  Quantitative models are an important decision-making factor for policy makers
and investors. Predicting an economic recession with high accuracy and
reliability would be very beneficial for the society. This paper assesses
machine learning technics to predict economic recessions in United States using
market sentiment and economic indicators (seventy-five explanatory variables)
from Jan 1986 - June 2022 on a monthly basis frequency. In order to solve the
issue of missing time-series data points, Autoregressive Integrated Moving
Average (ARIMA) method used to backcast explanatory variables. Analysis started
with reduction in high dimensional dataset to only most important characters
using Boruta algorithm, correlation matrix and solving multicollinearity issue.
Afterwards, built various cross-validated models, both probability regression
methods and machine learning technics, to predict recession binary outcome. The
methods considered are Probit, Logit, Elastic Net, Random Forest, Gradient
Boosting, and Neural Network. Lastly, discussed different models performance
based on confusion matrix, accuracy and F1 score with potential reasons for
their weakness and robustness.
