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Sparse Parametric Portfolio Selection
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Book Synopsis Sparse Parametric Portfolio Selection by : Roman Croessmann
Download or read book Sparse Parametric Portfolio Selection written by Roman Croessmann and published by . This book was released on 2018 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: This article shows how sparse solutions can be generated in parametric portfolio selection methods. Sparse mean-variance optimization procedures can be applied after the translation of parametric weight estimates into implied mean return estimates. The results of our empirical analysis suggest that such a translation is potentially helpful for sparse parametric portfolio selection. We however find that l1-penalized portfolio optimization methods have unintended properties and are outperformed by a simple heuristic approach in our data set.
Book Synopsis Sparse and Stable Portfolio Selection with Parameter Uncertainty by : Jiahan Li
Download or read book Sparse and Stable Portfolio Selection with Parameter Uncertainty written by Jiahan Li and published by . This book was released on 2015 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: A number of alternative mean-variance portfolio strategies have been recently proposed to improve the empirical performance of the classic Markowitz mean-variance framework. Designed as remedies for parameter uncertainty and estimation errors in portfolio selection problems, these alternative portfolio strategies deliver substantially better out-of-sample performance. In this paper, we first show how to solve a general portfolio selection problem in a linear regression framework. Then we propose to reduce the estimation risk of expected returns and the variance-covariance matrix of asset returns by imposing additional constraints on the portfolio weights. With results from linear regression models, we show that portfolio weights derived from new approaches enjoy two favorable properties: sparsity and stability. Moreover, we present insights into these new approaches as well as their connections to alternative strategies in literature. Four empirical studies show that the proposed strategies have better out-of-sample performance and lower turnover than many other strategies, especially when the estimation risk is large.
Book Synopsis A Simplified Approach to Parameter Estimation and Selection of Sparse, Mean Reverting Portfolios by : Norbert Fogarasi
Download or read book A Simplified Approach to Parameter Estimation and Selection of Sparse, Mean Reverting Portfolios written by Norbert Fogarasi and published by . This book was released on 2017 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we study the problem of finding sparse, mean reverting portfolios in multivariate time series. This can be applied to developing profitable convergence trading strategies by identifying portfolios which can be traded advantageously when their prices differ from their identified long-term mean. Assuming that the underlying assets follow a VAR(1) process, we propose simplified, dense parameter estimation techniques which also provide a goodness of model fit measure based on historical data. Using these dense estimated parameters, we describe an exhaustive method to select an optimal sparse mean-reverting portfolio which can be used as a benchmark to evaluate faster, heuristic methods such as greedy search. We also present a simple and very fast heuristic to solve the same problem, based on eigenvector truncation. We observe that convergence trading using these portfolio selection methods is able to generate profits on historical financial time series.
Book Synopsis Improved Parameter Estimation and Simple Trading Algorithm for Sparse, Mean-Reverting Portfolios by : Norbert Fogarasi
Download or read book Improved Parameter Estimation and Simple Trading Algorithm for Sparse, Mean-Reverting Portfolios written by Norbert Fogarasi and published by . This book was released on 2017 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: We examine the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping optimal portfolio selection into a generalized eigenvalue problem, two different heuristic algorithms are referenced for finding the solution in a subspace which satisfies the cardinality constraint. Having identified the optimal portfolio, we outline the known methods for finding the long-term mean and introduce a novel approach based on pattern matching. Furthermore, we present a simple convergence trading algorithm with a decision theoretic approach, which can be used to compare the economic viability of the different methods and test the effectiveness of our end-to-end process by extensive simulations on generated and historical real market data.
Book Synopsis Sparse and Robust Normal and T-Portfolios by Penalized Lq-Likelihood Minimization by : Davide Ferrari
Download or read book Sparse and Robust Normal and T-Portfolios by Penalized Lq-Likelihood Minimization written by Davide Ferrari and published by . This book was released on 2017 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two important problems arising in traditional asset allocation methods are the sensitivity to estimation error of portfolio weights and the high dimensionality of the set of candidate assets. In this paper, we address both issues by proposing a new minimum description length criterion for portfolio selection. The new criterion is a two-stage description of the available information, where the q-entropy, a generalized measure of information, is used to code the uncertainty of the data given the parametric model and the uncertainty related to the model choice. The information about the model is coded in terms of a prior distribution that promotes asset weights sparsity. Our approach carries out model selection and estimation in a single step, by selecting few assets and estimating their portfolio weights simultaneously. The resulting portfolios are doubly robust, in the sense that they can tolerate deviations from both, assumed data model and prior distribution for model parameters. Empirical results on simulated and real-world data support the validity of our approach in comparison to state-of-art benchmarks.
Book Synopsis A Sparse Learning Approach to Relative-Volatility-Managed Portfolio Selection by : Chi Seng Pun
Download or read book A Sparse Learning Approach to Relative-Volatility-Managed Portfolio Selection written by Chi Seng Pun and published by . This book was released on 2019 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes a self-calibrated sparse learning approach for estimating a sparse target vector, which is a product of a precision matrix and a vector, and investigates its application to finance to provide an innovative construction of relative-volatility-managed portfolio (RVMP). The proposed iterative algorithm, called DECODE, jointly estimates a performance measure of the market and the effective parameter vector in the optimal portfolio solution, where the relative-volatility timing is introduced into the risk exposure of an efficient portfolio via the control of its sparsity. The portfolio's risk exposure level, which is linked to its sparsity in the proposed framework, is automatically tuned with the latest market condition without using cross-validation. The algorithm is efficient as it costs only a few computations of quadratic programming. We prove that the iterative algorithm converges and show the oracle inequalities of the DECODE, which provide sufficient conditions for a consistent estimate of an optimal portfolio. The algorithm can also handle the curse of dimensionality that the number of training samples is less than the number of assets. Our empirical studies of over-12-year backtest illustrate the relative-volatility timing feature of the DECODE and the superior out-of-sample performance of the DECODE strategy, which beats the equally-weighted strategy and improves over the shrinkage strategy.
Book Synopsis Optimization of Complex Systems: Theory, Models, Algorithms and Applications by : Hoai An Le Thi
Download or read book Optimization of Complex Systems: Theory, Models, Algorithms and Applications written by Hoai An Le Thi and published by Springer. This book was released on 2019-06-15 with total page 1164 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains 112 papers selected from about 250 submissions to the 6th World Congress on Global Optimization (WCGO 2019) which takes place on July 8–10, 2019 at University of Lorraine, Metz, France. The book covers both theoretical and algorithmic aspects of Nonconvex Optimization, as well as its applications to modeling and solving decision problems in various domains. It is composed of 10 parts, each of them deals with either the theory and/or methods in a branch of optimization such as Continuous optimization, DC Programming and DCA, Discrete optimization & Network optimization, Multiobjective programming, Optimization under uncertainty, or models and optimization methods in a specific application area including Data science, Economics & Finance, Energy & Water management, Engineering systems, Transportation, Logistics, Resource allocation & Production management. The researchers and practitioners working in Nonconvex Optimization and several application areas can find here many inspiring ideas and useful tools & techniques for their works.
Book Synopsis Sparse Markowitz Portfolio Selection by Penalty Methods by : Qiyu Wang
Download or read book Sparse Markowitz Portfolio Selection by Penalty Methods written by Qiyu Wang and published by . This book was released on 2018 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider the framework of the classical Markowitz mean-variance model when multiple solutions exist, among which the sparse solutions are stable and cost-efficient. We study a least-$p$-norm sparse portfolio model with $p in(0,1)$ solved by the penalty method. This model finds the least-$p$-norm sparse asset allocation in the solution set of the Markowitz problem, which saves the transaction cost and stabilizes the optimization problem. We apply the sample average approximation (SAA) method to the least-$p$-norm sparse portfolio model and give a detailed convergence analysis. We implement this method on the data sets of 20 A &H stocks, Fama & French 12 industry sectors (FF12), and Fama & French 25 portfolios formed on size and book-to-market (FF25). Using portfolios constructed in the training sample, we test them in the out-of-sample data and find their Sharpe ratios outperform the $0$-norm sparse portfolio, $ ell_1$ penalty regularized portfolios, cardinality constrained portfolios, and $1/N$ investment strategy.
Book Synopsis Financial Signal Processing and Machine Learning by : Ali N. Akansu
Download or read book Financial Signal Processing and Machine Learning written by Ali N. Akansu and published by John Wiley & Sons. This book was released on 2016-05-31 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.
Book Synopsis Econometrics with Machine Learning by : Felix Chan
Download or read book Econometrics with Machine Learning written by Felix Chan and published by Springer Nature. This book was released on 2022-09-07 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.
Book Synopsis Sparse, Mean Reverting Portfolio Selection Using Simulated Annealing by : Norbert Fogarasi
Download or read book Sparse, Mean Reverting Portfolio Selection Using Simulated Annealing written by Norbert Fogarasi and published by . This book was released on 2014 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping the optimal portfolio selection problem into a generalized eigenvalue problem, we propose a new optimization approach based on the use of simulated annealing. This new method ensures that the cardinality constraint is automatically satisfied in each step of the optimization by embedding the constraint into the iterative neighbor selection function. We empirically demonstrate that the method produces better mean reversion coefficients than other heuristic methods, but also show that this does not necessarily result in higher profits during convergence trading. This implies that more complex objective functions should be developed for the problem, which can also be optimized under cardinality constraints using the proposed approach.
Book Synopsis Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers by : Stephen Boyd
Download or read book Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers written by Stephen Boyd and published by Now Publishers Inc. This book was released on 2011 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.
Book Synopsis Introduction to Risk Parity and Budgeting by : Thierry Roncalli
Download or read book Introduction to Risk Parity and Budgeting written by Thierry Roncalli and published by CRC Press. This book was released on 2016-04-19 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although portfolio management didn't change much during the 40 years after the seminal works of Markowitz and Sharpe, the development of risk budgeting techniques marked an important milestone in the deepening of the relationship between risk and asset management. Risk parity then became a popular financial model of investment after the global fina
Book Synopsis Sparse Tangent Portfolio Selection Via Semi-Definite Relaxation by : Min Jeong Kim
Download or read book Sparse Tangent Portfolio Selection Via Semi-Definite Relaxation written by Min Jeong Kim and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The high-cardinality of mean-variance portfolios is a concern in practice because it increases transaction costs and management fees. Therefore, we propose a method to resolve the cardinality problem by applying the semi-definite relaxation method to a cardinality constrained optimal tangent portfolio selection model. We find that the relaxed model becomes a semi-definite programming problem that is efficiently solved with existing optimization solvers. Numerical analyses with historical stock returns confirm that the proposed relaxed model effectively constructs sparse tangent portfolios.
Book Synopsis Online Portfolio Selection by : Bin Li
Download or read book Online Portfolio Selection written by Bin Li and published by CRC Press. This book was released on 2018-10-30 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: Introduce OLPS and formulate OLPS as a sequential decision task Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art Investigate possible future directions Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. Readers are encouraged to visit the authors’ website for updates: http://olps.stevenhoi.org.
Book Synopsis Construction, Management, and Performance of Sparse Markowitz Portfolios by : Julie Henriques
Download or read book Construction, Management, and Performance of Sparse Markowitz Portfolios written by Julie Henriques and published by . This book was released on 2014 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study different implementations of the sparse portfolio construction and rebalancing method introduced by Brodie et al. This technique is based on the use of a l1-norm (sum of the absolute values) type penalization on the portfolio weights vector that regularizes the Markowitz portfolio selection problem by automatically eliminating the dynamical redundancies present in the time evolution of asset prices. We make specific recommendations as to the different estimation techniques for the parameters needed in the use of the method and we prove its good performance in realistic situations involving different rebalancing frequencies and transaction costs. Our empirical findings show that the beneficial effects of the use of sparsity constraints are robust with respect to the choice of trend and covariance estimation methods used in its implementation.
Book Synopsis Artificial Intelligence in Asset Management by : Söhnke M. Bartram
Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
