Design and implementation of fuzzy expert system for Tehran Stock Exchange portfolio recommendation

https://doi.org/10.1016/j.eswa.2010.02.114Get rights and content

Abstract

The key issue for decision making in stock trading is selection of the right stock at the right time. In order to select the superior stocks (alternatives) for investment, a finite number of alternatives have to be ranked considering several and sometimes conflicting criteria that often are vague and have uncertainty conditions. Therefore, we are faced with a special Multiple Criteria Decision Making (MCDM) problem. The purpose of this paper is to develop a fuzzy expert system for selecting superior stocks in order to encounter the uncertainty of stock portfolio recommendation and model the recommendation rules which experts at Tehran Stock Exchange (TSE) use for portfolio recommendation. The results of implementing the designed fuzzy expert system at TSE were affirmative.

Introduction

Finance portfolio recommendation is a collection of investment suggested to an institution or private individual. Selection of the assets and their proportion is a key problem in portfolio recommendation. It is difficult to decide which securities should be selected because of the existence of uncertainty on their returns; hence, it is necessary to balance between the maximizing expected return and minimizing the risk of the selected portfolio for better recommendation (Bermúdez, Segura, & Vercher, 2007). To smooth portfolio recommendation, computerized and intelligent decision support has been used since past decades (Edward & Magee, 2001).

There are many analytical approaches for decision making in stock exchange, which are categorized in two groups of technical analysis and fundamental analysis (Edward & Magee, 2001). Technical analysts believe that the stock profitability prediction is possible through studying stock prices in the past (Edward & Magee, 2001). There are technical indicators for studying price trends of each stock such as moving averages, relative strength index (RSI), and moving average convergence divergence (MACD) (Abdollahzadeh, 2002). Fundamental analysts analyze audit reports, income statement, quarterly balance sheets, dividend records, sales records, management capabilities, and competitive situation of the company and then calculate intrinsic value of each stock based on prediction of cash flow for next few years. If the market price of a stock is lower than its intrinsic value, its price is expected to rise and they decide to buy it (Alexander, Sharpe, & Bailey, 1993).

Stock portfolio recommendation is a complex multiple attribute problem. Combination of scientific methodology and personal experience of the field of the stock portfolio recommendation is the vital point to be succeeded in this field. Thus, all advanced tools will be utilized in order to ensure the integration of the know-how of professionals in the field of portfolio management. A fundamental portfolio theory using model of mean–variance was developed by Markowitz (1952); anticipated return related to the undertaken risk is the main idea for portfolio recommendation. Then Sharpe (1964) developed a capital asset pricing model developing the mean–variance model and the multiple attribute utility theory, under performance conditions towards risk. Ross (1976) also developed arbitrage pricing theory, where the stock return common component is expressed through a number of effect factors.

Siskos and Despotis (1989) use multiple objectives interactive linear programming method to achieve a portfolio composition. Then Siskos, Spyridakos, and Yannacopoulos (1993) ranked the stocks with Minora method of preference analysis. Many methods are applied based on one of technical or fundamental approaches or the combination of them. For example, Sycara, Decker, Pannu, Williamson, and Zeng (1996) applied intelligent agents’ technology for retrieving stock market data from distributed Internet sources. Hurson and Zopounidis (1997) examined two types of problems: stock evaluation and portfolio composition; Spronk and Hallerbach (1997) proposed a system based on multiple criteria analysis for supporting individual financial decision making in portfolio management for any type of investor. Zopounidis, Despotis, and Kamaratou (1998) proposed a portfolio selection in Athens Stock Exchange (ASE), for the two-year period of 1989–1990. A proactive system for expressing and implementing high-level stock trading strategies was developed by Garc´ıa, Gollapally, Tarau, and Simari in 2000 which was suitable for implementing a deliberative multi-agent system in portfolio recommendation with technical and fundamental approaches. Zopounidis and Doumpos (2000) presented a multiple criteria expert system which deals with the problem of portfolio selection and composition. Kwas´nicka and Ciosmak (2001) also applied a fuzzy expert system for technical analysis and artificial neural network for fundamental analysis in order to analyze the stock market and calculate the attractiveness of the companies and identifying buy signals. Luo, Liu, and Davis (2002) designed a decision support system for buy or sell decisions based on fundamental analysis principles and technical indicators.

The most important elements of multiple criteria expert systems, an important category of expert systems, were developed by Matsatsinis, Samaras, Siskos, and Zopounidis (2002) for stock evaluation and portfolio management. Therefore, Matsatsinis et al. (2002) can consider many important criteria that derive from fundamental and technical analysis, as well as the investor’s profile that represents his goals, preferences and policies besides the two basic criteria of return and risk in portfolio management with an MCDM approach. In addition, Samaras and Matsatsinis (2003) use a multiple criteria expert system for which uses MCDM methods in order to rank the ASE stocks based on fundamental analysis, technical analysis and stock exchange analysis. Furthermore, Pohekar and Ramachandran (2004) used a genetic programming for discovering appropriate technical trading rules in stock exchange. Besides, Vranes, Stanojevic, Stevanovic, and Lucin (1996) suggested an investment program combining five different techniques: heuristics, expert system, fuzzy logic, investor risk model, and PROMETHEEII (a MCDM method) as an intelligent multiple criteria expert systems, in which multiple criteria methodologies and at least one artificial intelligent technology are combined. The Fineva system (Zopounidis & Doumpos, 1999) and Intelligent Investor Samaras and Matsatsinis (2003) in the ASE are the other example of intelligent multiple criteria expert systems.

In the stock portfolio recommendation problem, there are a finite number of stocks existing in stock exchange as alternatives which have to be ranked considering many different and conflicting criteria. Accordingly, this problem is considered as a MCDM problem. Fuzzy logic is initially introduced to deal with limitation in stock value reorganization and stock portfolio recommendation in TSE to encounter the uncertainty of criteria of decision making for stock portfolio recommendation. In this case, fuzzy decision making have been used in fuzzy expert system to recommend stock portfolio in TSE.

The outline of the paper is as follows: Next section, fuzzy expert system for portfolio recommendation has been described. Afterwards, the architecture of fuzzy expert system has been introduced and design of the proposed system for portfolio recommendation at TSE has been presented; then the designed system for stock portfolio recommendation at TSE is explained. Subsequently, the designed system has been evaluated and the obtained results have been described. Finally, conclusion has been showed the benefits of proposed system.

Section snippets

Fuzzy expert system

Problem solving mechanism is only a small part of intelligent computer system (Turban & Aronson, 1998). Thus, the necessity of expert system, a computing system capable of representing and reasoning about some knowledge-rich domain with a view to solving problems and giving advice (Jackson, 1990), was increased.

Usage of expert systems or knowledge-based system has extensively increased during last decade. The main difference of these systems with other software is that they process knowledge

Architecture of fuzzy expert system

A fuzzy expert system is comprised of four components: fuzzification unit, knowledge base, decision making logic, and defuzzification unit which should be embedded in the architecture detail for fuzzy expert system construction. The big picture of system architecture is composed of three main blocks as shown in Fig. 1.

Design the fuzzy expert system for portfolio recommendation

The proposed fuzzy expert system aims at evaluating stocks of TSE so that make the portfolio and recommend it to the target customers at TSE according to their preferences and stocks pay off. For stock portfolio recommendation, the proposed system ranks the stocks by starting with the best one towards the worst. Ranking criteria are fundamental analysis ratios and qualitative criteria from the TSE. The undertaken risk is also incorporated in the ranking process. Thus, the portfolio

Implementation and evaluation of the system

A prototype system is designed by use of MATLAB software. The procedure of building a prototype system has been widely used in software engineering research (Nunamaker & Purdin, 1990) because basic inherent problems emerge at an early stage and can be addressed promptly. In addition new concepts of user interface design can be evaluated and the developers gain insights into the application area and into the users’ work tasks and the problems they face (Ngai, 2003). Once the prototype system is

Conclusion

This paper described a new method for design of fuzzy expert system for portfolio recommendation at TSE. Seven critical parameters for portfolio recommendation at TSE have been considered, which have been obtained through distribution of questionnaires among expert of TSE, investment companies, and brokerage companies: market of stock, sale’s rules, EPS, projects, stockholders, legal audit report, and float shares. Comparing this system with the conventional one, the designed system in this

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