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Trading Algorithms with Learning in Latent Alpha Models

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 Sebastian Jaimungal, Tenured Full Professor at University of Toronto

 Tuesday, May 2, 2017

I will be delivering a seminar on one of my latest papers at the IAQF's Thalesians Seminar Series on May 15. [ https://lnkd.in/eqnviXB ] Trading Algorithms with Learning in Latent Alpha Models [ https://lnkd.in/eYiz2MR ] Alpha signals for statistical arbitrage strategies are often driven by latent factors. This paper analyses how to optimally trade with latent factors that cause prices to jump and diffuse. Moreover, we account for the effect of the trader's actions on quoted prices and the prices they receive from trading. Under fairly general assumptions, we demonstrate how the trader can learn the posterior distribution over the latent states, and explicitly solve the latent optimal trading problem. We provide a verification theorem, and provide a methodology for calibrating the model by deriving a variation of the expectation-maximization algorithm. To illustrate the efficacy of the optimal strategy, we demonstrate its performance through simulations and compare it to strategies which ignore learning in the latent factors. We also provide calibration results for a particular model using INTC as an example. [ joint work with Philippe Casgrain ] I hope to see you there!


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TRADING FUTURES AND OPTIONS INVOLVES SUBSTANTIAL RISK OF LOSS AND IS NOT SUITABLE FOR ALL INVESTORS
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