- [Instructor] Big data is changing finance.…The reality is that historically…in finance as in most other areas of business,…there have two ways of making investment decisions:…intuition or data.…Each of these methods have its adherents,…but when we talk about using data,…that's by and large become…a more and more important part of stock market investing,…so much so that there's a term that we use…for using data in making these decisions: quantitative.…
The term quantitative refers to using data…to try and identify attractive investment opportunities.…Now this falls into different categories or techniques…or schools of thought, whatever term you want to use:…smart data investors versus fundamentals investors…versus investors on technicals, things like that.…All of these different areas are using…different types of data in a little bit…different way to try to make decisions,…but they're all really at their core…looking at data and quantitative metrics…for evaluating stocks.…
Why is data so important?…The answer is what I like to call the HiPPO problem.…
Professor Michael McDonald provides a brief primer on securities markets. He explains how data helps investors forecast performance and automate trading. Then he moves into the practical steps: coming up with algorithmic trading rules and developing and testing an algorithm. Finally, he shows how the algorithm can be applied and eventually expanded to other securities. Anyone working in financial services, or interested in investing in the stock market, will be able to use these tutorials to understand and develop simple trading algorithms of their own.
- Define what a share of stock is.
- Classify the type of trading that attempts to capitalize on the bid-ask spread.
- Name the rule that can be used as a metric for Fed interference in the market.
- State the first step in a data analysis project.
- Identify the type of characteristic algorithmic trading relies on.
- Break down how VAR is used to manage risk.