Regret, valuation, and belief.

This will again be one of more speculative slash axiomatic type posts, mostly as an answer to the reason I’m reluctant to trust social data based trading strategies. From my limited experience, social sentiment is excellent for marketing that you do social data, and not terrible useful in practice. To explore why, we can consider the following dimensions:

  • Social data is noisy - Financial time series in general is a dimension of incredible noise, and in general models assume a strong stochastic component to various processes simply to match empirical observation. Social data, much like financial data, tends to not only have strong stochasticity but high dimensionality (suffering from the “curse of dimensionality”) and tends to resist many statistical learning techniques.

  • Social data is coincident - In general, most social data based strategies follow a fairly similar meta-strategy of “look at some quantized social interaction on some/multiple platforms, compare to baseline, and increase in relevant interaction is bullish, decrease is bearish”. There is nothing intrinsically wrong with this approach, but in practice it tends to not be predictive - rather, it’s difficult to argue there’s a stable lead-lag correlation between market metrics and social data. While you see it in short intervals and specific assets, any kind of predictive relationship usually falls apart at statistical scrutiny. This fails bidirectionally:

    • There are many cases where assets with strong social momentum tend to decline in value or otherwise fail to increase in value.

    • There are similarly many cases where assets with weak social momentum rise rapidly.

    That said, if we look at SwaggyStocks, a website service collating social media data from r/WallStreetBets, we can actually see a lagging social media relationship with price for CLOV:

    We can see pretty clearly in CLOV and other recent examples that certainly substantial increases in price, especially of former meme stocks (CLOV being one of Chamath’s SPACs) causes an influx of social attention, which may or may not coincide in magnitude with price increases (there usually isn’t a strong lead-lag effect with whether the top in social or price occurs first). However, it’s a much harder argue to look at this and assume social media is a good barometer of price increases to come, versus the other way around.

  • Sticky communities play liar’s poker: Online communities largely coalesce among some non-financial form of user incentivization; for example, karma in the case of reddit, or some metric like engagement or number of followers on Twitter or StockTwits. This reflects in agent (user) bias in three major different ways:

    • Clout chasers - These users tend to post in order to optimize the incentivization metric of the platform, in order to win “clout”.

    • Clout deniers/neutral - These users tend to post honestly, without regard for platform incentivization.

    • Captive influencers - These users tend to have a known social or financial incentive for posting outside of the platform itself. An example would be a corporate Twitter account.

    We can think about it simply as agents on an online platform interact an iterated game between content publishers and readers, with the content publishing agents looking to maximize utility largely according to maximizing the platform incentive (in the case of captive influencers, this is not the only rationale). In general, in matters of opinion, readers tend to prefer to provide incentive to publishers who act as honest social signalers - influencers who have a track record of honesty, or at least influencers who haven’t been caught or rumored to lie. This in theory acts as a platform self-moderation mechanism — influencers may trivially have an incentive to lie (for financial reasons, or simply to maximize the platform incentive of engagement or other factors), and the implicit penalization of dishonest social signalers serves to over time keep influencers “honest” (or at least not obviously lying). The iteration here is important - lies are often caught after an interaction, and for a single interaction between a reader and content publisher it’s a simple dominant strategy to lie (since there’s no cost, even future discounted) in order to maximize the incentive.

    However, in practice this hardly works perfectly, and is, like most network interactions between agents, largely dependent on risk versus reward. On a platform with easily fungible, anonymous identities such as Reddit, there’s strong asymmetry in risk versus reward non-linearly scaling with followers/prestige — at the low end of karma or for a new account, the payoff for “blowing up on reddit” is high even with lying, while the worst case (being caught lying and forced to make a new account) is fairly minor. Conversely, for a blue checkmark’d public figure with respectability in a field (for instance, Jim O’Shaughnessy of OSAM if he were to recommend some equity), there would be a much higher cost of being deemed a liar, versus gain from publishing misleading content for platform or financial gain.

    In our model above (clout chasers/clout deniers/captive influencers), we can determine that the most valuable social barometer for our data is clout deniers. Unlike clout chasers or captive influencers, their opinions should be an honest reflection fo what they believe in, and hopefully intend to do. We don’t know who they are, though. It isn’t like individuals are labelled - even captive influencers aren’t necessarily obvious, and can be subject to hidden conflicts of interest (e.g. paid promotion or job or familial links). Similarly, there is no “easy” proxy here - while in general larger, influential accounts tend to be clout chasers (for example, yours truly), they tend to have more compunction for honesty, given they persist over many iterations and hence incur more cost for being deemed liars.

    To make matters worse, there is a secondary dimension of cost in iterated social games - in-group dynamics. To keep it fairly straightforward, especially in insular communities whether intentional or not, cultural norms develop, largely created and maintained by influential members of the community (memetics). In r/WallStreetBets, for example, there is a strong in-group dynamic for “diamond handing” and risky, large bets on cult stocks. Admitting weakness and “selling early” is always cause for some light chastisement, especially during a high momentum period for a stock. However, the dominance of belief in a community shows strong proportionality to the cost of admitting heterodox belief — as we can see in the GME and AMC communities currently, for example, it is anathema to admit you sold, and treason to amplify flaws in cherished or popular-believed due diligence. This can present a paradox to the content creator between honest social signaling and in-group dynamics — if I disagree with a dominant community belief, I will be penalized, even if it is my honest social signal. In those scenarios, it is perhaps the dominant strategy for an influencer to abstain from commenting, thereby further skewing social sentiment-based data.

The Regret Principle

One of the anti-crypto rallying cries is that Bitcoin has no intrinsic value, which superficially is true, depending on what intrinsic actually means. The issue is that, with few exceptions, valuation is less a science and more an art — even fundamental bases of valuation tend to be based on a shared notion of “what is true” rather than a fixed, scientific relationship. The Shiller P/E, for example, the rallying cry of those who believe in our Everything Bubble, isn’t an axiomatically correct way to view the market; it is simply a metric with an empirically determined average (based on historical data, which I frequently talk about as a flawed approach), from which we have strongly deviated. Does this, though, mean the valuation of the whole market has strongly detached from reality? It’s hard to say.

A much more salient example here, however, is our favorite shiny weird rock, gold. Gold is one of those substances where we all know intuitively it is valuable, but never give much thought into why. There are certainly some industrial and productive applications of gold, and I’ll admit that gives it some non-zero economic value. But that value is still far less than the market value of gold. We have a shared belief gold is valuable, and this belief, more than anything, drives the price of gold. When the specter of hyperinflation or some bourgeoisie fantasy of the apocalypse (as Travis Kimmel noted, gold is essentially an options spread on the apocalypse — it becomes important when the world is fucked up, but not too fucked up) looms, gold becomes the zeitgeist, and gains value. When the world is fine and the economy is booming, gold shrinks in value.

At the end of the day, the basal truth of valuation is that it is simply what someone agrees to pay you for something (this is only true for marketable assets, of course). Our models exist as a contextual crutch on this, allowing us to believe (which holds true for the most part) that there is a scientific method for understanding and predicting the evolution of value.

And this understanding of value allows us finally to come back to our understanding of social data and interactions. Trivially, there must be value in the noise called social media — the market is human, and the nucleus of most price action in the markets tends to be human-driven (perhaps citation needed here). However, the key insight or linkage here comes from understanding what value is - value is an expression at the time of transaction of belief. When I buy or sell an asset at a certain price, explicitly I am providing information to the world of my view of the true price of the asset. When a transaction occurs, we are making two separate expressions of belief:

  • As a seller, I believe the risk of downside of continuing to hold the asset is greater than or equal to its upside (this does not factor in forced selling, and can be modified to include opportunity cost).

  • As a buyer, I believe the risk of downside of buying the asset is less than or equal to its upside (similar comments about forced buying and opportunity cost).

The essentially factor here is regret - what quantifies the strength of my belief in the asset is not how large my upside is, but how large my downside is. When I take a long position in an asset, I am trading the chance of regret of a downwards move for the upside exposure I receive.

We can codify this as the regret principle - as value is an expression of belief, the measure of an actor’s belief is proportional to the amount of regret they will incur in the worst case.

This is the key factor which will allow us a framework to understand the financial value of social data. Social data is not made equal - under the regret principle, we can explain the heterogenous behavior of our clout chasers/clout deniers/captive influencers, large vs small accounts, and anonymous versus non-anonymous account. Actions with strong ability to cause regret are bullish — it is quite likely that someone who publishes a photo of getting a Monster Energy tattoo, assuming they know how to read, would also similarly buy and evangelize MNST stock.

This is why expressions like espoused by Udi Wertheimer are darkly correct and bullish—cult-like behavior drives price. The more risk incurred by the evangelists of the community, the more gallows humor, the more exposure—the better.

I’m going to go more into the regret principle in later posts, and how that shapes interactions, but I hope this was a good introduction. As your neighborhood clout chaser, feel free to incentivize to remain an honest social signaler by smashing that like and subscribe button.

Goodnight,

Lily Francus