Surveying stock market forecasting techniques - Part II: Soft computing methods

By George S. Atsalakis, Kimon P. Valavanis. 2009

876 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Economics & Operations Research, Time-Series & Forecasting

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, background): “A number of surveys were published for stock market forecasting studies based on various soft computing methods at different times [24, 25, 26, 27, 28, 29, 30, 31].”
Deep Learning for Forecasting Stock Returns in the Cross-Section (2018, background): “Many studies on stock return predictability have been reported on neural networks [6, 7] .”
Developing a two level options trading strategy based on option pair optimization of spread strategies with evolutionary algorithms (2015, background): “Most of these studies were concentrated on stock market forecasting [1], [2], trend detection [3], and strategy development based on technical indicators.”
A review of unsupervised feature learning and deep learning for time-series modeling (2014, background): “prediction is the arti cial neural network (ANN) (Atsalakis and Valavanis, 2009).”
Using artificial neural network models in stock market index prediction (2011, methodology): “Atsalakis and Valavanis (2009a) used Adaptive Neuro Fuzzy Inference System (ANFIS) to determine the best stock trend prediction model and results show that ANFIS clearly demonstrates the potential of neurofuzzy based modeling for financial market prediction.”
Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches (2010, background): “Even related work uses the same index; the number of input variables used in these studies is different [3].”
Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations (2017, background): “For this purpose, filter-based feature selection methods were combined, SVM classifier was trained and finally the voting scheme was applied.”
Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange (2011, background): “The review paper of Atsalakis and Valavanis (2009), which summarizes the related literature, can be useful for interested readers.”
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting (2010, background): “There are several researches which have used GFSs for forecasting problems [14,35], but there is not any research in the literature that uses a GFS with the ability of learning rule base and tuning data base of fuzzy system by use of genetic algorithm for stock price forecasting problem (a complete ”
Applications of deep learning in stock market prediction: recent progress (2020, background): “Back to 2009, Atsalakis & Valavanis (2009) surveys more than 100 related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets, with the discussion of classifications of input data, forecasting methodology, performance evaluation and…”
CAN DEEP MACHINE LEARNING OUTSMART THE MARKET? A COMPARISON BETWEEN ECONOMETRIC MODELLING AND LONG- SHORT TERM MEMORY (2016, methodology): “The employed activation function were the Sigmoid activation function in equation (7), the Tangent activation function in equation (8), while in equation (9) the Tangent activation function expressed as Sigmoid function is exemplified, the Softplus activation function in equation (10), the Linear ac”
Stock prediction using deep learning (2016, neutral): “[1] have surveyed ANNs applied for stock prediction but they have not pointed out the feature extraction methods used for stock prediction.”
A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, background): “One year later, [12] surveyed more than 100 related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets.”