Predicting stock price using fuzzy grey prediction system

The purpose of this paper is to predict the stock price instantly at any given time. One problem with predicting stock prices is that there may be a large or small difference in two continuous sets of data. The other problem is that the volume of stock data is so large that it affects our ability to use it. To solve these problems, we constructed a data mart to reduce the size of stock data and combined fuzzification techniques with the grey theory to develop a fuzzy grey prediction as one of predicting functions in our system to predict the possible answer immediately. To demonstrate that our system is working correctly, we used our prediction system to analyse stock data and to predict the stock price promptly at a specific time. The system can effectively help stock dealers deal with day trading.     

Synthesis of fuzzy logic and Dempster–Shafer Theory for the simulation of the decision-making process in stock trading systems

Modern computerized stock trading systems (mechanical trading systems) are based on the simulation of the decision-making process and generate advice for traders to buy or sell stocks or other financial tools by taking into account the price history, technical analysis indicators, accepted rules of trading and so on. Two stock trading simulating systems based on trading rules defined using fuzzy logic are developed and compared. The first is based on the so-called “Logic-Motivated Fuzzy Logic Operators” (LMFL) approach and aims to avoid certain disadvantages of the classical Mamdani’s method, which has been developed for use in fuzzy logic controllers and not for solving the decision-making problems of stock trading. The LMFL   approach is based on the modified mathematical representation of t$t$-norm and Yager’s implication rule. The second trading system combines the tools of fuzzy logic and Dempster–Shafer Theory (DST  ) to represent the features of the decision-making process more transparently. The fuzzy representation of trading rules based on the theory of technical analysis is used in these expert systems. Since the theory of technical analysis is based on the indicators used by experts to predict stock price movements, the method maps these indicators into new inputs that can be used in a fuzzy logic system. The only required inputs to calculate these indicators are past sequences (history) of stock prices. The method relies on fuzzy logic to choose an appropriate decision when certain price movements or certain price formations occur. The optimization procedure based on historical (teaching) data is used as it significantly improves the performance of such expert systems. The efficiency of the developed expert systems is measured by comparing their outputs versus stock price movements. The results obtained using real NYSE$NYSE$ data allow us to say that the developed expert system based on the synthesis of fuzzy logic and DST provides better results and is more reliable. Moreover, such a conjunction of fuzzy logic, DST and technical analysis, makes it possible to make a profit even when trading against a dominating trend.     

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.     

应用人机交互的遗传算法挖掘股票投资风险规则

介绍了人机交互的遗传算法的基本原理 ,论述了用人机交互的遗传算法挖掘股票投资风险规则的算法思想和关键问题 ,包括规则的表示与编码、适应度函数的定义以及交叉、变异和选择操作 ,提出了基于人机交互遗传算法的股票投资风险规则挖掘算法 .应用该算法从股票交易数据库中挖掘一段时间内股票涨跌与其属性之间的关系 .实验结果表明该算法是可行的和有效的 .人机交互的遗传算法对于挖掘股票投资风险规则以及求解人机合作的“可操作性”问题 ,提供了一种方法和途径     

A multi-agent system to construct production orders by employing an expert system and a neural network

The authors describe the implementation of a multi-agent system, whose goal is to enhance production planning i.e. to improve the construction of production orders. This task has been carried out traditionally by the module known as production activity control (PAC). However, classic PAC systems lack adaptive techniques and intelligent behaviour. As a result they are mostly unfit to handle the NP Hard combinatorial problem underlying the construction of right production orders. To overcome this situation, we illustrate how an intelligent and collaborative multi-agent system (MAS) obtains a correct production order by coordinating two different techniques to emulate intelligence. One technique is performed by a feed-forward neural network (FANN), which is embedded in a machine agent, the objective being to determine the appropriate machine in order to fulfil clients’ requirements. Also, an expert system is provided to a tool agent, which in turn is in charge of inferring the right tooling. The entire MAS consists of a coordinator, a spy, and a scheduler. The coordinator agent has the responsibility to control the flow of messages among the agents, whereas the spy agent is constantly reading the Enterprise Information System. The scheduler agent programs the production orders. We achieve a realistic MAS that fully automates the construction and dispatch of valid production orders in a factory dedicated to produce labels.     

Fuzzy dual-factor time-series for stock index forecasting

There is an old Wall Street adage goes, “It takes volume to make price move”. The contemporaneous relation between trading volume and stock returns has been studied since stock markets were first opened. Recent researchers such as Wang and Chin [Wang, C. Y., & Chin S. T. (2004). Profitability of return and volume-based investment strategies in China’s stock market. Pacific-Basin Finace Journal, 12, 541–564], Hodgson et al. [Hodgson, A., Masih, A. M. M., & Masih, R. (2006). Futures trading volume as a determinant of prices in different momentum phases. International Review of Financial Analysis, 15, 68–85], and Ting [Ting, J. J. L. (2003). Causalities of the Taiwan stock market. Physica A, 324, 285–295] have found the correlation between stock volume and price in stock markets. To verify this saying, in this paper, we propose a dual-factor modified fuzzy time-series model, which take stock index and trading volume as forecasting factors to predict stock index. In empirical analysis, we employ the TAIEX (Taiwan stock exchange capitalization weighted stock index) and NASDAQ (National Association of Securities Dealers Automated Quotations) as experimental datasets and two multiple-factor models, Chen’s [Chen, S. M. (2000). Temperature prediction using fuzzy time-series. IEEE Transactions on Cybernetics, 30 (2), 263–275] and Huarng and Yu’s [Huarng, K. H., & Yu, H. K. (2005). A type 2 fuzzy time-series model for stock index forecasting. Physica A, 353, 445–462], as comparison models. The experimental results indicate that the proposed model outperforms the listing models and the employed factors, stock index and the volume technical indicator, VR(t), are effective in stock index forecasting.     

Learning rules of a card game from video

This paper presents a framework for automatically learning rules of a simple game of cards using data from a vision system observing the game being played. Incremental learning of object and protocol models from video, for use by an artificial cognitive agent, is presented. iLearn??a novel algorithm for inducing univariate decision trees for symbolic datasets is introduced. iLearn builds the decision tree in an incremental way allowing automatic learning of rules of the game.     

Multiagent System Simulations of Treasury Auctions

This study uses a multiagent system to determine which payment rule provides the most revenue in treasury auctions. The agents learn how to bid using straightforward bid adjustment rules that are based on impulse balance learning. The market model encompasses the when-issued, auction, and secondary markets, as well as bidding constraints for primary dealers. I find that when the number of primary bidders is less than 13 (Canada) the Discriminatory payment rule is revenue superior to the Uniform payment across most market price spreads. When the number of primary bidders is greater than 14 (United States), Uniform payment is revenue superior to Discriminatory payment for all market price spreads. In general, revenue increases with the minimum bid constraint and with the number of primary dealers for Uniform, Average, and Vickrey payment rules.      

EFFICIENT PERSONALIZATION OF E‐LEARNING ACTIVITIES USING A MULTI‐DEVICE DECENTRALIZED RECOMMENDER SYSTEM

Personalization is becoming a key issue in designing effective e‐learning systems and, in this context, a promising solution is represented by software agents. Usually, these systems provide the student with a student agent that interacts with a site agent associated with each e‐learning site. However, in presence of a large number of students and of e‐learning sites, the tasks of the agents are often onerous, even more if the student agents run on devices with limited resources. To face this problem, we propose a new multiagent learning system, called ISABEL. Our system provides each student, that are using a specific device, with a device agent able to autonomously monitor the student's behavior when accessing e‐learning Web sites. Each site is associated, in its turn, with a teacher agent. When a student visits an e‐learning site, the teacher agent collaborates with some tutor agents associated with the student, to provide him with useful recommendations. We present both theoretical and experimental results to show that this distributed approach introduces significant advantages in quality and efficiency of the recommendation activity with respect to the performances of other past recommenders.     

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.     

Combining technical trading rules using particle swarm optimization

Technical trading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this paper, a complex stock trading strategy, namely performance-based reward strategy (PRS), is proposed. PRS combines the two most popular classes of technical trading rules – moving average (MA) and trading range break-out (TRB). For both MA and TRB, PRS includes various combinations of the rule parameters to produce a universe of 140 component trading rules in all. Each component rule is assigned a starting weight, and a reward/penalty mechanism based on rules’ recent profit is proposed to update their weights over time. To determine the best parameter values of PRS, we employ an improved time variant particle swarm optimization (TVPSO) algorithm with the objective of maximizing the annual net profit generated by PRS. The experiments show that PRS outperforms all of the component rules in the testing period. To assess the significance of our trading results, we apply bootstrapping methodology to test three popular null models of stock return: the random walk, the AR(1) and the GARCH(1, 1). The results show that PRS is not consistent with these null models and has good predictive ability.     

A novel methodology for stock investment using high utility episode mining and genetic algorithm

In this paper, we present a novel methodology for stock investment using the technique of high utility episode mining and genetic algorithms. Our objective is to devise a profitable episode-based investment model to reveal hidden events that are associated with high utility in the stock market. The time series data of stock price and the derived technical indicators, including moving average, moving average convergence and divergence, random index and bias index, are used for the construction of episode events. We then employ the genetic algorithm for the simultaneous optimization on parameters and selection of subsets of models. The empirical results show that our proposed method significantly outperforms the state-of-the-art methods in terms of annualized returns of investment and precision. We also provide a set of Z-tests to statistically validate the effectiveness of our proposed method. Based upon the promising results obtained, we expect this novel methodology can advance the research in data mining for computational finance and provide an alternative to stock investment in practice.     

An intelligent stock trading system using comprehensive features

The aim of this study is to predict automatic trading decisions in stock markets. Comprehensive features (CF) for predicting future trend are very difficult to generate in a complex environment, especially in stock markets. According to related work, the relevant stock information can help investors formulate objects that may result in better profits. With this in mind, we present a framework of an intelligent stock trading system using comprehensive features (ISTSCF) to predict future stock trading decisions. The ISTSCF consists of stock information extraction, prediction model learning and stock trading decision. We apply three different methods to generate comprehensive features, including sentiment analysis (SA) that provides sensitive market events from stock news articles for sentiment indices (SI), technical analysis (TA) that yields effective trading rules based on trading information on the stock exchange for technical indices (TI), as well as the trend-based segmentation method (TBSM) that raises trading decisions from stock price for trading signals (TS). Experiments on the Taiwan stock market show that the results of employing comprehensive features are significantly better than traditional methods using numeric features alone (without textual sentiment features).     

Expert system for predicting stock market timing using a candlestick chart

It has been one of the greatest challenges to predict the stock market. Since stock prices vary dramatically, it is important to determine when to buy and sell stocks in order to get high returns from stock investment. In this study, we have developed a candlestick chart analysis expert system, or a chart interpreter, for predicting the best stock market timing. The expert system has patterns and rules which can predict future stock price movements. Defined patterns are classified into five groups with respect to their meanings: falling, rising, neutral, trend-continuation and trend-reversal patterns. The experimental results revealed that the developed knowledge base could provide excellent indicators with an average hit ratio of 72% to help investors get high returns from their stock investment. Through experiments from January 1992 to June 1997, it was proven that the developed knowledge base was time- and field-independent.     

A new ensemble method for gold mining problems: Predicting technology transfer

Of the many available innovative e-commerce technologies, only a small number have been successful in practice. Choosing and purchasing the right e-commerce technology is similar to finding gold in the mountains: there is a low frequency of a desirable state and a high frequency of an undesirable state. Thus, such scenarios are called gold mining problems. In such cases, the goal is to increase the probability of accurately predicting the desirable state. However, few prediction methods are sophisticated enough to predict gold mining problem results accurately. Hence, the purpose of this paper is to propose a novel ensemble method dedicated to increasing the probability of accurately predicting desirable states. We develop the vertical boosting with rewarded vote strategy, which generates classifiers for each attribute in a sample. Each classifier then generates individual rules with the assistance of a sensitivity level, to find desirable states. The individual rule sets are generated with adjustment by the multiplier, and then used in the ensemble method to generate combined rules. To show the method’s soundness, we perform an experiment with a representative gold mining problem: prediction of transferability of the intellectual properties of e-transaction technology.     

A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction

This study investigates stock market indices prediction that is an interesting and important research in the areas of investment and applications, as it can get more profits and returns at lower risk rate with effective exchange strategies. To realize accurate prediction, various methods have been tried, among which the machine learning methods have drawn attention and been developed. In this paper, we propose a basic hybridized framework of the feature weighted support vector machine as well as feature weighted K-nearest neighbor to effectively predict stock market indices. We first establish a detailed theory of feature weighted SVM for the data classification assigning different weights for different features with respect to the classification importance. Then, to get the weights, we estimate the importance of each feature by computing the information gain. Lastly, we use feature weighted K-nearest neighbor to predict future stock market indices by computing k weighted nearest neighbors from the historical dataset. Experiment results on two well known Chinese stock market indices like Shanghai and Shenzhen stock exchange indices are finally presented to test the performance of our established model. With our proposed model, it can achieve a better prediction capability to Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index in the short, medium and long term respectively. The proposed algorithm can also be adapted to other stock market indices prediction.     

The IPO window of opportunity for digital product and service firms

Recognizing the window of opportunity to go public for digital product and service (DPS) firms is especially critical because they have high fixed-to-variable cost (FCVC) ratios and winner-take-all industry competition. Too early and their very risky nature means a steep discount to their stock. Too late and they may not be able to sell stock or it may be too late to take advantage of a new product or service. We find that the size of the run-up in stock price at the IPO is higher (lower) for DPS firms the earlier (later) they go public and significantly more than for traditional firms. We also find that the DPS firms that go public earlier or later outside the window of opportunity are more likely to fail. This result is also stronger for DPS firms than for traditional firms.     

Efficient mining of intertransaction association rules

Most of the previous studies on mining association rules are on mining intratransaction associations, i.e., the associations among items within the same transaction. We extend the scope to include multidimensional, intertransaction associations. In a database of stock price information, an example of such an association is "if (company) A's stock goes up on day one, B's stock will go down on day two but go up on day four:" whether we treat company or day as the unit of transaction, the items belong to different transactions. Moreover, such an intertransaction association can be extended to associate multiple properties in the same rule, so that multidimensional intertransaction associations can also be defined and discovered. Mining intertransaction associations pose more challenges on efficient processing than mining intratransaction associations because the number of potential association rules is extremely large. We introduce the notion of intertransaction association rule and develop an efficient algorithm, FITI (first intra then inter), for mining intertransaction associations, which adopts two major ideas: 1) an intertransaction frequent itemset contains only the frequent itemsets of its corresponding intratransaction counterpart; and 2) a special data structure is built among intratransaction frequent itemsets for efficient mining of intertransaction frequent itemsets.     

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。     

数据流模式依赖挖掘及应用

针对数据流间“模式依赖”问题,给出了一种模式依赖挖掘算法,该算法包括：挖掘前时间序列分段和模式表示,条件规则元组的创建和维护,模式依赖的置信度和支持度计算,2个或N个数据流概要结构的设计等。股票数据实验和实际系统表明,该挖掘方法能够有效地发现数据流间的模式依赖,可用于预测。