January 15, 2020

Proportional Response

Proportional Response is the art of defusing geopolitical conflict.

Proportional Response is also efficient effort. With another earnings season underway in the stock market, efficient effort should animate both investment decisions, and investor-relations for public companies.

I’ve mentioned the book The Man Who Solved the Market, about Jim Simons, founder of Renaissance Technologies. There’s a point where an executive is explaining to potential investors how “RenTec” achieves its phenomenal returns.

The exec says, “We have a signal. Sometimes it tells us to buy Chrysler, sometimes it tells us to sell.”

The investors stare at him.

Chrysler hadn’t been a publicly traded stock for years (they invested anyway – a proportional response).

That executive – a math PhD from IBM – didn’t care about the companies behind stocks. It didn’t bear on returns. There are vast seas of money rifling through stocks with no idea what the companies behind them do.

RenTec is a quant-trading firm. Earnings calls are irrelevant save that its models might find opportunity in fleeting periods – even fractions of seconds – to trade divergences.

Divergences, tracking errors, are the bane of passive money benchmarked to indexes. If your stock veers up or down, it’ll cease for a time to be used in statistical samples for index and exchange-traded funds (ETFs).

And hedge funds obsess on risk-adjusted returns, the Sharpe Ratio (a portfolio’s return, minus the risk-free rate, divided by standard deviation), which means your fundamentals won’t be enough to keep you in a portfolio if your presence deteriorates it.

Before your eyes glaze over, I’m headed straight at a glaring point.  Active stock-pickers are machinating over financial results, answers to questions on earnings calls, corporate strategy, management capability, on it goes.

On the corporate side, IR people as I said last week build vast tomes to help execs answer earnings-call questions.

Both parties are expending immense effort to achieve results (investment returns, stock-returns). Is it proportional to outcomes?

IR people should have their executive teams prepared for Q&A. But let’s not confuse 2020 with the market in 1998 when thousands of people tuned to Yahoo! earnings calls (that was the year I started using a new search engine called Google).

It’s not 2001 when about 75% of equity assets were held by active managers and some 70% of volume was driven by fundamentals.

It’s 2020. JP Morgan claims combined indexed money, ETFs, proprietary trading and quant funds are 80% of assets. We see in our data every day that about 86% of volume comes from a motivation besides rational thought predicated on fundamental factors.

Proportional Response for IR people is a one-page fact sheet for execs with metrics, highlights, and expected Q&A.  The vast preparatory effort of 20 years ago is disproportionate to its impact on stock-performance now.

Proportional response for investors and public companies alike today should, rather than the intensive fundamental work of years past, now incorporate quantitative data science on market structure.

IR people, don’t report during options-expirations. You give traders a chance to drive brief and large changes to options prices. Those moves obscure your message and confuse investors (and cause execs to incorrectly blame IR for blowing the message).

Here’s your data science: Know your daily short-volume trends and what behaviors are corresponding to it, and how those trends compare to previous quarters. Know your market-structure Sentiment, your volatility trends, the percentages of your volume driven by Active and Passive Investment and how these compare to past periods.

Put these data in another fact sheet for your executive team and board.  Provide guidance on how price may move that reflects different motivations besides story (we have a model that does it instantly).

Measure the same data right after results and again a week and a month later. What changed? If you delivered a growth message, did growth money respond? That’s quantitatively measurable. How long before market structure metrics mean-reverted?

Investors, data science on market structure isn’t another way to invest. It’s core to predicting how prices will behave because it reflects the demographics driving supply and demand.

There are just a thousand stocks behind 95% of market cap. You won’t beat the market by owning something somebody else doesn’t. You’ll beat it by selling Overbought stocks and buying Oversold ones.  Not by buying accelerating earnings, or whatever.

The stock market today reflects broad-based mean-reversion interspersed with divergences. RenTec solved the market, we’re led to conclude, by identifying these patterns.  The proportional response for the rest of us is to learn patterns too.

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