Automated trading with boosting and expert weighting
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Publication:3564810
DOI10.1080/14697680903104113zbMath1203.91326OpenAlexW3124061827MaRDI QIDQ3564810
Yoav Freund, Germán G. Creamer
Publication date: 26 May 2010
Published in: Quantitative Finance (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/14697680903104113
Statistical methods; risk measures (91G70) Learning and adaptive systems in artificial intelligence (68T05) Actuarial science and mathematical finance (91G99)
Related Items (6)
Transaction cost optimization for online portfolio selection ⋮ Metalearning of time series: an approximate dynamic programming approach ⋮ Performance-weighted ensembles of random forests for predicting price impact ⋮ Editors' foreword ⋮ Online portfolio selection ⋮ Universal features of price formation in financial markets: perspectives from deep learning
Uses Software
Cites Work
- Unnamed Item
- Asymptotic optimality and asymptotic equipartiton properties of log- optimum investment
- The weighted majority algorithm
- A decision-theoretic generalization of on-line learning and an application to boosting
- Boosting the margin: a new explanation for the effectiveness of voting methods
- Generalized autoregressive conditional heteroscedasticity
- Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
- Generalization bounds for averaged classifiers
- Universal portfolios with and without transaction costs.
- GENETIC ALGORITHM LEARNING TO CHOOSE AND USE INFORMATION
- Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
- Universal Portfolios
- How to use expert advice
- 10.1162/153244303321897672
- Universal portfolios with side information
- Logistic regression, AdaBoost and Bregman distances
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