Chapter 4. Data-Driven Finance
If artificial intelligence is the new electricity, big data is the oil that powers the generators.
Kai-Fu Lee (2018)
Nowadays, analysts sift through non-traditional information such as satellite imagery and credit card data, or use artificial intelligence techniques such as machine learning and natural language processing to glean fresh insights from traditional sources such as economic data and earnings-call transcripts.
Robin Wigglesworth (2019)
This chapter discusses central aspects of data-driven finance. For the purposes of this book, data-driven finance is understood to be a financial context (theory, model, application, and so on) that is primarily driven by and based on insights gained from data.
“Scientific Method” discusses the scientific method, which is about generally accepted principles that should guide scientific effort. “Financial Econometrics and Regression” is about financial econometrics and related topics. “Data Availability” sheds light on which types of (financial) data are available today and in what quality and quantity via programmatic APIs. “Normative Theories Revisited” revisits the normative theories of Chapter 3 and analyzes them based on real financial time series data. Also based on real financial data, “Debunking Central Assumptions” debunks two of the most commonly found assumptions in financial models and theories: normality of returns and linear relationships.
Scientific Method
The scientific method refers ...