The Experts below are selected from a list of 13053 Experts worldwide ranked by ideXlab platform
Derek Snow - One of the best experts on this subject based on the ideXlab platform.
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machine learning in asset management part 2 Portfolio Construction weight optimization
The Journal of Financial Data Science, 2020Co-Authors: Derek SnowAbstract:This is the second in a series of articles dealing with machine learning in asset management. This article focuses on Portfolio weighting using machine learning. Following from the previous article (Snow 2020), which looked at trading strategies, this article identifies different weight optimization methods for supervised, unsupervised, and reinforcement learning frameworks. In total, seven submethods are summarized, with the code made available for further exploration. TOPICS:Big data/machine learning, analysis of individual factors/risk premia, Portfolio Construction, performance measurement Key Findings • Machine learning can help with most Portfolio Construction tasks, such as idea generation, alpha factor design, asset allocation, weight optimization, position sizing, and the testing of strategies. • This is the second of a series of articles dealing with machine learning in asset management and, more narrowly, weight optimization strategies equipped with machine learning. • Following from the previous article, different weight optimization methods are considered for supervised, unsupervised, and reinforcement learning frameworks.
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Machine Learning in Asset Management: Part 2: Portfolio Construction—Weight Optimization
The Journal of Financial Data Science, 2020Co-Authors: Derek SnowAbstract:This is the second in a series of articles dealing with machine learning in asset management. This article focuses on Portfolio weighting using machine learning. Following from the previous article (Snow 2020), which looked at trading strategies, this article identifies different weight optimization methods for supervised, unsupervised, and reinforcement learning frameworks. In total, seven submethods are summarized, with the code made available for further exploration. TOPICS:Big data/machine learning, analysis of individual factors/risk premia, Portfolio Construction, performance measurement Key Findings • Machine learning can help with most Portfolio Construction tasks, such as idea generation, alpha factor design, asset allocation, weight optimization, position sizing, and the testing of strategies. • This is the second of a series of articles dealing with machine learning in asset management and, more narrowly, weight optimization strategies equipped with machine learning. • Following from the previous article, different weight optimization methods are considered for supervised, unsupervised, and reinforcement learning frameworks.
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machine learning in asset management part 1 Portfolio Construction trading strategies
The Journal of Financial Data Science, 2020Co-Authors: Derek SnowAbstract:This is the first in a series of articles dealing with machine learning in asset management. Asset management can be broken into the following tasks: (1) Portfolio Construction, (2) risk management, (3) capital management, (4) infrastructure and deployment, and (5) sales and marketing. This article focuses on Portfolio Construction using machine learning. Historically, algorithmic trading could be more narrowly defined as the automation of sell-side trade execution, but since the introduction of more advanced algorithms, the definition has grown to include idea generation, alpha factor design, asset allocation, position sizing, and the testing of strategies. Machine learning, from the vantage of a decision-making tool, can help in all these areas. TOPICS:Big data/machine learning, analysis of individual factors/risk premia, Portfolio Construction, performance measurement Key Findings • Machine learning can help with most Portfolio Construction tasks like idea generation, alpha factor design, asset allocation, weight optimization, position sizing, and the testing of strategies. • This is the first in a series of articles dealing with machine learning in asset management and more narrowly on trading strategies equipped with machine-learning technologies. • Each trading strategy can end up using multiple machine learning frameworks. The author highlights nine different trading varieties each making use of a reinforcement-, supervised-, or unsupervised-learning framework or a combination of these learning frameworks.
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Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies
The Journal of Financial Data Science, 2019Co-Authors: Derek SnowAbstract:This is the first in a series of articles dealing with machine learning in asset management. Asset management can be broken into the following tasks: (1) Portfolio Construction, (2) risk management, (3) capital management, (4) infrastructure and deployment, and (5) sales and marketing. This article focuses on Portfolio Construction using machine learning. Historically, algorithmic trading could be more narrowly defined as the automation of sell-side trade execution, but since the introduction of more advanced algorithms, the definition has grown to include idea generation, alpha factor design, asset allocation, position sizing, and the testing of strategies. Machine learning, from the vantage of a decision-making tool, can help in all these areas. TOPICS:Big data/machine learning, analysis of individual factors/risk premia, Portfolio Construction, performance measurement Key Findings • Machine learning can help with most Portfolio Construction tasks like idea generation, alpha factor design, asset allocation, weight optimization, position sizing, and the testing of strategies. • This is the first in a series of articles dealing with machine learning in asset management and more narrowly on trading strategies equipped with machine-learning technologies. • Each trading strategy can end up using multiple machine learning frameworks. The author highlights nine different trading varieties each making use of a reinforcement-, supervised-, or unsupervised-learning framework or a combination of these learning frameworks.
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Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies
viXra, 2019Co-Authors: Derek SnowAbstract:This is the first in a series of articles dealing with machine learning in asset management. Asset management can be broken into the following tasks: (1) Portfolio Construction, (2) risk management, (3) capital management, (4) infrastructure and deployment, and (5) sales and marketing. This article focuses on Portfolio Construction using machine learning. Historically, algorithmic trading could be more narrowly defined as the automation of sell-side trade execution, but since the introduction of more advanced algorithms, the definition has grown to include idea generation, alpha factor design, asset allocation, position sizing, and the testing of strategies. Machine learning, from the vantage of a decision-making tool, can help in all these areas.
Larry Pohlman - One of the best experts on this subject based on the ideXlab platform.
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return forecasts and optimal Portfolio Construction a quantile regression approach
European Journal of Finance, 2008Co-Authors: Larry PohlmanAbstract:In finance there is growing interest in quantile regression with the particular focus on value at risk and copula models. In this paper, we first present a general interpretation of quantile regression in the context of financial markets. We then explore the full distributional impact of factors on returns of securities and find that factor effects vary substantially across quantiles of returns. Utilizing distributional information from quantile regression models, we propose two general methods for return forecasting and Portfolio Construction. We show that under mild conditions these new methods provide more accurate forecasts and potentially higher value-added Portfolios than the classical conditional mean method.
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Return forecasts and optimal Portfolio Construction: a quantile regression approach
The European Journal of Finance, 2008Co-Authors: Larry PohlmanAbstract:In finance there is growing interest in quantile regression with the particular focus on value at risk and copula models. In this paper, we first present a general interpretation of quantile regression in the context of financial markets. We then explore the full distributional impact of factors on returns of securities and find that factor effects vary substantially across quantiles of returns. Utilizing distributional information from quantile regression models, we propose two general methods for return forecasting and Portfolio Construction. We show that under mild conditions these new methods provide more accurate forecasts and potentially higher value-added Portfolios than the classical conditional mean method.return forecast, quantile regression, Portfolio Construction,
Judy Qiu - One of the best experts on this subject based on the ideXlab platform.
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Portfolio Construction and performance measurement when returns are non-normal
Australian Journal of Management, 2008Co-Authors: Karen L. Benson, Philip Gray, Egon Kalotay, Judy QiuAbstract:The foundation of popular approaches to Portfolio Construction and performance measurement lies in the mean-variance framework of Markowitz (1952, 1959). However, the suitability of such approaches in practice is questionable in light of considerable evidence of non-normalities in returns. This paper explores the potential usefulness of a non-parametric approach to Portfolio Construction and performance measurement recently proposed by Stutzer (2000). The Portfolio Performance Index (PPI) is based on the notion that investors associate risk with the failure to achieve a target return. Stutzer proposes that Portfolio Construction and performance measurement be approached by calculating the decay rate in the probability that a given Portfolio will underperform its designated benchmark. By comparing the PPI and Sharpe ratio metrics, this paper presents preliminary evidence of the economic significance of non-normalities in Australian equity returns, and documents the impact of such on Portfolio Construction and performance evaluation practice.
John Psarras - One of the best experts on this subject based on the ideXlab platform.
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Portfolio Construction on the athens stock exchange a multiobjective optimization approach
Optimization, 2010Co-Authors: Panagiotis Xidonas, George Mavrotas, John PsarrasAbstract:In this research article, our purpose is to propose a single-period multiobjective mixed-integer programming model for equity Portfolio Construction, in order to generate the Pareto optimal Portfolios, using a variant of the well-known e-constraint method. The decision maker's investment policy, i.e. constraints regarding the Portfolio structure, is strongly taken into account. An illustrative application in the Athens Stock Exchange market is also presented.
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Equity Portfolio Construction and selection using multiobjective mathematical programming
Journal of Global Optimization, 2009Co-Authors: Panagiotis Xidonas, George Mavrotas, John PsarrasAbstract:A multi-objective mixed integer programming model for equity Portfolio Construction and selection is developed in this study, in order to generate the Pareto optimal Portfolios, using a novel version of the well known e-constraint method. Subsequently, an interactive filtering process is also proposed to assist the decision maker in making his/her final choice among the Pareto solutions. The proposed methodology is tested through an application in the Athens Stock Exchange.
Alan Scowcroft - One of the best experts on this subject based on the ideXlab platform.
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Advances in Portfolio Construction and Implementation
2011Co-Authors: Stephen E. Satchell, Alan ScowcroftAbstract:Modern Portfolio Theory explores how risk averse investors construct Portfolios in order to optimize market risk against expected returns. The theory quantifies the benefits of diversification. Modern Portfolio Theory provides a broad context for understanding the interactions of systematic risk and reward. It has profoundly shaped how institutional Portfolios are managed, and has motivated the use of passive investment management techniques, and the mathematics of MPT is used extensively in financial risk management. Advances in Portfolio Construction and Implementation offers practical guidance in addition to the theory, and is therefore ideal for Risk Mangers, Actuaries, Investment Managers, and Consultants worldwide. Issues are covered from a global perspective and all the recent developments of financial risk management are presented. Although not designed as an academic text, it should be useful to graduate students in finance. *Provides practical guidance on financial risk management *Covers the latest developments in investment Portfolio Construction *Full coverage of the latest cutting edge research on measuring Portfolio risk, alternatives to mean variance analysis, expected returns forecasting, the Construction of global Portfolios and hedge Portfolios (funds)
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3 – A demystification of the Black-Litterman model: Managing quantitative and traditional Portfolio Construction
Forecasting Expected Returns in the Financial Markets, 2007Co-Authors: Stephen E. Satchell, Alan ScowcroftAbstract:Publisher Summary This chapter discusses the details of Bayesian Portfolio Construction procedures, which have become popular in the asset management industry as Black–Litterman models. It explains their Construction and presents some extensions and states the models as valuable tools for financial management. The chapter presents examples of Bayesian asset allocation Portfolio Construction models and illustrates the combination of judgmental and quantitative views. The Black–Litterman model has the potential to integrate diverse approaches, based on a Bayesian methodology that effectively updates currently held opinions with data to form new opinions. It concludes by stating that these models are potentially of considerable importance in the management of the investment process in modern financial institutions where both viewpoints are represented. The discussion includes an exposition of these models for the possibility of application by readers. It presents a theorem of Bayes' and related aassumptions..
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3 a demystification of the black litterman model managing quantitative and traditional Portfolio Construction
Forecasting Expected Returns in the Financial Markets, 2007Co-Authors: Stephen E. Satchell, Alan ScowcroftAbstract:Publisher Summary This chapter discusses the details of Bayesian Portfolio Construction procedures, which have become popular in the asset management industry as Black–Litterman models. It explains their Construction and presents some extensions and states the models as valuable tools for financial management. The chapter presents examples of Bayesian asset allocation Portfolio Construction models and illustrates the combination of judgmental and quantitative views. The Black–Litterman model has the potential to integrate diverse approaches, based on a Bayesian methodology that effectively updates currently held opinions with data to form new opinions. It concludes by stating that these models are potentially of considerable importance in the management of the investment process in modern financial institutions where both viewpoints are represented. The discussion includes an exposition of these models for the possibility of application by readers. It presents a theorem of Bayes' and related aassumptions..
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A demystification of the Black–Litterman model: Managing quantitative and traditional Portfolio Construction
Journal of Asset Management, 2000Co-Authors: Stephen E. Satchell, Alan ScowcroftAbstract:The purpose of this paper is to present details of Bayesian Portfolio Construction procedures which have become known in the asset management industry as Black–Litterman models. We explain their Construction, present some extensions and argue that these models are valuable tools for financial management.
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a demystification of the black litterman model managing quantitative and traditional Portfolio Construction
Journal of Asset Management, 2000Co-Authors: Stephen E. Satchell, Alan ScowcroftAbstract:The purpose of this paper is to present details of Bayesian Portfolio Construction procedures which have become known in the asset management industry as Black–Litterman models. We explain their Construction, present some extensions and argue that these models are valuable tools for financial management.