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Adaptive Configuration Oracle for Online Portfolio Selection Methods
[article]
2019
arXiv
pre-print
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We then propose an oracle based on adaptive Bayesian optimization for automatically and adaptively configuring online portfolio selection methods. ...
One fundamental problem is online portfolio selection, the goal of which is to exploit this data to sequentially select portfolios of assets to achieve positive investment outcomes while managing risks ...
We then propose a mechanism for using adaptive Bayesian optimization for tuning the parameters of online portfolio selection algorithms. ...
arXiv:1908.08258v1
fatcat:synx3iekf5do3ccijb3jj7idma
To personalize or not
2013
Proceedings of the 7th ACM conference on Recommender systems - RecSys '13
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From a risk management and portfolio retrieval perspective, there is no difference between the popularity-based and the personalized ranking as both of the recommendation outputs can be represented as ...
Item Reranking via Portfolio Optimization We present the performance for the two portfolio-based reranking algorithms (Opt and Greedy) based on the two base recommendation algorithms (BPR and POP). ...
Item Portfolio Optimization In this section, we focus on the item portfolio weighting optimization analysis. ...
doi:10.1145/2507157.2507167
dblp:conf/recsys/ZhangWCZ13
fatcat:be33yyzfqrfbffmv3oqdkech44
Multi-Model Generative Adversarial Network Hybrid Prediction Algorithm (MMGAN-HPA) for Stock Market Prices Prediction
2021
Journal of King Saud University: Computer and Information Sciences
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In this paper, overcome this problem with reinforcement learning and Bayesian optimization. A deep learning framework based on GAN, named Stock-GAN, is implemented with generator and discriminator. ...
In the financial domain, it is widely used for stock market prediction, trade execution strategies and portfolio optimization. Stock market prediction is a very significant use case in this domain. ...
In order to tune the hyperparameters dynamically, Stock-GAN incorporates reinforcement learning framework along with Bayesian optimization technique. ...
doi:10.1016/j.jksuci.2021.07.001
fatcat:rua3fjhdhbewnmprqthu3y6rpe
Bayesian Optimization for Adaptive MCMC
[article]
2011
arXiv
pre-print
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This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. ...
It proposes the use of Bayesian optimization (Brochu et al., 2009) to tune the parameters of the Markov chain. ...
Here, we show that this objective can be easily optimized with Bayesian optimization. ...
arXiv:1110.6497v1
fatcat:swkhgdjzjzbblkzb7z4poblwzy
A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization
[article]
2017
arXiv
pre-print
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Bayesian optimization through Gaussian process regression is an effective method of optimizing an unknown function for which every measurement is expensive. ...
All this is done without requiring the optimization of a nonlinear acquisition function. ...
Such optimization methods are suitable for tuning the parameters of systems with large amounts of uncertainty in an online data-based way. ...
arXiv:1604.00169v3
fatcat:tivcr6grvzg4hidfbsxlhk3fde
Adaptive MCMC with Bayesian Optimization
2012
Journal of machine learning research
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This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. ...
The proposed approach, Bayesian-optimized MCMC, has a few advantages over adaptive methods based on stochastic approximation. ...
It proposes the use of Bayesian optimization (Brochu et al., 2009) to tune the parameters of the Markov chain. ...
dblp:journals/jmlr/MahendranWHF12
fatcat:vwgkjotsenar3djevdlavbwchm
AI in Finance: Challenges, Techniques and Opportunities
[article]
2021
arXiv
pre-print
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Rule-based modeling and controlled experiments; simulation; self-organizing mechanisms; mechanism testing; etc. ...
Limited to evolutionary mechanisms; bias in local/global optimal and extremum; configuration and parameter tuning; etc. ...
arXiv:2107.09051v1
fatcat:g62cz4dqt5dcrbckn4lbveat3u
The Present and Future of Financial Risk Management
2004
Social Science Research Network
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We argue that consideration of the model risk arising from crude aggregation rules and inadequate data could lead to a new class of reduced-form Bayesian risk assessment models. ...
We explain how such a framework could also provide the essential links between risk control, risk assessments, and the optimal allocation of resources. ...
Fine tuning of market and credit VaR estimates of individual instruments or small portfolios may impact the relative risk capital allocations within a particular activity, but since very crude aggregation ...
doi:10.2139/ssrn.478802
fatcat:2gxwyneio5eingnloombslth5y
The Present and Future of Financial Risk Management
2005
Journal of Financial Econometrics
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We argue that consideration of the model risk arising from crude aggregation rules and inadequate data could lead to a new class of reduced-form Bayesian risk assessment models. ...
We explain how such a framework could also provide the essential links between risk control, risk assessments, and the optimal allocation of resources. ...
Fine tuning of market and credit VaR estimates of individual instruments or small portfolios may impact the relative risk capital allocations within a particular activity, but since very crude aggregation ...
doi:10.1093/jjfinec/nbi003
fatcat:ino54isow5gmpamvar5ajat2ny
Dealing with Drift Uncertainty: A Bayesian Learning Approach
2019
Risks
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To illustrate the value added of using the optimal Bayesian learning strategy, we compare it with an optimal nonlearning strategy that keeps the drift constant at all times. ...
Building on filtering techniques and learning methods, we use a Bayesian learning approach to solve the Markowitz problem and provide a simple and practical procedure to implement optimal strategy. ...
The fine-tuning parameter unc is a simple way to increase the uncertainty in the initial guess b 0 . The workflow above and Table 2 represent the Base Case. ...
doi:10.3390/risks7010005
fatcat:fpqraaummvaz3ilermgkn46ieq
Learning Convex Optimization Control Policies
[article]
2019
arXiv
pre-print
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These types of control policies are tuned by varying the parameters in the optimization problem, such as the LQR weights, to obtain good performance, judged by application-specific metrics. ...
Tuning is often done by hand, or by simple methods such as a crude grid search. ...
Learning optimization-based policies. Other work has considered tuning optimizationbased control policies. ...
arXiv:1912.09529v1
fatcat:acjmzbzozze5bpvjkrxqmmv4mm
Hyper-Parameter Optimization: A Review of Algorithms and Applications
[article]
2020
arXiv
pre-print
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To lower the technical thresholds for common users, automated hyper-parameter optimization (HPO) has become a popular topic in both academic and industrial areas. ...
Then, the research focuses on major optimization algorithms and their applicability, covering their efficiency and accuracy especially for deep learning networks. ...
Bayesian Optimization and Its Variants Bayesian optimization (BO) is a traditional algorithm with decades of history. ...
arXiv:2003.05689v1
fatcat:jjbt54gb2nagdegdjf5lj2pyvi
Can we imitate the principal investor's behavior to learn option price?
[article]
2022
arXiv
pre-print
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Eventually the optimal option price is learned by reinforcement learning to maximize the cumulative risk-adjusted return of a dynamically hedged portfolio over simulated price paths. ...
This paper presents a framework of imitating the principal investor's behavior for optimal pricing and hedging options. ...
There are two fine-tuned hyperparameters σ and λ for optimizing our model's performance. ...
arXiv:2105.11376v2
fatcat:ky6r72yiqnc35jyv7dhfaabbze
A Personalized Learning Service Compatible with Moodle E-Learning Management System
2022
Applied Sciences
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[22] developed a Moodle plugin, Middle, to infer personalized instruction for each student based on a Bayesian network model. Jeong et al. ...
[12] proposed a personalized e-course composition approach based on particle swarm optimization, which considers the requirements for meeting learning objectives, required concepts, the difficulty of ...
doi:10.3390/app12073562
fatcat:uducqbvo6vc5xggvebdnb35n44
Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 17191)
2017
Dagstuhl Reports
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Topics that have been intensively discussed at the seminar include population-based heuristics, constrained optimization, non-static parameter choices as well as connections to research in machine learning ...
This report summarizes the talks, breakout sessions, and discussions at the Dagstuhl Seminar 17191 on Theory of Randomized Optimization Heuristics, held during the week from May 08 until May 12, 2017, ...
Bayesian optimization. ...
doi:10.4230/dagrep.7.5.22
dblp:journals/dagstuhl-reports/DoerrIT017
fatcat:guma4eanyne6vlkwexkevs4v6i
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