Harish Gulati - SAS for Finance

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Harish Gulati is a consultant, analyst, modeler, and trainer based in London. He has 15 years' financial, consulting, and project management experience with leading banks, management consultancies, and media hubs. He enjoys demystifying his complex line of work in his spare time. This has led to him being an author and orator at analytical forums. He has also co-authored Role of a Data Analyst, published by the British Chartered Institute of IT (BCS). He has an MBA in brand communications and a degree in psychology and statistics.

Rashmi Gupta is an entrepreneur and consultant for established media and financial brands in the field of marketing and digital analytics. She is currently the director of Agile Fintech Partners. Artificial intelligence is a subject area that interests her, and she is currently building her expertise in the area.

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SAS is the world's largest privately held software business that offers an integrated suite of software solutions to manage data, produce reports, and build statistical models.

The book introduces statistical models in the finance industry in a simplified manner. It has real-world examples supported by data and code that reproduces the models. The chapters explain the relevance of the models to business problems, and the discussions about the diagnostics explains how the models can be implemented. The book uses various graphical illustrations, rather than having a focus on equations, to help the reader understand complex models. The book is designed to be a quick introduction to various modeling techniques by explaining their key concepts.

The intended reader is someone aspiring to work in the financial industry, or one of the many financial industry professionals who want to explore its various facets. The reader could also be a student curious to know how theoretical knowledge is applied in the industry, or a finance professional who wants to up-skill and move on to another role. The book's audience may also include any individual who works as a data analyst, data scientist, data architect, data engineer, analytics and insights professional, business analyst, or someone who integrates the outputs of models in business strategy but isn't aware of how problems are solved.

, Time Series Modeling in the Financial Industry, introduces time series modeling, and discusses its importance, the characteristics and challenges of data, and explains its use in the financial industry. The chapter also discusses the way forecasting is used across industries and what is meant by a good or bad forecast.

, Forecasting Stock Prices and Portfolio Decisions using Time Series, discusses the concept of portfolio forecasting and the decisions involved in managing portfolios. After exploring the forecasting process and the visualization of time series data, the chapter discusses modeling techniques and explains how to select the most suitable one based on real-world modeling examples.

, Credit Risk Management, provides context regarding the highly regulated nature of the industry. Basel norms and key terms such as PD, LGD, EAD, and EL are discussed. A PD model build methodology is briefly discussed.

, Budget and Demand Forecasting, helps create an understanding of the Markov model and showcases how to build a model. The chapter goes on to compare the Markov model forecast with ARIMA-generated forecasts. It also explains how Markov Chain Monte Carlo can be used for data imputation.