Optimal Machine Learning- and Deep Learning- driven algorithms for predicting the future value of investments: A systematic review and meta-analysis

Abstract

Abstract The COVID-19 pandemic and the increasing competitive landscape have led asset management companies to consider investing in applying Artificial Intelligence (AI)-driven technologies to minimise the risk and maximise the profitability of the investment funds they manage. Thus, a systematic review and a meta-analysis of the relevant literature were conducted to provide evidence-based informed recommendations on which AI-driven technologies could be leveraged for such purpose. Data on both Machine Learning (ML)- and Deep Learning (DL)-driven technologies applied to aid the management of investment funds in China and, specifically, in and around Shenzhen, were pooled from eleven eligible and recent studies (since 15 September 2017) and analysed accordingly. The key business-relevant and human-interpretable metrics representing their performance were identified in the root mean squared error (RMSE), in the same unit of currency of the investment funds, and the correlation strength between the predicted and actual values. One ML- and one DL-based algorithms were recommended to be used in the short and long terms respectively. In particular, the ML-based Gradient Boosting Decision Tree (GBDT) algorithm was found the most accurate in the relevant literature, e.g., 28.16% more accurate than the Support Vector Regressor (SVR), also having a highly competitive ability to capture trends in the actual values of investment funds (83.7% of correlation strength), whilst the Long-Short Term Memory (LSTM)-GBDT model was identified as the most accurate DL-based algorithm, 15.05% more accurate than the GBDT and with 13.2% higher ability to capture trends in the actual investment funds’ values than the GBDT.