This paper proposes a hybrid financial trading system that incorporates the application of chaos theory, non-linear statistical models and artificial intelligence/soft computing methods, specifically, Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs). This proposal forms the basis for the research direction of the Advanced Investment Technology Group at Bond University and is currently under consideration in the final round for a large ARC grant application. The methodology for this research can be defined in three phases.

The first is in selecting the time series for modelling using chaos theory to identify time series that display non-random behavior. The second phase is in forecasting the time series using ANNs and non-linear statistical modelling techniques. The final phase is in implementing a rule-based financial trading system that incorporate the forecast with trading rules and a money management system that may incorporate the use of GAs. An appendix on a primer on technical analysis, fundamental analysis, ANNs and trading systems as well as criteria for selection of data for ANN is provided.

Artificial Intelligence Applications in Finance

By and large, the evolution of commercial risk management technology has been characterized by computer technology lagging behind the theoretical advances of the field. As computers have become more powerful, they have permitted better testing and application of forecasting concepts. Recent years have seen a broadening of the array of computer technologies applied to forecasting. With the advent of the popularity of the Internet, data of various financial markets can be easily accessed. However, due to time and computational constraints, there still exist the need to select only a number of the time series from financial data that have the higher probability of providing abnormal returns.

One of the most exciting of these in terms of the potential for analyzing risk is Artificial Intelligence (AI)/Soft Computing and Nonlinear Statistical Forecasting methods. From the range of AI techniques, the one that deals best with uncertainty is Artificial Neural Network (ANN). Dealing with uncertainty with ANNs forecasting methods primarily involves recognition of patterns in data and using these patterns to predict future events. ANNs have been shown to handle time series problems better than other AI techniques because they deal well with large noisy data sets. Unlike expert systems, however, ANNs are not transparent, thus making them difficult to interpret. In our research proposal, we intend to use RULEX, a rule-extraction ANN program developed at the Queensland University of Technology that may enable the extraction of rules from the ANN model.

Expert systems and Artificial Neural Networks offer qualitative methods for business and economic systems that traditional quantitative tools in statistics and econometrics cannot quantify due to the complexity in translating the systems into precise mathematical functions. While artificial intelligence techniques have only recently been introduced in finance, they have a long history of application in other fields. Experience to date across a wide range of non-financial applications has been mixed. Patrick Winston, a leading AI researcher and the head of MIT’s AI Laboratory, conceded that the traditional AI methods such as search methods, predicate calculus, rule-based expert systems and game-playing, have achieved little progress [Gallant 1994].

The problem domain that traditional AI methods seem to fail in is in the trivial and common sense-type of tasks that humans find easy, such as recognizing faces, identifying objects and walking. Therefore, it was natural for AI researchers to turn to nature and the physical laws and processes for inspiration to find better solutions. As a result, many of the contemporary artificial intelligence tools developed in the natural sciences and engineering field have successfully found their way into the commercial world. These include wavelet transformations and finite impulse response filters (FIR) from the signal processing/electrical engineering field; genetic algorithms and artificial neural networks from the biological sciences; and, chaos theory and simulated annealing from the physical sciences.

These revolutionary techniques fall under the AI field as they represent ideas that seem to emulate intelligence in their approach to solving commercial problems. All these AI tools have a common thread in that they attempt to solve problems such as the forecasting and explanation of financial markets data by applying physical laws and processes. Pal and Srimani [1996] state that these novel modes of computation are collectively known as soft computing as they have the unique characteristic of being able to exploit the tolerance imprecision and uncertainty in real world problems to achieve tractability, robustness, and low cost.

They further state that soft computing is often used to find an approximate solution to a precisely (or imprecisely) formulated problem. Huffman [1994] of Motorola states that “At Motorola, we call neural networks, fuzzy logic, genetic algorithms and their ilk natural computing”. These contemporary tools are often used in combination with one another as well as with more traditional AI methods such as expert systems in order to obtain better solutions. These new systems that combine one or more AI methods (which may include traditional methods) are known as ‘hybrid systems’. An example of a hybrid system is the financial trading system described in a paper by Tan [1995ab]. According to Zahedi [1993], expert systems and Artificial Neural Networks offer qualitative methods for business and economic systems that traditional quantitative tools in statistics and econometrics cannot quantify due to the complexity in translating the systems into precise mathematical functions.

Medsker et al. [1996] list the following financial analysis task on which prototype neural network-based decisions aids have been built:

· Credit authorization screening

· Mortgage risk assessment

· Project management and bidding strategy

· Financial and economic forecasting

· Risk rating of exchange-traded, fixed income investments.

· Detection of regularities in security price movements

· Prediction of default and bankruptcy

Hsieh [1993] states the following potential corporate finance applications can be significantly improved with the adaptation to ANN technology:

· Financial Simulation

· Predicting Investor’s Behavior

· Evaluation

· Credit Approval

· Security and/or Asset Portfolio Management

· Pricing Initial Public Offerings

· Determining Optimal Capital Structure

Trippi and Turban [1996] noted in the preface of their book, that financial organizations are now second only to the US Department of Defense in the sponsorship of research in neural network applications. Despite the disappointing result from White’s [1988] initial seminal work in using ANNs for financial forecasting with a share price example, research in this field has generated growing interest.

Despite the increase in research activity in this area however, there are very few detailed publications of practical trading models. In part, this may be due to the fierce competition among financial trading houses to achieve marginal improvements in their trading strategies, which can translate into huge profits and their consequent reluctance to reveal their trading systems and activities. This reluctance notwithstanding, as reported by Dacorogna et al. [1994], a number of academicians have published papers on profitable trading strategies even when including transaction costs.

These include studies by Brock et al. [1992], LeBaron [1992], Taylor and Allen [1992], Surajaras and Sweeney [1992] and Levitch and Thomas [1993]. From the ANN literature, work by Refenes et al.[1995], Abu-Mostafa [1995], Steiner et al.[1995], Freisleben [1992], Kimoto et al.[1990], Schoneburg [1990], all support the proposition that ANNs can outperform conventional statistical approaches. Weigend et al. [1992] find the predictions of their ANN model for forecasting the weekly Deutshmark/US Dollar closing exchange rate to be significantly better than chance.

Pictet et. al. [1992] reports that their real -time trading models for foreign exchange rates returned close to 18% per annum with unleveraged positions and excluding any interest gains. Colin [1991] reports that Citibank’s proprietary ANNbased foreign exchange trading models for the US Dollar/Yen and US Dollar/Swiss Franc foreign exchange market achieved simulated trading profits in excess of 30% per annum and actual trading success rate of about 60% on a trade-by-trade basis. These studies add to the body of evidence contradicting the EMH.

Prof. Clarence N W Tan

Next: Description of Proposed Hybrid System

Summary: Index