December 10, 2019
Welcome to another episode of Data Science Interview Questions! In this episode, I discuss the Random Walk Hypothesis and Stock Price Prediction.
Why is stock price data often considered to be a random walk?
If your data is best modeled as a random walk, how can you do a time series forecast into the future?
How can you draw a confidence interval around the forecast?
What does this mean for stock price predictions?
Find out here:
What you will learn:
- How to make the best forecast possible if your data is from a random walk model
- How to find the confidence bounds for your forecast (also called confidence limits or prediction intervals)
- Why pretty much all the “data science” instructors out there are really just marketers who have been selling you lies for years
- Hint: No, LSTMs will not help you predict stock prices and in fact perform worse than the simple model described above