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on Financial Markets |
| By: | H. Christopher Kazemi; Christos A. Makridis |
| Abstract: | We develop a stylized model in which sentiment-driven demand creates a pre-announcement price wedge that is harder for arbitrageurs to offset when fundamental uncertainty is high. The model predicts that prior sentiment shifts expected post-announcement returns separately from the earnings surprise and that the shift is larger when firm-level and aggregate sentiment align. We test these predictions using LSEG MarketPsych sentiment, I/B/E/S earnings surprises, and CRSP returns for 83, 293 U.S. quarterly earnings announcements from 1998-2022. The 10-day low-minus-high cumulative abnormal-return spread is 1.38 percentage points for firm-level sentiment and 1.50 percentage points for aggregate sentiment. Sentiment enters separately from the earnings surprise, while sentiment-by-surprise interactions are generally small and statistically insignificant. The return spread is larger when analyst forecasts are noisier and when firm-level and aggregate sentiment have the same sign. Firm-level sentiment predicts a smaller spread on randomly selected non-announcement dates, while aggregate sentiment has little explanatory power on those dates. The results isolate the predictive content of daily pre-announcement sentiment from the immediate response to realized earnings news and show how it varies with valuation uncertainty and sentiment source. |
| Keywords: | investor sentiment, earnings announcements, earnings surprises, abnormal returns, behavioral finance, efficient market hypothesis, market reactions, asset pricing |
| JEL: | G14 G02 G11 G17 C58 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12929 |
| By: | Lee, Woongki (Yonsei University) |
| Abstract: | Given extensive evidence that firm characteristics predict expected returns, much of the literature asks whether these characteristics also capture the covariance structure of returns. If so, the next question is which modeling framework best explains the covariance structure revealed by those characteristics. This study addresses that question by comparing two alternative frameworks: a multifactor model and a conditional single-factor model. It formally examines how each specifies the covariance structure and how much overall return covariation each explains. The analysis then considers whether the covariance structure implied by the multifactor model can be nested within the conditional single-factor model. |
| Date: | 2026–08–05 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:nam4c_v1 |
| By: | Vives, Xavier; Ye, Zhiqiang |
| Abstract: | We provide a spatial framework to study competition between banks and fintechs in the lending market and examine the impact on investment and welfare. Based on the key differences between banks and fintechs, we derive results consistent with the empirical evidence available. We find that fintechs with inferior monitoring efficiency can successfully enter because of their superior flexibility in pricing and that higher bank concentration leads to higher fintech loan volume. If fintechs and banks have similar funding costs, fintech borrowers pay lower loan rates and have higher default rates than bank borrowers with similar characteristics; however, the result will flip if fintechs have much higher funding costs than banks. The advantage of fintechs in offering convenience can also induce them to charge higher loan rates than banks. Fintech entry will improve welfare if fintechs have high monitoring efficiency and interfintech competition intensity is intermediate. Fintech entry may induce banks’ exit and reduce investment; however, it will increase investment if inter-fintech competition is intense enough. |
| JEL: | G21 G23 I31 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19245 |