Regime Switching and Technical Trading with Dynamic Bayesian Networks in High-Frequency Stock Markets
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Technical analysis has been thwarted in academic circles, due to the Efficient Market Hypothesis, which had significant empirical support early on. However recently, there is accumulating evidence that the markets are not as efficient and a new theory of price discovery, Heterogenous Market Hypothesis, is being proposed. As such, there is renewed interest and possibility in technical analysis, which identifies trends in price and volume based on aggregate repeatable human behavioural patterns. In this thesis we propose a new approach for modeling and working with technical analysis in high-frequency markets: dynamic Bayesian networks (DBNs). DBNs are a statistical modeling and learning framework that have had successful applications in other domains such as speech recognition, bio-sequencing, visual interpretation. It provides a coherent probabilistic framework (in a Bayesian sense), that can be used for both learning technical rules and inferring the hidden state of the system. We design a DBN to learn price and volume patterns in TSE60 stock market and find that our model is able to successfully identify runs and reversal out-of-sample in a statistically significant way.