2026-09-19T03:47:57Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1389262022-01-15T03:39:27Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Chen, Hui
Yu, Jiaheng
Sloan School of Management
2022-01-14T14:38:20Z
2022-01-14T14:38:20Z
2021-06
2021-06-03T17:55:19.736Z
https://hdl.handle.net/1721.1/138926
I quantify how information frictions and learning from financial markets affect resource misallocation. I develop a dynamic model that features financial markets guiding managers in large investment decisions – mergers and acquisitions. Due to information frictions, mis-valuation of own firms and the potential gain from mergers and acquisitions prevent socially beneficial resource reallocation from happening. Compared to David et al. (2016), learning from the financial markets accumulates
over time, and also occurs upon the announcement of the mergers and acquisitions. In the structural estimation, I target novel data moments including sensitivity of merger deal cancellation to announcement period returns to identify learning. The estimates suggest that a 50% decline in stock price informativeness locally would lead to 1.64% output loss for the US economy.
S.M.
Massachusetts Institute of Technology
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http://rightsstatements.org/page/InC-EDU/1.0/
Learning from Financial Markets and Misallocation
Thesis
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Master
Master of Science in Management Research
Publication
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<Abstract>I quantify how information frictions and learning from financial markets affect resource misallocation. I develop a dynamic model that features financial markets guiding managers in large investment decisions – mergers and acquisitions. Due to information frictions, mis-valuation of own firms and the potential gain from mergers and acquisitions prevent socially beneficial resource reallocation from happening. Compared to David et al. (2016), learning from the financial markets accumulates
over time, and also occurs upon the announcement of the mergers and acquisitions. In the structural estimation, I target novel data moments including sensitivity of merger deal cancellation to announcement period returns to identify learning. The estimates suggest that a 50% decline in stock price informativeness locally would lead to 1.64% output loss for the US economy.</Abstract>
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