人工知能を用いた金融政策予想と市場予測分布に基づく為替の投資戦略

抄録

Central bank's monetary policy is one of the major interests for market participants. In this paper, we clarified Reserve Bank of Australia's monetary policy reaction function, predicted its policy change,and applied them to investment strategy. First of all, assuming perfect foresight by the central bank, we estimated an extended Taylor rule using bidirectional Recurrent Neural Network. Next, we combined it with distributed representation of Monetary Policy Committee minutes to develop a classifier of interest rate decision. Both the extended Taylor rule and the classifier showed improved performance. Finally, we formulated profitable Foreign Exchange strategy based on the classifier's prediction and market economists' forecasts.