We backtest 59 instruments and investigate the predictability of daily returns using
Bayesian var... more We backtest 59 instruments and investigate the predictability of daily returns using Bayesian variable selection methods. Through these models we show the importance of variable selection and reduction of overtting. We also visualize how the driving factors of daily returns from dierent classes vary over time. Predicting daily returns' magnitude is again conrmed to be a hard task but we show that for some instruments it is possible to achieve above average hit-rates that could lead to protable strategies. Simulation results show that predictability levels of daily returns also vary over time.
We backtest 59 instruments and investigate the predictability of daily returns using
Bayesian var... more We backtest 59 instruments and investigate the predictability of daily returns using Bayesian variable selection methods. Through these models we show the importance of variable selection and reduction of overtting. We also visualize how the driving factors of daily returns from dierent classes vary over time. Predicting daily returns' magnitude is again conrmed to be a hard task but we show that for some instruments it is possible to achieve above average hit-rates that could lead to protable strategies. Simulation results show that predictability levels of daily returns also vary over time.
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Bayesian variable selection methods. Through these models we show the importance
of variable selection and reduction of overtting. We also visualize how the driving
factors of daily returns from dierent classes vary over time. Predicting daily returns'
magnitude is again conrmed to be a hard task but we show that for some instruments
it is possible to achieve above average hit-rates that could lead to protable strategies.
Simulation results show that predictability levels of daily returns also vary over time.
Bayesian variable selection methods. Through these models we show the importance
of variable selection and reduction of overtting. We also visualize how the driving
factors of daily returns from dierent classes vary over time. Predicting daily returns'
magnitude is again conrmed to be a hard task but we show that for some instruments
it is possible to achieve above average hit-rates that could lead to protable strategies.
Simulation results show that predictability levels of daily returns also vary over time.