We propose a novel investment decision strategy (IDS) based on deep learning. The performance of many IDSs is affected by stock similarity. Most existing stock similarity measurements have the problems: (a) The linear nature of many measurements cannot capture nonlinear stock dynam- ics; (b) The estimation of many similarity metrics (e.g. co- variance) needs very long period historic data (e.g. 3K days) which cannot represent current market effectively; (c) They cannot capture translation-invariance. To solve these prob- lems, we apply Convolutional AutoEncoder to learn a stock representation, based on which we propose a novel portfolio construction strategy by: (i) using the deeply learned repre- sentation and modularity optimisation to cluster stocks and identify diverse sectors, (ii) picking stocks within each clus- ter according to their Sharpe ratio (Sharpe 1994). Overall this strategy provides low-risk high-return portfolios. We use the Financial Times Stock Exchange 100 Index (FTSE 100) data for evaluation. Results show our portfolio outperforms FTSE 100 index and many well known funds in terms of total return in 2000 trading days.
|Publication status||Accepted - 26 Jan 2018|
|Event||IEEE International Conference on Acoustics, Speech and Signal Processing - Calgary, Calgary, Canada|
Duration: 15 Jan 2018 → 20 Jan 2018
Conference number: 2018
|Conference||IEEE International Conference on Acoustics, Speech and Signal Processing|
|Period||15/01/2018 → 20/01/2018|