Stock performance modeling using neural networks: A comparative study with regression models

By A. Refenes, A. Zapranis, Gavin Francis. 1994

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “, sequential network construction (Fahlman, 1991; Ash, 1989; Moody, 1989), input pruning (Moody, 1992; Refenes et al., 1994), unit pruning (White, 1989; Mozer and Smolensky, 1989; Levin et al.”
A deep learning framework for financial time series using stacked autoencoders and long-short term memory (2017, background): “During the past decades, machine learning models, such as Artificial Neural Networks (ANNs) [2] and the Support Vector Regression (SVR) [3], have been widely used to predict financial time series and gain high predictive accuracy [4–8].”
A case study on using neural networks to perform technical forecasting of forex (2000, background): “Refenes et al. [11] also indicated that traditional statistical techniques for forecasting have reached their limitation in applications with nonlinearities in the data set such as stock indices.”
Fintech Model: The Random Neural Network with Genetic Algorithm (2018, background): “et al [20] examine the use of neural networks in stock performance modelling.”
A Hybrid Neurogenetic Approach for Stock Forecasting (2007, methodology): “It generates a random number in the range [0] [1] for each gene (entry) of the offspring; if the random number for the gene is smaller than a preset probability, it is replaced with an arbitrary number in the preset range.”
Discovering golden nuggets: data mining in financial application (2004, methodology): “[11] compared regression models with a back-propagation network using the same data”
Flat Minima (1997, background): “Examples are sequential network construction (Fahlman and Lebiere 1990; Ash 1989; Moody 1989), input pruning (Moody 1992; Refenes et al. 1994), unit pruning (White 1989; Mozer and Smolensky 1989; Levin et al.”
Multi-Task Learning for Stock Selection (1996, background): “…used to predict future returns of stocks in order to take nancial decisions Should one build a separate network for each stock or share the same network for all the stocks In this paper we also explore other alternatives in which some layers are shared and others are not shared When the…”