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Author : Matteo Bagnara
Publisher : Unknown
Page : 0 pages
File Size : 50,8 Mb
Release : 2023
Category : Electronic
ISBN : OCLC:1422875591
Author : Stefan Nagel
Publisher : Princeton University Press
Page : 156 pages
File Size : 42,5 Mb
Release : 2021-05-11
Category : Business & Economics
ISBN : 9780691218700
A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.
Author : Henry Schellhorn,Tianmin Kong
Publisher : SIAM
Page : 267 pages
File Size : 48,9 Mb
Release : 2024-03-26
Category : Computers
ISBN : 9781611977905
This textbook covers the latest advances in machine learning methods for asset management and asset pricing. Recent research in deep learning applied to finance shows that some of the (usually confidential) techniques used by asset managers result in better investments than the more standard techniques. Cutting-edge material is integrated with mainstream finance theory and statistical methods to provide a coherent narrative. Coverage includes an original machine learning method for strategic asset allocation; the no-arbitrage theory applied to a wide portfolio of assets as well as other asset management methods, such as mean-variance, Bayesian methods, linear factor models, and strategic asset allocation; recent techniques such as neural networks and reinforcement learning, and more classical ones, including nonlinear and linear programming, principal component analysis, dynamic programming, and clustering. The authors use technical and nontechnical arguments to accommodate readers with different levels of mathematical preparation. The book is easy to read yet rigorous and contains a large number of exercises. Machine Learning for Asset Management and Pricing is intended for graduate students and researchers in finance, economics, financial engineering, and data science focusing on asset pricing and management. It will also be of interest to finance professionals and analysts interested in applying machine learning to investment strategies and asset management. This textbook is appropriate for courses on asset management, optimization with applications, portfolio theory, and asset pricing.
Author : Steffen Windmüller
Publisher : Unknown
Page : 128 pages
File Size : 51,5 Mb
Release : 2021
Category : Electronic
ISBN : 3754155040
Author : Ehud Peleg
Publisher : ProQuest
Page : 356 pages
File Size : 52,5 Mb
Release : 2008
Category : Capital assets pricing model
ISBN : UCLA:L0099899015
Author : Martin Schneider (Professor of economics)
Publisher : Unknown
Page : 336 pages
File Size : 53,8 Mb
Release : 1999
Category : Electronic
ISBN : STANFORD:36105023750883
Author : Yongli Zhang
Publisher : ProQuest
Page : 198 pages
File Size : 54,9 Mb
Release : 2007
Category : Electronic
ISBN : 0549269487
G models without a monetary perspective are difficult to capture the dynamics of the real interest rates in the data of the US economy.
Author : Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari
Publisher : CFA Institute Research Foundation
Page : 95 pages
File Size : 52,5 Mb
Release : 2020-08-28
Category : Business & Economics
ISBN : 9781952927034
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 : Tae-Jin Kang
Publisher : Unknown
Page : 174 pages
File Size : 55,7 Mb
Release : 1991
Category : Electronic
ISBN : WISC:89039180773
Author : Guillaume Coqueret,Tony Guida
Publisher : CRC Press
Page : 498 pages
File Size : 50,8 Mb
Release : 2023-08-08
Category : Mathematics
ISBN : 9781000912821
Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additive trees and causal models. All topics are illustrated with self-contained Python code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material, along with the content of the book, is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance, this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.
Author : Pascal Alphonse
Publisher : Springer Nature
Page : 344 pages
File Size : 45,7 Mb
Release : 2024-06-17
Category : Electronic
ISBN : 9783031290503
Author : Xiaolong Li
Publisher : Springer Nature
Page : 2095 pages
File Size : 55,5 Mb
Release : 2024-06-17
Category : Electronic
ISBN : 9789819705238
Author : Chao Jiang
Publisher : Unknown
Page : 0 pages
File Size : 42,7 Mb
Release : 2016
Category : Electronic
ISBN : OCLC:1404858139
Author : Syed Hasan Jafar,Hemachandran K,Hani El-Chaarani,Sairam Moturi,Neha Gupta
Publisher : CRC Press
Page : 163 pages
File Size : 53,9 Mb
Release : 2023-05-15
Category : Computers
ISBN : 9781000867664
Artificial Intelligence for Capital Market throws light on the application of AI/ML techniques in the financial capital markets. This book discusses the challenges posed by the AI/ML techniques as these are prone to "black box" syndrome. The complexity of understanding the underlying dynamics for results generated by these methods is one of the major concerns which is highlighted in this book. Features: Showcases artificial intelligence in finance service industry Explains credit and risk analysis Elaborates on cryptocurrencies and blockchain technology Focuses on the optimal choice of asset pricing model Introduces testing of market efficiency and forecasting in the Indian stock market This book serves as a reference book for academicians, industry professionals, traders, finance managers and stock brokers. It may also be used as textbook for graduate level courses in financial services and financial analytics.
Author : Prasad Padmanabhan
Publisher : Unknown
Page : 828 pages
File Size : 55,6 Mb
Release : 1988
Category : Inflation (Finance)
ISBN : OCLC:427543922
"This dissertation consists of three essays in international asset pricing. The first essay develops a model where investors face barriers to foreign portfolio investment. Using the standard mean-variance framework, risk return relationships for all securities are developed. It is also shown that: (1) previous models adopting this approach are special cases of this model, and (2) all investors generally prefer complete removal of barriers over other market structures. Essay #2 empirically explores the issue of the degree of segmentation of the international capital market for risky securities. Using the 'emerging market' (EM) data base, it is shown that the international capital market is neither completely segmented nor completely integrated. Finally, the third essay investigates the relationship between stock returns and inflation for the EM securities. It is shown that stock returns are positively (negatively) related to inflation, for the group of high (low) inflation countries in the sample." --
