Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Sparse Mean Reverting Portfolios Via Penalized Likelihood Optimization
Download Sparse Mean Reverting Portfolios Via Penalized Likelihood Optimization full books in PDF, epub, and Kindle. Read online Sparse Mean Reverting Portfolios Via Penalized Likelihood Optimization ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
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 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 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 Optimizing Sparse Mean Reverting Portfolios by : I. Sipos
Download or read book Optimizing Sparse Mean Reverting Portfolios written by I. Sipos and published by . This book was released on 2013 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we investigate trading with optimal mean reverting portfolios subject to cardinality constraints. First, we identify the parameters of the underlying VAR(1) model of asset prices and then the quantities of the corresponding Ornstein-Uhlenbeck (OU) process are estimated by pattern matching techniques. Portfolio optimization is performed according to two approaches: (i) maximizing the predictability by solving the generalized eigenvalue problem or (ii) maximizing the mean return. The optimization itself is carried out by stochastic search algorithms and Feed Forward Neural Networks (FFNNs). The presented solutions satisfy the cardinality constraint thus providing sparse portfolios to minimize the transaction costs and to maximize interpretability of the results. The performance has been tested on historical data (SWAP rates, SP 500, and FOREX). The proposed trading algorithms have achieved 29.57% yearly return on average, on the examined data sets. The algorithms prove to be suitable for high frequency, intraday trading as they can handle financial data up to the arrival rate of every second.
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 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-Variance Portfolios by : David Puelz
Download or read book Sparse Mean-Variance Portfolios written by David Puelz and published by . This book was released on 2016 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers mean-variance optimization under uncertainty, specifically when one desires a sparsified set of optimal portfolio weights. From the standpoint of a Bayesian investor, our approach produces a small portfolio from many potential assets while acknowledging uncertainty in asset returns and parameter estimates. We demonstrate the procedure using static and dynamic models for asset returns.
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 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 Identifying Sparse L2-Norm Regularized Portfolios Via Semi-Definite Relaxation by : Min Jeong Kim
Download or read book Identifying Sparse L2-Norm Regularized Portfolios 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: For portfolio management in the real-world, it is required that a portfolio has a manageable number of assets and stable performance. However, much research has pointed out that the Markowitz model, which is a classical model in portfolio theory, forms a portfolio with many different assets that may have unstable performance. Therefore, in this paper, we focus on developing a portfolio selection model which constructs a sparse and stable optimal portfolio. In order to achieve our research goal we introduce a L2-norm regularization and a cardinality constraint on portfolio weights to the Markowitz model. Moreover, using semidefinite relaxation, we formulate a convex optimization problem for the proposed model. The outcomes of our empirical test show that portfolios obtained by our model have desired cardinalities and better out-of-sample performances than those of Markowitz optimal portfolios.
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 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 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 Quantitative Methods for Economics and Finance by : J.E. Trinidad-Segovia
Download or read book Quantitative Methods for Economics and Finance written by J.E. Trinidad-Segovia and published by MDPI. This book was released on 2021-02-12 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice.
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.
