Tutorial

Disclaimer: This series is converted from my project in Empirical Finance at VU Amsterdam, co-authoring with Denzel van Beek and Marcin Grobelny. The data used in this post is simulated data. This series has four parts: Part 1: Can We Predict Stock Returns? Testing the Fama-French Model <– You’re here Part 2: Did That Tax Law Actually Work? A Real-World Policy Analysis Part 3: Predicting Up or Down: When Will Stocks Rise Tomorrow? Part 4: Forecasting Trading Volume: When Simple Beats Complex Introduction: The Quest to Explain Stock Returns As an enthusiastic investor or an ambitious trader, you’ve probably asked yourself this question at least once in your life: “Can I predict tomorrow’s stock price?” ...

I am glad to have you for the third part of my complete guide on data cleaning. #1: Tidying Messy Data #2: Dealing with Missing Data #3.1: A discussion on the Nature of Outliers <– You are here #3.2: The Origin of Outliers & Detection Techniques #4.1: Where does Data Duplication come from? #4.2: A Practical Tutorial for Data Deduplication I like weird people. The black sheep, the odd ducks, the rejects, the eccentric, the loners, the lost and forgotten. More often than not, these people have the most beautiful souls. — Unknown ...

I am glad to have you for the fourth part of my complete guide on data cleaning. #1: Tidying Messy Data #2: Dealing with Missing Data #3.1: A discussion on the Nature of Outliers #3.2: The Origin of Outliers & Detection Techniques #4.1: Where does Data Duplication come from? #4.2: A Practical Tutorial for Data Deduplication <– You are here Can you spot the impostor? - Picture by the author ...

I am glad to have you for the first part of my complete guide on data cleaning. #1: Tidying Messy Data <– You are here #2: Dealing with Missing Data #3.1: A discussion on the Nature of Outliers #3.2: The Origin of Outliers & Detection Techniques #4.1: Where does Data Duplication come from? #4.2: A Practical Tutorial for Data Deduplication Photo by Vadim Sherbakov on Unsplash Data cleaning has always been a nightmare that every single analyst has to walk through (like through fire and flames). Although it is a hard pill to swallow, we cannot simply skip this step since the avoidance of data cleaning only brings more miseries to the analyst. ...

I am glad to have you for the second part of my complete guide on data cleaning. #1: Tidying Messy Data #2: Dealing with Missing Data <– You are here #3.1: A discussion on the Nature of Outliers #3.2: The Origin of Outliers & Detection Techniques #4.1: Where does Data Duplication come from? #4.2: A Practical Tutorial for Data Deduplication When you are missing someone, time seems to move slower, and when I’m falling in love with someone, time seems to be moving faster. — Taylor Swift ...