Stock market prediction with deep learning: The case of China

Liu, Qingfu; Tao, Zhenyi; Tse, Yiuman; Wang, Chuanjie

doi:10.1016/j.frl.2021.102209

Cited by 28 publications

(8 citation statements)

References 10 publications

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“…To compare the different deep learning models such as LSTM and GRU, Shahi et al ( 2020 ) showed that there was no significant difference while using the models, but when the authors added additional sentiment data to forecast the stock prices, the performance was significantly increased. The high predictive performance of deep and extreme machine learning algorithms for stock price prediction was also reported by Balaji et al ( 2018 ), Sen and Mehtab ( 2021 ), and Liu et al ( 2022 ). Furthermore, Wang and Fan ( 2021 ) show that by incorporating complex non-linear relations into the architecture of the deep learning networks, one can achieve high stock price prediction capacity.…”

Section: Related Worksupporting

confidence: 65%

Incorporating causality in energy consumption forecasting using deep neural networks

2022

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Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting energy consumption or demand methods included trend analyses, regression, and auto-regression. With advancements in machine learning methods, algorithms such as support vector machines, artificial neural networks, and random forests became prevalent. In recent times, with an unprecedented improvement in computing capabilities, deep learning algorithms are increasingly used to forecast energy consumption/demand. In this contribution, a relatively novel approach is employed to use long-term memory. Weather data was used to forecast the energy consumption from three datasets, with an additional piece of information in the deep learning architecture. This additional information carries the causal relationships between the weather indicators and energy consumption. This architecture with the causal information is termed as entangled long short term memory. The results show that the entangled long short term memory outperforms the state-of-the-art deep learning architecture (bidirectional long short term memory). The theoretical and practical implications of these results are discussed in terms of decision-making and energy management systems.

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Section: Related Worksupporting

confidence: 65%

Incorporating causality in energy consumption forecasting using deep neural networks

2022

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“…The other results presented an improved RNN using efficient discrete wavelet transform (DWT) to predict high-frequency time series, and it was concluded that the highorder B-spline wavelet model d (BSd-RNN) performed well [17]. Proposed stock market forecasting model based on deep learning taking into account investor sentiment, and combined with LSTM to predict the stock market is given in [18,19]. Table 2 recapitulates the implementation of deep learning methods to predict stock prices.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Long Short-Term Memory and Gated Recurrent Units on grouped time-series data to predict stock prices accurately

2022

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Stocks are an attractive investment option because they can generate large profits compared to other businesses. The movement of stock price patterns in the capital market is very dynamic. Therefore, accurate data modeling is needed to forecast stock prices with a low error rate. Forecasting models using Deep Learning are believed to be able to predict stock price movements accurately with time-series data input, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. Unfortunately, several previous studies and investigations of LSTM/GRU implementation have not yielded convincing performance results. This paper proposes eight new architectural models for stock price forecasting by identifying joint movement patterns in the stock market. The technique is to combine the LSTM and GRU models with four neural network block architectures. Then, the proposed architectural model is evaluated using three accuracy measures obtained from the loss function Mean Absolute Percentage Error (MAPE), Root Mean Squared Percentage Error (RMSPE), and Rooted Mean Dimensional Percentage Error (RMDPE). The three accuracies, MAPE, RMSPE, and RMDPE, represent lower accuracy, true accuracy, and higher accuracy in using the model.

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“…Hybrid models have become the most popular models in stock prediction. Other than that, deep learning neural networks (DLNNs) have powerful statistic probabilities for image modeling [5].…”

Section: Introductionmentioning

confidence: 99%

The Stock Price Prediction Based on Time Series Model, Multifactorial Regression, Machine Learnings

Jin¹,

Wei²,

Zhang³

2022

BCPBM

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In general, it is hard to forecast the prices the stock prices due to the stochastic fluctuations. This research aims to describe the process to use time series models, multifactorial regression, and machine learning to predict stock prices. ARIMA and EGARCH models are frequently used time series models to predict stock prices. Least-squares linear regression model, Lasso, and Polynomial Linear Regression model predict well in statistical regression methods. RNN and LSTM have higher prediction accuracy. Overall, time series models, statistical regression, and machine learning all can predict stock prices. Summarizing the different methods or models to forecast stock market trending can help investors to prepare relevant investing strategies. These results shed light on guiding further exploration of

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Stock market prediction with deep learning: The case of China

Cited by 28 publications

References 10 publications

Incorporating causality in energy consumption forecasting using deep neural networks

Incorporating causality in energy consumption forecasting using deep neural networks

Implementation of Long Short-Term Memory and Gated Recurrent Units on grouped time-series data to predict stock prices accurately

The Stock Price Prediction Based on Time Series Model, Multifactorial Regression, Machine Learnings

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