Adam Ghandar

Computational Intelligence for Evolving Trading Rules

This paper describes an adaptive computational intelligence system for learning trading rules. The trading rules are represented using a fuzzy logic rule base, and using an artificial evolutionary process the system learns to form rules that can perform well in dynamic market conditions. A comprehensive analysis of the results of applying the system for portfolio construction using portfolio evaluation tools widely accepted by both the financial industry and academia is provided.

Learning Fuzzy Rules with Evolutionary Algorithms -- An Analytic Approach

This paper provides an analytical approach to fuzzy rule base optimization. While most research in the area has been done experimentally, our theoretical considerations give new insights to the task. Using the symmetry that is inherent in our formulation, we show that the problem of finding an optimal rule base can be reduced to solving a set of quadratic equations that generically have a one dimensional solution space. This alternate problem specification can enable new approaches for rule base optimization.

A Computational Intelligence Portfolio Construction System for Equity Market Trading

This paper describes an adaptive computational intelligence system for learning trading rules used in equity market trading. The rules are represented using fuzzy logic, an evolutionary process facilitates the learning process. By controlling the evolutionary process and through selection of training data the trading rules are adapted to market conditions. Results of the systems performance are obtained using historical data from the Australian stock exchange (ASX).

An experimental study of Multi-Objective Evolutionary Algorithms for balancing interpretability and accuracy in fuzzy rulebase classifiers for financial prediction

This paper examines the advantages of simple models over more complex ones for financial prediction. This premise is examined using a genetic fuzzy framework. The interpretability of fuzzy systems is oftentimes put forward as a unique advantageous feature, sometimes to justify effort associated with using fuzzy classifiers instead of alternatives that can be more readily implemented using existing tools. Here we investigate if model interpretability can provide further benefits by realizing useful properties in computationally intelligent systems for financial modeling. We test an approach for learning momentum based strategies that predict price movements of the Bombay Stock Exchange (BSE). The paper contributes an experimental evaluation of the relationship between the predictive capability and interpretability of fuzzy rule based systems obtained using Multi-Objective Evolutionary Algorithms (MOEA.)

The performance of an adaptive portfolio management system

This paper describes the operation and performance of a computational intelligence rule-base system that manages a portfolio of stocks according to investment objectives. We present an overview of several improvements to the system presented in previous papers and provide detailed results from applying the system in representative scenarios toward determining the robustness of the approach.

Enhancing profitability through interpretability in algorithmic trading with a multiobjective evolutionary fuzzy system

This paper examines the interaction of decision model complexity and utility in a computational intelligence system for algorithmic trading. An empirical analysis is undertaken which makes use of recent developments in multiobjective evolutionary fuzzy systems (MOEFS) to produce and evaluate a Pareto set of rulebases that balance conflicting criteria. This results in strong evidence that controlling portfolio risk and return in this and other similar methodologies by selecting for interpretability is feasible. Furthermore, while investigating these properties we contribute to a growing body of evidence that stochastic systems based on natural computing techniques can deliver results that outperform the market.

Index tracking fund enhancement using evolving multi-criteria fuzzy decision models

An Index Tracking fund is designed to achieve similar investment performance to a market index by holding a portfolio of stocks in which each is weighted with consideration of its corresponding index weight. An ideal index tracking fund is exposed solely market risk. An enhanced index tracking fund should maintain a similar level of risk exposure through a high level of diversification (investing over a sufficiently wide base of assets) with a higher return on investment. In the approach suggested here, technical and fundamental valuation models found using an evolutionary fuzzy system provide enhancement by recommending under or over weighting some stocks in an index tracking portfolio.

Evaluation of intelligent quantitative hedge fund management

This paper examines an intelligent recommendation strategy implementation for managing a long short hedge fund and reports on performance during market conditions at the onset of the liquidity crisis. A hedge fund utilizes long and short trading to manage an investment portfolio consisting of allocations to cash and share equity positions. This results in a combined long short portfolio that is leveraged to obtain a potentially greater market exposure with borrowed cash from short selling and is also hedged to protect against market downturns. The paper also examines effects of parameters for fuzzy rule base specification on trading performance.

Intelligent Decision Support: A Fuzzy Stock Ranking System

This paper presents an intelligent decision support system for financial portfolio management. An adaptive business intelligence approach combines optimization, forecasting and adaptation with application specific financial information processing and quantitative investment paradigms. The methodology involves constructing a ranking of stocks by strength of a buy or sell recommendation which is inferred using an adapting forecasting model that considers a range of factors. These include company balance sheet information, market price and trading volume as well as the wider economy. The system adjusts its prediction model dynamically as market conditions change. An evolving fuzzy rule base mechanism encodes a model of relationships between model factors and a recommendation to buy, sell or hold securities.

Evolving Trading Rules

Summary. This chapter describes a computational intelligence system for portfolio management and provides a comparison of the relative performance of portfolios of managed by the system using stocks selected from the ASX (Australian Stock Exchange). The core of the system is the development of trading rules to guide portfolio management. The rules the system develops are adapted to dynamic market conditions. An integrated process for stock selection and portfolio management enables a search specification that produces highly adaptive and dynamic rule bases. Rule base solutions are represented using fuzzy logic and an evolutionary process facilitates a search for high performing fuzzy rule bases. Performance is defined using a novel evaluation function involving simulated trading on a recent historical data window. The system is readily extensible in terms of the information set used to construct rules, however to produce the results given in this chapter information derived only from price and volume history of stock prices was used. The approach is essentially referred to as technical analysis – as opposed to using information from outside the market such as fundamental accounting and macroeconomic data. A set of possible technical indicators commonly used by traders forms the basis for rule construction. The fuzzy rule base representation enables intuitive natural language interpretation of trading signals and implies a search space of possible rules that corresponds to trading rules a human trader could construct. An example of a typical technical �