Deep Learning in Finance: Prediction of Stock Returns with Long Short‐Term Memory Networks

Alonso, Miquel Noguer i; Batres-Estrada, Gilberto; Moulin, Aymeric

doi:10.1002/9781119522225.ch13

Cited by 10 publications

(9 citation statements)

References 12 publications

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“…Optimizing the financial risk management mechanism of colleges and universities and building financial risk models are important [14,15]. e innovation of financial management of colleges and universities in the background of the Internet can use the network data for reasonable control of financial risks and establish a financial risk model, and with the increase of financial data of colleges and universities on the Internet, the prediction accuracy of the financial risk model will be higher, so as to ensure the orderly development of financial management of colleges and universities [16].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Driven Financial Management Innovation Upgrade for Universities

Guan

Chen

2022

Mobile Information Systems

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This study aims to improve the quality of college financial management and reduce the risk of college financial management, and a college financial system based on multiscale deep learning is designed in this paper. This paper designs a university financial system based on multiscale deep learning. In the hardware design, the system adds multiple sensors and scans all the information in the financial database using a coordinator. In the software design, the weights that can connect the financial information of the same attribute are set by establishing a database form; according to the multilayer perceptual network topology, a full interconnection model based on multiscale deep learning is designed to realize the system’s deep extraction of data. The experimental results show that the financial risk is based on the risk warning capability for university finance, and compared with the system under the traditional design, the university finance system designed at this time has the most categories of financial information parameters extracted.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Driven Financial Management Innovation Upgrade for Universities

Guan

Chen

2022

Mobile Information Systems

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“…However, the IIP has the least impact in predicting the Indian stock market. The prediction accuracy obtained by our model is better than the accuracy obtained by previous studies such as Chen et al (2001), Kim and Lee (2004), Hadavandi et al (2010) and Alonso et al (2018). The results of the present study have implications for all categories of investors such as portfolio managers, investment houses, HNIs and DIIs in predicting the stock price more precisely.…”

Section: Discussionmentioning

confidence: 40%

“…Relative error during the training process is found to be 0.068 (6.8%) which implies that the model achieved 93.2% accuracy. Similarly, during the training process, the relative error increased to 0.112 (11.2%) which implies the model achieved Kim and Lee (2004); Hadavandi et al (2010) and Alonso et al (2018). Table 6 depicts the importance of the independent variables in the model.…”

Section: Empirical Findingsmentioning

confidence: 88%

“…Under the statistical model category, autoregressive integrated moving average (Banerjee, 2014;Babu and Reddy, 2014), regression methods (Dutta et al, 2012) and others have been used. Under AI technique, primarily machine learning algorithms (Ariyo et al, 2014) and ANNs (Chen et al, 2001;Kim and Lee, 2004;Hadavandi et al, 2010;Alonso et al, 2018) have been identified as the most prominently used methods or techniques to forecast time series or the stock market. It is evident from the literature that there is hardly any study that has used ANNs with macroeconomic economic and global stock market variables as input variables to forecast the Indian stock market at BSE.…”

Section: Dynamic Prediction Of Indian Stock Marketmentioning

confidence: 99%

“…Later, Alonso et al (2018) explained the benefits of deep learning and the advantages that users can take while using it. The authors evaluated different ANN models to achieve the highest level of accuracy in time series and long short term memory (LSTM) to be best suited because of the fact that autocorrelations, cycles and non-linearity are present in time series.…”

Section: Review Of Literaturementioning

confidence: 99%

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Dynamic prediction of Indian stock market: an artificial neural network approach

Goel

Singh

2021

IJOES

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Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

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