A data driven approach for optimal stochastic control in finance

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Many problems in finance naturally arise as stochastic optimal control problems, including for example optimal trade execution, pricing and hedging of variable annuities and multi-period asset allocation. We will investigate a new, data driven approach to handling such stochastic optimal control problems arising from finance. In particular, we propose to use a machine learning approach to constructing the optimal controls directly from the observed data. To understand the issues of this new paradigm, we will use this approach on three problems in finance. The first test problem will involve the optimal multi-period rebalancing of portfolios over a long time frame with taxable accounts. At each of these multiple rebalancing times one would need to take into consideration tax scenarios such as marginal tax rates, capital gains and capital losses.The second study will involve the timing of optimal multi-period portfolio rebalancing where we seek both optimal times to rebalance and the amounts to reallocate during these times. Our final study involves strategies for portfolios that combines selling options on single assets balanced by the concept of trend following involving baskets of assets. Here one needs to find the best weighting of each part. Canada is a world leader in Machine Learning and AI. The field of financial technology (FinTech) is one area which will benefit from better data analytics and more effective learning methods. This is particularly truewhen it comes to managing the risks involved with long term investment allocation problems.