Tesla is a good example of why alternative data hasn’t helped the hedge fund industry as much as hoped - even if you knew sales or earnings in advance, it’d have been hard or impossible to make $. We live in cash flow from financing world, not cash flow from operations world
Hmm is that true? Have never tested, but I feel like a portfolio which is long biggest future earnings beats, short misses, adjusted for size/sector, would do extremely well even over the last 2-5 years.
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My response to “why hasn’t alternative data helped the hedge fund industry” is two-fold. First, much of the data is bad (eg low breadth, short history, poor data quality, not updated in a timely manner etc) or useless (already baked into earnings estimates or reflected in price)
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Second, it’s not true that alternative data hasn’t helped. Some of it has been and still is very predictive, you just need to know where to look (and have tested a lot of data sets out of sample).
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Not sure if how much alpha it has anymore since so many eyes are on TSLA these days... But Musk used to love bringing forward earnings calls by a few weeks every time they had a large beat.
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trying to do the arb?
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AQR does something sort of like this to show why value has sucked. Knowing future earnings expectations obviously would gives you a great sharpe ratio but has deteriorated in the last few years meaning knowing future fundamentals have mattered less. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3554267 …pic.twitter.com/vBWnnA0zUa
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Think it should be “Tesla is a good reminder that alternative data, like other data, can not be applied to individual names and should be used in portfolio manner instead.”
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Tesla such an outlier on several fronts that it's not a good example - Alex says alt data, I say alt KPIs. You're talking about an earnings revision factor. Generally great to know future earnings, but sector-neutral, would be like pod HF book, with associated crowding risk.
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Some shops abandoned that exact strat last yr, esp w credit card. It’s a 1ish sharpe strat. Gotten worse lately. Part of the issues is biases in the data, but also because participants start crowding into names as panels are released - and get caught wrong footed together
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