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Trade sentiment and the stock market : new evidence based on big data textual analysis of Chinese media
Amstad, Marlene; Gambacorta, Leonardo; He, Chao; Xia, … - 2021
Persistent link: https://www.econbiz.de/10012483485
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Intraday market predictability : a machine learning approach
Huddleston, Dillon; Liu, Fred; Stentoft, Lars - In: Journal of financial econometrics 21 (2023) 2, pp. 485-527
Persistent link: https://www.econbiz.de/10014314759
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Trade sentiment and the stock market : new evidence based on big data textual analysis of Chinese media
Amstad, Marlene; Gambacorta, Leonardo; He, Chao; Xia, … - 2021
Persistent link: https://www.econbiz.de/10012416565
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Application and performance of data mining techniques in stock market : a review
Kaur, Jasleen; Dharni, Khushdeep - In: Intelligent systems in accounting, finance & management 29 (2022) 4, pp. 219-241
Persistent link: https://www.econbiz.de/10014245473
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The applications of artificial neural networks, support vector machines, and long-short term memory for stock market prediction
Chhajer, Parshv; Shah, Manan; Kshirsagar, Ameya - In: Decision analytics journal 2 (2022)
The future is unknown and uncertain, but there are ways to predict future events and reap the rewards safely. One such opportunity is the application of machine learning and artificial intelligence for stock market prediction. The stock market is turbulent, yet using artificial intelligence to...
Persistent link: https://www.econbiz.de/10013163098
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Artificial intelligence applied to African stock market
Amzile, Karim - In: International journal of management practice : IJMP 17 (2024) 4, pp. 463-477
Persistent link: https://www.econbiz.de/10015069643
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State-dependent asset allocation using neural networks
Bradrania, Reza; Pirayesh Neghab, Davood - In: The European journal of finance 28 (2022) 11, pp. 1130-1156
Persistent link: https://www.econbiz.de/10013373381
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Application of artificial intelligence in stock market forecasting : a critique, review, and research agenda
Chopra, Ritika; Sharma, Gagan Deep - In: Journal of risk and financial management : JRFM 14 (2021) 11, pp. 1-34
The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal and external environmental variables. Artificial intelligence (AI) techniques can detect such non-linearity, resulting in much-improved forecast results. This paper reviews 148 studies utilizing...
Persistent link: https://www.econbiz.de/10012795264
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Application of machine learning in quantitative investment strategies on global stock markets
Grudniewicz, Jan; Ślepaczuk, Robert - 2021
Persistent link: https://www.econbiz.de/10012816704
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Predicting stock return and volatility with machine learning and econometric models: a comparative case study of the Baltic stock market
Nõu, Anders; Lapitskaya, Darya; Eratalay, M. Hakan; … - 2021
Persistent link: https://www.econbiz.de/10012694117