Fear, Greed & the Whitepaper Expectation Fallacy

The impact that behavioral biases exhibit on the financial markets has been well documented and studied by Nobel Prize recipients and scholars throughout numerous white papers. These findings tend to arrive at similar conclusions: value investing outperforms over market cycles, and momentum is a positive contributor to various investment styles.

But here is the problem: these well-thought-out papers have shaped a perception of how value "should" work and in what markets value investing "should" outperform. Are these perceptions correct—or do they just create additional biases? Have the studies created a fallacy of their own when it comes to the expectations of real-world value investing?

We call this the whitepaper expectation fallacy.

Greed, for the Lack of a Better Word, Is Good. But So Is Fear.

We all have emotions, and these emotions are learned traits passed down through generations, hardwired in our DNA to improve the odds of survival. On the plains of the Serengeti, a flight or fight response can help you live another day. Within the world of investing, however, these same behavioral reactions can lead to decisions that are less than optimal.

Fear, when associated with a company, can drive a stock price down beyond the long-term fundamental prospects of that company. If fear is acute and prolonged enough, it can lead to a panic—and when this panic spreads across industries and sectors, it can cause second-derivative effects on tangential companies: the proverbial throwing out the baby with the bathwater.

The data supports acting in these moments. Across our investable small cap universe—companies with a market cap range of $80 million to $2.5 billion—the lowest valuation quintile consistently outperforms the highest quintile over time. This lowest quintile is our fishing pond and the area of the market where we believe fear has created opportunities to capture alpha.

The Alter Ego: Greed

Just as fear drives prices lower, greed drives them to elevated levels. We capitalize on this by trimming or selling stocks that rise above the median valuation of the industry, then recycling the proceeds into another value opportunity. Buy a low-value stock at the bottom. Gradually exit as it becomes more expensive. Reinvest into another low-value stock. Rinse and repeat.

That is the ideal. In real time, however, other biases creep in. One in particular is recency bias—the tendency to overweight events that happened in the near past versus those that occurred in the distant past, and then project these trends into perpetuity. This can create undue pressure on a security or drive shares higher without consideration for the risks that remain.

The Momentum Factor

To separate stocks that are cheap for a reason—think structural impairment—versus those that are temporarily undervalued, we utilize a momentum factor. We decile-rank the performance of all stocks on a 49-week moving average relative to the entire universe. We avoid the decile tens with the thought that while the stock is inexpensive, it has yet to bottom.

The data shows that utilizing a momentum factor adds roughly 100 basis points annually on average through a market cycle—with the notable exception of 2009, when correlations approached one and sentiment was indiscriminate.

Not All Fear and Greed Are Created Equal

The most important—and most overlooked—point from the academic literature is this: extended periods of value underperformance can occur when investors flock to high-flying growth stocks and shun value ones, such as the late 1990s, or in periods where long-term rates are kept artificially low, forcing investors into long-duration assets.

We are currently in one of the second-longest periods of value underperforming growth since the 1920s. But history is instructive: after each such period, value went on to outperform growth by a wide margin—each subsequent period of recovery lasting between five years on the short end and nearly two decades on the long end.

The conclusion is not that value doesn't work. It's that the whitepapers set the wrong expectations about when and how it works. Knowing the difference is the edge.