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Open AccessArticle

The Determinants of the U.S. Consumer Sentiment: Linear and Nonlinear Models

1
Questrom School of Business—Boston University, 595 Commonwealth Avenue, Boston, MA 02215, USA
2
USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon
*
Author to whom correspondence should be addressed.
Int. J. Financial Stud. 2020, 8(3), 38; https://doi.org/10.3390/ijfs8030038
Received: 10 April 2020 / Revised: 13 June 2020 / Accepted: 16 June 2020 / Published: 1 July 2020
(This article belongs to the Special Issue Econophysics Applications to Financial Markets)
We examined the determinants of the U.S. consumer sentiment by applying linear and nonlinear models. The data are monthly from 2009 to 2019, covering a large set of financial and nonfinancial variables related to the stock market, personal income, confidence, education, environment, sustainability, and innovation freedom. We show that more than 8.3% of the total of eigenvalues deviate from the Random Matrix Theory (RMT) and might contain pertinent information. Results from linear models show that variables related to the stock market, confidence, personal income, and unemployment explain the U.S. consumer sentiment. To capture nonlinearity, we applied the switching regime model and showed a switch towards a more positive sentiment regarding energy efficiency, unemployment rate, student loan, sustainability, and business confidence. We additionally applied the Gradient Descent Algorithm to compare the errors obtained in linear and nonlinear models, and the results imply a better model with a high predictive power. View Full-Text
Keywords: U.S. consumer sentiment; consumer perception; financial markets; cross-correlation; Random Matrix Theory; Switching Regime Regression; Gradient Descent Algorithm U.S. consumer sentiment; consumer perception; financial markets; cross-correlation; Random Matrix Theory; Switching Regime Regression; Gradient Descent Algorithm
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El Alaoui, M.; Bouri, E.; Azoury, N. The Determinants of the U.S. Consumer Sentiment: Linear and Nonlinear Models. Int. J. Financial Stud. 2020, 8, 38.

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