PREDICTING PRICES OF FINANCIAL ASSETS: FROM CLASSICAL ECONOMICS TO INTELLIGENT FINANCE

Determining the circumstances under which it is possible to make any sort of prediction is a fundamental question in financial research. The presence of complex and robust statistical characteristics, exhibited by most financial time series, raise doubts on the simple relationship between information and price changes, as implied by the efficient market hypothesis. In this paper, we consider the main competing economic hypotheses and examine different approaches for learning the price behaviour in financial markets. Our analysis reveals the need to approach the problem from a new perspective. In financial markets, traders are not only adapting, but also determine and form the economic mechanism essentially by their actions. In these settings, financial markets are evolutionary structures of competing trading strategies; prices in such markets are driven endogenously by the induced expectations. A combination of economics, computer and cognitive science in cross-disciplinary study of intelligent finance, which aims to explore information about financial markets from multiple perspectives, is expected to expand the boundary of pure economic analysis.