Financial Forecasting Using Genetic Algorithms

A new genetic-algorithm-based system is presented and applied to the task of predicting the future performances of individual stocks. The system, in its most general form, can be applied to any inductive machine-learning problem: given a database of examples, the system will return a general description applicable to examples both within and outside the database. This differs from traditional genetic algorithms, which perform optimization. The genetic algorithm system is compared to an established neural network system in the domain of financial fore- casting, using the results from over 160 stocks and roughly 5000 experiments. Synergy between the two systems is also examined. This study presents a new system that utilizes

genetic algorithms (GAs) top redict the future performances of individual stocks. More generally, the system extends GAs from their traditional domain of optimization to inductive machine learning or clasification. The overall learning system incorporates a GA, a niching method (for finding multiple solutions), and several other components (discussed in the section entitled Genetic Algorithms for Inductive Learning). Time-series forecasting is a special type of classification on which this study concentrates. Specifically, for any financial time series related to the performance of an individual stock, the goal is to forecast the value of the time series k steps into the future. The experiments of this study forecast the relative return of a stock12 weks into the future. We define a stock’s relative return as the stock’s return minus the average return of the over 1600 stocks we model. We make predictions for all 1600+ stocks at thre different points in time and sumarize the results.As a benchmark, the GA system is compared to an established neural network(NN) system (Mani & Bar, 1994) using the same 1600+ stocks and thre points in time. (We have used the N system and its predecesors to forecast stock prices and manage portfolios for approximately 3 years.) We examine the potential synergy from combining the GA and N forecasts, as well as other ways in which the two algorithms complement each other.The remainder of this article discusses inductive machine learning, casting financial forecasting as an inductive machine-learning problem; reviews genetic Aplied Artificial Intelligence, 10:543± 565, 1996Copyright bullet2 1996 Taylor & Francis0883-9514/96 $12.00 +.00 543 The authors thank Dean Bar, K. K. Quah, and Doug Case for their advice and assistance, and Steve Ward of Ward Systems Group for help with neural network implementations. The authors also thank the referees for their suggestions. Adress correspondence to Sam Mahfoud, LBS Capital Management, Inc., 311 Park Place Boulevard, Suite330, Clearwater, FL 34619, USA. E-mail:sam@lbs.com algorithms; examines genetic algorithms in inductive machine learning and financial forecasting; explains the GA-based system of this study; discusses the chosen applications domain predicting the performances of individual stocks; presents two sets of experiments and their asociated results; examines the results as well as experimental biases; and presents paths for future research. FINANCIAL FORECASTING AS INDUCTIVE MACHINE LEARNING This section briefly reviews inductive machine learning and proceds to cast the problem of financial time-series forecasting as a specific type of inductive machine learning. Inductive learning (Michalski, 1983) can be defined as acquiring conceptsthrough examining data items. It is the similarities among various data...

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