Prediction of SSE Shanghai Enterprises index based on bidirectional LSTM model of air pollutants

Liu, Bingchun; Yu, Zhecheng; Wang, Qingshan; Du, Peng; Zhang, Xinming

doi:10.1016/j.eswa.2022.117600

Cited by 25 publications

(8 citation statements)

References 25 publications

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“…53,63,89 However, DL models require significant computational power and resources for training, which may not be accessible to all. 51,61 The efficacy of these models heavily depends on the quality and quantity of the data; insufficient data can reduce their performance. The complexity of DL models can result in overfitting, particularly when the model is very complex relative to the simplicity of the task or the volume of available data.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…50,55,59,63 These models can deal with the inherent non-linear and non-smooth features of stock price data. 61,64 Their versatility enables them to handle various data types and structures, utilizing diverse variable sets from different markets. 19 Moreover, they provide flexibility in exploring time series data of varying lengths, which is particularly beneficial for stocks with inconsistent trading histories.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…Research on SPF is primarily distributed through articles (61), indicating a robust presence in academic journals, followed by conference papers (11), which suggest active discussions and explorations in conference settings, and is minimally represented in book chapters (1), as shown in Figure 5. The dominance of articles points towards depth and precision in the field, while conference papers highlight ongoing, dynamic discussions and potential collaborations among researchers.…”

Section: Statistics Of Selected Papersmentioning

confidence: 99%

“…A DL model can effectively outperform traditional SPF systems in terms of accuracy. CNNs 50–54 and recurrent neural network (RNN) such as LSTM or gated recurrent unit (GRU), 55–60 and BiLSTM 61–64 are extensively employed for SPF systems. Table 3 presents a summary of DL-based approaches for SPF.…”

Section: Related Workmentioning

confidence: 99%

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A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

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We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Statistics Of Selected Papersmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

View full text Add to dashboard Cite

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“…Various applications of LSTM can be witnessed in the past literature including traffic speed prediction [ 53 ], image classification [ 54 ], and so on. In the financial fields, LSTM and its improved model have shown an outstanding ability to forecast stock prices [ 55 , 56 , 57 ]. The mechanism of LSTM is as follows.…”

Section: Appendix A1 Least Absolute Shrinkage and Selection Operatormentioning

confidence: 99%

Stock Index Spot–Futures Arbitrage Prediction Using Machine Learning Models

Sheng

2022

Entropy

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With the development of quantitative finance, machine learning methods used in the financial fields have been given significant attention among researchers, investors, and traders. However, in the field of stock index spot–futures arbitrage, relevant work is still rare. Furthermore, existing work is mostly retrospective, rather than anticipatory of arbitrage opportunities. To close the gap, this study uses machine learning approaches based on historical high-frequency data to forecast spot–futures arbitrage opportunities for the China Security Index (CSI) 300. Firstly, the possibility of spot–futures arbitrage opportunities is identified through econometric models. Then, Exchange-Traded-Fund (ETF)-based portfolios are built to fit the movements of CSI 300 with the least tracking errors. A strategy consisting of non-arbitrage intervals and unwinding timing indicators is derived and proven profitable in a back-test. In forecasting, four machine learning methods are adopted to predict the indicator we acquired, namely Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Long Short-Term Memory neural network (LSTM). The performance of each algorithm is compared from two perspectives. One is an error perspective based on the Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and goodness of fit (R2). Another is a return perspective based on the trade yield and the number of arbitrage opportunities captured. Finally, a performance heterogeneity analysis is conducted based on the separation of bull and bear markets. The results show that LSTM outperforms all other algorithms over the entire time period, with an RMSE of 0.00813, MAPE of 0.70 percent, R2 of 92.09 percent, and an arbitrage return of 58.18 percent. Meanwhile, in certain market conditions, namely both the bull market and bear market separately with a shorter period, LASSO can outperform.

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