Determining of stock investments with grey relational analysis

Selecting stock is important problem for investors. Investors can use related financial ratios in stock selection. These kind of worthy financial ratios can be obtained from financial statements. The investors can use these ratios as criteria while they are selecting the stocks. Since dealing with more than one financial ratio, the investing issue becomes multi-criteria decision making (MCDM) problem for the investors. There are various techniques for solving MCDM problems in literature. In this study grey relational analysis (GRA) is used for ordering some financial firms’ stocks which are in Financial Sector Index of Istanbul Stock Exchange (ISE). Besides, because of the importance of criteria weights in decision making, three different approaches – heuristic, Analytic Hierarchy Process, learning via sample – were experimented to find best values of criteria weights in GRA process.     

Fuzzy performance evaluation in Turkish Banking Sector using Analytic Hierarchy Process and TOPSIS

The performance evaluation of banks has important results for creditors, investors and stakeholders since it determines banks’ capabilities to compete in the sector and has a critical importance for the development of the sector. The aim of this study is to propose a fuzzy multi-criteria decision model to evaluate the performances of banks.The largest five commercial banks of Turkish Banking Sector are examined and these banks are evaluated in terms of several financial and non-financial indicators. Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) methods are integrated in the proposed model. After the weights for a number of criteria are determined based on the opinions of experts using the FAHP method, these weights are input to the TOPSIS method to rank the banks. The results show that not only financial performance but also non-financial performance should be taken into account in a competitive environment.     

An integrated TOPSIS crow search based classifier ensemble: In application to stock index price movement prediction

Predicting future stock index price movement has always been a fascinating research area both for the investors who wish to yield a profit by trading stocks and for the researchers who attempt to expose the buried information from the complex stock market time series data. This prediction problem can be addressed as a binary classification problem with two class labels, one for the increasing movement and other for the decreasing movement. In literature, a wide range of classifiers has been tested for this application. As the performance of individual classifier varies for a diverse dataset with respect to different performance measures, it is impractical to acknowledge a specific classifier to be the best one. Hence, designing an efficient classifier ensemble instead of an individual classifier is fetching increasing attention from many researchers. Again selection of base classifiers and deciding their preferences in ensemble with respect to a variety of performance criteria can be considered as a Multi Criteria Decision Making (MCDM) problem. In this paper, an integrated TOPSIS Crow Search based weighted voting classifier ensemble is proposed for stock index price movement prediction. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), one of the popular MCDM techniques, is suggested for ranking and selecting a set of base classifiers for the ensemble whereas the weights of the classifiers used in the ensemble are tuned by the Crow Search method. The proposed ensemble model is validated for prediction of stock index price over the historical prices of BSE SENSEX, S&P500 and NIFTY 50 stock indices. The model has shown better performance compared to individual classifiers and other ensemble models such as majority voting, weighted voting, differential evolution and particle swarm optimization based classifier ensemble.     

Forecasting PGR of the financial industry using a rough sets classifier based on attribute-granularity

In the financial industry, continually changing economic conditions and characteristics involving uncertainty and risk have made financial forecasts even more difficult, increasing the need for more reliable ways to forecast a bank’s operating performance. However, early related studies of performance analysis for using statistical methods usually become more complex when relationships in input/output data are nonlinear. Furthermore, strict data assumptions, such as linearity, normality, and independence, limit real-world applications often. Additionally, a drawback of traditional rough sets is that data must be discretized first for improving classification accuracy. To remedy the existing shortcomings above, the study proposes a hybrid procedure, which mixes professional knowledge, an attribute granularity, and a rough sets classifier, for automatically classifying profit growth rate (PGR) to solve real problems faced by investors. The proposed procedure is illustrated by examining a practical dataset for publicly traded financial holding stocks in Taiwan‘s stock markets. The experimental results reveal that the proposed procedure outperforms listing methods in terms of accuracy, and they provide useful insights in responsiveness to rapidly changing stock market conditions. Importantly, the output created by the rough sets LEM2 (Learning from Examples Module, version 2) algorithm is a set of comprehensible rules applied in a knowledge-based investment system for investors.     

Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods

In today’s competitive environment evaluating firms’ performance properly, is an important issue not only for investors and creditors but also for the firms that are in the same sector. Determining the competitiveness of the firms and evaluating the financial performance of them is also crucial for the sector’s development.The aim of this study is developing a fuzzy model to evaluate the performance of the firms by using financial ratios and at the same time, taking subjective judgments of decision makers into consideration. Proposed approach is based on Fuzzy Analytic Hierarchy Process (FAHP) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods. FAHP method is used in determining the weights of the criteria by decision makers and then rankings of the firms are determined by TOPSIS method. The proposed method is used for evaluating the performance of the fifteen Turkish cement firms in the Istanbul Stock Exchange by using their financial tables. Then the rankings of the firms are determined according to their results.     

FuzzyTree crossover for multi-valued stock valuation

Stock valuation is very important for fundamental investors in order to select undervalued stocks so as to earn excess profits. However, it may be difficult to use stock valuation results, because different models generate different estimates for the same stock. This suggests that the value of a stock should be multi-valued rather than single-valued. We therefore develop a multi-valued stock valuation model based on fuzzy genetic programming (GP). In our fuzzy GP model the value of a stock is represented as a fuzzy expression tree whose terminal nodes are allowed to be fuzzy numbers. There is scant literature available on the crossover operator for our fuzzy trees, except for the vanilla subtree crossover. This study generalizes the subtree crossover in order to design a new crossover operator for the fuzzy trees. Since the stock value is estimated by a fuzzy expression tree which calculates to a fuzzy number, the stock value becomes multi-valued. In addition, the resulting fuzzy stock value induces a natural trading strategy which can readily be executed and evaluated. These experimental results indicate that the fuzzy tree (FuzzyTree) crossover is more effective than a subtree (SubTree) crossover in terms of expression tree complexity and run time. Secondly, shorter training periods produce a better return of investment (ROI), indicating that long-term financial statements may distort the intrinsic value of a stock. Finally, the return of a multi-valued fuzzy trading strategy is better than that of single-valued and buy-and-hold strategies.     

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).     

An integrated fuzzy multi-criteria approach for the performance evaluation of multiple manufacturing plants

The performance evaluation is regarded as a multiple criteria decision making (MCDM) problem and has a significant impact on the operations of the enterprise. This paper develops an integrated MCDM approach that combines the voting method and the fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method to evaluate the performance of multiple manufacturing plants in a fuzzy environment. Fuzzy TOPSIS helps decision-makers carry out analysis and comparisons in ranking their preference of the alternatives with vague or imprecise data. Since the evaluation result is often greatly affected by the weights used in the evaluation process, the voting method is used in this study to determine the appropriate criteria weights. A case study demonstrating the applicability of the proposed model is presented. The case company is the world’s largest manufacturer of power supplies. It has three primary manufacturing bases located in Wujiang, Dongguan, and Tianjin, China. The proposed approach is used to evaluate the performance of the company’s five manufacturing plants in Wujiang, which produce switch power, telecom power, DC/DC converters, uninterruptible power systems (UPS) and AC/DC adapters.     

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.     

Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology

Energy planning is a complex issue which takes technical, economic, environmental and social attributes into account. Selection of the best energy technology requires the consideration of conflicting quantitative and qualitative evaluation criteria. When decision-makers’ judgments are under uncertainty, it is relatively difficult for them to provide exact numerical values. The fuzzy set theory is a strong tool which can deal with the uncertainty in case of subjective, incomplete, and vague information. It is easier for an energy planning expert to make an evaluation by using linguistic terms. In this paper, a modified fuzzy TOPSIS methodology is proposed for the selection of the best energy technology alternative. TOPSIS is a multicriteria decision making (MCDM) technique which determines the best alternative by calculating the distances from the positive and negative ideal solutions according to the evaluation scores of the experts. In the proposed methodology, the weights of the selection criteria are determined by fuzzy pairwise comparison matrices. The methodology is applied to an energy planning decision-making problem.     

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.     

How are social and mass media different in relation to the stock market? A study on topic coverage and predictive value

Although investors in financial markets have access to information from both mass media and social media, trading platforms that curate and provide this information have little to go by in terms of understanding the difference between these two types of media. This paper compares social media with mass media in the stock market, focusing on information coverage diversity and predictive value with respect to future stock absolute returns. Based on a study of nearly a million stock-related news articles from the Sina Finance news platform and 12.7 million stock-related social media messages from the popular Weibo platform in China, we find that social media covers less stocks than mass media, and this effect is amplified as the volume of media information increases. We find that there is some short-term predictive value from these sources, but they are different. Although mass media information coverage is more predictive than social media information coverage in a one-day horizon, it is the other way around in a two-to five-day horizon. These empirical results suggest that social media and mass media serve stock market investors differently. We draw connections to theories related to how crowds and experts differ and offer practical implications for the design of media-related IS systems.     

PERFORMANCE EVALUATION OF SUGAR PLANTS BY FUZZY TECHNIQUE FOR ORDER PERFORMANCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS)

This study presents the performance evaluation of sugar plants using the technique for order performance by similarity to ideal solution (TOPSIS) under a fuzzy environment. First, the decision criteria used to evaluate the performances are determined, and then the data from financial statements are collected from sugar plants. Accordingly, the ratings of various alternatives under various criteria and the importance weights of various criteria are assessed by evaluators using linguistic terms. The data obtained are converted into a fuzzy triangular number system and then the fuzzy TOPSIS method is applied to make a final decision. According to the closeness coefficients, the sugar plants are ranked from strong to weak. A real case study involving eight evaluation criteria and nine sugar plants assessed by nine evaluators is provided to illustrate the proposed method. The results show that this method is an effective tool for evaluating investment risks based on the heuristic knowledge acquired from experts.     

A model based on 2-tuple fuzzy linguistic representation and Analytic Hierarchy Process for supplier segmentation using qualitative and quantitative criteria

The literature on supply base segmentation has increasingly adopted multi-criteria decision making (MCDM) techniques into recently proposed models. However, most proposals segment the supply base from the standpoint of the purchased item, which prevents them from providing guidelines that are specific to each supplier. Some authors have attempted to overcome these limitations by putting forward portfolio models based on the relationship with suppliers. These approaches use fuzzy variables and MCDM methods that take qualitative judgements by experts as the only input for decision making. However, many companies have databases with historical data about the performance of past transactions with suppliers that should be considered by expert systems that aim to comprehensively evaluate suppliers’ performance. This paper seeks to address this gap by proposing a segmentation model based on the relationship with suppliers capable of aggregating quantitative and qualitative criteria. Analytic Hierarchy Process (AHP) was used to determine the relative importance of each criteria. Fuzzy 2-tuple, a prominent computing with word (CWW) approach, was used to evaluate suppliers with a mixture of historical quantitative data and qualitative judgements by purchasing experts. An illustrative application of the proposed model was carried out in the pharmaceutical supply center (PSC) of a teaching hospital. The proposed model can be viewed as a decision support system capable of aggregating the qualitative judgements of experts and quantitative historical performance measures, thus providing guidelines to improve the relationship between suppliers and the buyer firm.     

Hybridizing nonlinear independent component analysis and support vector regression with particle swarm optimization for stock index forecasting

One of the major activities of financial firms and private investors is to predict future prices of stocks. However, stock index prediction is regarded as a challenging task of the prediction problem since the stock market is a complex, chaotic and nonlinear dynamic system. As stock markets are highly dynamic and exhibit wide variation, it may be more realistic and practical that assumed the stock index data are a nonlinear mixture data. In this study, a hybrid stock index prediction model by utilizing nonlinear independent component analysis (NLICA), support vector regression (SVR) and particle swarm optimization (PSO) is proposed. In the proposed model, first, the NLICA is used to deal with the nonlinearity property of the stock index data. The proposed model utilizes NLICA to extract features from the observed stock index data. The features which can be used to represent underlying/hidden information of the data are then served as the inputs of SVR to build the stock index prediction model. Finally, PSO is applied to optimize the parameters of the SVR prediction model since the parameters of SVR must be carefully selected in establishing an effective and efficient SVR model. In order to evaluate the performance of the proposed approach, the closing indexes of the Taiwan stock exchange capitalization weighted stock index, Shanghai stock exchange composite index and Bombay stock exchange index are used as illustrative examples. Experimental results showed that the proposed hybrid stock index prediction method significantly outperforms the other six comparison models. It is an efficient and effective alternative for stock index forecasting.     

Combined MCDM techniques for exploring stock selection based on Gordon model

Basing on the Gordon model perspective and applying multiple criteria decision making (MCDM), this research explores the influential factors and relative weight of dividend, discount rate, and dividend growth rate. The purpose is to establish an investment decision model and provides investors with a reference/selection of stocks most suitable for investing effects to achieve the greatest returns. Taking full consideration of the interrelation effect among variables of the decision model, this paper introduced analytical network process (ANP) and examined leading electronics companies spanning the hottest sectors of lens, solar, and handset by experts. Empirical findings indicated that dividend was affected by industry outlook, earnings, operating cash flow, and dividend payout rate; discount rate was affected by market β and risk-free rate; and dividend growth rate was affected by earnings growth rate and dividend payout growth rate. Also, according to literatures, discount rate possessed a self-effect relationship. Among the eight evaluation criteria, market β was the most important factor influencing investment decisions, followed by dividend growth rate and risk-free rate. In stock evaluations, leadership companies in the solar industry outperformed those in handset and lens, becoming investors’ favorite stock group at the time that this research was conducted.     

The role of algorithmic trading systems on stock market efficiency

The rapid development of information technology has changed the dynamics of financial markets. The main purpose of this study is laid on examining the role of IT based stock trading on financial market efficiency. This research specifically focused on algorithmic trading. Algorithmic trading enables investors to trade stocks through a computer program without the need for human interventions. Based on an empirical analysis of the Korean stock market, this study discovered the positive impact of algorithmic trading on stock market efficiency at three-fold. First, the study results indicate that algorithmic trading contributes to the reduction in asymmetric volatility, which causes inefficiency of information in a stock market. Second, an algorithmic trading also increases the operation efficiency of a stock market. Arbitrage trading contributes on the equilibrium between the spot market and futures market as well as on the price discovery. Third, algorithmic trading provides liquidity for market participants contributing to friction free transactions. The research results indicate that stock exchanges based on electronic communications networks (ECNs) without human intervention could augment a financial market quality by increasing trading share volumes and market efficiency so that it can eventually contribute to the welfare of market investors.     

Generalizing TOPSIS for fuzzy multiple-criteria group decision-making

In this paper, we generalize TOPSIS to fuzzy multiple-criteria group decision-making (FMCGDM) in a fuzzy environment. TOPSIS is one of the well-known methods for multiple-criteria decision-making (MCDM). Most of the steps of TOPSIS can be easily generalized to a fuzzy environment, except max and min operations in finding the ideal solution and negative ideal solution. Thus we propose two operators Up and Lo which satisfy the partial ordering relation on fuzzy numbers to the generalization of TOPSIS. In generalized TOPSIS, these two operations (Up and Lo) are employed to find ideal and negative ideal solutions under a fuzzy environment. Then the FMCGDM problem can be solved effectively and efficiently.     

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.