where is the number of explanatory variables. can be estimated by regressing log change in asset prices on log change in oil prices over the sample period using the standard instrumental variable approach, with the instrument LL : αα ∆ss (11) ww Simple algebra shows that the estimated value of is _asymptotically_ identical to (9). To assess the effect αα̂ = (ww<sup>′</sup> ∆pp)<sup>−1</sup> (ww<sup>′</sup> ∆ss) of oil prices on U.S. oil firms’ stock prices, we set up the regression framework as follows: αα = + − + + − + f<sup>+εε</sup><sup>**(12)**</sup> where is logarithm change in the stock price of firm ii,tt 00 11 ii,tt 11 22 tt _i_ at day 33 tt 11 _t;_ and ii tt is logarithm change in firm ∆yy ββ ββ ∆yy ββ ∆̂pp ββ ∆̂pp i’s stock prices at day t-1; is logarithm change in WTI oil price at day t, instrumented by _w_ ; −1 is ii,tt ii,tt logarithm change in WTI oil price at day t-1; ∆yy f captures firm fixed effects. Standard errors are ∆yy tt tt clustered at the firm level. ∆̂pp ∆pp ii To assess the role of debt, we set up the regression framework as follows: = + − + + − + ̂× + f<sup>+εε</sup><sup>**(13)**</sup> where ̂× is the interaction between logarithm change in WTI and firm i’s debt at the end of ii,tt 00 11 ii,tt 11 22 tt 33 tt 11 44 tt ii ii tt ∆yy ββ ββ ∆yy ββ ∆̂pp ββ ∆̂pp ββ ∆pp DDffbbtt 2019, instrumented by . tt ii ∆pp DDffDDtt **Identifying Oil-Specific Events** ii ww ×̂DDffDDtt Identifying oil-specific events or news (as opposed to aggregate demand news) is challenging. We define oil-specific news to be news exclusively affecting the current or potential future oil supplies. An example of news on future oil supplies is OPEC agreeing to production change, which could cause market participations to update their demand for oil on their expectation that oil supply will change in the future.

There is no fixed calendar for oil-specific events. Hence, we screened these event days from financial news. Since multiple events could happen on any one day, we employ several rounds of screening to identify oil-specific events. First, we used the “Seeking Alpha” news portal. Seeking Alpha is a community-based platform for investment research, with broad coverage of stocks, asset classes, ETFs, and investment strategy.<sup>12</sup> “Seeking Alpha” records all surprising events and announcements that arguably affect current or future oil supply. From 1/1/2020 to 6/30/2020, we identified 36 events. We restrict our attention to the six-month window to focus on the period when the oil market crashed. The window for our event study is one day. We examine the change in financial markets on the following trading day for announcements after trading hours. The relevant dates are in Appendix Table A2, with links to in-depth financial news discussing the events.

There are potential concerns with this approach. First, recorded events could reflect ad-hoc expost explanations by the analysts. For example, an analyst might see oil prices drop and look for news about current or future oil supply that could explain the event. In other words, oil prices drop because of aggregate demand factors, but the analysts interpret this as oil-specific shocks. We minimize the ad-hoc ex-post problem by _not_ considering the days with important aggregate demand announcements recorded

> 12 In contrast to other equity research platforms, insights are provided by investors and industry experts rather than sell-side analysts. Seeking Alpha has 4 million registered users, about 500,000 to 1 million unique users per day as of August 2013, over 18.5% of the audience are financial professionals, making it one of the biggest investment-related social media websites in the US. We use Seeking because it is popular among investors and because it is a free, opensource platform from which we could draw news about oil prices. See Chen et al (2014) for an example of usage of Seeking Alpha to study how social media affects stock values.

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