Automatic Relevance Determination Bayesian Neural Networks for Credit Card Default Modelling

Mbuvha, Rendani; Boulkaibet, Ilyes; Marwala, Tshilidzi

doi:10.48550/arxiv.1906.06382

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Financial applications involving BNNs are somewhat limited. An application for automatic relevance determination in option pricing is that of Mbuvha et al (2019). A recent example of stock-price prediction is found in Chandra and He (2021), where exploitative MCMC-based learning is used to forecast daily closing prices of four stocks, showing that in terms of RMSE performance metric, their BNN outperforms non-Bayesian counterparts.…”

Section: Literature Reviewmentioning

confidence: 99%

Bayesian bilinear neural network for predicting the mid‐price dynamics in limit‐order book markets

Magris

Shabani

Iosifidis

2023

Journal of Forecasting

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The prediction of financial markets is a challenging yet important task. In modern electronically driven markets, traditional time‐series econometric methods often appear incapable of capturing the true complexity of the multilevel interactions driving the price dynamics. While recent research has established the effectiveness of traditional machine learning (ML) models in financial applications, their intrinsic inability to deal with uncertainties, which is a great concern in econometrics research and real business applications, constitutes a major drawback. Bayesian methods naturally appear as a suitable remedy conveying the predictive ability of ML methods with the probabilistically oriented practice of econometric research. By adopting a state‐of‐the‐art second‐order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention, suitable for the challenging time‐series task of predicting mid‐price movements in ultra‐high‐frequency limit‐order book markets. We thoroughly compare our Bayesian model with traditional ML alternatives by addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts. Our results underline the feasibility of the Bayesian deep‐learning approach and its predictive and decisional advantages in complex econometric tasks, prompting future research in this direction.

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Section: Literature Reviewmentioning

confidence: 99%

Bayesian bilinear neural network for predicting the mid‐price dynamics in limit‐order book markets

Magris

Shabani

Iosifidis

2023

Journal of Forecasting

View full text Add to dashboard Cite

show abstract

“…Financial applications involving BNNs are rather limited. An applications for automatic relevance determination in option pricing is that of [52], a recent example on stock-price prediction is found in [53] where exploitative MCMC-based learning is used to forecast daily closing prices of four stocks, showing that in terms of RMSE performance metric, their BNN outperforms non-Bayesian counterparts. A forecasting study based on electricity prices is provided in [54], [55], while bitcoin data is used in [56].…”

Section: Literature Reviewmentioning

confidence: 99%

Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets

Magris¹,

Shabani²,

Iosifidis³

2022

Preprint

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The prediction of financial markets is a challenging yet important task. In modern electronically-driven markets traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. While recent research has established the effectiveness of traditional machine learning (ML) models in financial applications, their intrinsic inability in dealing with uncertainties, which is a great concern in econometrics research and real business applications, constitutes a major drawback. Bayesian methods naturally appear as a suitable remedy conveying the predictive ability of ML methods with the probabilistically-oriented practice of econometric research. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention, suitable for the challenging time-series task of predicting mid-price movements in ultra-high-frequency limit-order book markets. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives. Our results underline the feasibility of the Bayesian deep learning approach and its predictive and decisional advantages in complex econometric tasks, prompting future research in this direction.

show abstract

Automatic Relevance Determination Bayesian Neural Networks for Credit Card Default Modelling

Cited by 2 publications

References 13 publications

Bayesian bilinear neural network for predicting the mid‐price dynamics in limit‐order book markets

Bayesian bilinear neural network for predicting the mid‐price dynamics in limit‐order book markets

Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets

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