Reading Notes

Conference Information 

Academic Notes

I share below with (1) my self-written notes (constantly updating); (2) lecture notes from renowned scholars, influencing me greatly during my past studies.

• Economics & Finance Theory

  • My Notes (updating): mainly on asset pricing

  • Corporate Finance Theory: Ph.D. level lecture notes by Dr. Itay Goldstein (UPenn)

  • Information Economics: by Christopher Nimark (Cornell)

• Econometric Theory

  • My Notes (updating)

  • Time Series Analysis: by Dr. Anna Mikusheva (MIT)

  • Economic Forecasting: by Dr. Francis Diebold (UPenn)

• Math Prerequisites

  • My Notes (updating)

  • Probability and Measure Theory: by Dr. Robert Wolpert (Duke)

• Others

  • Machine Learning: Stanford CS229, led by Andrew Ng

Empirical Helpers

I benefit a lot from following sources when I am swamped into the massive preprocessing of asset pricing datasets. Hope this may also help you!

Data

• • [U.S.] open source asset pricing: 212 replicated portfolio-level asset pricing anomalies & underlying firm-level firm characteristic

  • [U.S] macroeconomic attention index: monthly & daily time series, by Dr. Charles Martineau (Toronto)

  • [U.S.] EDGAR log file: data and code, by Dr. James Ryans (LBS)

  • [U.S.] Google Search Volume index: firm coverage of S&P 500 and Russell 3000, by Dr. Robin Litjens (Tilburg)

  • [U.S] extracting headquarter information from 10K/Q filings

    • by Dr. Mingze Gao (Macquarie): state-level history during 1969-2018, and zip code-level history during 2004-2022

    • 10-X header data: contributed by Dr. Bill McDonald (Notre Dame)

  • [U.S.] information of zip code-level geographic areas: contributed by Ready Signal

  • [global] Standardize Security Code: link table across many security identifiers, by Dr. Wenzhi Ding (HKPoly)

  • [global] asset pricing factors

    • global factor premia: by Hanuer-Windmüller team (Tech. Uni. of Munich), using Datastream and Worldscope

    • global factor data: by Jensen-Kelly-Pedersen team (Yale, Copenhagen), using Compustat

Code

• [Python & R] Tidy Finance Package - primarily for asset pricing and portfolio optimization

• [Python] modules for regression discontinuity 

• [Python] Finance Research tools, by Dr. Lira Mota (MIT)

• [Python] Markov Chain Monte Carlo sampling

• [Python] Insturmental PCA (Kelly, Pruitt and Su, 2019 JFE) - implemented by Liz Chen (AQR)

• [R] Machine Learning for Factor Investing

• [R] Multi-period Diff-in-Diff (Callaway and Sant'Anna, 2021 JoE), by Dr. Brantly Callaway (Georgia)

• [R] package for analyzing high-frequency transaction data