A Bayesian regularized artificial neural network for stock market forecasting

By Jonathan L. Ticknor. 2013

503 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research

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Citations in the corpus

Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, background): “With the developement of deep learning, more research efforts have been paid on exploiting deep neurual networks for financial prediction [2, 8, 12, 14, 21, 25].”
Stock trend forecasting in turbulent market periods using neuro-fuzzy systems (2015, background): “Low-level features, i.e. plain technical indicators as shown in Ticknor (2013).”
Pattern graph tracking-based stock price prediction using big data (2018, methodology): “As the second issue, many related papers have already been published, and stock prices are still being predicted using a variety of methods, such as machine learning and feature selection [22, 42, 43].”
Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data (2016, unknown): “In many studies [8,31–33], the BR training algorithm has given either moderate or the best performance in terms of comparison with other training algorithms.”
Applications of deep learning in stock market prediction: recent progress (2020, background): “…(2019) Python N/A Cao et al. (2019); Wu et al. (2018); Li et al. (2019a); Siami-Namini et al. (2019); Jin et al. (2019); Chen et al. (2018b) Matlab N/A Ticknor (2013); Zhong & Enke (2017); Singh & Srivastava (2017); Wu & Gao (2018); Lei (2018); Pang et al. (2018); Chen et al. (2018a) R nnet…”
Natural language based financial forecasting: a survey (2017, background): “The effects of these hidden energy terms on the visible stock price energy can be modeled and fused as a Bayesian network [115].”
AI in Finance: Challenges, Techniques, and Opportunities (2021, unknown): “Examples include integrating expert systems and evolutionary computing for advising investment [131]; combining evolutionary computing and neural computing to generate evolutionary neural models to simulate and analyze a capital market [18]; integrating complexity science and game theory to model an”
Back propagation neural network with adaptive differential evolution algorithm for time series forecasting (2015, background): “The advantages of ANNs are their flexible nonlinear modeling capability, strong adaptability, as well as their learning and massive parallel computing abilities (Ticknor, 2013).”
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns (2017, methodology): “A Harmony Search based neural network [8] is proposed to forecast the stock market; [2] compared capability of neural networks with Autoregressive Integrated Moving Average (ARIMA) to predict stock trends; a Bayesian regularized Artificial Neural Networks [27] was presented to forecast stock indices”
Advances in Bayesian network modelling: Integration of modelling technologies (2019, methodology): “Bayesian neural networks have been used to forecast energy load requirements (Lauret et al., 2008), solar irradiation (Yacef et al., 2012), stock market performance (Ticknor, 2013), and internet traffic loads (Auld et al., 2007).”
Predicting the Price of Bitcoin Using Machine Learning (2018, background): “For this reason, grid search is favoured over random search in this research where Bayesian optimization is not suitable (33).”