Financial Forecasting with Neural Networks

INTRODUCTION Financial forecasting has become highly complicated by the increasingly interconnected nature of financial market variables. This calls for use of analytical tools that can detect interrelationships among a large number of diverse market variables. The ability of artificial neural networks (ANN) to detect correlations among hundreds of variables makes it a popular forecasting tool. Since the 1980s ANNs they have assumed importance in scientific and technical use. They have been applied in fields ranging from automated automobile inspection to sensor signal processing (see Exhibit 1). More recently they have been applied to economics and finance to resolve issues like predicting the economy, picking stocks, constructing portfolios, spotting insider dealing, analysis of corporate financial health, assessing bond risk, recognizing financial distress, detecting credit card fraud, improving real estate appraisal, and identifying good credit or insurance candidates, exchange rate prediction, commodity trading. Academic research works involving the technology have been devoted predominantly to two areas: financial distress prediction and prediction of stock price/stock index. Exhibit 2 lists some of the areas in which the efficacy of using neural networks could be further researched. For example, the technology can be applied to corporate finance (financial simulation, prediction, evaluation, etc.), IPO pricing, identifying arbitrage opportunities, security risk profiling, locating tax evaders, etc. Evaluation of uses in these areas is yet to be seen. This paper shows the use of ANN for out-of-sample volatility forecasts of SP Lenard, Alam and Madey, 1995; Fletcher and Goss, 1993; Salchenberger, Cinar and Lash, 1992). Yet other studies found both approaches yield balanced degree of accuracy (Altman, Marco and Varetto, 1994; Boritz, Kennedy, de Mirande e Albuquerque, 1995; Yang, Platt and Platt, 1999). Boritz and Kennedy (1995) show that the performance of ANNs is sensitive to the choice of variables selected and that the networks cannot be relied upon to evaluate and focus on the most important variables. Zurada, Foster, Ward, and Barker (1998) find neural networks are not superior to logistic regression models for traditional dichotomous response variables, but are superior for more complex financial distress response variables. Perez (2006) analyzes thirty studies in which the authors use neural networks to solve companies' classification problems (healthy and failing firms). ANNs have been employed with success to make stock market predictions and stock selection (e.g., White, 1988, Yoon and Swales, 1991; Kryzanowski, Galler and Wright, 1993; Rhee, 1994, Gencay, 1998; Qi, 1999; Qi and Maddala, 1999; Ao, 2003; Armano, Marchesi and Murru, 2005; Yu, Wang, and Lai, 2005c), and prediction of stock index futures (Kim, 2004). The networks have been used to determine optimal buy and sell timing for an equity index (Kimoto, Asakawa, Yoda, and Takeoka, 1990; Leigh, Paz and Purvis, 2002; Leigh, Purvis, and Ragusa, 2002; Chen, Lueng and Daouk, 2003) and recognize a specific price pattern, such as the Japanese "candlestick" triangle (Kamijo and Tanigawa, 1990). Ko and Lin (2008) introduce a resource allocation neural network model to optimize investment weight of a portfolio. …