Resources
This section contains resources and links to outside miscellaneous materials that I found useful when doing research or programming.
Links in the External Programming Resources section go to sites and blog posts that do not belong to me. Links in Workflow section go to the official sites for the corresponding apps.
You can find my teaching materials in the Teaching page. For a list of teaching and research assistantship experiences, please see the CV section.
Please let me know if any links are dead/outdated. You can email me at ben.charoenwong@nus.edu.sg.
July 2022: Official Announcement https://www.set.or.th/th/education-research/research/database/factor-library/overview
Overview: The factor library with Thai market Fama-French and Q-factors was constructed in collaboration with Kanis Saengchote at Chulalongkorn University. The data are maintained live by the Stock Exchange of Thailand.
Data: All data come from the Stock Exchange of Thailand from official filings and recording of trading, with no lookahead or survivorship bias.
Programming/Scripting
Optimal Trading Execution Code for Minimizing Execution Cost: This script contains the functions for the base (parametric with closed form solutions) version of the optimal trading execution according to Bertsimas and Lo (1998). It shows how optimal trading should depend on how aggressive other market participants are, how noisy the fundamental information is, and other fundamental parameters. All parameters are defined according to the original paper.
I also have some intro to R slides, intro to R memory management, and some sample code:
Script downloading Useful Packages in R.
Chicago Booth PhD Student Website Template: This links to my BitBucket repository that contains instructions and sample files to set up a website, following the template and color scheme of Chicago Booth. The site scales with screens of different sizes.
External Programming Resources
These range from basic to intermediate R, and do not belong to me.
R Tidyverse Style Guide provides great guidelines, but I also adopt the deviations from Google's style guide (fork of the Tidyverse Guide).
Tidyverse Data Science Course Textbook from John Hopkins University.
Why R?: Great slide deck on the power of R with great graphics and visualizations written in R.
New to R? Try Swirl! : This website contains lots of useful tutorials.
Tidy Finance with R: Free e-book with sample code on using tidyverse to do quantitative finance.p
R: Intro to data.table Package: Once you get past the learning curve, you will not go back to base R.
R: The apply function family: R users should know about this. However, to apply functions by groups (rather than by rows), use data.table or dplyr.
R: Example plots using ggplot2 with code: Shows some of the capabilities of ggplot.
R: ggplot2: slides by Hadley Wickham.
R: ggplot2 Visualization Gallery: Useful examples for inspiration.
R: ggplot2 Extension Visualization Gallery: Useful examples for inspiration using extensions to ggplot2.
R: Benchmarking data.table vs. pandas vs. dplyr: I have gotten into lots of discussion about whether python or R is faster. The answer: It doesn't matter much for small data, but data.table seems to win for larger data sets.
R: 11 Tips to Handle Big Data: A short, incomplete list but useful nonetheless. Turns out R isn't great at handling big data (where big data:= data > 1 TB).
R for Big Data: A cool mindmap of useful R packages for managing and analyzing big data. I would personally add ggplot2 into the Visualization category.
R: Compressing Data Files to Save Space: This saved me an unbelievable amount of space when working with TAQ data.
R: Stargazer Cheat-sheet: In case you need to customize output.
R: Tips with data.table: Fairly advanced looping and editing data in memory.
R: Preparing your Own Package and Shiny Apps: Some tips and instructions on unit testing for packages prior to pushing to GitHub and submitting to CRAN.
R: Different file formats: A blog covering rds, feather, fst file formats for fast read/write and interoperability between R and Python.
R: Visualization from VIS 2017 Conference: Good examples of data visualization with R for inspiration.
Data Handling: LocustDB > Clickhouse. Also an overview of Online Analytical Processing (OLAP).
Primer on Advanced Text Analysis: Guide on considering different text similarity measures that can take into account the semantics and grammar in addition to simple word similarity.
Visualizing Transition Matrices: Snippet showing how to visualize transitions with different features. Does not use graph theory.
Introduction to Git for Beginners, Grant McDermott's Version Control Slides with GitHub, and Jenny Bryan's Introduction
R: What They Forgot (wtf): For intermediate R users who are mostly self-taught. More advanced topics like code maintenance, library maintenance, version maintenance.
Misleading Visualization from the Economist: 40 Examples from one of the Economist's data journalists, showing both the original (misleading) and better plots.
Data Visualization Lesson Materials from Dr. Andrew Heiss at Georgia State University: Great e-book, lesson plan, assignments, sample code.
Data Science Bootcamp for High Schoolers: by David Kane.
Conference Discussion Slides
Workflow
Mendeley: Having used it since I started grad school, I can't imagine what a pain it must be to maintain a huge library of PDF's without this organizer.
LyX: For doing all things LaTeX (although more recently I tend to use PowerPoint more for slides).
Slack: For an organised chat group where we can share files.
Asana Project Management: I learned about this from Matt Gentzkow and Jesse Shapiro's Research guide. This has been immensely useful for collaboration. The integration with Google Calendar is particularly useful for me. However, like Slack, I feel this works better for more stable research/co-author teams.
BitBucket: For writing and code documentation, particularly for team collaboration. Also useful for storing different iterations of output.
I post my conference discussion slides here. Please contact the related authors directly for the paper or the presentation slides. Shown in reverse chronological order. In general, I try to send the slides to the presenters at least one day before the discussion.
December 2023 (Scheduled). Global AI Finance Conference. Domestication of Cryptoassets: Theory and Evidence.
November 2023. Singapore Scholar Symposium. Data Specialists and Market Efficiency by Massimo Massa, Hong Zhang, and Yijun Zhou.
October 2023. Vietnam Symposium in Banking and Finance. Bank lending during the 2008 credit crunch: Only what matters, capital or liquidity? by Helyoth Hessou and Van Son Lai.
August 2023. European Finance Association. Stablecoins and the financing of the real economy by Jean Barthélémy, Paul Gardin, and Benoît Nguyen.
June 2023. Asian Finance Association. Housing Speculation and Entrepreneurship by Xuan Tian and Yichu Wang.
June 2023. SMU Summer Camp. Does Trading Volume Mitigate or Amplify Mispricing? by Xinrui Duan, Li Guo, Jun Tu, Luying Wang.
March 2023. ABFER Innovation, Productivity and Challenges in the Digital Era. Preparing for the (non-existent?) future of work by Anton Korinek and Megan Juelfs.
December 2022. FIRN. Understanding the role of dealer-client relationships in bond trading by Simon Jurkatis, Andreas Schrimpf, Karamfil Todorov, Nicholas Vause.
December 2022. ANU RFAS: More Debt More Leverage? by Antje Berndt, Bruce Grundy and Yue Wang.
November 2022. PRN Tokyo. CompNet Discussion.
October 2022. CAFM. Does Divergence of Opinion make better minds? Evidence from Social Media.
May 2022. CityUHK Conference for Fintech, AI, and Big Data in Business: Technology and Cryptocurrency Valuation by Yukun Liu, Jinfei Sheng, Wanyi Wang.
January 2022. AFA: Investor Protections and Stock Market Participation: An Evaluation of Financial Advisor Oversight by Juhani Linnainmaa, Brian Melzer, Alessandro Previtero, Stephen Foerster.
July 2021. CICF: Common Ownership, Competition, and Top Management Incentives by Miguel Anton, Florian Ederer, Mireia Gine, Martin Schmalz.
November 2020. Asia-Pacific Corporate Finance Online Workshop. Fintech Adoption and Household Risk-Taking by Claire Yurong Hong, Xiaomeng Lu, Jun Pan.
November 2020. Singapore Scholar Symposium: Is Carbon Risk Priced in the Cross-Section of Corporate Bond Returns? by Tinghua Duan, Frank Weikai Li, Quan Wen.
May 2020. ABFER: FinTech Platforms and Mutual Fund Distribution by Claire Yurong Hong, Xiaomeng Lu, Jun Pan.
June 2019. CUHK-Review of Corporate Finance Studies Conference: Court Congestion and Economic Fragility by Dimas Fazio, Thiago Silva, Janis Skrastins.
May 2019. ABFER: Financial Technology Adoption by Sean Higgins.
May 2019. ABFER: Do Tokens Behave Like Securities? An Anatomy of Initial Coin Offerings by Evgeny Lyandres, Berardino Palazzo, Daniel Rabett.
2019. 4th Productivity Research Network Conference: Which Firms Get Credit? Evidence from Firm-Level Data by Gabriela Araujo and Jonathan Hambur.
2019. 4th Productivity Research Network Conference: Industry- and State-Level Value Added and Productivity Decompositions by Shipei Zeng, Stephanie Parsons, Erwin Diewert, and Kevin Fox.
2018. SFS Cavalcade Asia Pacific: Credit Risk Propagation along Supply Chains: Evidence from the CDS Market by Senay Agca, Volodymyr Babich, John Birge, Jing Wu.
2018. 2nd Productivity Research Network Conference: The Redistributive Role of Automation by Giorgio Presidente.
2018. 2nd Productivity Research Network Conference: Is Informal Credit Supplement or Complementary for Financing SME's Investment During the Crisis? Evidence from Vietnam by Long Q Trinh & Peter J Morgan.
2018. SMU Finance Summer Camp: Sentiment, Limited Attention and Mispricing by Xinrui Duan, LiGuo, Frank Weikai Li, Jun Tu.
2018. NTU Finance Conference: Regulation of Charlatans in High-Skill Professions by Jonathan Berk & Jules van Binsbergen.
2018. 29th NBER-EASE: Corruption, Political Stability and Efficiency of Government Expenditure on Health Care – Evidence from Asian Countries by Nobuo Akai & Zhenyu Cui.
2017. RMI Conference: Industry Competition, Credit Spreads, and Levered Equity Returns by Alexandre Corhay.
Photo Credit: Byoung-Hyoun Hwang. Singapore Scholar Symposium, Nov 2023. NTU.
Random
Really random stuff that are nice short breaks from work.
Course and Textbook on writing research by Econscribe. Disclaimer: I went through this program as well.
Online Textbook on Data Visualization: by Claus Wilke, with everything built in R and source code available through GitHub.
Tips for Economists: A collection of tips from various great sources all collected in one place.
Language-Agnostic Coding Advice for Economists by Ljubica Ristovska, from a presentation at the Harvard Economics Professional Development workshop. Spring 2019.
A very useful, free Econometrics Textbook by Scott Cunningham at Baylor University. Comes with a Spotify playlist of accompanying rap!
Open Source Etiquette: Best be nice; especially when help is needed.
Best Figures in Economics: Curated best pictures in economics research (credit to Paul Goldsmith-Pinkham at Yale and others.)
R for Data Journalism: Useful packages, examples, and tips to integrate data into articles.
A Bunch of Spurious Correlations: Do you find yourself coming up with stories for them?
Typing Test - Check Your Speed!: Comes in handy for checking how quick students can type, which affects the speed to go over code in class.
Library of Statistical Techniques (LOST): Open-sourced e-book on statistical techniques, created by Nick Huntington-Klein at Seattle University.
Econometrics Resources for R (and Stata) from Nick Huntington-Klein at Seattle University. The Causal Inference Animated Plots are particularly cool.
Lecture Notes for PhD Economics. An amalgamation of different course syllabus and lecture notes.
AFA Presidential Addresses from 1972 through 2026, compiled by Malcolm Wardlaw.
Financial History Resources: by Investor Amnesia.
A tool to split rent, a bill, or even workload.
Guideline and introspection framework on picking a career, from waitbutwhy.
How to pick a career, and why it is so important, from 80,000 Hours.
Intro to Computational Finance with R & Financial Econometrics - by Eric Zivot.