The term “market noise” refers to transactions that are erratic and unrelated to fundamental value. Theory suggests that without market noise profitable trading would be impossible. Yet, while irrational and erratic trading may occur, most of what we call “noise” reflects rationality disguised by complexity. Illustrating that point, a new paper shows that the effect of rebalancing cascades on the net demand for individual assets is not predictable, even if we know everything about the underlying rules and if they are fully rational. Predictions become infeasible because of alternating buy and sell orders, feedback loops and threshold-based execution rules. This cautions against dismissing seemingly non-fundamental market flows as irrational and betting against them.

Chinco, Alex and Vyacheslav Fos (2019), “The Sound Of Many Funds Rebalancing”, CEPR discussion paper 13561.

The below are excerpts from the paper. Emphasis and cursive text have been added.
The post ties in with the SRSV summary on price distortions.

The importance of market noise

In a market without noise, a trader who discovers that an asset is under-priced cannot take advantage of this discovery. The moment he tries to buy a share, other traders will immediately realize he must have uncovered some good news. And, no one will agree to sell him any shares at the old price.”

“Noise pulls the rug out from under this no-trade theorem. In a market with noise, there are always some shares of the asset being bought or sold for erratic non-fundamental reasons. So, if the trader tries to buy a share, other traders will not immediately realize he has uncovered some good news. His buy order might just be some more random noise. This cover story allows the trader to profit from his discovery.”

A plausible origin of apparent market noise

“This paper proposes an alternative noise-generating mechanism: computational complexity. In modern financial markets, the same asset is often held for completely different reasons by funds following a wide variety of threshold-based trading rules. We show that, under these conditions, it is computationally infeasible to predict how the various trading rules will interact with one another. As a result, the net demand coming from an interacting mass of funds will appear random even if market participants are fully rational and the individual trading rules involved are simple.”

“To show how computational complexity generates noise, we study a theoretical model motivated by three common features of modern financial markets. First, we assume that there are a large number of funds, which follow a wide variety of different trading rules. This statement is self-evident, and it applies…to hedge funds as to mutual funds, pension funds, algorithmic traders, and index funds. Second, because the market is populated by such a large heterogeneous collection of funds, we assume that the same asset is often held by different funds for completely different reasons. For example, one fund’s value stock might be another fund’s low-volatility stock. Third, we assume that many funds use threshold-based trading rules.”

When there are so many different funds with overlapping holdings using so many different threshold-based trading rules…the problem of determining how that asset will be affected (buy? or sell?) is computationally infeasible. .. Complexity can generate noise even if individual agents are following extremely simple decision rules… There is no way to guess how a rebalancing cascade will affect an asset by examining the set of rebalancing rules involved, even though these rules are completely deterministic.”

“In a large market, it is computationally infeasible to predict the demand coming from a rebalancing cascade in response to any initial shock…The sign of the resulting demand shock may as well be thought of as a coin flip, i.e. it may as well be noise. And, this remains true even if agents are fully rational and each fund involved in the cascade follows a simple deterministic trading rule.”

“[There are] three key features that make rebalancing cascades so hard to predict.

  • Rebalancing cascades are only hard to predict if they involve alternating sequences of buy and sell orders… Alternation is a ubiquitous feature of modern financial markets. When a fund rebalances, it necessarily exchanges an existing position in one asset for a new position in another. And, there are many funds following a wide variety of different trading rules…
  • Rebalancing cascades are only hard to predict in a market structure that involves cancellation due to feedback loops. It is important that different cascade paths have the potential to cancel each other out… There is no central-planning committee that limits the number of funds holding a single asset. There is nothing stopping 20 different smart-beta ETFs from holding the same company at the same time for different reasons. Thus, the associated collection of rebalancing rules will contain market structures with feedback loops.
  • Rebalancing cascades are only hard to predict if funds follow threshold-based trading rules… Without threshold-based rebalancing rules, longer cascade paths would necessarily have smaller effects on the demand for stock.”

Consequences of rebalancing noise

Market participants will treat demand shocks as random noise even if they are fully rational. This noise-generating mechanism can produce noise in a wide range of markets and also predicts how noise will vary across assets.”

“An asset’s demand might appear random, not because individual investors are actually behaving randomly, but because it is too computationally complex to predict how a wide variety of simple deterministic trading rules will interact with one another.”

“We empirically verify predictions using data from ETF Global on the daily holdings of ETFs. We document that stocks on the cusp of more ETF rebalancing thresholds experience more noise.”

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Ralph Sueppel is founder and director of SRSV Ltd, a research company dedicated to socially responsible macro trading strategies. He has worked in economics and finance for almost 25 years for investment banks, the European Central Bank and leading hedge funds. At present he is head of research and quantitative strategies at Macrosynergy Partners.