Nick Clark's repositories
awesome-normalizing-flows
Awesome resources on normalizing flows.
bayesian_nonparametrics
Slides and code for Bayesian nonparametrics in R and Julia
CrossSection
Code to accompany our paper Chen and Zimmermann (2020), "Open source cross-sectional asset pricing"
dataflowr
code for deep learning courses
deep-learning-drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Deep_Partial_Least_Squares
Source code for Deep Partial Least Squares for Empirical Asset Pricing.
finBERT
Financial Sentiment Analysis with BERT
GoogleScraper
A Python module to scrape several search engines (like Google, Yandex, Bing, Duckduckgo, Baidu and others) by using proxies (socks4/5, http proxy) and with many different IP's, including asynchronous networking support (very fast).
gt-nlp-class
Course materials for Georgia Tech CS 4650 and 7650, "Natural Language"
lxmls-toolkit
Machine Learning applied to Natural Language Processing Toolkit used in the Lisbon Machine Learning Summer School
mlfactor.github.io
Website dedicated to a book on machine learning for factor investing
NonparametricBayes
Dummy code for nonparametric bayes
nstrayer.github.io
Personal website
Rebellion_Research_NLP
In this repository you will find code for our capstone project "How to Predict Stock Movements Using NLP Techniques". The code has been adapted in the first 3 pairs to illustrate how to scrape the EDGAR webpage. However, the word2vec analysis and FinBERT analysis use as data the data uploaded in this repository. Get in touch with me if you have any questions or if you find any mistakes.
Scraping-13F-Filings
Tutorial on Scraping 13F Filings
statsmodels
Statsmodels: statistical modeling and econometrics in Python
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.