Time Series Prediction of US Swap Rates
نویسندگان
چکیده
In this project machine learning techniques were used to generate technical trading strategies in the US interest rate swap markets. Leela, a correlation algorithm that is closely related to autoregression, was developed to detect short term repeating patterns to predict future market moves. This algorithm exhibited a Sharpe Ratio of 1 when applied to a single swap. When additional swaps were used the Sharpe Ratio increased to 1.5. Finally, when PCA decomposition was employed, the time series of residuals exhibited a Sharpe Ratio in excess of 2.
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