REVIEW 2 cited by
Deep Learning in Finance
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
We explore the use of deep learning hierarchical models for problems in financial prediction and classification. Financial prediction problems -- such as those presented in designing and pricing securities, constructing portfolios, and risk management -- often involve large data sets with complex data interactions that currently are difficult or impossible to specify in a full economic model. Applying deep learning methods to these problems can produce more useful results than standard methods in finance. In particular, deep learning can detect and exploit interactions in the data that are, at least currently, invisible to any existing financial economic theory.
Forward citations
Cited by 2 Pith papers
-
Integration of Wavelet Transform Convolution and Channel Attention with LSTM for Stock Price Prediction based Portfolio Allocation
A wavelet-convolution channel-attention LSTM model improves one-step-ahead stock price prediction and long-short portfolio backtests on four large-cap US stocks, with reported Sharpe ratios above 1.8.
-
Multilayer Perceptron Neural Network Models in Asset Pricing: An Empirical Study on Large-Cap US Stocks
MLP models with two hidden layers outperform deeper networks and traditional linear benchmarks for pricing large-cap US stocks with portfolio factors.
Discussion (0). Continue with ORCID to comment.