Are Financial Markets Too Short-Term?

Financial market prices embody info that helps others to make decisions. For example, firms decide activity levels based in part on their stock prices. Thus traders who add info to such markets do a public service, even if they do this for a private profit.

Such traders can choose to focus their info-collection efforts on “slow” info, which stays relevant for a long time, or on “fast” info, which is quickly forgotten. Many have said that such markets focus too much on fast info, relative to slow. In this post I will analyze this question. My tentative conclusion will be: yes, financial markets do indeed seem to focus too much on fast info. But first, let’s review the basics.

Each financial trade has an asset type, a buyer, a seller, a quantity, and a price. Each simple financial market trades one kind of asset, and its sequence of trade prices follows a random walk over time, a walk that reveals info about the value of that asset to observers. The expected price change variance during a time period is proportional to the amount of info revealed in that period.

Each trade happens via one trader first putting an offer into an “order book”, after which the another trader accepts that offer. While the act of posting a book order could reveal info to observers, it usually doesn’t. This is because a trader with substantial info prefers to instead profit from it by accepting a book order. If your info suggests that the price should rise, you buy, and if your info suggests that the price should fall, you sell.

However, the profits of traders who accept book orders come from the traders who posted those orders. So book order traders adjust their book prices to include the average info held by accepting traders. And competition typically moves book prices to where book traders make zero expected profits. There is a “bid-ask spread” between the “bid”, the highest book offer to buy, and the “ask”, the lowest book offer to sell. The size of this spread says how much info is expected to be embodied on average in each accepting trader.

However, some traders have little or no info. They instead want to trade for reasons other than profiting from info. If they could post competitive book orders, they should. But doing that well is hard. (For example, ~95% of book orders are cancelled before being filled.) So most low info traders instead accept book orders. Their trades lower the average info per trade, and thus allow traders with higher than average info to profit from their trades. These “fools” are the engine that drives the whole system.

For any given piece of info that a trader holds, they could profit more by trading a higher quantity at the same price. But those who make book orders foresee this strategy, and so their spread increases with order quantity; larger trades are presumed to carry more info.

As a result, a trader with an unusually big chunk of info prefers to reveal it more slowly over time, via a slower sequence of smaller trades (Vayanos, Kyle). And to avoid other traders noticing a pattern in their trades and jumping ahead to grab their profits, a trader who can find no other trades to hide among may need to make an apparent random walk of trades. For example, N2 trades on both the buy and sell side can hide N trades all on the same side.

So why not spread informed trades out over longer time periods? Because each piece of valuable info comes with a deadline. You can only profit from by telling a market about somethings that it will eventually learn in other ways. However, once many traders all know that many of them all have the same piece of info, then that info should be incorporated into the book order prices. Thus one can only profit by trading on such info before its everyone-knows-it deadline.

This duration-til-deadline varies greatly with info type. For example, slow info on future product fashions, or the success of innovation projects, may take years or decades to be revealed. In contrast, ~20% of trades are by “high frequency traders” (HFT), who typically trade on very fast info re prices in other markets. The deadline for the fastest HFT to arrive at a market with such other-market info is roughly when the second-faster HFT arrives. This is typically ~20-200 ns later for other markets at the same site, and ~50-500 μs for different sites (source: Kelvin Santos).

Thus five-year duration “slow” info is roughly a factor of a trillion to quadrillion times slower than HFT “fast” info. This huge dynamic range for info duration offers a big chance for duration effects to have big impacts. If there are problems with poor incentives re info duration, they could plausibly be really big problems. 

To evaluate whether financial markets focus too much on fast info, we should consider how social value, and also private trader costs and benefits, vary with info duration.

Let’s start with social value. As social value of info revealed to a market comes from its ability to influence decisions, decisions which are typically spread out across time, this value is roughly proportion to info size (i.e, price-change) times info duration. So, for example, if no relevant decisions are made using the market price in the few milliseconds duration of a high frequency trade, then the info in that trade induced zero social value.

Now let’s consider the private net revenue to be gained from a trade. As discussed above, that trade revenue is also proportional to info size times duration, at least for traders who have access to enough capital to support the required trading strategy, whose cost goes roughly as info size times duration.

How about trading costs? While there are fixed costs to design a trading strategy and arrange to implement it, and there can be mechanical marginal costs to execute a trade, the main other marginal cost is the opportunity cost of the assets used to make a trade. Any one asset can’t be simultaneously used to support an arbitrary number of arbitrary trades. The opportunity cost of these assets is also roughly proportional to info size times duration. (Yes, orgs that trade on margin and make many fast independent trades, may seem to face no opportunity costs of assets, but this is an illusion; they just have especially low opportunity costs per trade.)

So far all the factors we’ve considered have depended in the same way on duration; social value, trade revenue, and marginal trading cost all go as info size times duration. But a few considerations remain that depend differently.

For example, traders often do not have sufficient capital to fully profit from info that has a very large size times duration. In addition, long duration info apparently comes in larger chunks, which makes size and duration positively correlated. For example, an insight about whether some product innovation will succeed over the next decade is usually just a much bigger chunk of price-change-times-duration than is the last market price tick typically used by a HFT trader. This effect suggest insufficient attention to long duration info.

Finally, ambitious traders, and the systems that train and select them, prefer that traders show their abilities over many small fast trades, instead of over a few big slow trades. It is just not very useful to prove your trading abilities via finding and trading on info that takes decades to be proven right. This effect also suggests insufficient attention to long duration info.

Bottom line: while social value, trading revenue, and marginal trading cost all scale as price-change times info duration, the existence of large info chunks and the desire to prove trader abilities over career-sized durations suggests that financial markets pay too much attention to fast, relative to slow, info.

In my next related post, I’ll discuss how alternative trading institutions might mitigate this problem.

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