This paper studies the implications of disclosure repetitiveness on firm performance, information processing costs, and future stock returns. I propose a compression-based method to measure disclosure repetitiveness, which is assumption-free with respect to the underlying language models. I then decompose my measure into news repetitiveness ratio (NRR) and stale-news repetitiveness ratio (SNR), which allow me to separately analyze managers' disclosure behaviors regarding new and old contents compared to the previous year. In contrast with previous studies, I find that operating performance and filing announcement returns are positively (negatively) correlated with NRR (SRR) and that investors under-react to information in NRR. A portfolio with long (short) positions on high (low) NRR stocks generates value-weighted alphas of 5%-7% per annum. These results are consistent with the notion that managers present good (bad) news with more (less) repetition and repeat stale news to obfuscate unfavorable information, while the investors incorporate this information slowly due to limited attention.
| Date of Award | 2021 |
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| Original language | English |
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| Awarding Institution | - The Hong Kong University of Science and Technology
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| Supervisor | Chu ZHANG (Supervisor) |
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Asymmetric news repetition and the cross-section of stock returns
LI, R. (Author). 2021
Student thesis: Doctoral thesis