Table 10.
Applications of deep learning for financial economics
| Application subfield | Article | Aim of study | Data set | Date size | Time span | Used models |
|---|---|---|---|---|---|---|
| Financial market | (Heaton et al., 2017) | Predict and classify financial market | Component stocks of the biotechnology IBB index | Weekly returns data | 2012–2016 | Stacked AE |
| (Mishev et al., 2020) | Analyze sentiment in finance | Financial Phrase-Bank dataset, SemEval-2017 task dataset | 4,845 English sentences, 2,510 news headlines | - | RNN, RNN, Attention, CNN, Dense Network | |
| Stock market | (Zhong & Enke, 2019) | Forecast daily stock return | SPDR S&P 500 ETF (ticker symbol: SPY) | 60 factors over 2,518 trading days | 2003–2013 | DNN, ANN |
| (Chatzis et al., 2018) | Forecast crisis events | FRED and the SNL | More than 5,000 records | 1996–2017 | MXNET, DNN | |
| (Nikou et al., 2019) | Predict stock price | iShares MSCI United Kingdom exchange | 869 data | 2015–2018 | LSTM | |
| (Sharaf et al., 2021) | Predict stock price | Quandl dataset | - | 2000–2019 | LSTM, CNN, Stacked-LSTM, Bi-LSTM | |
| (Tao et al., 2020) | Evaluate the impact of the Northridge Earthquake | http://finance.yahoo.com/ | 616 listed companies | 1992–1994 | LSTM, NAR neural network | |
| (Katayama et al., 2019) | Identify sentiment polarity in financial news | Economy Watchers Survey | 234,626 samples | 2000–2018 | LSTM, Convolution model | |
| (Ji et al., 2021) | Forecast stock indices | Australian stock market index | 2,523 records | 2010–2020 | LSTM-IPSO | |
| (Jin et al., 2020) | Predict stock closing price | Stock of Apple from (https://stocktwits.com/) | 96,903 comments | 2013–2018 | LSTM, sentiment analysis, attention mechanism, empirical modal decomposition | |
| (Liu et al., 2020) | Forecast stock price | CSMAR and WIND | - | 2008–2016 | CNN, Gboost | |
| (Xu et al., 2016) | Select feature and forecast price | Apple, S&P 500 in Yahoo Finance | 6,423 financial news headlines | 2011–2017 | TransE Model, CNN, LSTM | |
| (Niu et al., 2020) | Predict stock price index | HIS, SPX, FTSE and IXIC | - | 2010–2019 | VMD-LSTM | |
| (Sattarov et al., 2020) | Recommend cryptocurrency trading points | Bitcoin, Litecoin, and Ethereum—hourly historical data from (https://www.cryptodatadownload.com) | - | 2019 | DRL | |
| (Chakole & Kurhekar, 2020) | Make trading decisions | DJIA, NASDAQ, NIFTY and SENSEX index stocks | - | 2001–2018 | Deep Q-learning | |
| Insurance mathematics | (Kremsner et al., 2020) | Compute risk measure | Dataset coming from references | - | - | DNN |
Note: IPSO (Improved Particle Swarm Optimization), VMD (Variational Mode Decomposition), S&P 500 (Standard & Poor’s 500 index), FRED (Federal Reserve Economic Database), SNL (S&P Global Market Intelligence), CSMAR (China Stock Market & Accounting Research Database), HIS (Daily closing prices of the Hong Kong Hang Seng Index), SPX (S&P 500 Index), FTSE (London FTSE Index) and IXIC (Nasdaq Index), DJIA (Dow Jones Industrial Average), NASDAQ (National Association of Securities Dealers Automated Quotations)