We designed an algorithm for predicting the behavior of US Sector ETF prices, as a proxy for the sectors themselves, to function as an asset allocation framework. Using API calls from existing frameworks in R, we mined monthly price data for a myriad of assets that best represented broad market information to use as features in modelling the sector ETFs. We tested mutliple machine learning techniques to cluster like asset classes and select only the most significantly correlated from each group as components for each model. We then used both linear and decision tree models to predict future prices. All combinations of filters and models were evaluated against one another for accuracy and error rates.
-
Notifications
You must be signed in to change notification settings - Fork 1
An algorithmic approach to predicting US Sector ETF price movement using machine learning techniques. The goal was to create an asset allocation framwrok using ETFs as proxies for secotr behavior, and to test different clustering and predictive models to accomplish this goal using the R programming language.
wyattm94/US-Sector-Forecasting-with-Machine-Learning
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
An algorithmic approach to predicting US Sector ETF price movement using machine learning techniques. The goal was to create an asset allocation framwrok using ETFs as proxies for secotr behavior, and to test different clustering and predictive models to accomplish this goal using the R programming language.
Topics
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published