Improving portfolio construction using deep generative machine learning models applying generative models on financial market data

Abstract

The lack of availability of financial data has posed serious challenges to practitioners and researchers alike, especially to address portfolio and risk management problems using machine learning techniques. In order to alleviate these challenges in recent years there has been a rising interest in the use of deep generative models to generate synthetic time series data and augment existing datasets. Apart from being successful in generating synthetic images in the computer vision domain, Generative Adversarial Networks (GANs) have recently been also used in practical time series applications. For synthetic time series data to be useful in portfolio management, not only does it need to possess comparable statistical properties, but in addition it requires to feature similar correlational structure as ground truth data. In this work we examine the correlation characteristics of synthetic financial time series data generated by a deep generative model, TimeGAN, and carry out a holistic assessment that includes visual evaluation of stylized facts and quantitative evaluation of correlation similarity. We run experiments with a dataset containing features of a single stock following the original authors of TimeGAN, and additional datasets containing the market data of multiple stocks featuring a broad range of pairwise correlation coefficients. We demonstrate that TimeGAN-generated market data preserves well the correlation structure for the multi-stock datasets examined. Moreover, we propose a GAN-assisted portfolio construction technique that can be incorporated with traditional portfolio management methods. The proposed scheme is framed as an extension of an established deep learning portfolio optimisation technique proposed by Cai et al. (2019), against which we benchmark our study, where we utilise TimeGAN to generate correlated synthetic future price paths of a set of stocks. Using the generated price paths we introduce an efficient way of de-risking the portfolio by filtering stocks that are expected to exhibit high volatility out-of-sample. We carry out experiments with two filtering approaches, global and adaptive, and demonstrate that using correlation-aware synthetic data together with real historic market data in a systematic manner improves out-of-sample portfolio Sharpe Ratio by 18.1% and cumulative portfolio return by 46.8% when compared with a benchmark portfolio constructed with historic data only. The encouraging results achieved in this study suggest that in financial settings where time series data is limited, combining historic data with correlation-informed synthetic data in the construction of risky portfolios, can potentially help financial practitioners make better investment decisions.