- [Narrator] We previously put together…a set of data looking at light vehicle sales,…as it relates to various other macro economic variables,…gas prices, Moody's Bond yields, Jobless Claims, et cetera.…Then we've gone through and run a regression based on that,…so we understood the relationship between…light vehicle sales and some of these other figures.…Now it's time to try and make a prediction…for what light vehicle sales might be in the future.…Now, the math and formulas are a little bit tedious…so I've put together something basic for you to start with.…
What we've got here is a simple model…that'll help us to forecast light vehicle sales.…And the idea is that we can use the coefficients…from our regression analysis to understand…what light vehicle sales might be.…The way to interpret these coefficients is as follows,…a one unit increase in gas prices…would lead to a negative zero point seven two…change in car sales.…
So what this means is that a one dollar rise per gallon…in gas prices causes car sales, or is associated with,…
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.