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Yin, A. (2024) Unravelling managerial bad news hoarding: evidence from stock price crash risk. PhD thesis, University of Reading. doi: 10.48683/1926.00117930
Abstract/Summary
With three empirical essays, this thesis endeavours to contribute further empirical insights into stock price crash risk (managerial bad news hoarding). Specifically, the first essay (Chapter 3) investigates the impact of board gender diversity on mitigating future stock price crash risk. The second essay (Chapter 4) and third essay (Chapter 5) delve into the effects of managerial bad news hoarding on corporate innovation and financial analysts’ forecasts, respectively. The findings reveal a positive correlation between board gender diversity and a reduction in stock price crash risk. This effect is attributed to the reinforcement of board monitoring mechanisms and the alleviation of agency conflicts. Notably, firms with three or more female independent directors exhibit a more pronounced decrease in future stock price crash risk, supporting the critical mass theory. Furthermore, this thesis empirically establishes that firms inclined towards withholding bad news demonstrate higher levels of corporate innovation, both in terms of input and output. This phenomenon stems from managerial overconfidence spurred by the tail events associated with stock price crash risk, leading to increased innovative activities. Additionally, the practice of bad news hoarding enables managers to alleviate short-term market pressures, allowing a focus on implementing strategic innovation initiatives. In addition, this study identifies a positive correlation between managerial bad news hoarding and the quality of financial analysts’ forecasts, characterised by enhanced accuracy and reduced forecast dispersion. This relationship is driven by intensified herding behaviour among financial analysts when forecasting firms with higher levels of bad news hoarding. Analysts’ herding behaviour leads to reduced forecast dispersion and facilitates the aggregation of valuable information, ultimately improving forecast accuracy.
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| Item Type | Thesis (PhD) |
| URI | https://reading-clone.eprints-hosting.org/id/eprint/117930 |
| Identification Number/DOI | 10.48683/1926.00117930 |
| Divisions | Henley Business School |
| Download/View statistics | View download statistics for this item |
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