Links to Code & Data

Useful Links

• Critical Finance Review: Seeks controversial, unusual and critical papers. Publishes replications of famous articles.
• Forecasting: Principles and Practice: An excellent EBook on time-series forecasting with R
• bookdown.org: A growing number of excellent EBooks on using R to do all matter of things
• Reproducible Research Workshop (UZH)

Replications and codes

• Open Source Asset Pricing: Data and Code for more than 202 asset pricing factors from Andrew Y. Chen and Tom Zimmermann
• Equity Risk factor Toolkit: Very cool Shiny-Website from T. Evgeniou that allows to calculate Asset Pricing factors from CRSP and Compustat “on-the-fly”
• Interacting Anomalies: A website entirely dedicated to the interaction of asset pricing factors (by Karsten Müller and Simon N. M. Schmickler)
• Wayne Changs Website: Lots of nice R-code for Standard Errors (OLS, White, Clustered, Fama-MacBeth, Newey-West, FE, GLS and bootstrap) as well as replication code for the Fama-French Factors (do work reasonably well, but not exactly the same)
• Christoph Jäckel’s replication-Blog
• Mikhail Simutin’s Homepage: SAS Code to replicate the key results from Doshi, H., Elkamhi, R., & Simutin, M. (2015): Managerial Activeness and Mutual Fund Performance, Review of Asset Pricing Studies, 5 (2), 156-184.
• Byoung-Hyoun Hwang’s Website: A lot of SAS/Stata-Code to for replication of papers.
• Quant-Blogs (some with trading code and others with paper discussions):
  • https://opensourcequant.wordpress.com/ (good articles with code, e.g. on block bootstrapping or testing Harvey & Liu (2015))
  • http://blog.fosstrading.com/ (R code and blog articles related to trading and quant finance)
  • http://www.turingfinance.com/ (lots of code (partly R), e.g. for testing the random walk hypothesis)
  • http://systematicinvestor.github.io/ (developer of the Systematic Investor Toolbox (SIT), gives lot of code)
  • http://quantocracy.com/ (gives lots of references to quantitative trading startegies/code)
  • http://www.financial-hacker.com/ (mainly ideas about algorithmic trading)

Machine Learning

• Tensorflow Playground (visualize deep learning with a Neural Network)
• Machine Learning for Factor Investing Absolutely marvelous book on Machine Learning and Portfolio Management with data and R codes

ESG Investing

• Thierry Roncalli’s Webpage - Course Sustainable Finance (massive slideset [736 pages] as well as all latex source files)
• Guillaume Coqueret’s Website - Explaining Green Returns*

Data

• Ivo Welch’s Webpage: Annual Goyal-Welch Factors
• Amit Goyal’s Webpage: Monthly Goyal-Welch-Factors (replication code available here)
• Dave Rapach’s Website: Data & Matlab programs (e.g. for Short interest data, technical indicators, and many more programs and files)
• Sydney Ludvigson’s Webpage: Lots of Data (e.g. CAY, Real, Macro and Financial Uncertainty)
• Lubos Pastor’s Webpage: Liquidity Risk factor (until Dec 2014)
• Jeffrey Wurgler’s Webpage: Investor Sentiment (1965-2014)
• Hao Zhou’s Website: Economic Uncertainty and Variance Risk premium (until Dec 2016)
• Robert Shiller’s Online Data Repository
• Harvard Dataverse: large collection of replication data and codes, e.g. the Low-Risk anomaly and matching (CRSP data etc)
• Journal of Financial Economics Data Repository (e.g.: Giglio, Kelly & Pruitt, 2016 or Bali, 2017)
• Andrea Frazzini’s Webpage: e.g. BAB (D/M), QMJ (D/M), HML Devil (D/M), Global Market, SMB, HML and MOM factors (D/M)
• Lasse Pedersen’s Webpage: e.g. Time Series Momentum
• Jeremiah Green’s Website: SAS file for code to create firm characteristics and returns pulling from Compustat, CRSP, and IBES through WRDS for their RFS Paper

Best R environment for programming with large financial Datasets: The TIDYVERSE

• NEW: How to use git with R and RStudio
• NEW: Learn the Tidyverse
• Time-Series Anaylsis with dplyr
• Tibbletime for rolling regressions etc.
• ggplot2 for nice plots
• AND: For learning R I highly recommend DataCamp

Other scientific/academic R code and applications

• Justin E. Esarey’s website: PhD-level courses on Bayesian Statistics and Nonparametric Models and Machine Learning including R-scripts, videos of the lectures and lecture notes!