Stock Portfolio Selection Research Articles

Mechanical Selection System of Stock Portfolio Based on Grey Relational Analysis Attribute Reduction Mechanism

A stock portfolio selection system based on a Grey Relational Analysis (GRA) model, a Fuzzy C-Means (FCM) clustering scheme and Rough Set (RS) theory is proposed. In the proposed approach, 53 financial indices are collected automatically for each stock item every quarter and a GRA model is used to consolidate these indices into six predetermined financial ratios (Grey Relational Grades (GRGs)). The GRGs of the stock items are then clustered using a FCM scheme and the resulting cluster indices are processed using RS theory to identify the lower approximate set within the stock system. The stock items within the lower approximate set are filtered in accordance with established investment principles and the six GRGs of each surviving stock item are then consolidated to a single GRG indicating the overall merit of the corresponding stock item in terms of its ability to maximize the rate of return on the investment portfolio. The validity of the proposed approach is demonstrated using electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). It is shown that the rate of return on the investment portfolio selected using the proposed GRA/FCM/RS system is higher than the average rate of return predicted by the variation in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) over the same period. Thus, the feasibility of the GRA-based attribute reduction mechanism and the overall viability of the proposed portfolio selection system are confirmed. Introduction

Stock returns in emerging markets and the use of GARCH models

We use the Hinich portmanteau bicorrelation test to detect for the adequacy of using GARCH (Generalized Autoregressive Conditional Heteroscedasticity) as the data-generating process to model conditional volatility of stock market index rates of return in 13 emerging economies. We find that a GARCH formulation or any of its variants fail to provide an adequate characterization for the underlying process of the 13 emerging stock market indices. We also study whether there exist evidence of ARCH effects, over windows of 200, 400 and 800 observations, using Engle's LM (Lagrange Multiplier) test, and find that there exist long periods of time with no evidence of ARCH effects. The results suggest that policymakers should use caution when using autoregressive models for policy analysis and forecast because the inadequacy of GARCH models has strong implications for the pricing of stock index options, portfolio selection and risk management. Specially, measures of spillover effects and output volatility may not be accurate when using GARCH models to evaluate economic policy.

Application of enhanced cluster validity index function to automatic stock portfolio selection system

This paper presents an automatic stock portfolio selection system. In the proposed approach, 53 financial indices are collected for each stock item and are consolidated into six financial ratios [Grey relational grades (GRGs)] using a Grey relational analysis model. The GRGs are processed using a modified form of the PBMF index method (designated as the Huang index function) to determine the optimal number of clusters per GRG. The resulting cluster indices are then processed using rough set theory to identify the stocks within the lower approximate sets. Finally, the GRGs of each stock item in the lower approximate sets are consolidated into a single GRG, indicating the ability of the stock item to maximize the rate of return. It is demonstrated that the proposed stock selection mechanism yields a higher rate of return than several existing portfolio selection systems.

A Portfolio Selection Using Fuzzy Analytic Hierarchy Process: A Case Study of Iranian pharmaceutical industry

Article history: Received 31 January 2010 Received in revised form 28 March 2010 Accepted 1 May 2010 Available online 7 May 2010 Portfolio selection is one of the important problems encountered by any investor. The purpose of this paper is to solve a real stock portfolio selection problem in Iran. According to the uncertain environments in which financial decisions are made, most of the recent works in this field use fuzzy sets theory in order to incorporate these uncertainties into their analysis. The problem is to determine how to allocate a limited fund among the stocks of some pharmaceutical companies in Tehran stock exchange. For this purpose we apply two fuzzy analytic hierarchy process (FAHP) methods to this problem. Finally, the results obtained from the two methods are compared in terms of the solution quality. © 2011 Growing Science Ltd. All rights reserved.

An Enhanced Approach to Optimizing the Stock Portfolio Selection based on Modified Markowitz MV Method

This study presents an expert system for automatically predicting the future behavior of the stock market and determining an investment portfolio which maximizes the rate of return using a hybrid GRA / MV Model. The proposed system involved a moving Average Autoregressive Exogenous (ARX) prediction model, an enhanced clustering / classification method based on Huang-index function, RS theory ,Grey Relational Analysis (GRA) model, and Markowitz MV method. ARX is used to forecast the future trends of the collected data over the next quarter or half-year period. The enhanced clustering method is used to determine the optimal number of clusters per attribute. A RS classification module then is supplied to identify the stocks within the lower approximate sets. Finally, the selected stocks are using a hybrid GRA / MV scheme in order to maximize the rate of return of the stock portfolio. The validity and effectiveness of the clustering / classification method based on Huang-index function is first evaluated prior to that of the expert system. After that, the validity of the proposed expert system is demonstrated using electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ).

NONLINEARITIES AND GARCH INADEQUACY FOR MODELING STOCK MARKET RETURNS: EMPIRICAL EVIDENCE FROM LATIN AMERICA

In this paper, we analyze the adequacy of using GARCH as the data-generating process to model conditional volatility of stock market index rates-of-return series. Using the Hinich portmanteau bicorrelation test, we find that a GARCH formulation or any of its variants fail to provide an adequate characterization for the underlying process of the main Latin American stock market indices. Policymakers need to be careful when using autoregressive models for policy analysis and forecast because the inadequacy of GARCH models has strong implications for the pricing of stock index options, portfolio selection, and risk management. In particular, measures of spillover effects and output volatility may not be correct when GARCH-type models are used to evaluate economic policy.

A New Fuzzy DSS/ES for Stock Portfolio Selection using Technical and Fundamental Approaches in Parallel

A Decision Support System/Expert System for stock portfolio selection presented where at first step, both technical and fundamental data used to estimate technical and fundamental return and risk (1 phase); Then, the estimated values are aggregated with the investor preferences (2 phase) to produce convenient stock portfolio. In the 1 phase, there are two expert systems, each of which is responsible for technical or fundamental estimation. In the technical expert system, for each stock, twenty seven candidates are identified and with using rough sets-based clustering method (RC) the effective variables have been selected. Next, for each stock two fuzzy rulebases are developed with fuzzy C-Mean method and Takai-SugenoKang (TSK) approach; one for return estimation and the other for risk. Thereafter, the parameters of the rule-bases are tuned with backpropagation method. In parallel, for fundamental expert systems, fuzzy rule-bases have been identified in the form of “IF-THEN” rules through brainstorming with the stock market experts and the input data have been derived from financial statements; as a result two fuzzy rule-bases have been generated for all the stocks, one for return and the other for risk. In the 2 phase, user preferences represented by four criteria and are obtained by questionnaire. Using an expert system, four estimated values of return and risk have been aggregated with the respective values of user preference. At last, a fuzzy rule base having four rules, treats these values and produce a ranking score for each stock which will lead to a satisfactory portfolio for the user. The stocks of six manufacturing companies and the period of 2003-2006 selected for data gathering. Keywords—Stock Portfolio Selection, Fuzzy Rule-Base Expert Systems, Financial Decision Support Systems, Technical Analysis, Fundamental Analysis.

Stock portfolio selection with full-scale optimization and differential evolution

Full-Scale Optimization (FSO) is a utility maximization approach to portfolio choice problems that has theoretical appeal but that suffers from computational burden in large scale problems. We apply the heuristic technique differential evolution to solve FSO-type asset selection problems of 97 assets under complex utility functions rendering rough utility search surfaces. We show that this problem is computationally feasible and that solutions retrieved with random starting values are converging to one optimum. Furthermore, the study constitutes the first FSO application to stock portfolio optimization. The results indicate that when investors are loss averse, FSO improves stock portfolio performance compared to Mean Variance (MV) portfolios. This finding widens the scope of applicability of FSO, but it is also stressed that out-of-sample success will always be dependent on the forecasting ability of the input return distributions.

A New Decision-Making Method for Stock Portfolio Selection Based on Computing with Linguistic Assessment

The purpose of stock portfolio selection is how to allocate the capital to a large number of stocks in order to bring a most profitable return for investors. In most of past literatures, experts considered the portfolio of selection problem only based on past crisp or quantitative data. However, many qualitative and quantitative factors will influence the stock portfolio selection in real investment situation. It is very important for experts or decision-makers to use their experience or knowledge to predict the performance of each stock and make a stock portfolio. Because of the knowledge, experience, and background of each expert are different and vague, different types of 2-tuple linguistic variable are suitable used to express experts' opinions for the performance evaluation of each stock with respect to criteria. According to the linguistic evaluations of experts, the linguistic TOPSIS and linguistic ELECTRE methods are combined to present a new decision-making method for dealing with stock selection problems in this paper. Once the investment set has been determined, the risk preferences of investor are considered to calculate the investment ratio of each stock in the investment set. Finally, an example is implemented to demonstrate the practicability of the proposed method.

Application of VPRS model with enhanced threshold parameter selection mechanism to automatic stock market forecasting and portfolio selection

This study proposes a technique based upon Fuzzy C-Means (FCM) classification theory and related fuzzy theories for choosing an appropriate value of the Variable Precision Rough Set (VPRS) threshold parameter ( β) when applied to the classification of continuous information systems. The VPRS model is then combined with a moving Average Autoregressive Exogenous (ARX) prediction model and Grey Systems theory to create an automatic stock market forecasting and portfolio selection mechanism. In the proposed mechanism, financial data are collected automatically every quarter and are input to an ARX prediction model to forecast the future trends of the collected data over the next quarter or half-year period. The forecast data are then reduced using a GM(1, N) model, classified using a FCM clustering algorithm, and then supplied to a VPRS classification module which selects appropriate investment stocks in accordance with a pre-determined set of decision-making rules. Finally, a grey relational analysis technique is employed to weight the selected stocks in such a way as to maximize the rate of return of the stock portfolio. The validity of the proposed approach is demonstrated using electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The portfolio results obtained using the proposed hybrid model are compared with those obtained using a Rough Set (RS) selection model. The effects of the number of attributes of the RS lower approximation set and VPRS β-lower approximation set on the classification are systematically examined and compared. Overall, the results show that the proposed stock forecasting and stock selection mechanism not only yields a greater number of selected stocks in the β-lower approximation set than in the RS approximation set, but also yields a greater rate of return.

A Hybrid RS Model for Stock Portfolio Selection Allied with Weight Clustering and Grey System Theories

In this study, the weight clustering model, which consists of Dependency of Attributes of Rough Set (RSDA) with K-means Clustering is combined with Grey Systems theory and Rough Set (RS) theory to create an automatic stock market forecasting and portfolio selection mechanism. In our proposed approach, financial data are collected every quarter and are inputted to an GM (1, 1) predicting model to forecast the future trends of the collected data over the next quarter. Next, the forecasted data of financial statement is transformed into financial ratios using a RSDA measures and clustered by using a K-means clustering algorithm, and then supplied to a RS classified module, which selects appropriate investment stocks by adopting a set of decision-making rules. Finally, a grey relational analysis technique is applied to specify an appropriate weighting of the selected stocks to maximize the portfolio's rate of return. The validity of our proposed approach is demonstrated to use the electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The portfolio's results derived by using our proposed weight clustering model are compared with those portfolio's results of a conventionally clustering method. It is found that our proposed method yielded a greater average annual rate of return (23.42%) on the selected stocks from 2004 to 2006 in Taiwan stock market.

Common stock portfolio selection: a multiple criteria decision making methodology and an application to the Athens Stock Exchange

An integrated multiple-criteria methodological framework is proposed to support decisions that concern the selection of common stock portfolios. At the first stage of the methodology, two multiple-criteria methods are employed, within the context of the outranking relations theory, towards the initial appraisal of the stocks that are examined. We then utilize a non-linear optimization model, to generate portfolios that consist of the stocks that are classified as those with the optimal characteristics, during of the first stage. Finally, the portfolios designed at the previous stage are evaluated by using a wide set of well-known sophisticated evaluation criteria. The preferences and experiences of professionals and experts in the field of portfolio management were taken into consideration, through all the stages of the process. The validity of the above methodology is tested through a large scale illustrative application on the stocks that constitute the FTSE-140 index of the Athens Stock Exchange. The advantages of the proposed model against contextual approaches are finally stressed.

A hybrid model for stock market forecasting and portfolio selection based on ARX, grey system and RS theories

In this study, the moving average autoregressive exogenous (ARX) prediction model is combined with grey systems theory and rough set (RS) theory to create an automatic stock market forecasting and portfolio selection mechanism. In the proposed approach, financial data are collected automatically every quarter and are input to an ARX prediction model to forecast the future trends of the collected data over the next quarter or half-year period. The forecast data is then reduced using a GM(1,N) model, clustered using a K-means clustering algorithm and then supplied to a RS classification module which selects appropriate investment stocks by applying a set of decision-making rules. Finally, a grey relational analysis technique is employed to specify an appropriate weighting of the selected stocks such that the portfolio’s rate of return is maximized. The validity of the proposed approach is demonstrated using electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The predictive ability and portfolio results obtained using the proposed hybrid model are compared with those of a GM(1,1) prediction method. It is found that the hybrid method not only has a greater forecasting accuracy than the GM(1,1) method, but also yields a greater rate of return on the selected stocks.

Towards a Multiple Criteria Decision Making framework for common stock portfolio selection

An integrated multiple criteria methodological framework is proposed to support decisions that concern the construction and selection of common stock portfolios. The proposed framework combines discrete multiple criteria evaluation methods with a non-linear continuous optimisation model. The preferences and experiences of professionals and experts in the field of portfolio management are strongly taken into consideration, through all the stages of the process. The validity of the above methodology is tested through a large scale illustrative application on the stocks that constitute the FTSE-140 index of the Athens Stock Exchange.

The Stock Portfolio Game

One of the objectives of the stock portfolio game is to introduce undergraduate engineering students to the application of optimization techniques for stock portfolio selection. Ideally students playing the game would have basic background in linear algebra, calculus, and statistics. The general idea of the game is to present teams of students with real historical performance data for stocks with different levels of returns and variability so that they make decisions concerning the allocation of limited funds among these stocks. The game is played in four rounds within an hour-long session. The teams, comprised of no more than four students, are provided with an initial virtual investment fund, which they have to allocate or reallocate based on the information presented. The team with the most money in its investment account at the end of the game is declared the winner. Once the results of the game are known, one or more teams are asked to share their investment strategy with the rest of the group. This discussion forms the starting point for the last part of the session when the lecturer puts the investment game in the perspective of an optimization problem. At the end of the session basic principles of Markowitz's portfolio theory are presented and applied to the data given to the students.

Portfolio Selection in Stochastic Environments

In this article, I explicitly solve dynamic portfolio choice problems, up to the solution of an ordinary differential equation (ODE), when the asset returns are quadratic and the agent has a constant relative risk aversion (CRRA) coefficient. My solution includes as special cases many existing explicit solutions of dynamic portfolio choice problems. I also present three applications that are not in the literature. Application 1 is the bond portfolio selection problem when bond returns are described by ‘‘quadratic term structure models.’’ Application 2 is the stock portfolio selection problem when stock return volatility is stochastic as in Heston model. Application 3 is a bond and stock portfolio selection problem when the interest rate is stochastic and stock returns display stochastic volatility. (JEL G11) There is substantial evidence of time variation in interest rates, expected returns, and asset return volatilities. Interest rates change over time, and although expected stock returns are not directly observed, future stock returns seem to be predictable using term structure variables and scaled prices such as dividend yields. 1 Similarly, there is well-documented evidence of stochastic volatility, 2 whose existence is also supported by the ‘‘smile curve’’ of volatilities implied by option prices. Therefore, any serious study of dynamic portfolio choice must take account of stochastic variation in investment opportunities. The seminal work of Merton (1971) establishes the framework for dynamic portfolio choice with stochastic variation in investment opportunities. The portfolio weights in Merton’s framework are expressed in terms of the solution to a nonlinear partial differential equation (PDE), and because there is no closed-form solution of a nonlinear PDE in general, explicit portfolio weights are not available in general. There are approximate solutions to

Profilio Selection Using the Adelais Multiobjective Linear Programming System

The ADELAIS (Aide a la DEcision pour systemes Lineaires multicriteres par AIde a la Structuration des preferences), multiobjective linear programming system is proposed as a decision tool for the selection of stock portfolios. A portfolio selection model is developed and applied to a set of fifty two stocks from the Athens Stock Exchange for the two years period of 1989–1990. On the basis of this model, ADELAIS is used to design and evaluate alternative portfolios by considering a set of well known criteria such as return, price earnings ratio, volume of transactions and dividend yield. A final portfolio of maximal utility is obtained as an outcome of an interactive process of individual inter-alternative preference modelling.

Intelligent Stock Portfolio Management System

Abstract: For the selection of stock portfolios, the optimiastion models or expert systems are used separately. To take advantage of both approaches, the integration of optimisation model and expert system is attempted. The proposed architecture—ISPMS (Intelligent Stock Portfolio Management System)—accomplishes the integration by interpreting the knowledge as a part of the formulation of optimisation model. The other generic issues in ISPMS are the integration of personal preference with the expert knowledge and knowledge acquisition by machine learning. This paper thus describes the representation and the inference of an expert knowledge, the representation of personal preference and its integration with the expert knowledge, the interpretation of the knowledge and preference to associate with optimisation model, and the solution algorithm of the modified optimisation model accordingly.

Ectopic Pregnancy after Pelvic Tuberculosis

This article addresses stock selection modeling and portfolio selection and implementation in EMs. The authors view EM investing as a special case of global investing, demonstrating how stock-selection models in EMs with price momentum and forecast earnings acceleration factors can enhance returns. The authors construct index-enhanced portfolios for EMs that offer superior return-to-risk ratios relative to domestic portfolios. On the basis of back test and in real-time performance, the authors show how an EM portfolio using forecast earnings and price momentum anomalies can be developed, estimated, and implemented to generate statistically significant excess returns.