1 research outputs found
Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions
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 dynamics; (b) The estimation of
many similarity metrics (e.g. covariance) 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 problems, 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 representation and modularity optimisation to cluster stocks and
identify diverse sectors, (ii) picking stocks within each cluster 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.Comment: Accepted to International Conference on Acoustics, Speech and Signal
Processing (ICASSP) 201