A Survey of Forex and Stock Price Prediction Using Deep Learning

Hu, Zexin; Zhao, Yiqi; Khushi, Matloob

doi:10.3390/asi4010009

Cited by 211 publications

(108 citation statements)

References 86 publications

(554 reference statements)

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“…Lastly, traditional machine learning methods and transformer-based can be used in combination with an Ensemble-based method [32]. The ensemble method would assign weights to prediction result based on the accuracy of individual models and give the best results out of them.…”

Section: Future Workmentioning

confidence: 99%

Text Mining of Stocktwits Data for Predicting Stock Prices

Jaggi

Mandal

Narang

et al. 2021

ASI

Self Cite

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Stock price prediction can be made more efficient by considering the price fluctuations and understanding people’s sentiments. A limited number of models understand financial jargon or have labelled datasets concerning stock price change. To overcome this challenge, we introduced FinALBERT, an ALBERT based model trained to handle financial domain text classification tasks by labelling Stocktwits text data based on stock price change. We collected Stocktwits data for over ten years for 25 different companies, including the major five FAANG (Facebook, Amazon, Apple, Netflix, Google). These datasets were labelled with three labelling techniques based on stock price changes. Our proposed model FinALBERT is fine-tuned with these labels to achieve optimal results. We experimented with the labelled dataset by training it on traditional machine learning, BERT, and FinBERT models, which helped us understand how these labels behaved with different model architectures. Our labelling method’s competitive advantage is that it can help analyse the historical data effectively, and the mathematical function can be easily customised to predict stock movement.

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Section: Future Workmentioning

confidence: 99%

Text Mining of Stocktwits Data for Predicting Stock Prices

Jaggi

Mandal

Narang

et al. 2021

ASI

Self Cite

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“…This is the first task to use the best of our cryptocurrency prediction experience. Although the LSTM model has a greater computing burden than nonlinear pattern brutality, profound learning was essentially highly efficient in predicting the volatile dynamics inherent in cryptocurrency markets [27]- [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Time-Series Prediction of Cryptocurrency Market using Machine Learning Techniques

Iqbal¹,

Iqbal²,

Jaskani³

et al. 2021

EAI Endorsed Transactions on Creative Technologies

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In the market of cryptocurrency the Bitcoins are the first currency which has gain the significant importance. To predict the market price and stability of Bitcoin in Crypto-market, a machine learning based time series analysis has been applied. Time-series analysis can predict the future ups and downs in the price of Bitcoin. For this purpose we have used ARIMA, FBProphet, XG Boosting for time series analysis as a machine learning techniques. The parameters on the basis of which we have evaluated these models are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R 2 . We conduct experiments on these three techniques but after conducting time series analysis, ARIMA considered as the best model for forecasting Bitcoin price in the crypto-market with RMSE score of 322.4 and MAE score of 227.3. Additionally, this research can be helpful for investors of crypto-market.

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“…If derivatives are small values, multiplication makes it so small. In this situation weights are not updated effectively which increases inaccuracy in a model prediction (Charniak, 2019;Hu, Zhao & Khushi, 2021).…”

Section: Deep Learningmentioning

confidence: 99%

“…Last layer is fully connected where each neuron is connected to the neurons in the previous layer. An important feature of CNN is weight sharing which makes it less complex than fully connected neural network (Goodfellow et al, 2016;Charniak, 2019;Hu, Zhao & Khushi, 2021).…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

“…Information in RNN travels through loop which makes it possible to use same parameters and reduces the parameter complexity than other NN models. But RNN doesn't support long-term memory and faces vanishing gradient problem (Goodfellow et al, 2016;Charniak, 2019;Hu, Zhao & Khushi, 2021). The architecture of RNN is shown in Fig.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

See 1 more Smart Citation

News sensitive stock market prediction: literature review and suggestions

Usmani¹,

Shamsi²

2021

PeerJ Computer Science

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Stock market prediction is a challenging task as it requires deep insights for extraction of news events, analysis of historic data, and impact of news events on stock price trends. The challenge is further exacerbated due to the high volatility of stock price trends. However, a detailed overview that discusses the overall context of stock prediction is elusive in literature. To address this research gap, this paper presents a detailed survey. All key terms and phases of generic stock prediction methodology along with challenges, are described. A detailed literature review that covers data preprocessing techniques, feature extraction techniques, prediction techniques, and future directions is presented for news sensitive stock prediction. This work investigates the significance of using structured text features rather than unstructured and shallow text features. It also discusses the use of opinion extraction techniques. In addition, it emphasizes the use of domain knowledge with both approaches of textual feature extraction. Furthermore, it highlights the significance of deep neural network based prediction techniques to capture the hidden relationship between textual and numerical data. This survey is significant and novel as it elaborates a comprehensive framework for stock market prediction and highlights the strengths and weaknesses of existing approaches. It presents a wide range of open issues and research directions that are beneficial for the research community.

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A Survey of Forex and Stock Price Prediction Using Deep Learning

Cited by 211 publications

References 86 publications

Text Mining of Stocktwits Data for Predicting Stock Prices

Text Mining of Stocktwits Data for Predicting Stock Prices

Time-Series Prediction of Cryptocurrency Market using Machine Learning Techniques

News sensitive stock market prediction: literature review and suggestions

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