Practical Bayesian support vector regression for financial time series prediction and market condition change detection

DOI10.1080/14697688.2016.1267868zbMath1406.62120OpenAlexW2594182135MaRDI QIDQ4555150

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Publication date: 19 November 2018

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2016.1267868

zbMATH Keywords

Gaussian processBayesian inferencemachine learningsupport vector machines regressionkernel scalingmulti-armed bandit Bayesian optimization

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15)

Related Items (5)

Short-term volatility forecasting with kernel support vector regression and Markov switching multifractal model ⋮ Stock market prediction based on adaptive training algorithm in machine learning ⋮ Manifold regularization based on Nyström type subsampling ⋮ Factor-based portfolio optimization ⋮ Enhancing the momentum strategy through deep regression

Uses Software

• LIBSVM
• BayesOpt
• Spearmint

Cites Work

• Predicting credit default swap prices with financial and pure data-driven approaches
• Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis
• Learning to Optimize via Posterior Sampling
• A probabilistic framework for SVM regression and error bar estimation
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