Herd Behaviour

Profiting From Investors’ Herd Behaviour Following Analyst Recommendations

Abstract: Investors are often most at ease buying a stock with a high percentage of analyst BUY recommendations. This is herd behaviour bias in action; that is, the tendency to feel more comfortable belonging to the consensus or the herd. In reality, companies with fewer BUY recommendations have historically outperformed those with more. This creates a problem for investors who rely on analysts’ calls and who stay with the herd.

Q1 hedge fund letters, conference, scoops etc, Also read Lear Capital: Financial Products You Should Avoid?

In this paper, we share the story of how we researched a strategy designed to profit from this herd behaviour bias. In addition, we demonstrate how we improved the efficacy of the strategy by applying machine learning.

Introduction

Many investors, advisors and portfolio managers rely on sell-side analyst ratings. This complete or partial outsourcing of due diligence is attributable to a number of behavioural biases, including overconfidence, confirmation and herd behaviour.

Herd Behaviour

We believe these flaws are present in many investment processes and can lead to mispriced assets in the marketplace. For active managers such as ourselves, a mispriced asset provides an opportunity to profit on behalf of our investors.

In theory, most investors behave rationally and most assets are priced fairly. However, in practice, the markets are more complex. Sometimes, behavioural biases – driven by emotions – cause investors to act irrationally. This can lead to mispriced assets. These biases are human in nature. As a result, they should continue to elicit bad behaviours that result in predictable mispricings. Investors who identify these patterns are set to potentially reap the benefits.

Herd Behaviour

In this report, we share our analysis on how to profit from the herd behaviour elicited by analyst recommendations. We also show how investing in unloved companies may lead to outperformance versus the benchmark and relative to companies that are more loved by analysts. Lastly, we demonstrate how applying machine learning can help to improve upon a base strategy. In sum, we believe that investors can benefit from using strategies that are built to capitalize on mistakes caused by other investors’ behavioural biases.

The problem: the herd is often wrong

The herd mentality is a behavioural tendency that is hard-coded in our DNA. We tend to feel more comfortable when we are in agreement with the consensus. On the subject of analyst ratings, investors generally prefer to buy, own or add to companies that have a higher percentage of BUY ratings. Conversely, investors tend to neglect or overlook unloved companies with a very low percentage of BUY ratings.

Two factors likely drive herd behaviour. First, when more analysts have positive ratings for a stock, there is a preponderance of reports with positive views. This makes it easier for investors to come across reports that recommend a company. The second factor is the desire to belong to the herd. If you invest in a company with mostly BUY recommendations, then you are part of the consensus. This has the benefit of protecting you because you can say that “everyone said to buy” if the recommendation does not work out. Alternatively, if you invest in an unloved company with few BUY ratings, then you own that decision because if it does not go well then there is no one else to blame.

Behavioural finance

Behavioural finance, which combines finance and psychology, is the study of how we, as investors, make decisions, and more poignantly, how we often make poor investment decisions. Psychology has uncovered many heuristics that our brains use to help us make decisions quickly. These are rules of thumb or mental shortcuts that enable us to navigate the enormous number of decisions we make every day. Unfortunately, many of these heuristics can lead to predictable decision-making errors.

In a low-stakes environment, our heuristics often help us because emotions are low. In a high-stakes investing environment; however, the opposite is true. Investing is emotionally charged because our successes or failures have real consequences. That is especially true during periods of heightened volatility.

Research has shown that behavioural biases are often systematic. There are certain market events – such as large price moves, overreactions to earnings and heavy news flow – that trigger the same biases in many investors. These biases cause them to act in irrational ways, resulting in mispriced assets. If certain identifiable circumstances cause investors to act in predicable and irrational ways, then perhaps we can profit from their misbehaviour. That is exactly what we set out to do with the unloved to less unloved strategy.

History has shown that companies with fewer BUY recommendations typically outperform the companies with many BUY recommendations

In both Canada and the U.S., we found that companies with a lower percentage of BUY recommendations outperformed those with a higher percentage. In our analysis, we ranked index constituents based on their percentage of BUY recommendations over total recommendations. We then tracked the subsequent performance of the top and bottom quintile until the next rebalancing. The analysis covered the past 20 years and our findings were consistent across the S&P/TSX Composite Index, S&P/TSX 60 Index, S&P 500 Index and S&P 100 Index. We used multiple indices so as to avoid a potential company size influence. The analysis was rebalanced monthly and quarterly, with similar results.

Unloved to Less Unloved White Paper – RGMP v2 Herd Behaviour

The outperformance of unloved over loved companies was not consistent during all periods of the analysis. That said, based on our analysis, on average, investors may want to avoid loved companies and to seek unloved ones.

There is some logic behind these findings. The market tends to move on new information. In this case, the new information was analysts changing their recommendations. The market likely knows and discounts 10 pre-existing BUY ratings. If a company has all BUY recommendations, then a downgrade is the next logical ratings change. Conversely, for a company with very few BUYs, many more analysts could upgrade their ratings should they reconsider the prospects of or see more value in the company.

If the herd, based on the percentage of BUY recommendations, is so often wrong, how can we profit from this? The solution: the unloved to less unloved investment strategy The simplest solution based on the above findings would have been to develop a strategy that went long companies with low percentages of BUY recommendations (unloved) and went short those with high percentages (loved). However, that would have proved difficult given that loved companies may outperform unloved ones for extended periods. Instead, we chose to focus more on unloved companies that began to receive upgrades.

The unloved to less unloved strategy attempts to harvest gains from hated or neglected companies that have remained unloved for an extended period. A company that starts to receive upgrades that move its BUY / TOTAL recommendations ratio above a predefined threshold would trigger a potential investment opportunity. If those early upgrades are from forward-looking analysts, then more upgrades could follow, creating the potential for a recovery in the share price.

Article by Craig Basinger, Chris Kerlow, Shane Obata and Derek Benedet – RichardsonGMP

Se the full PDF below.

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