Multi-contextual Recommender Using 3D Latent Factor Models and Online Tensor Decomposition

Suleiman, Basem; Anaissi, Ali; Alibasa, Muhammad Johan; Truong, Ha H.

doi:10.1007/978-3-031-08751-6_20

Cited by 2 publications

(2 citation statements)

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“…Stock price forecasting has earned a lot of interests in recent years and has attracted many researchers working in academia and industry. Although the EMH 15 indicates that it is impossible to predict future prices based on historical data, many researchers have attempted to demonstrate the validity of price prediction 16‐21 . In recent years, scholars have pinned their hopes on AI technology to improve the accuracy of the prediction models.…”

Section: Related Workmentioning

confidence: 99%

“…Although the EMH 15 indicates that it is impossible to predict future prices based on historical data, many researchers have attempted to demonstrate the validity of price prediction. [16][17][18][19][20][21] In recent years, scholars have pinned their hopes on AI technology to improve the accuracy of the prediction models. Thus, conference papers, journal articles and academic reports related to this area in the past 20 years have been analysed.…”

Section: Related Workmentioning

confidence: 99%

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ResNLS: An improved model for stock price forecasting

Jia,

Anaissi,

Suleiman

2023

Computational Intelligence

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Stock prices forecasting has always been a challenging task. Although many research projects adopt machine learning and deep learning algorithms to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices. In this paper we introduce a hybrid model that improves stock price prediction by emphasizing the dependencies between adjacent stock prices. The proposed model, ResNLS, is mainly composed of two neural architectures, ResNet and LSTM. ResNet serves as a feature extractor to identify dependencies between stock prices across time windows, while LSTM analyses the initial time‐series data with the combination of dependencies which considered as residuals. In predicting the SSE Composite Index, our experiment reveals that when the closing price data for the previous five consecutive trading days is used as the input, the performance of the model (ResNLS‐5) is optimal compared to those with other inputs. Furthermore, ResNLS‐5 outperforms vanilla CNN, RNN, LSTM, and BiLSTM models in terms of prediction accuracy. It also demonstrates at least a 20% improvement over the current state‐of‐the‐art baselines. To verify whether ResNLS‐5 can help clients effectively avoid risks and earn profits in the stock market, we construct a quantitative trading framework for back testing. The experimental results show that the trading strategy based on predictions from ResNLS‐5 can successfully mitigate losses during declining stock prices and generate profits in the periods of rising stock prices.

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Section: Related Workmentioning

confidence: 99%