the data from Chinese companies show that the GA‐based approach can extract fewer features with a higher accuracy compared with statistical filter approaches, such as analysis of variance, the T‐W test (which is the t test applied to variables satisfying a normal distribution and the Wilcoxon test applied to other variables not satisfying a normal distribution), logit regression and multiple discriminant analysis. Moreover, the experiments indicate that the proposed GA‐based approach is robust and suitable for selecting features for the SVM.     

A hybrid fuzzy regression-fuzzy cognitive map algorithm for forecasting and optimization of housing market fluctuations

This paper presents a hybrid algorithm based on fuzzy linear regression (FLR) and fuzzy cognitive map (FCM) to deal with the problem of forecasting and optimization of housing market fluctuations. Due to the uncertainty and severe noise associated with the housing market, the application of crisp data for forecasting and optimization purposes is insufficient. Hence, in order to enable the decision-makers to make decisions with respect to imprecise/fuzzy data, FLR is used in the proposed hybrid algorithm. The best-fitted FLR model is then selected with respect to two indicators including Index of Confidence (IC) and Mean Absolute Percentage Error (MAPE). To achieve this objective, analysis of variance (ANOVA) for a randomized complete block design (RCBD) is employed. The primary objective of this study is to utilize imprecise/fuzzy data in order to improve the analysis of housing price fluctuations, in accordance with the factors obtained through the best-fitted FLR model. The secondary objective of this study is the exhibition of the resulted values in a schematic way via FCM. Hybridization of FLR and FCM provides a decision support system (DSS) for utilization of historical data to predict housing market fluctuation in the future and identify the influence of the other parameters. The proposed hybrid FLR-FCM algorithm enables the decision-makers to utilize imprecise and ambiguous data and represent the resulted values of the model more clearly. This is the first study that utilizes a hybrid intelligent approach for housing price and market forecasting and optimization.