Handbook of Alternative Data in Finance, Volume I (CRC Press/OptiRisk Series in Finance)
The Handbook of Alternative Data in Finance, Volume I explores in depth profound significance of Alternative Data in the realm of finance. This handbook motivates and challenges readers to delve into the dynamic world of Alternative Data, its characteristics and its transformative potential compared to conventional data. The book covers an array of Alternative Data categories, and providers, and delves into its application processes, providing valuable insights for researchers and practitioners alike.
Cutting-edge applications in machine learning, fintech, and more, the handbook caters to quantitative analysts, postgraduates, financial mathematics researchers, and other market participants. Featuring contributions from prominent experts, it offers a 360-degree view of Alternative Data’s role in predicting market trends, guiding investments, and managing risks.
OptiRisk’s USP is research, knowledge acquisition and sharing, in the domain of News Analytics, Sentiment Analysis and Alternative Data in Finance. Our journey started with The Handbook of News Analytics in Finance (2011), which was followed by The Handbook of Sentiment Analysis in Finance (2016). We have stayed the course, and as a Financial Analytics company continued to research in the domain of trading and fund management; this has culminated in our latest work: The Handbook of Alternative Data in Finance (2023), a vital resource in an era where data-driven decisions reign supreme. Unlike single-authored works, this collaborative effort provides a diverse and comprehensive perspective on Alternative Data’s implications.
In the ever-evolving landscape of data’s ascendancy, this handbook serves as a compass, guiding financial professionals and academics through the intricate relationship between data and decision-making. This handbook is an indispensable guide to informed decision making and creating trading and fund management strategies.
Handbook of Sentiment Analysis in Finance (2016)
Building on the success of the Handbook of News Analytics in Finance, the editors have researched and compiled this updated volume of the Handbook; the publication date is May 2016. In the last four years there has been explosive developments in the domain of sentiment analysis in general and sentiment classification in particular. There has been a growing consumer interest in social media and these new media sources have become the leading ‘influencers’ of market sentiment. The latest edition includes multiple sources of information such as:
- News Wires
- Macro-economic Announcements
- Social Media
- Online (search) Information e.g. Google Trends
The applications of sentiment analysis are considered for multiple asset classes including:
- Fixed Income Instruments
- Foreign Exchange
- Commodities (Oil, Gas, Energy and others)
- Green Commodities
Respected Academic, Author, Practicing Fund Manager
Entrepreneur and Founder of PredictNow.AI
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 are 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
Professor Gautam Mitra and his team unpack the topic of alternative data in finance, an ambitious endeavour 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.
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
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