Pinned Repositories
2016.M3.TQF-ML.Project
ai-for-trading-1
Artificial Intelligence for Trading
baruch-mfe-lab
Baruch MFE program quant lab
BaruchAdvancedCpp
Code developed for the Advanced C++ and Modern Design Online Certificate taken with Baruch MFE program and taught by Dr. Daniel Duffy from QuantNet
Empirical-Asset-Pricing
finmath-lib
Mathematical Finance Library: Algorithms and methodologies related to mathematical finance.
MTH9845_Final_Project
This repo contains the files for the 9845 Final project
MTH9875_Volatility_Surface
This repo contains lecture notes and HW for Baruch MTH9875 Volatility Surface
Quantitative-Fund-Selection-Framework
Quantitative Fund Selection in R
UCLA_MGMTMFE405-2_Computational_Methods
UCLA_MGMTMFE405-2_Computational_Methods
Jmaihuire's Repositories
Jmaihuire/ai-for-trading-1
Artificial Intelligence for Trading
Jmaihuire/BaruchAdvancedCpp
Code developed for the Advanced C++ and Modern Design Online Certificate taken with Baruch MFE program and taught by Dr. Daniel Duffy from QuantNet
Jmaihuire/AI-for-trading-2
Udacity AI for Trading programme. Using AI to generate signals for Risk-Factor models in quantitative trading
Jmaihuire/AI_For_Trading_Udacity_Nanodgree
Jmaihuire/AssetPricingML
Codes for Empirical Stock Analysis: A comparative Analyses between CAPM and Machine Learning Algorithms
Jmaihuire/Baruch-Cplusplus-programming-for-FE-Certificate
Jmaihuire/Benchmarking_Economic_Efficiency_Julia_Notebooks
These are the notebooks containing the replication code for the book Benchmarking Economic Efficiency by Pastor, Aparicio, and Zofío
Jmaihuire/causalbook
Replication code and downloadable example data sets for The Effect
Jmaihuire/CausalitySlides
Slides for the Seattle University Causal Inference Class
Jmaihuire/Deep_Fundamental_Factors
Source code for Deep Fundamental Factor Models, https://arxiv.org/abs/1903.07677
Jmaihuire/dlsa-public
Deep Learning Statistical Arbitrage
Jmaihuire/EconometricsSlides
This is the repository for the slides used in the Seattle University Econometrics course
Jmaihuire/Empirical-Asset-Pricing-via-Machine-Learning-Evidence-from-the-German-Stock-Market
Machine learning methods for identifing investment factors
Jmaihuire/fastbook
The fastai book, published as Jupyter Notebooks
Jmaihuire/finance-courses
Notes and examples about Portfolio Construction and Analysis with Python (Jupyter notebooks)
Jmaihuire/forecasting-realized-volatility-using-supervised-learning
Traditionally, volatility is modeled using parametric models. This project focuses on predicting EUR/USD volatility using more flexible, machine-learning methods.
Jmaihuire/Investment-Strategies-Final
Final code and data for the replication of the BAB paper of Frazzini and Pedersen (2014)
Jmaihuire/MachineLearning
NYU ECE GY-6143
Jmaihuire/ML_Advanced_Spring_2023
NYU Tandon Advanced Machine Learning
Jmaihuire/ML_Spring_2022
NYU Tandon Machine Learning and Finance Spring 2022
Jmaihuire/ML_Spring_2023
NYU Tandon Machine Learning and Finance Spring 2023
Jmaihuire/NYU-MFE-Projects
Projects for NYU MFE Program
Jmaihuire/NYU_MFE_2708_2022_Spring
A code repo for class project
Jmaihuire/Python-Code-for-My-Empirical-Asset-Pricing-Paper
Python code for my paper: What’s in the Moneyness? Moneyness Spread and Future Stock Returns
Jmaihuire/ReplicationCrisis
Code for "Is There a Replication Crisis in Finance" by Jensen, Kelly and Pedersen (2021)
Jmaihuire/Research
Open sourced research notebooks by the QuantConnect team.
Jmaihuire/Research_Codes_2021
Empirical Asset Pricing with Individual Stocks on the JSE: Betas Versus Characteristics
Jmaihuire/struct
Code for UTexas Structural Econometrics and IO
Jmaihuire/SwapsBook
Interest Rate Swaps – Theory, Pricing and Practice
Jmaihuire/Tutorials
Jupyter notebook tutorials from QuantConnect website for Python, Finance and LEAN.