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 misallocation, fueling booms that subsequently correct through contractions. These results contribute to the behavioral finance literature by empirically demonstrating that sentiment and expectations influence credit cycles beyond macroeconomic fundamentals. They also offer practical implications for policymakers seeking to mitigate the risks of credit booms and busts by integrating sentiment-based indicators into financial stability assessments.
","abstract_html":"<p>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 misallocation, fueling booms that subsequently correct through contractions. These results contribute to the behavioral finance literature by empirically demonstrating that sentiment and expectations influence credit cycles beyond macroeconomic fundamentals. They also offer practical implications for policymakers seeking to mitigate the risks of credit booms and busts by integrating sentiment-based indicators into financial stability assessments.</p>","abstract_has_math":false,"creators":["Xiao, Luoyi"],"institution":null,"degree_name":"Economics, PhD","degree_level":"Restricted to Claremont Colleges Dissertation","degree_discipline":"School of Social Science, Politics, and Evaluation","degree_department":null,"school":null,"contributors":["Thomas Willett","Levan Efremidze"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-01-01T08:00:00Z","date_published":"2025-01-01T08:00:00Z","updated_at":"2026-07-24T01:41:09Z","subjects":["Behavioral Finance","Credit Growth","Econometrics","Sentiment Indexe","Economics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/976","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Thomas Willett","Levan Efremidze"]},{"key":"dc:creator","label":"Author","values":["Xiao, Luoyi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-12T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["School of Social Science, Politics, and Evaluation"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Restricted to Claremont Colleges Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Economics, PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Behavioral Finance","Credit Growth","Econometrics","Sentiment Indexe","Economics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/976"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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 misallocation, fueling booms that subsequently correct through contractions. These results contribute to the behavioral finance literature by empirically demonstrating that sentiment and expectations influence credit cycles beyond macroeconomic fundamentals. They also offer practical implications for policymakers seeking to mitigate the risks of credit booms and busts by integrating sentiment-based indicators into financial stability assessments.
"]},{"key":"dc:title","label":"Title","values":["Quantifying the Effects of Sentiment and Expectations on the United States Credit Growth"]}]}],"canonical_facts":{"dc:contributor":["Thomas Willett","Levan Efremidze"],"dc:creator":["Xiao, Luoyi"],"dc:date.available":["2025-07-12T07:00:00Z"],"dc:description.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 misallocation, fueling booms that subsequently correct through contractions. These results contribute to the behavioral finance literature by empirically demonstrating that sentiment and expectations influence credit cycles beyond macroeconomic fundamentals. They also offer practical implications for policymakers seeking to mitigate the risks of credit booms and busts by integrating sentiment-based indicators into financial stability assessments.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/976"],"dc:subject":["Behavioral Finance","Credit Growth","Econometrics","Sentiment Indexe","Economics"],"dc:title":["Quantifying the Effects of Sentiment and Expectations on the United States Credit Growth"],"thesis:degree_discipline":["School of Social Science, Politics, and Evaluation"],"thesis:degree_level":["Restricted to Claremont Colleges Dissertation"],"thesis:degree_name":["Economics, PhD"]},"updated_at":"2026-07-24T01:41:09Z"}