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Weiwei Shen

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7 papers

1 author row

AAAI Conference 2019 Conference Paper

The Kelly Growth Optimal Portfolio with Ensemble Learning

Weiwei Shen
Bin Wang
Jian Pu
Jun Wang

As a competitive alternative to the Markowitz mean-variance portfolio, the Kelly growth optimal portfolio has drawn sufficient attention in investment science. While the growth optimal portfolio is theoretically guaranteed to dominate any other portfolio with probability 1 in the long run, it practically tends to be highly risky in the short term. Moreover, empirical analysis and performance enhancement studies under practical settings are surprisingly short. In particular, how to handle the challenging but realistic condition with insufficient training data has barely been investigated. In order to fill voids, especially grappling with the difficulty from small samples, in this paper, we propose a growth optimal portfolio strategy equipped with ensemble learning. We synergically leverage the bootstrap aggregating algorithm and the random subspace method into portfolio construction to mitigate estimation error. We analyze the behavior and hyperparameter selection of the proposed strategy by simulation, and then corroborate its effectiveness by comparing its out-of-sample performance with those of 10 competing strategies on four datasets. Experimental results lucidly confirm that the new strategy has superiority in extensive evaluation criteria.

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AAAI Conference 2017 Conference Paper

Portfolio Selection via Subset Resampling

Weiwei Shen
Jun Wang

As the cornerstone of the modern portfolio theory, Markowitz’s mean-variance optimization is a major model adopted in portfolio management. However, the estimation errors in its input parameters substantially deteriorate its performance in practice. Speciﬁcally, loss could be huge when the number of assets for investment is not much smaller than the sample size of historical data. To hasten the applicability of Markowitz’s portfolio optimization to large portfolios, in this paper, we propose a new portfolio strategy via subset resampling. Through resampling subsets of the original large universe of assets, we construct the associated subset portfolios with more accurately estimated parameters without requiring additional data. By aggregating a number of constructed subset portfolios, we attain a well-diversiﬁed portfolio of all assets. To investigate its performance, we ﬁrst analyze its corresponding efﬁcient frontiers by simulation, provide analysis on the hyperparameter selection, and then empirically compare its out-of-sample performance with those of various competing strategies on diversiﬁed datasets. Experimental results corroborate that the proposed portfolio strategy has marked superiority in extensive evaluation criteria.

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AAAI Conference 2017 Conference Paper

Random Features for Shift-Invariant Kernels with Moment Matching

Weiwei Shen
Zhihui Yang
Jun Wang

In order to grapple with the conundrum in the scalability of kernel-based learning algorithms, the method of approximating nonlinear kernels via random feature maps has attracted wide attention in large-scale learning systems. Speciﬁcally, the associated sampling procedure is one critical component that dictates the quality of random feature maps. However, for high-dimensional features, the standard Monte Carlo sampling method has been shown to be less effective in producing low-variance random samples. In consequence, it demands constructing a large number of features to attain the desired accuracy for downstream use. In this paper, we present a novel sampling algorithm powered by moment matching techniques to reduce the variance of random features. Our extensive empirical studies and comparisons with several highly competitive peer methods verify the superiority of the proposed algorithm in Gram matrix approximation and generalization errors in regression. Our rigorous theoretical proofs justify that the proposed algorithm is guaranteed achieving lower variance than the standard Monte Carlo method in high dimensional settings.

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IJCAI Conference 2016 Conference Paper

Portfolio Blending via Thompson Sampling

Weiwei Shen
Jun Wang

As a definitive investment guideline for institutions and individuals, Markowitz's modern portfolio theory is ubiquitous in financial industry. However, its noticeably poor out-of-sample performance due to the inaccurate estimation of parameters evokes unremitting efforts of investigating effective remedies. One common retrofit that blends portfolios from disparate investment perspectives has received growing attention. While even a naive portfolio blending strategy can be empirically successful, how to effectually and robustly blend portfolios to generate stable performance improvement remains less explored. In this paper, we present a novel online algorithm that leverages Thompson sampling into the sequential decision-making process for portfolio blending. By modeling blending coefficients as probabilities of choosing basis portfolios and utilizing Bayes decision rules to update the corresponding distribution functions, our algorithm sequentially determines the optimal coefficients to blend multiple portfolios that embody different criteria of investment and market views. Compared with competitive trading strategies across various benchmarks, our method shows superiority through standard evaluation metrics.

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IJCAI Conference 2015 Conference Paper

Portfolio Choices with Orthogonal Bandit Learning

Weiwei Shen
Jun Wang
Yu-Gang Jiang
Hongyuan Zha

The investigation and development of new methods from diverse perspectives to shed light on portfolio choice problems has never stagnated in financial research. Recently, multi-armed bandits have drawn intensive attention in various machine learning applications in online settings. The tradeoff between exploration and exploitation to maximize rewards in bandit algorithms naturally establishes a connection to portfolio choice problems. In this paper, we present a bandit algorithm for conducting online portfolio choices by effectually exploiting correlations among multiple arms. Through constructing orthogonal portfolios from multiple assets and integrating with the upper confidence bound bandit framework, we derive the optimal portfolio strategy that represents the combination of passive and active investments according to a risk-adjusted reward function. Compared with oft-quoted trading strategies in finance and machine learning fields across representative real-world market datasets, the proposed algorithm demonstrates superiority in both risk-adjusted return and cumulative wealth.

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AAAI Conference 2015 Conference Paper

Transaction Costs-Aware Portfolio Optimization via Fast Lowner-John Ellipsoid Approximation

Weiwei Shen
Jun Wang

Merton’s portfolio optimization problem in the presence of transaction costs for multiple assets has been an important and challenging problem in both theory and practice. Most existing work suffers from curse of dimensionality and encounters with the difficulty of generalization. In this paper, we develop an approximate dynamic programing method of synergistically combining the Löwner-John ellipsoid approximation with conventional value function iteration to quantify the associated optimal trading policy. Through constructing Löwner-John ellipsoids to parameterize the optimal policy and taking Euclidean projections onto the constructed ellipsoids to implement the trading policy, the proposed algorithm has cut computational costs up to a factor of five hundred and meanwhile achieved nearoptimal risk-adjusted returns across both synthetic and real-world market datasets.

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AAAI Conference 2014 Conference Paper

Doubly Regularized Portfolio with Risk Minimization

Weiwei Shen
Jun Wang
Shiqian Ma

Due to recent empirical success, machine learning algorithms have drawn sufficient attention and are becoming important analysis tools in financial industry. In particular, as the core engine of many financial services such as private wealth and pension fund management, portfolio management calls for the application of those novel algorithms. Most of portfolio allocation strategies do not account for costs from market frictions such as transaction costs and capital gain taxes, as the complexity of sensible cost models often causes the induced problem intractable. In this paper, we propose a doubly regularized portfolio that provides a modest but effective solution to the above difficulty. Specifically, as all kinds of trading costs primarily root in large transaction volumes, to reduce volumes we synergistically combine two penalty terms with classic risk minimization models to ensure: (1) only a small set of assets are selected to invest in each period; (2) portfolios in consecutive trading periods are similar. To assess the new portfolio, we apply standard evaluation criteria and conduct extensive experiments on well-known benchmarks and market datasets. Compared with various state-of-the-art portfolios, the proposed portfolio demonstrates a superior performance of having both higher risk-adjusted returns and dramatically decreased transaction volumes.

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