Categories | Subcategories | Definition | |
1. Neural networks/ algorithms used in research | A-LSTM | Stock price projection with the—RNN LSTM. | |
B-Compared to LSTM | Stock price prediction with other artificial neural networks and results compared to RNN LSTM. | ||
C-Combined with LSTM | Predict stock prices with blended neural networks including LSTM. | ||
D-Others | Other topics unrelated to subcategories 1A to 1C. | ||
2. Types of data analyzed | A-Closing prices | Daily stock closing prices. | |
B-Opening prices | Daily stock opening prices. | ||
C-Highest and lowest prices | Daily highest and lowest stock prices. | ||
D-Volumes | Stock trading volumes. | ||
E-Index | Daily closing of the Stock Price Index. | ||
F-Others | Others not related to subcategories 2A to 2E. | ||
3. Analysis period | A-Up to 5 years | Data from 0 to 5 years. | |
B-More than 5 to 10 years | Data from 5.1 to 10 years. | ||
C-More than 10 years | More than 10 years. | ||
D-Not applicable/not informed | Studies that do not inform the period of analysis. | ||
4. Objectives | A-Tests with new neural networks models | Improved share price accuracy tested with other neural networks algorithms and/or hybrid models. | |
B-Tests with other assets | Check whether using price and volatility indices of other assets (except stocks) can help predict stock prices. | ||
C-Sentiment Analysis | Improved accuracy in stock price projection with sentiment analysis. | ||
D-Others | Other topics unrelated to subcategories 7A to 7C. | ||
5. Data origin | A-NYSE, NASDAQ, DJI, S&P, CBOE, FTSE | US Stock Exchanges. | |
B-CSI, SSE, NSE, HS, SH, SZSE | China and Hong Kong Stock Exchanges. | ||
C-B3 | Brazil Stock Exchange. | ||
D-TWSE | Thailand Stock Exchange. | ||
E-IMKB | Turkey Stock Exchange. | ||
F-TSE | Tehran Stock Exchange. | ||
G-GSE | Ghana Stock Exchange. | ||
H-ASX | Australia Stock Exchange. | ||
I-DAX | Germany Stock Exchange. | ||
J-KOSPI, KOSDAQ | Korea Stock Exchanges. | ||
K-NSE | India Stock Exchange. | ||
N-NIKKEI | Japan Stock Exchange. | ||
O-IDX | Indonesia Stock Exchange. | ||
P-FTSE | UK Stock Exchange. | ||
L-Texts | News agencies/websites, for sentiment analysis. | ||
M-No information or other | There is no identification of information that can be considered as inputs for the evaluation models. | ||
6. Results | A-Outperforms compared methods | The results of the proposed model surpass the results of the compared model(s). |
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B-Promising model | The results of the proposed model are promising. |
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C-Others | Other results unrelated to subcategories 8A to 8B. |
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7. Conclusions | A-New conclusions | Presentation of new findings—adjustment to already tested neural networks models, improvement in the quality of input information, and other innovations to existing models. |
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B-New perspectives | Presentation of a new theory, new models of projections, with models of isolated, hybrid or combined neural networks. |
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C-Conclusions similar to works presented previously | Studies that do not present new perspectives or new conclusions. |
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D-Others | Other results unrelated to subcategories 9A to 9C. |
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8. Pathways for future studies | A-Hybrid models with LSTM | Studies with other hybrid models using LSMT. |
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B-Other ANN | Studies with other ANN, pure or hybrid. |
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C-Other types of data | In addition to opening, closing, high, low and trading volume data, sentiment analysis tests with other types of news and stock data, in periods such as intraday. |
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D-Data from other sources | Study the model’s performance on other Stock Exchanges. |
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E-Other analysis periods | Study and test data from different periods. |
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F-No path commented by the author(s) | No future path detailed by author(s). |
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