February 2018
Intermediate to advanced
400 pages
10h 17m
English
Content preview from Advances in Financial Machine Learning
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CHAPTER 2 Financial Data Structures
2.1 Motivation
In this chapter we will learn how to work with unstructured financial data, and from that to derive a structured dataset amenable to ML algorithms. In general, you do not want to consume someone else's processed dataset, as the likely outcome will be that you discover what someone else already knows or will figure out soon. Ideally your starting point is a collection of unstructured, raw data that you are going to process in a way that will lead to informative features.
2.2 Essential Types of Financial Data
Financial data comes in many shapes and forms. Table 2.1 shows the four essential types of financial data, ordered from left to right in terms of increasing diversity. Next, we will discuss their different natures and applications.
Table 2.1 The Four Essential Types of Financial Data
| Fundamental Data | Market Data | Analytics | Alternative Data |
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2.2.1 Fundamental Data
Fundamental data encompasses information that can be found in regulatory filings and business analytics. It is mostly accounting data, reported quarterly. A particular aspect of this data is that it is reported with a lapse. ...
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