Methodology of Learning Optimal Decisions from Market Data in Financial Technology

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As pension crises deepen in the western world and beyond, efficiently and effectively solving long term dynamic asset allocation problems becomes more critical than ever. The trend from Defined Benefit to Defined Contribution is particularly problematic for lower income population. Automatic pension investing can potentially boost retirement confidence, benefiting particularly women and financially disadvantaged people. Can machine learning offer a new solution to dynamic asset allocation in general? This is a question which can best be addressed by combining expertise from the financial mathematics and machine learning optimization. The need for research on machine learning in financial technology is illustrated by recent creations of Borealis AI Institute by RBC and other similar organizations. The standard approach in mathematical finance has always started from extracting information from market data. Until recently, the approach has been a 2-tiered solution. First, a parametric stochastic model is estimated from the data. Second, relevant answers, e.g., investment strategies, are computed from the assumed parametric model. Serious obstacles have been encountered from this 2-tirered approach in mathematical finance. Since the market dynamics are complex and changeable, the assumed model is inevitably erroneous. When optimal decisions are subsequently derived, errors in the assumed model are often magnified. This limits the applicability of the computed solution. In addition, many dynamic portfolio optimization and risk management problems, following the classical parametric approach, are incredibly hard to solve, even though the model is flawed. A new paradigm is emerging with the promise of a brand new data driven solution. Often, the relevant financial solutions are optimal decisions. Data driven optimization aims to determine the optimal solutions directly from market data, representing market stochasticity. This direct optimization framework bypasses the intermediate step of making erroneous model assumptions. Surprisingly, this approach often avoids computational challenges from solving model based optimal control problems. To succeed in this emerging interdisciplinary field, research experience in optimization, financial modelling, and machine learning is crucial. My research expertise in all three fields has uniquely positioned me to explore the landscape of this exciting area. The goal of this research is to develop a data driven methodology, focusing specifically on financial markets , to learn more complex but practically relevant dynamic decisions directly from market data. Specifically we will (a) develop efficient algorithms solving (non-parametric) sample path based optimization problems with suitable objectives for financial strategies; (b) ensure that optimal data driven model is effective and robust but interpretable through thorough empirical validation; (c) augment sufficiently high quality data.