We develop a portfolio management model based on an integrated analysis of asset return prediction, risk control and portfolio optimization. This project narrows the gap between theoretical research and practical applications, thus facilitating the commercialization of AI powered FinTech products. In practice there are three key factors for successful portfolio management: Asset return prediction, risk control and portfolio optimization. Practical portfolio management is in turn a combination of these three factors. Up to this stage, these three aspects have been typically investigated separately, thus causing shortcomings in each traditional model. We aim to supersede this methodology with an integrated model.
Traditional forecasting methods are mainly econometric models based on historical data, but these models are actually “backward-looking” methods. Investment, on the other hand, is a “forward-looking” activity, and the market is relatively effective, which means that the “backward-looking” prediction is invalid, at least theoretically. In terms of risk control, in traditional portfolio models there is usually only single total risk measure, which is unable to cope with the requirements of the actual portfolio risk management. In terms of portfolio optimization, most models simply consider “optimization” itself. In practice, portfolio optimization should combine prediction and optimization with respect to asset class and risk measure.