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