On the Comparison of Quantitative Predictabilities of Different Financial Instruments

Abstract

Huge amount of liquidity flows into a number of financial instruments such as stocks, commodities, currencies, futures, and so on every day. Investment decisions are mainly based on predicting the future movements of the instrument(s) in question. However, high frequency financial data are somewhat hard to model or predict. It would be valuable information for the investor if he or she knew which financial instruments were quantitatively more predictable. The data used in the model consisted of intraday frequencies covering the period between 1993 and 2013. An Artificial Neural Network model using Radial Basis Functions containing only past data of three different types of instruments (stocks, currencies, and commodities) to predict future high values on six different frequencies was applied. A total of 72 different artificial neural networks representing 12 different instruments were trained five times each, and their prediction performances were recorded on average. Considerably clear distinctions were observed on prediction performances of different financial instruments.