DijunLiu1995

EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Empirical-Method-in-Finance

Winter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices  Class intro: Forecasting and Finance  The random walk hypothesis  Stationarity  Time-varying volatility and General Least Squares  Robust standard errors and OLS Topic 2: Time-dependence and predictability  ARMA models  The likelihood function, exact and conditional likelihood estimation  Predictive regressions, autocorrelation robust standard errors  The Campbell-Shiller decomposition  Present value restrictions  Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter Topic 3: Heteroscedasticity  Time-varying volatility in the data  Realized Variance  ARCH and GARCH models, application to Value-at-Risk Topic 4: Time series properties of the cross-section of stock returns  Single- and multifactor models  Economic factors: Models and data exploration  Statistical factors: Principal Components Analysis  Fama-MacBeth regressions and characteristics-based factors

sec-edgar

Download all companies periodic reports, filings and forms from EDGAR database.

codechella

Data, Code and other material for CodeChella concert

CommonOwnerReplication

Replication for Common Owner 1980-2017

corporate_governance

Scrapers used for corporate governance searching on Factiva and Nexis Uni. Designed for a Wilfrid Laurier University project (cancelled).

cpi

Quickly adjust U.S. dollars for inflation using the Consumer Price Index (CPI)

CrossSection

Code to accompany our paper Chen and Zimmermann (2020), "Open source cross-sectional asset pricing"

DiD_Codes

This repo contains the codes for Baker, Larcker, Wang - "How Much Should We Trust Staggered Difference-in-Differences Estimates?"

dijunliu.github.io

doshi-replicate

This repo replicates Doshi et al (2019) and extends the analysis with Stata and Python

EDGAR-Parsing

This repo contains all the code necessary to download, extract, and parse 13F filings on EDGAR.

EquityCharacteristics

Calculate U.S. equity (portfolio) characteristics

factiva-analytics-process

Dow Jones DNA collection, storage and analytics process.

factiva-rtf-db

Script to import Factiva RTF files into a database

Firm-Specific-Intangible-Capital

Firm-Specific Intangible Capital

Gamesmanship-and-Seasonality-in-Stock-Returns

Value Investing Research Awaiting Publication

identifying_price_informativeness

Replication Code for Identifying Price Informativeness

intangiblevalue

IPP_USLS

Data files and STATA codes for the project "Labor Share Decline and Intellectual Property Products Capital" by Dongya Koh, Raul Santaeulalia-Llopis, and Yu Zheng

lexpredict-lexnlp

LexNLP by LexPredict

lp_var_simul

Simulation study of Local Projections, VARs, and related estimators

openedgar

OpenEDGAR (openedgar.io)

pin-code

python-causality-handbook

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and sensitivity analysis.

replication-of-liquidity-chapter

ReplicationCrisis

Code for "Is There a Replication Crisis in Finance" by Jensen, Kelly and Pedersen (2021)

starter-hugo-academic

🎓 Hugo Academic Theme 创建一个学术网站. Easily create a beautiful academic résumé or educational website using Hugo, GitHub, and Netlify.

Technological-Innovation-Resource-Allocation-and-Growth-Replication-Kit

This repository provides the replication code and data for Kogan, L., Papanikolaou, D., Seru, A. and Stoffman, N., QJE 2017.

Value_Return_Predictability_Across_Asset_Classes_and_Commonalities_in_Risk_Premia

This repository contains data and replication code for the paper "Value Return Predictability Across Asset Classes and Commonalities in Risk Premia" forth coming "Review of Finance".