Stock Market Prediction Using Ensemble of Graph Theory, Machine Learning and Deep Learning Models

Patil, Pratik; Wu, Ching-Seh; Potika, Katerina; Orang, Marjan

doi:10.1145/3378936.3378972

Cited by 22 publications

(18 citation statements)

References 11 publications

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“…15 shows the details of each paper's RMSE based on the numerical range. In this table, papers [11,12,24] used the CNN-based model; papers [32,[35][36][37][38][39][45][46][47]57,65] used the LSTM-based model; papers [71][72][73] used the DNN-based model; papers [27,29,31] used the RNN-based model; and paper [86] used another deep learning method. [27,35] It was clear that papers [24,57] achieved the best performance using the DNN model, having RMSEs smaller than 0.001.…”

Section: Discussion and Analysis Based On Performance Metricsmentioning

confidence: 99%

“…Patil, P. proposed a new network using graph theory and CNN, which leveraged spatiotemporal relationship information between different stocks by modelling the stock market as a complex network. Meanwhile, the model used both stock indicators and financial news as input [12].…”

Section: Selected Paper Statisticsmentioning

confidence: 99%

“…Table 17 shows the details of the papers' MAE. In this table, papers [12,24] used the CNN-based model; papers [34,37,39,40,[42][43][44] used the LSTM-based model; papers [71,72] used the DNN-based model; and papers [29,31] used the RNN-based model. It could be found that paper [24] performed best, which used the CNN model.…”

Section: Analysis Based On Maementioning

confidence: 99%

See 2 more Smart Citations

A Survey of Forex and Stock Price Prediction Using Deep Learning

Zhao

Khushi

2021

ASI

144

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Predictions of stock and foreign exchange (Forex) have always been a hot and profitable area of study. Deep learning applications have been proven to yield better accuracy and return in the field of financial prediction and forecasting. In this survey, we selected papers from the Digital Bibliography & Library Project (DBLP) database for comparison and analysis. We classified papers according to different deep learning methods, which included Convolutional neural network (CNN); Long Short-Term Memory (LSTM); Deep neural network (DNN); Recurrent Neural Network (RNN); Reinforcement Learning; and other deep learning methods such as Hybrid Attention Networks (HAN), self-paced learning mechanism (NLP), and Wavenet. Furthermore, this paper reviews the dataset, variable, model, and results of each article. The survey used presents the results through the most used performance metrics: Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), accuracy, Sharpe ratio, and return rate. We identified that recent models combining LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning methods yielded great returns and performances. We conclude that, in recent years, the trend of using deep-learning-based methods for financial modeling is rising exponentially.

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Section: Discussion and Analysis Based On Performance Metricsmentioning

confidence: 99%

Section: Selected Paper Statisticsmentioning

confidence: 99%

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A Survey of Forex and Stock Price Prediction Using Deep Learning

Zhao

Khushi

2021

ASI

144

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“…A different model was proposed by [52], which uses a CNN network with graph theory implemented. Two models were proposed for tests and comparisons, the first based on correlation and the second on causality.…”

Section: ) Analysis Based On Predictor Techniquesmentioning

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%

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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Forecasting asset returns with network‐based metrics: A statistical and economic analysis

Baitinger¹

2021

Journal of Forecasting

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One of the main challenges facing researchers and industry professionals for decades is the successful prediction of asset returns. This paper enriches this endeavor by applying topological metrics of correlation networks to the challenge of financial forecasting. These network-based metrics are retrieved with the help of graph theory and quantify the interconnectedness of financial assets. In this paper, we show that this network-based information statistically significantly predicts future asset returns. Because industry professionals are more interested in the economic value-added of competing forecasting approaches, we also devote our attention to an economic analysis. Considering economic performance metrics, network-based predictors generate a clear value-added, which also applies to the multi-asset allocation case.

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Stock Market Prediction Using Ensemble of Graph Theory, Machine Learning and Deep Learning Models

Cited by 22 publications

References 11 publications

A Survey of Forex and Stock Price Prediction Using Deep Learning

A Survey of Forex and Stock Price Prediction Using Deep Learning

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

Forecasting asset returns with network‐based metrics: A statistical and economic analysis

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