Maringer Dietmar, Zhang Jin (2014), Transition Variable Selection for Regime Switching Recurrent Reinforcement Learning, in IEEE Computational Intelligence for Financial Engineering and Economics conference
, London, UK.
Zhang Jin, Maringer Dietmar (2013), Indicator Selection for Daily Equity Trading with Recurrent Reinforcement Learning, in 2013 Genetic and Evolutionary Computational Conference
, Amsterdam, NL.
Maringer Dietmar, Zhang Jin, Two Parameter Update Scheemes for Recurrent Reinforcement Learning, in IEEE World Congress on Computational Intelligence (IEEE WCCI)
, Beijing, China.
This project has two main objectives: (i) providing a reliable method for continuous calibration of forecasting models for financial assets which are used for automated trading systems with a special focus that can deal with alternative regimes and situations; and (ii) assessing how well such forecasting methods could perform in the Swiss market. The former problem will be approached with innovative search and optimization techniques. The latter will be tested using historical data in a traditional ceteris paribus analysis. Additional tests are planned using an artificial market to assess the market impact of these strategies and whether the identified strategies could have a potentially destabilizing effect on the market.