The goal of our project is to find a better prediction of the S&P 500 with external data including monetary policies and decisions from the Federal reserve, profitability and financial decisions of s&p 500 companies, etc. (Please see our dataset description for a complete list). We studied a couple of machine learning models for and improved performance of the best one --- the Facebook Prophet model, with added external features. We used a unique nonlinear model and a special computing scheme and a feature selection algorithm. Our model is able to reduce the Mean Squared Error and reflect the COVID19 period market crash and rebound when that period of time is included.
(sorted alphabetically by last names) Shelby Cox, Yuqing Dai, Malavika Mukundan, Qiang Wu, and Hao Xing
https://youtu.be/dAVS5EECk30
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Copyright <2021> <COPYRIGHT Shelby Cox, Yuqing Dai, Malavika Mukundan, Qiang Wu, and Hao Xing> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.