Deep Learning for Stock Market Prediction Using Event Embedding and Technical Indicators

Oncharoen, Pisut; Vateekul, Peerapon

doi:10.1109/icaicta.2018.8541310

Cited by 52 publications

(39 citation statements)

References 11 publications

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“…However, these results cannot be analysed in isolation, since the model is used for financial time series, it is essential also to analyse the profitability obtained. Works such as [49], [50], [57], [61] corroborate this statement, showing that it is possible to create a model with high accuracy, but reporting losses.…”

Section: Databasementioning

confidence: 65%

“…Several authors have developed research using hybrid models for forecasting, and all models had LSTM or RNN layers linked to CNN layers. In the works of [46], [50], [51], [60], [61], the authors used CNN to extract textual data patterns, such as news channels and social networks, thus generating more information than just the asset price. All of them showed better results than a network with only LSTM implemented.…”

Section: ) Analysis Based On Predictor Techniquesmentioning

confidence: 99%

“…According to the model forecasting, other works implemented long or short strategies and finished the operation after a certain period. Matsubara et al [49] and Oncharoen and Vateekul [50] chose to buy or sell the asset at the trading session opening and closed the operation at the end of the same day. While Lee and Soo [46] made the purchase and closed it after five days, once that they trained the model with a 5-day forecast horizon.…”

Section: ) Analysis Based On Trading Strategiesmentioning

confidence: 99%

“…Considering the costs, Fazeli and Houghten [40] presented the results using ROI, percentage of profit as a function of costs. While the publications by [46], [50], [51], [61] provided the net profitability, that is, the gross profit discounted the costs. Oncharoen and Vateekul [50] and Vargas et al [61] obtained losses in some of their tests, despite the models reaching accuracy above 50% (69% and 51%, respectively).…”

Section: ) Analysis Based On Profitability Metricsmentioning

confidence: 99%

“…Total Articles Yahoo Finance 18 11 [34], [40], [41], [46], [50]- [52], [55], [60], [61] Regarding the timeframe, those who used daily data add up to 23 works, while the other 11 chose DT with varying graphic times, as can be seen in Figure 4. Most works in the literature use daily data, as this information is easily and freely obtained from finance sites, such as Yahoo Finance.…”

Section: Databasementioning

confidence: 99%

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Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review

Bastos

2020

IEEE Access

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Stock market forecasting is one of the biggest challenges in the financial market since its time series has a complex, noisy, chaotic, dynamic, volatile, and non-parametric nature. However, due to computing development, an intelligent model can help investors and professional analysts reduce the risk of their investments. As Deep Learning models have been extensively studied in recent years, several studies have explored these techniques to predict stock prices using historical data and technical indicators. However, as the objective is to generate forecasts for the financial market, it is essential to validate the model through profitability metrics and model performance. Therefore, this systematic review focuses on Deep Learning models implemented for stock market forecasting using technical analysis. Discussions were made based on four main points of view: predictor techniques, trading strategies, profitability metrics, and risk management. This study showed that the LSTM technique is widely applied in this scenario (73.5%). This work significant contribution is to highlight some limitations found in the literature, such as only 35.3% of the studies analysed profitability, and only two articles implemented risk management. Therefore, despite the widely explored theme, there are still interesting open areas for research and development. INDEX TERMS Deep learning, profitability metrics, risk management, stock market forecasting, systematic review, technical analysis, technical indicators.

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Section: Databasementioning

confidence: 65%

Section: ) Analysis Based On Predictor Techniquesmentioning

confidence: 99%

Section: ) Analysis Based On Trading Strategiesmentioning

confidence: 99%

Section: ) Analysis Based On Profitability Metricsmentioning

confidence: 99%

Section: Databasementioning

confidence: 99%

See 3 more Smart Citations

Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review

Bastos

2020

IEEE Access

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Forecasting financial markets with semantic network analysis in the COVID‐19 crisis

Colladon

Grassi

Ravazzolo

et al. 2022

Journal of Forecasting

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This paper uses a new textual data index for predicting stock market data. The index is applied to a large set of news to evaluate the importance of one or more general economic‐related keywords appearing in the text. The index assesses the importance of the economic‐related keywords, based on their frequency of use and semantic network position. We apply it to the Italian press and construct indices to predict Italian stock and bond market returns and volatilities in a recent sample period, including the COVID‐19 crisis. The evidence shows that the index captures the different phases of financial time series well. Moreover, results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility.

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Artificial and Human Intelligence in Mental Health

et al. 2021

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Spring 2021 39 Computer: What is your problem? patient: i am worried about the weather. Comp: How long have you been worried about the weather? pt: Since my mother passed away. Comp: Tell me more about your family. pt: They are all into computers. Comp: Do machines frighten you? pt: no. Comp: please do not be so short with me. pt: i am sorry. Comp: Earlier you talked about your mother. pt: Yes, she didn't like me very much. Comp: please go on. 2

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Deep Learning for Stock Market Prediction Using Event Embedding and Technical Indicators

Cited by 52 publications

References 11 publications

Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review

Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review

Forecasting financial markets with semantic network analysis in the COVID‐19 crisis

Artificial and Human Intelligence in Mental Health

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