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Forecasting salmon market volatility using long short-term memory (LSTM)

Zitti, Mikaella - In: Aquaculture economics & management : official journal … 28 (2024) 1, pp. 143-175

Persistent link: https://www.econbiz.de/10015053133

Optimised hybrid CNN bi-LSTM model for stock price forecasting

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Persistent link: https://www.econbiz.de/10015065340

Optimised hybrid CNN-LSTM model for stock price prediction

Patnaik, Deepti; Rao, N. V. Jagannadha; Padhiari, … - In: International journal of management and decision making … 23 (2024) 4, pp. 438-460

Persistent link: https://www.econbiz.de/10015067129

Multiple-predictor regressions : hypothesis testing

Amihud, Yakov; Hurvich, Clifford M.; Wang, Yi - In: The review of financial studies 22 (2009) 1, pp. 413-434

Persistent link: https://www.econbiz.de/10003836279

Multivariate nonlinear forecasting : using financial information to forecast the real sector

Jaditz, Theodore Mark; Riddick, Leigh A.; Sayers, Chera L. - In: Macroeconomic dynamics 2 (1998) 3, pp. 369-382

Persistent link: https://www.econbiz.de/10001617659

Consistent quasi-maximum likelihood estimation with limited information

Miller, Douglas R.; Lee, Sang-hak - In: Maximum likelihood estimation of misspecified models : …, (pp. 149-164). 2003

Persistent link: https://www.econbiz.de/10001916311

Modelling and predicting the real GDP rate in Romania

Bratu, Mihaela - In: Journal of academic research in economics 6 (2014) 3, pp. 305-314

Persistent link: https://www.econbiz.de/10011348112

Macroeconomic variables and stock price

Tripathy, Tapas Kumar; Gautam, Mitra - In: Research bulletin / The Institute of Cost Accountants … 41 (2015) 2, pp. 19-28

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Forecasting the stock exchange of Thailand using data mining techniques

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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