Behavioral finance means a lot of things to a lot of people.
In this short piece I’ll outline what “behavioral finance” means to academic researchers.
The image below highlights the foundations of behavioral finance. There are 2 pieces to the puzzle: Limits to Arbitrage and Psychology.
Limits to Arbitrage
The efficient market hypothesis predicts that prices reflect fundamental value. Why? People are greedy and any mispricings are immediately corrected by arbitragers.
But in the real world, true arbitrage–profits earned with zero risk after all possible costs–rarely, if ever, exist. Most arbitrage-like trades involve some form of cost or risk. This could mean fundamental or basis risk, transaction costs, or noise trader risk.
Nick Barberis and Dick Thaler outline the fundamental misunderstanding with respect to the efficient market hypothesis (EMH). First, EMH correctly identifies that if prices are actually correct, there are no free lunches. Supporters of EMH often point to the extreme difficulty professional money managers have trying to beat simple passive indexes as evidence for efficient prices. However, the evidence that managers rarely add value doesn’t mean the EMH is correct. The limits to arbitrage concept highlights that evidence for no free lunch DOES NOT necessarily imply prices are correct.
- Oranges in Florida cost $1 per orange.
- Oranges in California cost $2 per orange.
- The fundamental value of an orange is $1 (Assumption for the example).
- The EMH suggests arbitragers will buy oranges in Florida and sell oranges in California until California oranges drop to $1. Prices will quickly correct and there is no free lunch.
- But what if it costs $1 to ship oranges from Florida to California? Prices are decidedly not correct–the fundamental value of an orange is $1. But there is also not a free lunch. So therefore, No Free Lunch does not imply prices are correct. QED.
What can cause limits to arbitrage?
Consider an example where Ford has a fundamental value of $10 and GM has a fundamental value of $10. Currently, Ford trades at $8 and GM at $10. In order to complete the arbitrage, a trader would need to buy Ford, but then simultaneously hedge market risk and car company risk. GM is probably a good option, since it is a car company and shares some of the same fundamental risks as Ford. The trade is to long Ford, short GM.
What are the potential issues with this “arbitrage” trade?
- Idiosyncratic risk–What if an earthquake destroys Ford HQ, but GM finds a hidden gold mine buried under one of their manufacturing plants?
- Noise trader risk–markets can remain irrational longer than you can remain solvent. What if Ford drops to $6 and GM goes to $15?
- Horizon risk–What if it takes 10 years for Ford to reach fundamental value? One may have been better off investing in treasury bills.
- Transaction Costs–B/A spreads? Price impacts? Short sell costs (rebate fees)?
After considering the various limits of arbitrage, the mispriced Ford shares, while interesting, are certainly not a free lunch.
Some other examples:
Royal Dutch/Shell parity. Royal Dutch owns 60% of the cash flow rights; Shell owns 40%. In theory, the price of Royal Dutch should equal 1.5 times the price of Shell.
Relative Value Arbitrage. Negative stub investments are a classic case of “arbitrage.” But as Mitchell, Pulvino, and Stafford (2002) show, it ain’t a free lunch!
Porsche/Volkswagen. Who can forget the massive short squeeze on the “great trade” hawked by every hedge fund on the planet. They all got their faces ripped off…
The literature from psychology makes it fairly clear that humans are not 100% rational all the time. As we have mentioned many times on this site, Kahneman tells a story of 2 modes of thinking: System 1 and System 2. System 1 is an efficient heuristics-based decision-making component of the human brain. System 2 is the analytic and calculated portion of the brain–~100% rational. Unfortunately, the efficiency of System 1 comes with drawbacks–we can make instinctive decisions that are irrational.
Below I laundry list a plethora of bias that can affect investment decisions:
- Self-attribution bias
- Endowment effect
- Status Quo/Default effect
- narrow framing
- mental accounting
- Hot hand fallacy
- Availability/Saliency bias
- And so forth…
My favorite bias: Overconfidence. Shark jugglers are a good example.
How do Limits of Arbitrage and Psychology interact to create behavioral finance?
Well, academics like to study things that are actually interesting. As stand-alone topics, limits of arbitrage (also referred to as market frictions) and psychology are interesting, but they have limited mojo to get published in top-tier academic finance journals.
Nonetheless, what turns researchers on is when they can identify anomalous empirical findings and explain them in an intuitive and interesting way. Examples of what doesn’t work:
- If some investors were simply stupid (suffering from cognitive bias), this would not be interesting to financial economists. Okay, so some investors are stupid, but professional traders counteract these dumb traders and prices are unaffected. No results in the data. Research paper = denied at journals = no tenure.
- Markets have frictions. Investors pay transaction costs. We all know this. Research paper on transaction costs = denied at journals = no tenure
However, combining a story that involves elements of silly investors and limits of arbitrage is a potent combination. For example, consider the concept of noise traders. Brad De Long, Andrei Shleifer, Larry Summers, and Robert Waldmann wrote an article called, “Noise Trader Risk in Financial Markets” in the Journal of Political Economy in 1990. I’d say this a pretty influential group of economists!
Here is the abstract from the paper:
We present a simple overlapping generations model of an asset market in which irrational noise traders with erroneous stochastic beliefs both affect prices and earn higher expected returns. The unpredictability of noise traders’ beliefs creates a risk in the price of the asset that deters rational arbitrageurs from aggressively betting against them. As a result, prices can diverge significantly from fundamental values even in the absence of fundamental risk. Moreover, bearing a disproportionate amount of risk that they themselves create enables noise traders to earn a higher expected return than rational investors do. The model sheds light on a number of financial anomalies, including the excess volatility of asset prices, the mean reversion of stock returns, the underpricing of closed-end mutual funds, and the Mehra-Prescott equity premium puzzle.
Combining biased investors with the realities of the marketplace can create some potent and publishable research.
This combination also describes what behavioral finance is all about: How does cognitive bias, in conjunction with limits of arbitrage, create interesting impacts on the marketplace.
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