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Constructing Sparse And Fast Mean Reverting Portfolios
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Book Synopsis Constructing Sparse and Fast Mean Reverting Portfolios by : Xiaolong Long
Download or read book Constructing Sparse and Fast Mean Reverting Portfolios written by Xiaolong Long and published by . This book was released on 2014 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study the problem of constructing sparse and fast mean reverting portfolios based on a set of financial data arising in convergence trading. The problem is formulated as a generalized eigenvalue problem with a cardinality constraint \cite{ismp}. We develope a new proxy of mean reversion coefficient, the direct OU estimator, which can be used for both stationary and non-stationary data. In addition, we introduce two different methods to enforce the sparsity of the solutions instead of predetermining the cardinality. One method uses the ratio of $l_1$ and $l_2$ norms and the other one uses /1 norm and prior knowledge. We analyze various formulations of the resulting non-convex optimization problems and develop efficient algorithms to solve them on portfolio sizes as large as hundreds. By adopting a simple convergence trading strategy, we test the performance of our sparse mean reverting portfolios on both generated and historical real market data. In particular, the $l_1$ norm regularization method gives robust results on large out-of-sample data set. We formulated a new type of problems for recovering fastest mean reverting process. It is a generalization of recovering sparse element in a subspace. From the numerical tests, we successfully recovered the hidden fastest OU process.
Book Synopsis Sparse Mean-Reverting Portfolios Via Penalized Likelihood Optimization by : Jize Zhang
Download or read book Sparse Mean-Reverting Portfolios Via Penalized Likelihood Optimization written by Jize Zhang and published by . This book was released on 2019 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: An optimization approach is proposed to construct sparse portfolios with mean-reverting price behaviors. Our objectives are threefold: (i) design a multi-asset long-short portfolio that best fits an Ornstein-Uhlenbeck process in terms of maximum likelihood, (ii) select portfolios with desirable characteristics of high mean reversion and low variance though penalization, and (iii) select a parsimonious portfolio using l0-regularization, i.e. find a small subset of a larger universe of assets that can be used for long and short positions. We present the full problem formulation, and develop a provably convergent algorithm for the nonsmooth, nonconvex objective based on partial minimization and projection. The problem requires custom analysis because the objective function does not have a Lipschitz-continuous gradient. Through our experiments using simulated and empirical price data, the proposed algorithm significantly outperforms standard approaches that do not exploit problem structure.
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 Mean Reverting Portfolios Via Penalized OU-Likelihood Estimation by : Jize Zhang
Download or read book Mean Reverting Portfolios Via Penalized OU-Likelihood Estimation written by Jize Zhang and published by . This book was released on 2019 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study an optimization-based approach to construct a mean-reverting portfolio of assets. Our objectives are threefold: (1) design a portfolio that is well-represented by an Ornstein-Uhlenbeck process with parameters estimated by maximum likelihood, (2) select portfolios with desirable characteristics of high mean reversion, and (3) select a parsimonious portfolio, i.e. find a small subset of a larger universe of assets that can be used for long and short positions. We present the full problem formulation, a specialized algorithm that exploits partial minimization, and numerical examples using both simulated and empirical price data.
Book Synopsis Constructing Optimal Sparse Portfolios Using Regularization Methods by : Bjoern Fastrich
Download or read book Constructing Optimal Sparse Portfolios Using Regularization Methods written by Bjoern Fastrich and published by . This book was released on 2014 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ideas of Markowitz indisputably constitute a milestone in portfolio theory, even though the resulting mean-variance portfolios typically exhibit an unsatisfying out-of-sample performance, especially when the number of securities is large and that of observations is not. The bad performance is caused by estimation errors in the covariance matrix and in the expected return vector that can deposit unhindered in the portfolio weights. Recent studies show that imposing a penalty in form of a l1-norm of the asset weights regularizes the problem, thereby improving the out-of-sample performance of the optimized portfolios. Simultaneously, l1-regularization selects a subset of assets to invest in from a pool of candidates that is often very large. However, l1-regularization might lead to the construction of biased solutions. We propose to tackle this issue by considering several alternative penalties proposed in non-financial contexts. Moreover we propose a simple new type of penalty that explicitly considers financial information. We show empirically that these alternative penalties can lead to the construction of portfolios with superior out-of-sample performance in comparison to the state-of-the-art l1-regularized portfolios and several standard benchmarks, especially in high dimensional problems. The empirical analysis is conducted with various U.S.-stock market datasets.
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 Optimal Mean Reversion Trading by : Tim Leung (Professor of industrial engineering)
Download or read book Optimal Mean Reversion Trading written by Tim Leung (Professor of industrial engineering) and published by World Scientific. This book was released on 2015-11-26 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Optimal Mean Reversion Trading: Mathematical Analysis and Practical Applications provides a systematic study to the practical problem of optimal trading in the presence of mean-reverting price dynamics. It is self-contained and organized in its presentation, and provides rigorous mathematical analysis as well as computational methods for trading ETFs, options, futures on commodities or volatility indices, and credit risk derivatives. This book offers a unique financial engineering approach that combines novel analytical methodologies and applications to a wide array of real-world examples. It extracts the mathematical problems from various trading approaches and scenarios, but also addresses the practical aspects of trading problems, such as model estimation, risk premium, risk constraints, and transaction costs. The explanations in the book are detailed enough to capture the interest of the curious student or researcher, and complete enough to give the necessary background material for further exploration into the subject and related literature. This book will be a useful tool for anyone interested in financial engineering, particularly algorithmic trading and commodity trading, and would like to understand the mathematically optimal strategies in different market environments."--
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 2016-04-21 with total page 312 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 Multi-Period Trading Via Convex Optimization by : Stephen Boyd
Download or read book Multi-Period Trading Via Convex Optimization written by Stephen Boyd and published by . This book was released on 2017-07-28 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph collects in one place the basic definitions, a careful description of the model, and discussion of how convex optimization can be used in multi-period trading, all in a common notation and framework.
Book Synopsis Handbook of Portfolio Construction by : John B. Guerard, Jr.
Download or read book Handbook of Portfolio Construction written by John B. Guerard, Jr. and published by Springer Science & Business Media. This book was released on 2009-12-12 with total page 796 pages. Available in PDF, EPUB and Kindle. Book excerpt: Portfolio construction is fundamental to the investment management process. In the 1950s, Harry Markowitz demonstrated the benefits of efficient diversification by formulating a mathematical program for generating the "efficient frontier" to summarize optimal trade-offs between expected return and risk. The Markowitz framework continues to be used as a basis for both practical portfolio construction and emerging research in financial economics. Such concepts as the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT), for example, provide the foundation for setting benchmarks, for predicting returns and risk, and for performance measurement. This volume showcases original essays by some of today’s most prominent academics and practitioners in the field on the contemporary application of Markowitz techniques. Covering a wide spectrum of topics, including portfolio selection, data mining tests, and multi-factor risk models, the book presents a comprehensive approach to portfolio construction tools, models, frameworks, and analyses, with both practical and theoretical implications.
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.
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 Monte Carlo Simulation and Finance by : Don L. McLeish
Download or read book Monte Carlo Simulation and Finance written by Don L. McLeish and published by John Wiley & Sons. This book was released on 2011-09-13 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monte Carlo methods have been used for decades in physics, engineering, statistics, and other fields. Monte Carlo Simulation and Finance explains the nuts and bolts of this essential technique used to value derivatives and other securities. Author and educator Don McLeish examines this fundamental process, and discusses important issues, including specialized problems in finance that Monte Carlo and Quasi-Monte Carlo methods can help solve and the different ways Monte Carlo methods can be improved upon. This state-of-the-art book on Monte Carlo simulation methods is ideal for finance professionals and students. Order your copy today.
Book Synopsis Derivatives in Financial Markets with Stochastic Volatility by : Jean-Pierre Fouque
Download or read book Derivatives in Financial Markets with Stochastic Volatility written by Jean-Pierre Fouque and published by Cambridge University Press. This book was released on 2000-07-03 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, first published in 2000, addresses pricing and hedging derivative securities in uncertain and changing market volatility.
Book Synopsis Adaptive Learning of Polynomial Networks by : Nikolay Nikolaev
Download or read book Adaptive Learning of Polynomial Networks written by Nikolay Nikolaev and published by Springer Science & Business Media. This book was released on 2006-08-18 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and predict well. The book further facilitates the discovery of polynomial models for time-series prediction.
Book Synopsis A Signal Processing Perspective on Financial Engineering by : Yiyong Feng
Download or read book A Signal Processing Perspective on Financial Engineering written by Yiyong Feng and published by . This book was released on 2016 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial engineering and electrical engineering are seemingly different areas that share strong underlying connections. Both areas rely on statistical analysis and modeling of systems; either modeling the financial markets or modeling wireless communication channels. Having a model of reality allows us to make predictions and to optimize the strategies. It is as important to optimize our investment strategies in a financial market as it is to optimize the signal transmitted by an antenna in a wireless link. This monograph provides a survey of financial engineering from a signal processing perspective, that is, it reviews financial modeling, the design of quantitative investment strategies, and order execution with comparison to seemingly different problems in signal processing and communication systems, such as signal modeling, filter/beamforming design, network scheduling, and power allocation.
