Quantifying the Effects of Sentiment and Expectations on the United States Credit Growth
type: CreativeWork
description: This dissertation examines the role of sentiment and expectations in predicting U.S. credit growth, drawing upon insights from behavioral finance and macroeconomic forecasting. While traditional economic theories emphasize fundamental determinants of credit expansion, growing evidence suggests that psychological biases, investor sentiment, and extrapolative expectations contribute significantly to credit market fluctuations. Using a dataset comprising sixteen sentiment and expectations indices—including the Consumer Sentiment Index, Anxious Index, Crash Confidence Index, and Loan Officer Opinions Index—this study applies time-varying parameter models and out-of-sample forecasting techniques to assess their predictive power relative to benchmark models. The sentiment indices used in this dissertation capture both psychological attitudes and rational expectations about the economic outlook. While sentiment is often associated with behavioral biases or sub-optimal decisions under uncertainty (Lo, 2004), these indices also reflect informed, rational forecasts made by economic agents. This dual nature—blending both rational and potentially biased components—is crucial for understanding how expectations shape market dynamics, especially in environments where equilibrium may not hold. The findings reveal that while sentiment measures provide valuable information about credit growth at time t, t-2, and t-2 (t is quarterly), they do not consistently outperform a random walk process in forecasting accuracy. However, the optimal rolling window size selection method proposed by Rossi and Inoue (2018) enhances forecast precision relative to ordinary least squares (OLS) regressions. Sentiment effects exhibit heterogeneity across credit types: consumer sentiment significantly influences consumer credit and total credit growth, while financial stress indicators and market-based expectations influence short-term bond issuance. Importantly, elevated sentiment can lead to credit misal…
abstract: This dissertation examines the role of sentiment and expectations in predicting U.S. credit growth, drawing upon insights from behavioral finance and macroeconomic forecasting. While traditional economic theories emphasize fundamental determinants of credit expansion, growing evidence suggests that psychological biases, investor sentiment, and extrapolative expectations contribute significantly to credit market fluctuations. Using a dataset comprising sixteen sentiment and expectations indices—including the Consumer Sentiment Index, Anxious Index, Crash Confidence Index, and Loan Officer Opinions Index—this study applies time-varying parameter models and out-of-sample forecasting techniques to assess their predictive power relative to benchmark models. The sentiment indices used in this dissertation capture both psychological attitudes and rational expectations about the economic outlook. While sentiment is often associated with behavioral biases or sub-optimal decisions under uncertainty (Lo, 2004), these indices also reflect informed, rational forecasts made by economic agents. This dual nature—blending both rational and potentially biased components—is crucial for understanding how expectations shape market dynamics, especially in environments where equilibrium may not hold. The findings reveal that while sentiment measures provide valuable information about credit growth at time t, t-2, and t-2 (t is quarterly), they do not consistently outperform a random walk process in forecasting accuracy. However, the optimal rolling window size selection method proposed by Rossi and Inoue (2018) enhances forecast precision relative to ordinary least squares (OLS) regressions. Sentiment effects exhibit heterogeneity across credit types: consumer sentiment significantly influences consumer credit and total credit growth, while financial stress indicators and market-based expectations influence short-term bond issuance. Importantly, elevated sentiment can lead to credit misal…
author: Xiao, Luoyi
keywords: Behavioral Finance, Credit Growth, Econometrics, Sentiment Indexe, Economics
date published: 2025-01-01T08:00:00Z
date modified: 2026-07-24T01:41:09Z
provider: Claremont Graduate University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Claremont Graduate University