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2020-uzh-course-material

Course material for the Winter 2020 Edition of PP4RS at Uni Zurich

academia-hugo

Academia Hugo theme is fork from Hugo Academic Template.

alphaai

Use unsupervised and supervised learning to predict stocks

applied-methods-phd

Repo for Yale Applied Empirical Methods PHD Course

applied_metrics

A PhD course in Applied Econometrics and Panel Data

awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

backtesting

Backtesting on 22 firm-characteristic risk factors ( R, python, cross-sectional linear regression, Fama-MacBeth)

beamer-tips

beamercolortheme-owl

A Beamer colour theme that maximizes visibility in dark and unfavourable conditions

block_codes

This depository uses SEC EDGAR data in Schedule 13D and Schedule 13G data to find all positions above 5% in all US stocks between 1994 and 2018.

causal-effect-identification

continuous_time_methods

cookiecutter-research-project-template

Cookiecutter Template for Academic research projects

course-text-analysis-in-r

Text Analysis in R. Winter School in Data Analytics and Machine Learning, Université de Fribourg.

crosssection

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

cs-video-courses

List of Computer Science courses with video lectures.

dealing_with_data

Material that I use for a variety of classes and tutorials

deep-reinforcement-learning

Repo for the Deep Reinforcement Learning Nanodegree program

deep-reinforcement-learning-for-automated-stock-trading-ensemble-strategy-icaif-2020

Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020. Please star.

doshi-replicate

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

dse2021_tutorials

dsge_mod

A collection of Dynare models

econ546_2018

Course Materials for ECON546 in winter of 2018

econet

:exclamation: This is a read-only mirror of the CRAN R package repository. econet — Estimation of Parameter-Dependent Network Centrality Measures

econometric-algorithms

Popular Econometrics content for students and researchers who wants to learn about regression analysis (in STATA/Python/R), how to test hypothesis and perform statistical tests.

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

eval_stochastic_seeding

Replication materials for the paper: Chin, A., Eckles, D., & Ugander, J. Evaluating stochastic seeding strategies in networks.

event_study

experimentdata