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Author: Alessandra Congedo
Publisher: Roma TrE-Press
ISBN:
Category : Business & Economics
Languages : en
Pages : 34
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Book Description
Uno dei principi fondamentali dei modelli di selezione del portafoglio è la minimizzazione del rischio attraverso la diversificazione degli investimenti. Tuttavia, i benefici della diversificazione si riducono in presenza di un'elevata correlazione tra gli asset. È noto che la diversificazione attraverso l'uso di portafogli più ampi non è il modo migliore per ottenere un miglioramento della performance fuori campione. Inoltre, l'inclusione di un numero elevato di posizioni nel portafoglio aumenta i costi di gestione e di transazione. Mentre i modelli classici di selezione del portafoglio si concentrano sulla minimizzazione del rischio e sulla massimizzazione del rendimento, lo scopo di questo lavoro è quello di includere un terzo obiettivo: la norma-1. Ciò consente di selezionare portafogli sparsi, cioè con un numero limitato di attività, che sono più facili da gestire e consentono di ottenere buoni risultati in termini di rischio-rendimento. La nostra analisi empirica si basa su un dataset di riferimento disponibile pubblicamente e spesso utilizzato in letteratura. DOI: 10.13134/979-12-5977-332-6
Author: Alessandra Congedo
Publisher: Roma TrE-Press
ISBN:
Category : Business & Economics
Languages : en
Pages : 34
Get Book Here
Book Description
Uno dei principi fondamentali dei modelli di selezione del portafoglio è la minimizzazione del rischio attraverso la diversificazione degli investimenti. Tuttavia, i benefici della diversificazione si riducono in presenza di un'elevata correlazione tra gli asset. È noto che la diversificazione attraverso l'uso di portafogli più ampi non è il modo migliore per ottenere un miglioramento della performance fuori campione. Inoltre, l'inclusione di un numero elevato di posizioni nel portafoglio aumenta i costi di gestione e di transazione. Mentre i modelli classici di selezione del portafoglio si concentrano sulla minimizzazione del rischio e sulla massimizzazione del rendimento, lo scopo di questo lavoro è quello di includere un terzo obiettivo: la norma-1. Ciò consente di selezionare portafogli sparsi, cioè con un numero limitato di attività, che sono più facili da gestire e consentono di ottenere buoni risultati in termini di rischio-rendimento. La nostra analisi empirica si basa su un dataset di riferimento disponibile pubblicamente e spesso utilizzato in letteratura. DOI: 10.13134/979-12-5977-332-6
Author: Stephen Boyd
Publisher: Now Publishers Inc
ISBN: 160198460X
Category : Computers
Languages : en
Pages : 138
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Book Description
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.
Author: Gerard Cornuejols
Publisher: Cambridge University Press
ISBN: 9780521861700
Category : Mathematics
Languages : en
Pages : 358
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Book Description
Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational finance more efficiently and accurately. Chapters discussing the theory and efficient solution methods for all major classes of optimization problems alternate with chapters illustrating their use in modeling problems of mathematical finance. The reader is guided through topics such as volatility estimation, portfolio optimization problems and constructing an index fund, using techniques such as nonlinear optimization models, quadratic programming formulations and integer programming models respectively. The book is based on Master's courses in financial engineering and comes with worked examples, exercises and case studies. It will be welcomed by applied mathematicians, operational researchers and others who work in mathematical and computational finance and who are seeking a text for self-learning or for use with courses.
Author: Brandon M. Greenwell
Publisher: CRC Press
ISBN: 1000595315
Category : Business & Economics
Languages : en
Pages : 405
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Book Description
Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level, which lie at the cutting edge of modern statistical and machine learning methodology. The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example, users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit), and more. The core chapters also end with a detailed section on relevant software in both R and other opensource alternatives (e.g., Python, Spark, and Julia), and example usage on real data sets. While the book mostly uses R, it is meant to be equally accessible and useful to non-R programmers. Consumers of this book will have gained a solid foundation (and appreciation) for tree-based methods and how they can be used to solve practical problems and challenges data scientists often face in applied work. Features: Thorough coverage, from the ground up, of tree-based methods (e.g., CART, conditional inference trees, bagging, boosting, and random forests). A companion website containing additional supplementary material and the code to reproduce every example and figure in the book. A companion R package, called treemisc, which contains several data sets and functions used throughout the book (e.g., there’s an implementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree). Interesting examples that are of practical use; for example, how to construct partial dependence plots from a fitted model in Spark MLlib (using only Spark operations), or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining, or even improving performance.
Author: Söhnke M. Bartram
Publisher: CFA Institute Research Foundation
ISBN: 195292703X
Category : Business & Economics
Languages : en
Pages : 95
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Book Description
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.
Author: Renata Mansini
Publisher: Springer
ISBN: 3319184822
Category : Business & Economics
Languages : en
Pages : 131
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Book Description
This book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features. Other linear models, such as models for portfolio rebalancing and index tracking, are also covered. The book discusses computational issues and provides a theoretical framework, including the concepts of risk-averse preferences, stochastic dominance and coherent risk measures. The material is presented in a style that requires no background in finance or in portfolio optimization; some experience in linear and mixed integer models, however, is required. The book is thoroughly didactic, supplementing the concepts with comments and illustrative examples.
Author: Giuseppe C. Calafiore
Publisher: Cambridge University Press
ISBN: 1107050871
Category : Business & Economics
Languages : en
Pages : 651
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Book Description
This accessible textbook demonstrates how to recognize, simplify, model and solve optimization problems - and apply these principles to new projects.
Author: Bernhard Pfaff
Publisher: John Wiley & Sons
ISBN: 1119119685
Category : Mathematics
Languages : en
Pages : 448
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Book Description
Financial Risk Modelling and Portfolio Optimization with R, 2nd Edition Bernhard Pfaff, Invesco Global Asset Allocation, Germany A must have text for risk modelling and portfolio optimization using R. This book introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. This edition has been extensively revised to include new topics on risk surfaces and probabilistic utility optimization as well as an extended introduction to R language. Financial Risk Modelling and Portfolio Optimization with R: Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk concepts and optimization with risk constraints. Is accompanied by a supporting website featuring examples and case studies in R. Includes updated list of R packages for enabling the reader to replicate the results in the book. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.
Author: Francis Bach
Publisher:
ISBN: 9781601985101
Category : Computers
Languages : en
Pages : 124
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Book Description
Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It turns out that many of the related estimation problems can be cast as convex optimization problems by regularizing the empirical risk with appropriate nonsmooth norms. Optimization with Sparsity-Inducing Penalties presents optimization tools and techniques dedicated to such sparsity-inducing penalties from a general perspective. It covers proximal methods, block-coordinate descent, reweighted ?2-penalized techniques, working-set and homotopy methods, as well as non-convex formulations and extensions, and provides an extensive set of experiments to compare various algorithms from a computational point of view. The presentation of Optimization with Sparsity-Inducing Penalties is essentially based on existing literature, but the process of constructing a general framework leads naturally to new results, connections and points of view. It is an ideal reference on the topic for anyone working in machine learning and related areas.
Author: Julien Mairal
Publisher: Now Publishers
ISBN: 9781680830088
Category : Computers
Languages : en
Pages : 216
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Book Description
Sparse Modeling for Image and Vision Processing offers a self-contained view of sparse modeling for visual recognition and image processing. More specifically, it focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.
