DijunLiu1995

10K-MDA-Section

Extract the Management Discussion and Analyses (MD&A) section from 10K Financial Statements

analyst-forecast-errors

A new approach to predicting analyst forecast errors: Do investors overweight analyst forecasts?https://www.sciencedirect.com/science/article/pii/S0304405X13000329

anlp19

Course repo for Applied Natural Language Processing (Spring 2019)

CF-Paper-replication

Replication of Philippon and Guitierrez (2017), “Investment less Growth: An Empirical Investigation”

CHIIA

COMP8715 Group Project: CHIIA-NLP project is to identify the relevant data for CHIIA database by using natural language processing and machine learning models which calculate the likelihood between the data extracted from Factiva and the relevant datasets. Our project will automatically search for the most obvious relevant data, and save them to CHIIA database.

coblog

how co-attention affect beta

codechella

Data, Code and other material for CodeChella concert

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.

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.

DijunLiu1995/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

DijunLiu1995/sec-edgar

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

DijunLiu1995/codechella

Data, Code and other material for CodeChella concert

DijunLiu1995/CommonOwnerReplication

Replication for Common Owner 1980-2017

DijunLiu1995/corporate_governance

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

DijunLiu1995/cpi

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

DijunLiu1995/CrossSection

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

DijunLiu1995/DiD_Codes

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

DijunLiu1995/dijunliu.github.io

DijunLiu1995/doshi-replicate

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

DijunLiu1995/EDGAR-Parsing

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

DijunLiu1995/EquityCharacteristics

Calculate U.S. equity (portfolio) characteristics

DijunLiu1995/factiva-analytics-process

Dow Jones DNA collection, storage and analytics process.

DijunLiu1995/factiva-rtf-db

Script to import Factiva RTF files into a database

DijunLiu1995/Firm-Specific-Intangible-Capital

Firm-Specific Intangible Capital

DijunLiu1995/Gamesmanship-and-Seasonality-in-Stock-Returns

Value Investing Research Awaiting Publication

DijunLiu1995/identifying_price_informativeness

Replication Code for Identifying Price Informativeness

DijunLiu1995/intangiblevalue

DijunLiu1995/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

DijunLiu1995/lexpredict-lexnlp

LexNLP by LexPredict

DijunLiu1995/lp_var_simul

Simulation study of Local Projections, VARs, and related estimators

DijunLiu1995/openedgar

OpenEDGAR (openedgar.io)

DijunLiu1995/pin-code

DijunLiu1995/python-causality-handbook

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

DijunLiu1995/replication-of-liquidity-chapter

DijunLiu1995/ReplicationCrisis

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

DijunLiu1995/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.

DijunLiu1995/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.

DijunLiu1995/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".