Automated generation of new knowledge to support managerial decision-making: case study in forecasting a stock market

Abstract

The deluge of data available to managers underscores the need to develop intelligent systems to generate new knowledge. Such tools are available in the form of learning systems from artificial intelligence. This paper explores how the novel tools can support decision-making in the ubiquitous managerial task of forecasting. For concreteness, the methodology is examined in the context of predicting a financial index whose chaotic properties render the time series difficult to predict. The study investigates the circumstances under which enough new knowledge is extracted from temporal data to overturn the efficient markets hypothesis. The efficient markets hypothesis precludes the possibility of anticipating in financial markets. More precisely, the markets are deemed to be so efficient that the best forecast of a price level for the subsequent period is precisely the current price. Certain anomalies to the efficient market premise have been observed, such as calendar effects. Even so, forecasting techniques have been largely unable to outperform the random walk model which corresponds to the behavior of prices under the efficient markets hypothesis. This paper tests the validity of the efficient markets hypothesis by developing knowledge-based tools to forecast a market index. The predictions are examined across several horizons: single-period forecasts as well as multiple periods. For multiperiod forecasts, the predictive methodology takes two forms: a single jump from the current period to the end of the forecast horizon, and a multistage web of forecasts which progresses systematically from one period to the next. These models are first evaluated using neural networks and case-based reasoning, and are then compared against a random walk model. The computational models are examined in the context of forecasting a composite for the Korean stock market.