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advanced-financial-economics

Repo of homework and research code for ECON 525: Advanced Financial Economics

afedr

Repository for package afedR ("Analyzing Financial and Economic Data with R")

ai-for-trading

Artificial Intelligence for Trading

applieddatascience

artificial-intelligence-for-trading

Content for Udacity's AI in Trading NanoDegree.

asset-class

Interactive analysis of asset class returns' relationship with economic indicators.

asset-pricing

UChicago Asset Pricing

awesome-ai-in-finance

🔬 A curated list of awesome machine learning strategies & tools in financial market.

binder

A binder of my Jupyter Notebooks for reproducible science!

bookdown

Authoring Books and Technical Documents with R Markdown

cad_factor_regression_analysis

A limitation of the factor regression available on portfoliovisualizer.com is that it does not take FX into account. To address this, I have developed a python module that considers the CAD:USD data when performing a regression analysis on securities listed on the Toronto Stock Exchange (TSX). For Canadian Equities listed on the TSX run CDN_listed_CDN_Equity.py. For US Equities listed on the TSX run CDN_listed_US_Equity.py. US equities are analyzed using the Fama-French 5 factor model using the daily data. Canadian equities are analyzed using AQR data for MKT-RF, SMB, HML(FF), QMJ, and UMD (FF data not available for Canada). For comparative purposes, the file US_listed_US_Equity replicates the results from portfoliovisualizer.com for US listed US Equities analyzed using the FF 5-factor model with daily data. X, Custom_start_date, and Custom_end_date can be modified as required by the user. If the user does not wish to enter a custom start or end date, a value of zero will use the longest dataset possible. Prior to running the scripts, the following lines of code must be executed if their respective packages have yet to be installed: pip install pandas pip install numpy pip install DateTime pip install statsmodels pip install urllib3 pip install zipfile37 pip install investpy pip install yfinance Prior to running the CDN_listed_CDN_Equity.py script for the first time, run importAQR_QMJ.py to download the AQR dataset onto the local hard drive. Once the dataset is downloaded, the importAQR_QMJ.py script is not required to be executed unless updated data is required.

coursera-compinvest1

Georgia Institute of Technology - Computational Investing by Tucker Balch

coursera-courses

Coursera Course

coursera_ap2013

My code for Coursera Asset Pricing course, by John H. Cochrane, fall 2013.

cram_ws20

credit_risk_model_loandefaults

Credit Risk Modeling to Compute Expected Loss of Loans (logistic regression, linear regression)

custom-factor-model

Data and R code related to my medium article "Custom Factor Models - Build your own in R with a few lines of codes"

danieltitman1997

Replication of the methodology of Daniel and Titman (1997) for constructing pre-formation and constant-weight allocation Fama-French factors.

data-r-value

Repository dedicated to blog data R Value

datacamp

Working through some data camp courses.

dcc-esg

Scripts & data used for calculations described in "Risk management opportunities between socially responsible investments and selected commodities"

deeplearningcreditrisk

In this project deep learning models are applied to solve a problem of credit risk analysis

deeplearningcreditriskmodeling

Replication codes for Deep Learning Credit Risk Modeling by Manzo, Qiao

develop

generic project files

e4040-2020fall-project-wnzh-jr4001-xy2419-gl2664

Deep Learning and the Cross-Section of Stock Returns

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

esg-bert

Domain Specific BERT Model for Text Mining in Sustainable Investing

esg-investment-portfolio

esg-stock-data

S&P 500 ESG / Financial Performance Data. ESG data web-scraped from Yahoo Finance; stock metrics from 3rd party. Merged into `sp_esg_stock_data.csv` df.

esg_investment