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Foreign exchange market prediction with multiple classifiers
Author(s) -
Qian Bo,
Rasheed Khaled
Publication year - 2010
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1124
Subject(s) - predictability , random walk , artificial intelligence , random forest , computer science , machine learning , random walk hypothesis , decision tree , autoregressive integrated moving average , artificial neural network , foreign exchange market , bayesian probability , econometrics , k nearest neighbors algorithm , us dollar , foreign exchange , exchange rate , time series , stock market , statistics , mathematics , economics , paleontology , macroeconomics , horse , monetary economics , biology
Foreign exchange market prediction is attractive and challenging. According to the efficient market and random walk hypotheses, market prices should follow a random walk pattern and thus should not be predictable with more than about 50% accuracy. In this article, we investigate the predictability of foreign exchange spot rates of the US dollar against the British pound to show that not all periods are equally random. We used the Hurst exponent to select a period with great predictability. Parameters for generating training patterns were determined heuristically by auto‐mutual information and false nearest‐neighbor methods. Some inductive machine‐learning classifiers—artificial neural network, decision tree, k ‐nearest neighbor, and naïve Bayesian classifier—were then trained with these generated patterns. Through appropriate collaboration of these models, we achieved a prediction accuracy of up to 67%. Copyright © 2009 John Wiley & Sons, Ltd.