Project Title: The Risky Business of Safe Investing: Optimizing Conservative Portfolios
This project is a part of the AAI-500 course in the Applied Artificial Intelligence Program at the University of San Diego (USD).
-- Project Status: [Complete]
Installation N/A at this time
Project Intro/Objective: The main purpose of this project is Explore new investment strategies using liquid funds, comparing Risk Parity, Minimum Variance, and Maximum Diversification approaches to provide enhanced returns while managing risks.
Partner(s)/Contributor(s): [Carrie Little] [Ian Lucas]
Website for partner, if available. N/A
Partner contact: [Carrie Little], [email protected], [email protected] [Ian Lucas], [email protected], [email protected]
Keywords: Finanacial Portfolio, Risk Parity, Minimum Variance, Maximum Diversification, Correlation, Optimization, Python, Juniper Notebook, Statistics, Data Visualization, Machine Learning, Monthly Return Time Series Plots, Raw Return Histograms with Fitted Probability Distributions, QQ-Plots, Rolling 36-Month Means, Standard Deviations, Risk-Adjusted Returns, Asset Risk-Return Characteristics, Model Analysis, Portfolio Weights, Risk Contribution, Performance Metrics, Annual Return, Regression, Fama French Factors, Monte Carlo Analysis, Quantitative Goodness of Fit
Project Description: We are looking at a sample of broadly diversified mutual funds and exchange-traded funds that have varying levels of correlation to a conservative bond-stock asset allocation. We seek to understand the characteristics of these funds through our Exploratory Data Analysis, their correlations with one another, which probability distributions may be the best fit, an examination of one or more portfolio constructions methods using optimization (Risk Parity, Minimum Variance, and Maximum Diversification), and recommendations for further development.
License N/A