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Author: Wayne Ferson
Publisher: MIT Press
ISBN: 0262039370
Category : Business & Economics
Languages : en
Pages : 497
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Book Description
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Author: Wayne Ferson
Publisher: MIT Press
ISBN: 0262039370
Category : Business & Economics
Languages : en
Pages : 497
Get Book Here
Book Description
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Author: Asian Development Bank
Publisher:
ISBN: 9789292623135
Category :
Languages : en
Pages : 54
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Book Description
This special supplement to the Key Indicators for Asia and the Pacific 2020 discusses how poverty estimates can be enhanced by integrating household surveys and censuses with data extracted from satellite imagery. As part of a special ADB knowledge initiative, computer vision techniques and machine-learning algorithms were applied on datasets from the Philippines and Thailand to demonstrate increased granularity of poverty estimation using artificial intelligence. The report identifies practical considerations and technical requirements for this novel approach to mapping the spatial distribution of poverty. It also outlines the investments required by national statistics offices to fully capitalize on the benefits of incorporating innovative data sources into conventional work programs.
Author: Asian Development Bank
Publisher: Asian Development Bank
ISBN: 9292627694
Category : Business & Economics
Languages : en
Pages : 141
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Book Description
The “leave no one behind” principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country’s population. This entails detailed, granular data on population groups that extend beyond national trends and averages. The Asian Development Bank (ADB), in collaboration with the National Statistical Office of Thailand and the Word Data Lab, conducted a feasibility study to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in Thailand. This report documents the results of the study, providing insights on data collection requirements, advanced algorithmic techniques, and validation of poverty estimates using artificial intelligence to complement traditional data sources and conventional survey methods.
Author: Xiaofeng Meng
Publisher: Springer Nature
ISBN: 9819758033
Category : Big data
Languages : en
Pages : 487
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Book Description
This book constitutes the refereed proceedings of the 9th China National Conference on Big Data and Social Computing, BDSC 2024, held in Harbin, China, during August 810, 2024. The 28 full papers presented in this volume were carefully reviewed and selected from a total of 141 submissions. The papers in the volume are organized according to the following topics: digital society and public security; modelling and simulation of social systems; internet intelligent algorithm governance; social network and group behavior; innovation, risks, and network security of large language models; and artificial intelligence and cognitive science.
Author: Margaret Grosh
Publisher: World Bank Publications
ISBN: 1464818150
Category : Business & Economics
Languages : en
Pages : 397
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Book Description
Targeting is a commonly used, but much debated, policy tool within global social assistance practice. Revisiting Targeting in Social Assistance: A New Look at Old Dilemmas examines the well-known dilemmas in light of the growing body of experience, new implementation capacities, and the potential to bring new data and data science to bear. The book begins by considering why or whether or how narrowly or broadly to target different parts of social assistance and updates the global empirics around the outcomes and costs of targeting. It illustrates the choices that must be made in moving from an abstract vision to implementable definitions and procedures, and in deciding how the choices should be informed by values, empirics, and context. The importance of delivery systems and processes to distributional outcomes are emphasized, and many facets with room for improvement are discussed. The book also explores the choices between targeting methods and how differences in purposes and contexts shape those. The know-how with respect to the data and inference used by the different household-specific targeting methods is summarized and comprehensively updated, including a focus on “big data†? and machine learning. A primer on measurement issues is included. Key findings include the following: · Targeting selected categories, families, or individuals plays a valuable role within the framework of universal social protection. · Measuring the accuracy and cost of targeting can be done in many ways, and judicious choices require a range of metrics. · Weighing the relatively low costs of targeting against the potential gains is important. · Implementing inclusive delivery systems is critical for reducing errors of exclusion and inclusion. · Selecting and customizing the appropriate targeting method depends on purpose and context; there is no method preferred in all circumstances. · Leveraging advances in technology—ICT, big data, artificial intelligence, machine learning—can improve targeting accuracy, but they are not a panacea; better data matters more than sophistication in inference. · Targeting social protection should be a dynamic process.
Author: Asian Development Bank
Publisher: Asian Development Bank
ISBN: 9292621327
Category : Business & Economics
Languages : en
Pages : 159
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Book Description
The “leave no one behind” principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country’s population. This entails detailed, granular data on population groups that extend beyond national trends and averages. The Asian Development Bank, in collaboration with the Philippine Statistics Authority and the World Data Lab, conducted a feasibility study to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in the Philippines. This report documents the results of the study, which capitalized on satellite imagery, geospatial data, and powerful machine learning algorithms to augment conventional data collection and sample survey techniques.
Author: Renos Vakis
Publisher: World Bank Publications
ISBN: 1464806616
Category : Business & Economics
Languages : en
Pages : 192
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Book Description
One out of every five Latin Americans or around 130 million people have never known anything but poverty, subsisting on less than US$4-a-day throughout their lives. These are the region ́s chronically poor, who have remained so despite unprecedented inroads against poverty in Latin America and the Caribbean since the turn of the century. Left Behind: Chronic Poverty in Latin America and the Caribbean takes a closer look at the region’s entrenched poor, who and where they are, and how existing policies need to change in order to effectively assist them. The book shows significant variations of rates of chronic poverty both across and within countries. Within a single country, some regions show incidence rates up to eight times higher than the lowest. Despite the higher rates of chronic poverty in rural areas, chronic poverty is as much an urban as a rural issue. In fact, considering absolute numbers, urban areas in many countries, including Chile, Brazil, Mexico, Colombia and the Dominican Republic, have more chronic poor than rural areas. Undoubtedly the region has come a long way during the decade in terms of poverty reduction, guided by a mix of sustained growth and increased levels in amounts and quality of public spending and programs targeted directly or indirectly to the chronic poor. While improving endowments and the context where the chronic poor live is a necessary condition going forward, the decade’s experience suggests that it may not be enough to reach the chronic poor. The book posits that refinements to the existing policy toolkit †“ as opposed to more programs †“ may come a long way in helping the remaining poor. These refinements include intensifying efforts to improve coordination between different social and economic programs, which can boost the income generation process and deal with the intergenerational transmission of chronic poverty by investing in early childhood development. Equally important though, there is an urgent need to adapt programs to directly address the psychological toll of chronic poverty on people’s mindset and aspirations, which currently undermines the effectiveness of the existing policy efforts.
Author: Stefan Jansen
Publisher: Packt Publishing Ltd
ISBN: 1839216786
Category : Business & Economics
Languages : en
Pages : 822
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Book Description
Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Author: OECD
Publisher: OECD Publishing
ISBN: 926420685X
Category :
Languages : en
Pages : 146
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Book Description
National Accounts at a Glance presents information using an "indicator" approach, focusing on cross-country comparisons.
Author: Felix Chan
Publisher: Springer Nature
ISBN: 3031151496
Category : Business & Economics
Languages : en
Pages : 385
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Book Description
This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.
