基于理性指标的马尔可夫链股市态势预测方法

由于股票市场存在人为扰动性,使得基于情绪的股市预测算法效果不佳。针对股市的诱多诱空问题,提出一种基于理性指标的马尔可夫链股市态势预测算法（RI_MCA）。提取股市的主要理性特征,并对这些理性特征进行量化;通过主成分分析将这些理性特征融合成理性指标,并利用理性指标获取股市的买卖点;将买卖点所对应的股市状态引入到马尔可夫链中,实现股市态势预测。在理性指标和股市状态相背离情况下会降低买卖点的可靠性,因而通过将特征背离引入到RI_MCA算法中提出了RICD_MCA算法,RICD_MCA算法根据特征背离程度对RI_MCA算法的结果进行调整优化。在上证指数上的实验比较与分析结果表明,RICD_MCA算法具有更高的预测精度。     

Forecasting IBEX-35 moves using support vector machines

This research aims at examining the application of support vector machines (SVMs) to the task of forecasting the weekly change in the Madrid IBEX-35 stock index. The data cover the period between 10/18/1990 and 10/29/2010. A trading simulation is implemented so that statistical efficiency is complemented by measures of economic performance. The inputs retained are traditional technical trading rules commonly used in the analysis of equity markets such as the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD) decision rules. The SVMs with given values of the RSI and MACD indicators are used in order to determine the best situations to buy or sell the market. The two outputs of the SVM are both the direction of the market and the probability attached to each forecast market move. The best result that it has been achieved is a hit ratio of 100% using the SVM classifier under some chosen risk-aversion parameters. However, these results are obtained analyzing recent periods rather than using all the dataset information.     

A cooperative exchanging mechanism among seller agents for group-based sales

Agent-mediated electronic markets have been a growing area in intelligent agent research and development in recent years. Agents can act autonomously and cooperatively in an electronic market on behalf of their users. In such an electronic market, if a seller agent does not have enough of a particular item, it misses the opportunity to sell the item. Buyers also miss the opportunity to purchase the item. Namely, the overall negotiation utility is decreased. Thus, we propose a new cooperation mechanism among seller agents based on exchanging their goods in our agent-mediated electronic market system, G-Commerce. In G-Commerce, seller agents and buyer agents negotiate with each other. In our model, seller agents cooperatively negotiate in order to sell goods in stock. Buyer agents cooperatively form coalitions in order to buy goods based on discount prices. Seller agents’ negotiations are completed by using an exchanging mechanism for selling goods. Our experiments show that this exchanging mechanism enables seller agents to sell goods in stock effectively. We also demonstrate how our exchanging mechanism satisfies Pareto optimality.     

Clustering stock price time series data to generate stock trading recommendations: An empirical study

Predicting the stock market is considered to be a very difficult task due to its non-linear and dynamic nature. Our proposed system is designed in such a way that even a layman can use it. It reduces the burden on the user. The user's job is to give only the recent closing prices of a stock as input and the proposed Recommender system will instruct him when to buy and when to sell if it is profitable or not to buy share in case if it is not profitable to do trading. Using soft computing based techniques is considered to be more suitable for predicting trends in stock market where the data is chaotic and large in number. The soft computing based systems are capable of extracting relevant information from large sets of data by discovering hidden patterns in the data. Here regression trees are used for dimensionality reduction and clustering is done with the help of Self Organizing Maps (SOM). The proposed system is designed to assist stock market investors identify possible profit-making opportunities and also help in developing a better understanding on how to extract the relevant information from stock price data.     

意愿计算的股市突变点预测方法

由于传统分段线性表示方法没有考虑股市数据分布变化导致分段不合理,同时股市突变点相关特征的局部性导致突变点难以有效预测,所以在分段线性表示方法的基础上提出一种意愿计算的股市突变点预测方法(WC-WSVM)。首先,给出一种波动率分布变化的分段线性表示(V-PLR)方法,通过波动率分布变化自适应地优化PLR分段阈值;然后,提取与主力买卖股票意愿相关的股市特征并进行量化,利用逻辑回归(LR)对于所提取的特征进行融合得到意愿计算结果;最后,将意愿计算结果与PLR-WSVM算法输入特征共同代入到WSVM中,进行突变点预测。在真实数据上的实验结果表明,算法具有强适应性,预测精度得到有效提升。     

Planning stock portfolios by means of weighted frequent itemsets

Planning stock portfolios is a challenging task, because investors have to forecast stock market trends. To limit losses due to wrong forecasts a common strategy is diversification, which consists in buying stocks belonging to different sectors/markets to spread bets across different assets. Since the amount of stock market data is continuously growing, an appealing research strategy is to first apply data mining algorithms to discover significant patterns from potentially large stock datasets and then exploit them to support investor decision-making.This article presents an itemset-based approach to supporting buy-and-hold investors in technical analyses by automatically identifying promising sets of high-yield yet diversified stocks to buy. Specifically, it investigates the use of itemsets to generate stock portfolios from historical stock data and recommend them for buy-and-hold investments. To achieve this goal, it analyzes stock market datasets, which contain for each stock the closing prices on different trading days. Datasets are enriched with (analyst-provided) taxonomies, which are used to classify stocks as the corresponding sectors. Unlike previous approaches, it generates a model composed of a subset of potentially interesting itemsets, which are then used to support investors in decision-making. The selected itemsets represent promptly usable stock portfolios satisfying expert’s requirements on minimal average return and minimal level of diversification across sectors.The experiments performed on real stock datasets acquired under different market conditions demonstrate the effectiveness of the proposed approach compared to real stock funds.     

A parallel evolutionary algorithm for technical market indicators optimization

This paper deals with the optimization of parameters of technical indicators for stock market investment. Price prediction is a problem of great complexity and, usually, some technical indicators are used to predict market trends. The main difficulty in using technical indicators lies in deciding a set of parameter values. We proposed the use of Multi-Objective Evolutionary Algorithms (MOEAs) to obtain the best parameter values belonging to a collection of indicators that will help in the buying and selling of shares. The experimental results indicate that our MOEA offers a solution to the problem by obtaining results that improve those obtained through technical indicators with standard parameters. In order to reduce execution time is necessary to parallelize the executions. Parallelization results show that distributing the workload of indicators in multiple processors to improve performance is recommended. This parallelization has been performed taking advantage of the idle time in a corporate technology infrastructure. We have configured a small parallel grid using the students Labs of a Computer Science University College.     

基于神经网络集成学习算法的金融时间序列预测

本文在传统神经网络(NN)、循环神经网络(RNN)、长短时记忆网络(LSTM)与门控循环单元(GRU)等神经网络时间预测模型基础上, 进一步构建集成学习(EL)时间序列预测模型, 研究神经网络类模型、集成学习模型和传统时间序列模型在股票指数预测上的表现. 本文以16只A股和国际股票市场指数为样本, 比较模型在不同预测期间和不同国家和地区股票市场上的表现.本文主要结论如下: 第一, 神经网络类时间序列预测模型和神经网络集成学习时间序列预测模型在表现上显著稳健优于传统金融时间序列预测模型, 预测性能提高大约35%; 第二, 神经网络类模型和神经网络集成学习模型在中国和美国股票市场上的表现优于其他发达国家和地区的股票市场.     

Trend following deep Q-Learning strategy for stock trading

Computers and algorithms are widely used to help in stock market decision making. A few questions with regards to the profitability of algorithms for stock trading are can computers be trained to beat the markets? Can an algorithm take decisions for optimal profits? And so forth. In this research work, our objective is to answer some of these questions. We propose an algorithm using deep Q-Reinforcement Learning techniques to make trading decisions. Trading in stock markets involves potential risk because the price is affected by various uncertain events ranging from political influences to economic constraints. Models that trade using predictions may not always be profitable mainly due to the influence of various unknown factors in predicting the future stock price. Trend Following is a trading idea in which, trading decisions, like buying and selling, are taken purely according to the observed market trend. A stock trend can be up, down, or sideways. Trend Following does not predict the stock price but follows the reversals in the trend direction. A trend reversal can be used to trigger a buy or a sell of a certain stock. In this research paper, we describe a deep Q-Reinforcement Learning agent able to learn the Trend Following trading by getting rewarded for its trading decisions. Our results are based on experiments performed on the actual stock market data of the American and the Indian stock markets. The results indicate that the proposed model outperforms forecasting-based methods in terms of profitability. We also limit risk by confirming trading actions with the trend before actual trading.     

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.     

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.     

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MPCA-RBF模型的证券市场指数时间序列预测*

针对证券市场指数内部结构的复杂性和影响因素的高维性,提出基于MPCA-RBF（多线性主成分分析法-径向基神经网络）模型的证券市场指数时间序列预测方法。由于证券市场间存在关联性,选取了7个证券市场及34个技术指标构建三维张量模型,采用张量方法—MPCA进行特征提取,使降维的同时充分保留数据内部结构,之后利用RBF神经网络进行回归预测,提高了预测精度。对恒生指数和日经225指数的实验结果显示,与非张量模型相比,该模型预测误差较小,预测精度有较显著的提高,表明该模型能充分地保留证券时间序列内部结构,证明了其在证券预测领域的有效性和实用性。     

Ensemble learning of rule-based evolutionary algorithm using multi-layer perceptron for supporting decisions in stock trading problems

Classification is a major research field in pattern recognition and many methods have been proposed to enhance the generalization ability of classification. Ensemble learning is one of the methods which enhance the classification ability by creating several classifiers and making decisions by combining their classification results. On the other hand, when we consider stock trading problems, trends of the markets are very important to decide to buy and sell stocks. In this case, the combinations of trading rules that can adapt to various kinds of trends are effective to judge the good timing of buying and selling. Therefore, in this paper, to enhance the performance of the stock trading system, ensemble learning mechanism of rule-based evolutionary algorithm using multi-layer perceptron (MLP) is proposed, where several rule pools for stock trading are created by rule-based evolutionary algorithm, and effective rule pools are adaptively selected by MLP and the selected rule pools cooperatively make decisions of stock trading. In the simulations, it is clarified that the proposed method shows higher profits or lower losses than the method without ensemble learning and buy&hold.     

Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods

The study of financial markets has been addressed in many works during the last years. Different methods have been used in order to capture the non-linear behavior which is characteristic of these complex systems. The development of profitable strategies has been associated with the predictive character of the market movement, and special attention has been devoted to forecast the trends of financial markets. This work performs a predictive study of the principal index of the Brazilian stock market through artificial neural networks and the adaptive exponential smoothing method, respectively. The objective is to compare the forecasting performance of both methods on this market index, and in particular, to evaluate the accuracy of both methods to predict the sign of the market returns. Also the influence on the results of some parameters associated to both methods is studied. Our results show that both methods produce similar results regarding the prediction of the index returns. On the contrary, the neural networks outperform the adaptive exponential smoothing method in the forecasting of the market movement, with relative hit rates similar to the ones found in other developed markets.     

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).     

基于技术分析的股价预测系统的研究与开发

分析了影响股票价格的因素,讨论了常用的股票价格预测方法。使用SQL Server2000作为后台数据库,Java和.NET作为前台开发工具,使用IKVM实现Java和.NET两种开发工具之间的互通性,研究和开发了使用技术分析法的股票价格预测系统。系统对于投资者的买入和卖出操作具有重要的指导意义。     

Characterising trader manipulation in a limit-order driven market

Use of trading strategies to mislead other market participants, commonly termed trade-based market manipulation, has been identified as a major problem faced by present day stock markets. Although some mathematical models of trade-based market manipulation have been previously developed, this work presents a framework for manipulation in the context of a realistic computational model of a limit-order market. The Maslov limit order market model is extended to introduce manipulators and technical traders. We show that “pump and dump” manipulation is not possible with traditional Maslov (liquidity) traders. The presence of technical traders, however, makes profitable manipulation possible. When exploiting the behaviour of technical traders, manipulators can wait some time after their buying phase before selling, in order to profit. Moreover, if technical traders believe that there is an information asymmetry between buy and sell actions, the manipulator effort required to perform a “pump and dump” is comparatively low, and a manipulator can generate profits even by selling immediately after raising the price.     

基于Elman神经网络的股市决策模型

利用具有时变适应能力的Elman动态回归神经网络,建立了股市的预测和决策模型,并利用两只股票进行了实验检验,实验结果表明,该模型具有较高的预测精度、较为稳定的预测效果和较快的收敛速度,说明该模型应用于股票市场的预测与决策是可行和有效的,对于短期的买卖决策具有指导意义,有着良好的应用前景.     

A novel stock trading prediction and recommendation system

The prices of the stock are influence by many factors and emerge extremely nonlinear structure. Therefore, the stock trading prediction and recommendation is an extremely challenging task. In this paper, a novel stock trading prediction and recommendation system is proposed in user-friendly form. The recommendation system can inform the user whether is to buy or sell the stocks in the next step. Information granulation is applied to transform raw time series into meaningful and interpretable granules, and the more effective non-uniform partitioning method for prediction is presented. The system first determines the intervals based on information granules, and then define the fuzzy sets and fuzzify the historical data. Third, construct fuzzy relationships and assign weights to each period. Finally, the prediction and recommendation is implemented. The experimental results show the proposed system yields better prediction performance, and increases profit-making opportunities.