Estimating the effects of a slow news cycle

A the moment, the collapse of Silicon Valley Bank is the dominant story in the news cycle. It seemed like a big deal to me at first, then less of a big deal, then of enormous consequence again. At the moment, my estimation has settled into “A negative event that will hurt some people but will only be of long run consequence unless it yields sufficiently bad new economic policy out of it i.e. receive a bailed that entirely shields them from consequences. But honestly I don’t know. My estimation really shouldn’t move your priors too much unless you were previously sitting at one of the extremes of “Nothing actually happened” or “This is the beginning of a new Great Depression”. I’m quite confident neither of those is correct. If you want a solid accounting, read Noah Smith’s post. I think he probably nailed it.

What I do want to consider is enthusiasm within the “take marketplace” for breathless concerns this was the beginning of a financial meltdown, a desperate situation that calls for a federal bailout, the beginning of inevitable hyperinflation, evidence even that catastrophic consequences of “wokeism” for <checks notes> risk hedging within bank portfolios. All of these seem somewhere between overwrought and stupid, all got a non-trivial amount of oxygen within the news cycle. [UPDATE: Depositors were maintained through federal liquidity, shareholders were not bailed out. Seems pretty reasonable to me.] Many takes were no doubt motivated by personal assets at stake or economic hobbyhorses, but I’m more concerned with how much traction they got than their origin stories.

So here’s a research idea so quarter-baked I haven’t even looked on google scholar to see if it’s been done, let alone would work. What is the relationship between a slow news cycle and pessimistic affect in event coverage? Here’s I’d go about it:

  1. Create an idex of news story variation. Variation in news coverage is an indicator that nothing is happening. When important things happen, they get covered alot, which means there is less variation in stories across outlets.
  2. Run an natural language algorithm for measuring “pessimistic affect” i.e. doomerism in news stories.
  3. Estimate the relationship between lagged news story variation and current pessimistic affect.
  4. ?
  5. Publish

The hypothesis is simple: when the news cycle is slow, outlets and pundits have an incentive to not just hype the importance of any event, but accentuate it’s potential negative consequences going forward so they can keep talking about it.

That’s it. Thats the idea. I hope you will include me in the acknowledgments when accepting your various research awards and accolades.

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