Abstract
Existing literature has extensively discussed the impact of economic policy uncertainty (EPU) on firm-related activities, but there is sparse evidence of its impact on the behavior of institutional investors. Using quarterly U.S firm-level data for 1980Q1-2020Q4, we find heterogeneous responses of institutional investors to EPU shocks to different horizons. Specifically, long-term institutional investors respond positively to EPU shocks, while short-term institutional investors reduce their holdings during periods of uncertainty. We posit that different expectations about the future of firms between long- and short-term investors may account for the heterogeneous responses. We test this hypothesis by investigating how firm growth opportunities, volatility, and investment activity influence the relationship between EPU and institutional investor horizons. The results show that the positive (negative) effect of EPU on long-term (short-term) institutional investors becomes stronger for firms with higher growth opportunities, higher volatility, and more investment. Our paper has important economic implications that the countercyclical behavior of long-term institutional investors improves firm value and liquidity during uncertain periods.
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Notes
Our sample period starts from 1980 when the Security Exchange Commission started to require institutional investors with more than $100 million in assets under management to report their holdings every quarter (Lewellen and Lewellen 2022). According to Wharton Research Data Services, “The Thomson/Refinitiv Institutional (13f) Holdings are available on WRDS as part of the Thomson Financial Network (TFN) group with holdings data starting in the first quarter of 1980. The data provide detail on holdings of US equities, and a limited set of other securities, including foreign stocks, and are based on documents filed by registered investment companies and professional money managers." Our sample period ends in 2020 to avoid the potential impact of COVID-19. We have added the discussion for the justification of our sample period in the revised manuscript.
For more details on variable construction, see https://www.policyuncertainty.com/us_monthly.html
Several other measures are also used in existing literature. For instance, Gulen and Ion (2016) uses the first dimension of the DW-NOMINATE scores and calculates the political polarization as the average of these scores for the Republican party members in the Senate minus the average for the Democratic party members in the Senate. Xu (2020) considers a simple political polarization defined as the average dispersion about bills or joint resolutions considered in the House across both parties. Specifically, it is calculated as \(\frac{1}{N} \sum _{n=1}^N 1-|Yea_{n,t}\% - Nay_{n,y}\%|\), where \(Yea_{n,t}\%\) (\(Nay_{n,t}\%\)) is the percentage of Yea (Nay) votes among all votes for bill n in year t. N is the total number of bills or joint resolutions in year t. Note that this measure does not distinguish the disagreement between Republican and Democratic legislators in the House. Clearly, our measure of political polarization is an extension of Xu (2020) by highlighting the disagreement between two parties. As a robustness check, we also construct Xu (2020)’s measure and find the correlation between these two is 0.673. In unreported results, we obtain similar estimate results.
We only report the results using the news-based EPU and the predicted news-based EPU to simplify the discussions as the results are robust to other EPU and IV measures.
Since the illiquidity is measured as the average of the daily Amihud (2002) illiquidity measure within a quarter, a higher value of the measure entails lower stock liquidity.
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Wang, X., Wei, S. & Zhu, X. Economic policy uncertainty and heterogeneous institutional investor horizons. Rev Quant Finan Acc 62, 39–67 (2024). https://doi.org/10.1007/s11156-023-01191-y
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DOI: https://doi.org/10.1007/s11156-023-01191-y