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Course material for the Winter 2020 Edition of PP4RS at Uni Zurich
Academia Hugo theme is fork from Hugo Academic Template.
Use unsupervised and supervised learning to predict stocks
Repo for Yale Applied Empirical Methods PHD Course
A PhD course in Applied Econometrics and Panel Data
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
Backtesting on 22 firm-characteristic risk factors ( R, python, cross-sectional linear regression, Fama-MacBeth)
A Beamer colour theme that maximizes visibility in dark and unfavourable conditions
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.
Cookiecutter Template for Academic research projects
Text Analysis in R. Winter School in Data Analytics and Machine Learning, Université de Fribourg.
Code to accompany our paper Chen and Zimmermann (2020), "Open source cross-sectional asset pricing"
List of Computer Science courses with video lectures.
Material that I use for a variety of classes and tutorials
Repo for the Deep Reinforcement Learning Nanodegree program
Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020. Please star.
This repo replicates Doshi et al (2019) and extends the analysis with Stata and Python
A collection of Dynare models
Course Materials for ECON546 in winter of 2018
:exclamation: This is a read-only mirror of the CRAN R package repository. econet — Estimation of Parameter-Dependent Network Centrality Measures
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.
A tutorial of empirical finance (It will be not updated)
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
Replication materials for the paper: Chin, A., Eckles, D., & Ugander, J. Evaluating stochastic seeding strategies in networks.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.