Performance of Deep Learning in Prediction of Stock Market Volatility

Moon, Kyoung-Sook; Kim, Hongjoong

doi:10.24818/18423264/53.2.19.05

Cited by 32 publications

(25 citation statements)

References 9 publications

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“…Employing optimal long short-term memory (O-LSTM), Agrawal et al [38] proposed a model for the stock price Many researchers have strived to forecast the stock value relying on the LSTM algorithm, either a single long short-term memory (LSTM) or a hybrid model of LSTM. Adapting the LSTM algorithm, Moon and Kim [32] proposed an algorithm to predict the stock market index and volatility. Fischer and Krauss [33] expand the LSTM networks to forecast out-of-sample directional movements in the stock market.…”

Section: Lstmmentioning

confidence: 99%

“…The ability of DL algorithms in prediction and finding the patterns among the raw data has grabbed the attraction of many researchers from various fields. As explored above the stock price prediction, e.g., [32][33][34] and consumer behavior, e.g., [75] have well benefited with a wide range of a deep learning algorithms. Among the methods, the LSTM, CNN, and DNN are respectively the most popular DL [32][33][34].…”

Section: Cryptocurrencymentioning

confidence: 99%

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Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

et al. 2020

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This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.

show abstract

Section: Lstmmentioning

confidence: 99%

Section: Cryptocurrencymentioning

confidence: 99%

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

et al. 2020

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show abstract

“…Relying on LSTM algorithm, either a single long short-term memory (LSTM) or a hybrid model of LSTM, many researchers strived to forecast the stock value. Adapting LSTM algorithm, Moon and Kim [26] propose an algorithm to predict the stock market index and the stock market volatility. Fischer and Krauss [27] expand the LSTM networks to forecast out-of-sample directional movements in the stock market.…”

Section: Lstmmentioning

confidence: 99%

“…LSTM, CNN, and DNN are respectively the most applied DL models among the database of the study. LSTM is applied to stock price prediction [26][27][28], portfolio management [29], automated stock trading [30], and cryptocurrencies price prediction [82]. Among the reviewed papers, the LSTM method has only applied to find the patterns among financial time series data.…”

Section: Cryptocurrencymentioning

confidence: 99%

Data Science in Economics

Nosratabadi¹,

Mosavi²,

Duan³

et al. 2020

Preprint

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This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The novel data science methods and applications are investigated in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide range of economics research, from stock market, marketing, E-commerce, to corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on the advancement of hybrid models. On the other hand, based on the accuracy metric it is also reported that the hybrid models outperform other learning algorithms. It is further expected that the trends would go toward the advancements of sophisticated hybrid deep learning models.

show abstract

“…Relying on the LSTM algorithm, either a single long short-term memory (LSTM) or a hybrid model of LSTM, many researchers have strived to forecast the stock value. Adapting the LSTM algorithm, Moon and Kim [32] proposed an algorithm to predict the stock market index and volatility. Fischer and Krauss [33] expand the LSTM networks to forecast out-of-sample directional movements in the stock market.…”

Section: Lstmmentioning

confidence: 99%