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E-raamat: Handbook of Alternative Data in Finance, Volume I

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"Handbook of Alternative Data in Finance, Volume I Handbook of Alternative Data in Finance, Volume I motivates and challenges the reader to explore and apply Alternative Data in finance. The book provides a robust and in-depth overview of Alternative Data, including its definition, characteristics, difference from conventional data, categories of Alternative Data, Alternative Data providers, and more. The book also offers a rigorous and detailed exploration of process, application and delivery that should be practically useful to researchers and practitioners alike. Features Includes cutting edge applications in machine learning, fintech, and more Suitable for professional quantitative analysts, and as a resource for postgraduates and researchers in financial mathematics Features chapters from many leading researchers and practitioners"--

Handbook of Alternative Data in Finance, Volume I motivates and challenges the reader to explore and apply Alternative Data in finance. The book provides a robust and in-depth overview of Alternative Data, including its definition, characteristics, difference from conventional data, categories of Alternative Data, Alternative Data providers, and more. The book also offers a rigorous and detailed exploration of process, application and delivery that should be practically useful to researchers and practitioners alike.

Features

  • Includes cutting edge applications in machine learning, fintech, and more
  • Suitable for professional quantitative analysts, and as a resource for postgraduates and researchers in financial mathematics
  • Features chapters from many leading researchers and practitioners



This book motivates and challenges the reader to explore and apply Alternative Data in finance. It provides a robust and in-depth overview of Alternative Data, including its definition, characteristics, difference from conventional data, categories of Alternative Data, Alternative Data providers, and more.

Arvustused

"Alternative data has become a hot topic in finance. New kinds of data, new data sources, and of course new tools for processing such data offer the possibility of new and previously unsuspected signals. In short alternative data lead to the promise of enhanced predictive power. But such advance does not come without its challenges - in terms of the quality of the data, the length of its history, reliable data capture, the development of appropriate statistical, AI, machine learning, and data mining tools, and, of course, the ethical challenges in the face of increasingly tough data protection regimes. Gautam Mitra and his colleagues have put together a superb collection of chapters discussing these topics, and more, to show how alternative data, used with care and expertise, can reveal the bigger picture." Professor David J. Hand, Emeritus Professor of Mathematics and Senior Research Investigator, Imperial College, London

"Digital capital is now so important that it can rightly be viewed as a factor of production, especially in the financial sector. This handbook does for the field of alternative data what vendors of alternative data do for data itself; and that is to provide structure, filter noise, and bring clarity. It is an indispensable work which every financial professional can consult, be it for an overview of the field or for specific details about alternative data." Professor Hersh Shefrin, Mario L. Belotti Professor of Finance, Santa Clara University An impressive and timely contribution to the fast developing discipline of data driven decisions in the trading and management of financial risk. Automated data collection, organization, and dissemination is part and parcel of Data Science and the Handbook covers the current breadth of these activities, their risks, rewards, and costs. A welcome addition to the landscape of quantitative finance. Professor Dilip Madan, Professor of Finance, Robert H. Smith School of Business"The Handbook of Alternative Data in Finance is the most comprehensive guide to alternative data I have seen. It could be called the Encyclopaedia of Alternative Data. It belongs to the desktop, not the bookshelf, of every investor." Ernest Chan, Respected Academic, Author, Practicing Fund Manager, Entrepreneur and Founder of PredictNow.AI

"Professor Gautam Mitra and his team unpack the topic of alternative data in finance, an ambitious endeavor given the fast-expanding nature of this new and exciting space. Alternative data powered by Natural Language Processing and Machine Learning has emerged as a new source of insights that can help investors make more informed decisions, stay ahead of competition and mitigate emerging risks. This handbook provides a strong validation of the substantial added value that alternative data brings. It also helps promote the idea that data driven decisions are better and more sustainable something we, at RavenPack, firmly believe." Armando Gonzalez, CEO and Founder of RavenPack

"As the 1st Duke of Marlborough, John Churchill, wrote in 1715: 'No war can be conducted successfully without early and good intelligence.' The same can be said for successful trading. In that light, the Handbook of Alternative Data in Finance contains vital insights about how to gather and use alternative data in short, intelligence to facilitate successful trading." Professor Steve H. Hanke, Professor of Applied Economics, The Johns Hopkins University, Baltimore, USA

"The Handbook of Alternative Data in Finance is cutting edge and it bridges a huge gap in the representative studies on emerging areas of finance where alternative data can be profitably utilised for better informed decisions. The practical insights in the book would come very handy to both investors and researchers who look for fresh ideas." Ashok Banerjee, Director, Indian Institute of Management Udaipur, Formerly Dean, and Faculty-in-charge of the Finance Lab at Indian Institute of Management Calcutta

1. Alternative Data: Overview. Part I. Alternative Data: Processing and Impact.
2. Contemplation and Reflection on Using Alternative Data for Trading and Fund Management.
3. Global Economy and Markets Sentiment Model. Part II. Coupling Models with Alternative Data for Financial Analytics.
4. Enhanced Corporate Bond Yield Modelling Incorporating Macroeconomic News Sentiment.
5. AI, Machine Learning and Quantitative Models. Part III. Handling Different Alternative Datasets.
6. Asset Allocation Strategies: Enhanced by Micro-Blog.
7. Asset Allocation Strategies: Enhanced by News.
8. Extracting Structured Datasets from Textual Sources: Some Examples.
9. Comparative Analysis of NLP Approaches for Earnings Calls.
10. Sensors Data. Part IV. Alternative Data Use Cases in Finance. Part IV.A. Application in Trading and Fund Management (Finding New Alpha).
11. Media Sentiment Momentum.
12. Defining Market States with Media Sentiment. Part IV.B. Application in Risk Control. 13. A Quantitative Metric for Corporate Sustainability.
14. Hot off the Press: Predicting Intraday Risk and Liquidity with News Analytics.
15. Exogenous Risks Alternative Data Implications for Strategic Asset Allocation: Multi-Subordination Levy Processes Approach. Part IV.C. Case Studies on ESG.
16. ESG Controversies and Stock Returns.
17. Oil and Gas Drilling Waste: A Material Externality. 18: ESG Scores and Price Momentum Are Compatible: Revisited. Part V. Directory of Alternative Data Vendors.

Gautam Mitra is founder and MD of Optirisk Systems. He is internationally renowned research scientist in the field of Operational Research in general and computational optimization and modeling in particular. He is an alumni of UCL and currently a visiting professor at UCL. In 2004 he was awarded the title of distinguished professor by Brunel University in recognition of his contributions in the domain of computational optimization, risk analytics and modeling. Professor Mitra is also the founder and chairman of the sister company UNICOM seminars.

Christina Erlwein-Sayer is a consultant and associate researcher at OptiRisk Systems. Her research interests lie in financial analytics, portfolio optimisation and risk management with sentiment analysis, involving time series modelling and machine learning techniques. She holds a professorship in Financial Mathematics and Statistics at HTW University of Applied Sciences, Berlin. She completed her PhD in Mathematics at Brunel University, London in 2008. She was then a researcher and consultant in the Financial Mathematics Department at Fraunhofer ITWM, Kaiserslautern, Germany. Between 2015 and 2018, prior to joining HTW Berlin in 2019, she was a full-time senior quantitative analyst and researcher at OptiRisk Systems, London, UK. She teaches modules on statistics, machine learning and financial mathematics and is part of the CSAF faculty. Christina is an experienced presenter at conferences and workshops: amongst others, she presented at workshops in London, IIM Calcutta in Kolkata and Mumbai and in Washington to World Bank.

Kieu Thi Hoang is a Financial Analyst and Relationship Manager at OptiRisk Systems. Kieu has a bachelors degree (with high distinction) in International Economics from Foreign Trade University, Hanoi, Vietnam. She was among the top 10% of all the global candidates in her CFA level 2 examination (December 2020). Kieu has a strong foundation in advanced financial analysis and work experience in the finance industry. She has years of experience working at different renowned BFSI firms in Vietnam. Joining OptiRisk Systems as a Financial Analyst and Relationship Manager, she has done a lot of thorough research on alternative data in company projects. She also works with a variety of alternative data providers who are partners of her firm.

Diana Roman is a Consultant and Research Associate at OptiRisk Systems. After completing her PhD at Brunel University under late Professor Darby-Dowman and Professor Mitra, Dr Roman joined OptiRisk Systems as a software developer. She had designed the scenario-generator library which was used inSPInEthe first version of the SP Tool developed by OptiRisk Systems. Together with Professor Mitra she has written a few seminal papers on the topic of portfolio construction with downside risk control in general and use of Second Order Stochastic Dominance (SSD) in particular. Dr Roman is a senior lecturer in the Department of Mathematics at Brunel University London.

Zryan Sadik is a senior Quantitative Analyst and Researcher at OptiRisk Systems. Dr Sadik has a bachelors degree in Mathematics from Salahaddin University Erbil in the Kurdistan region of Iraq. After working as an IT technician, he pursued an MSc Degree in Computational Mathematics with Modelling at Brunel University, London (2012). Dr Sadik completed his PhD in Applied Mathematics with a thesis on the Asset Price and Volatility Forecasting Using News Sentiment at Brunel University, London (2018). His research interests include news sentiment analysis, macroeconomic sentiment analysis, stochastic volatility models, filtering in linear and nonlinear time series applying Kalman filters, volatility forecasting as well as optimization and risk assessment. His current research interests lie in the areas of empirical finance and quantitative methods, and the role of Alternative data in financial markets. He has been involved in developing predictive models of sentiment analysis, and sentiment-based trading strategies for the last seven years. These models and strategies are developed in C, C++, MATLAB, Python and R as appropriate. His prior studies include the impact of macroeconomic news on the spot and futures prices of crude oil, and the impact of firm-specific news on the movement of asset prices and on the volatility of asset price returns. Dr Sadik is fluent in Kurdish (his native language), as well as in English and Arabic.
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