dijunliu1995 Goto Github PK

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

commonownerreplication

Replication for Common Owner 1980-2017

computational-text-analysis-spring-2019

Computational text analysis for Spring 2019 by Caroline Le Pennec-Caldichoury

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"

csr_earnings

CSR and Earnings

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

dsqf

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.

edgar

R scripts for scraping form data from the SEC's Edgar database

edgar-parsing

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

empirical-finance

A tutorial of empirical finance (It will be not updated)

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

equitycharacteristics

Calculate U.S. equity (portfolio) characteristics

factiva

Scrap data from FACTIVA

factiva-analytics-process

Dow Jones DNA collection, storage and analytics process.

factiva-pipelines-python

Python package to read, transform, enrich and load news data. Patterns can be found in the Dow Jones Developer Portal.

factiva-pypckgs-sample

Jupyter notebooks that guide through the use of the Factiva Python packages.

factiva-rtf-db

Script to import Factiva RTF files into a database

factiva-sentiment

The project implements processing of articles downloaded from Factiva page and classifies them according to the Naive Bayes algorithm.

factiva_sentiment_analysis

Program for analyzing the tone of news messages for a course project