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CORN: Correlation-Driven Nonparametric Learning Approach for Portfolio Selection -- an Online Appendix
[article]
2013
arXiv
pre-print
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One of Bin's PhD thesis examiner (Special thanks to Vladimir Vovk from Royal Holloway, University of London) suggested that CORN is universal and provided sketch proof of Lemma 1.6, which is the key of ...
The portfolio scheme CORN is universal with respect to the class of all ergodic processes such that E log X (j) < ∞, for j = 1, 2, ..., d. PROOF. ...
PROOF OF CORN'S UNIVERSAL CONSISTENCY In this note, we give a detailed proof that the portfolio scheme CORN [Li et al. 2011 ] is universal with respect to the class of all ergodic processes. ...
arXiv:1306.1378v1
fatcat:zq6vm5bc5vgmncri4lpfieuw54
CORN
2011
ACM Transactions on Intelligent Systems and Technology
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In this paper, we present a novel learning to trade strategy for the (sequential) portfolio selection problem, termed the CORrelation-driven Nonparametric learning (CORN) algorithm. ...
In this paper, we propose a novel learning to trade algorithm termed the CORrelation-driven Nonparametric learning strategy (CORN) for actively trading stocks, which effectively exploits statistical relations ...
CORN: CORRELATION-DRIVEN NONPARAMETRIC LEARNING STRATEGY In this section, we present a new learning to trade strategy termed CORrelation-driven Nonparametric learning algorithm (CORN). ...
doi:10.1145/1961189.1961193
fatcat:gmmqnkmqmnamznychzjuvqx7ku
Moving average reversion strategy for on-line portfolio selection
2015
Artificial Intelligence
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On-line portfolio selection, a fundamental problem in computational finance, has attracted increasing interest from artificial intelligence and machine learning communities in recent years. ...
To overcome this limitation, this article proposes a multiple-period mean reversion, or so-called "Moving Average Reversion" (MAR), and a new on-line portfolio selection strategy named "On-Line Moving ...
Acknowledgements We want to thank the associate editor and anonymous reviewers for their helpful comments and suggestions. ...
doi:10.1016/j.artint.2015.01.006
fatcat:ncomxpppcffa7cvmkmrsaonjuu
Online Portfolio Selection: A Survey
[article]
2013
arXiv
pre-print
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From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then survey a variety of state-of-the-art approaches, which are grouped ...
This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in the financial industry to help them ...
Acknowledgements The authors would like to thank VIVEKANAND GOPALKRISHNAN for his comments on an early discussion of this work. ...
arXiv:1212.2129v2
fatcat:hkrum5sevncvvpe3aksikhqmtq
On-Line Portfolio Selection with Moving Average Reversion
[article]
2012
arXiv
pre-print
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On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. ...
To overcome the limitation, this article proposes a multiple-period mean reversion, or so-called Moving Average Reversion (MAR), and a new on-line portfolio selection strategy named "On-Line Moving Average ...
)searches for ℓ nearest neighbors to the recent market window.Recently,Li et al. (2011a)proposed Correlation-driven Nonparametric learning (CORN)
Table 2 . 2 Illustration of growth of mean reversion ...
arXiv:1206.4626v1
fatcat:izop5mn6kbamtddj2ryidbanma
Online portfolio selection
2014
ACM Computing Surveys
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From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then survey a variety of state-of-the-art approaches, which are grouped ...
This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in the financial industry to help them ...
ACKNOWLEDGMENTS The authors would like to thank VIVEKANAND GOPALKRISHNAN for his comments on an early discussion of this work, and AMIR SANI for his careful proofreading. ...
doi:10.1145/2512962
fatcat:dfjxdk2e2fcovm24otoq2qhceq
Kernel-Based Aggregating Learning System for Online Portfolio Optimization
2020
Mathematical Problems in Engineering
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Recently, various machine learning techniques have been applied to solve online portfolio optimization (OLPO) problems. ...
To overcome these drawbacks, this paper proposes a novel kernel-based aggregating learning (KAL) system for OLPO. ...
[31] propose correlation-driven nonparametric learning (CORN) approach that identifies the linear similarity among two market windows via correlation, which also adopts the idea of pattern-matching. ...
doi:10.1155/2020/6595329
fatcat:y642ebkbbnaqtjclb4rgscpvfq
Combination Forecasting Reversion Strategy for Online Portfolio Selection
2018
ACM Transactions on Intelligent Systems and Technology
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Machine learning and artificial intelligence techniques have been applied to construct online portfolio selection strategies recently. ...
Despite gaining promising results on some benchmark datasets, these strategies often adopt a single model based on a selection criterion (e.g., breakdown point) for predicting future price. ...
[36] proposed correlation-driven nonparametric learning (CORN), which measures the similarity via correlation. ...
doi:10.1145/3200692
fatcat:5kh6z4guqzg3pb6volayta2sim
PAMR: Passive aggressive mean reversion strategy for portfolio selection
2012
Machine Learning
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Equipped with online passive aggressive learning technique from machine learning, the proposed portfolio selection strategy can effectively exploit the mean reversion property of markets. ...
This article proposes a novel online portfolio selection strategy named "Passive Aggressive Mean Reversion" (PAMR). ...
Along this direction, Li et al. (2011a) recently proposed Correlation-driven Nonparametric learning (CORN) strategy to search for similar price relatives via correlation coefficient and considerably ...
doi:10.1007/s10994-012-5281-z
fatcat:m2h3o47mszcq5ms7c4h5bgrffy
Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection
2013
ACM Transactions on Knowledge Discovery from Data
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The CWMR strategy is able to effectively exploit the power of mean reversion for on-line portfolio selection. ...
This paper proposes a novel on-line portfolio selection strategy named "Confidence Weighted Mean Reversion" (CWMR). ...
[24] further proposed Correlation-driven Nonparametric learning (CORN) strategy by locating the similar price relatives via correlation. ...
doi:10.1145/2435209.2435213
fatcat:tfun74pkefdbnlfy2ssi2eystm
Robust Median Reversion Strategy for Online Portfolio Selection
2016
IEEE Transactions on Knowledge and Data Engineering
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Online portfolio selection has attracted increasing attention from data mining and machine learning communities in recent years. ...
An important theory in financial markets is mean reversion, which plays a critical role in some state-of-the-art portfolio selection strategies. ...
ACKNOWLEDGMENTS This work was partially done when the first author was visiting the Computer Science Department, University of California Santa Cruz, he would like to thank Professor Manfred Warmuth for ...
doi:10.1109/tkde.2016.2563433
fatcat:d6yuw6zepbfezehwkiyh3cwytq
STATISTICAL ARBITRAGE PAIRS TRADING STRATEGIES: REVIEW AND OUTLOOK
2016
Journal of economic surveys (Print)
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The time series approach focuses on finding optimal trading rules for mean-reverting spreads. ...
The stochastic control approach aims at identifying optimal portfolio holdings in the legs of a pairs trade relative to other available securities. ...
Clearly, this selection metric is stricter than simply demanding for high return correlation, as in Chen et al. ...
doi:10.1111/joes.12153
fatcat:mtwqfyja6va3fiddtxztluuyzm
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
[article]
2017
arXiv
pre-print
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This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem. ...
They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market. ...
(UP) (Cover, 1991) , Exponential Gradient (EG) (Helmbold et al., 1998) , Nonparametric Kernel Based Log Optimal Strategy (B K ) (Györfi et al., 2006) , Correlation-driven Nonparametric Learning Strategy ...
arXiv:1706.10059v2
fatcat:3eafh5qeunhu5eoxxcnijsa76m
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods
2018
Surveys in geophysics
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combine RTM simulations with machine learning regression methods. ...
regression algorithms; (3) physically-based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up-table approaches; and (4) hybrid regression methods, which ...
illustrations), or (3) hybrid approaches in which LUTs are generated as input for machine learning approaches (see Sect. 5). ...
doi:10.1007/s10712-018-9478-y
fatcat:brcn45xg5zfihn6enxm6vn2fv4
Improving Generalization in Reinforcement Learning–Based Trading by using a Generative Adversarial Market Model
2021
IEEE Access
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This strategy has been applied in the M0 [46] and correlation-driven nonparametric learning (CORN) [3] methods, which we tested. ...
[12] focus on portfolio selection and use an RL framework in which an agent for asset pool selection optimizes the selection strategy. Wang et al. ...
doi:10.1109/access.2021.3068269
fatcat:uvhptcu5jbcbtmcsra3jw7x32q
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