גיא עוזיאל, הרצאה סמינריונית לדוקטורט
יום רביעי, 4.3.2020, 13:30
Online Portfolio Selection, aiming to optimize the allocation of wealth across a set of assets, is a fundamental research problem in computational finance and machine learning. Despite the theoretical challenges, the implementation of a real-world trading system is extremely challenging. This issue has been extensively studied across several research communities, including finance, statistics, coding and information theory and machine learning. This long-standing problem, however, still poses many challenges.
We begin with a discussion of the problem of online portfolio selection in the presence of transaction costs, and we present two novel algorithms, enabling a trader to enhance any existing commission-oblivious algorithms. Our experiments which were conducted on common benchmarks show that the two new algorithms achieve state-of-the-art results.
We then present an approach to handle the risk incurred while trading. First, we review multi-objective online learning, where we propose a novel framework and algorithm to address this problem in case the underlying process is stationary and ergodic. We prove that under mild conditions our algorithm is universal and thus asymptotically achieves the best possible outcome in hindsight. Later on, we show how this method can be utilized to incorporate the well-known risk proxy, conditional value at risk (CVaR) in online portfolio selection.
Finally, we deal with the pattern matching algorithms and propose a novel approach to incorporate the learning of a suitable kernel using a deep neural network, in an online manner.
The talk summarizes work presented in 5 papers, 4 of which were published/accepted to NIPS, AISTATS.