Recently, we wrote about how Systematic Investing Works Great and the growing body of research which provides evidence to the superiority of models and algorithms in decision-making versus relying on expert judgment of active managers. In the article we compared two Graycell quantitative portfolios with index benchmarks, (a) the Graycell S&P Quant Portfolio with the S&P 500 index, and (b) the Graycell Small Cap Quant Portfolio with the Russell 2000 index, demonstrating the significant risk-adjusted excess returns of quantitative portfolios over benchmark indexes.
One of the key reasons that quantitative portfolios are well-positioned to outperform active management, in which money managers or individual investors buy and sell positions based on their company analysis, is the mitigation of behavioral biases.
What are Biases?
Behavioral biases are our tendencies to react in a certain way. Such tendencies are embedded in our thinking and influence decision-making to a significant extent. These biases are the mental short-cuts, important to speed up our thinking and reduce cognitive load, and often times our biases save us time and energy.
Along with the natural cognitive biases, we are also influenced by the pool of emotions and experiences accumulated over our lifetime, which create certain proclivities in our thinking. We lean on these preferences to aid our decisions and judgments.
All these cognitive and emotional tendencies are collectively referred to as behavioral biases. These are the subjective elements of our decision-making, the ones which we possess by default.
Biases and Investing
Making rational decisions was not really a priority on the plains of Africa as the human species evolved a few million years ago. But surviving the day from the lions and other threats was a priority, and instincts and biases became imprinted on our mind, in place of rational analysis, to boost our survival chance.
But there are times when our inclinations can result in irrational or unproductive decisions. Investing is one area where behavioral biases can significantly hurt our investment returns.
Psychologically, our opinions are filtered through our biases. This creates irrational choices and prevents sound decision making, particularly when decisions involve investing, money, time constraints, and emotion. As Richard Thaler, 2017 Nobel Prize winner for his work in behavioral economics, has observed that humans are predictably irrational.
We are prone to over-or under-estimating, being overconfident in our analysis while doubting data contrary to our opinions, and using perceptions to create or fill-up what doesn’t exist. Our decisions are not always rooted in facts, logic, and reason.
A 2015 survey of over 700 financial practitioners by the CFA Institute revealed some of the biases affecting investment decision-makers.
Source: CFA Institute
Biases can interfere with optimal judgment. By being aware of these biases and mitigating them, investors can work towards arriving at more objective and rational decisions, leading to enhanced portfolio performance. This is easier said than done, and behavioral bias is an issue that affects everyone from individual investors, to traders with multi-million dollar positions for banks, to portfolio managers responsible for investing billions of dollars.
No wonder even Benjamin Graham, considered the Father of Value Investing, observed:
These behavioral biases to a meaningful extent explain why many active managers are unable to outperform standard industry benchmarks like the S&P 500 (SPY), Nasdaq (QQQ), Russell 2000 (IWM) and Dow Jones (DIA). This has led to net cash outflows of over $500 billion in the 10-year period from 2007 to 2016. At the same time, quantitative and passive management has experienced growing cash inflows.
We have to accept that we are simply not the rational investors that are assumed in theories and hypotheses, like the Efficient Markets Hypothesis. People develop biases in the normal course of life, which creates irrational investors and market inefficiencies.
Quantitative models have an edge because computer algorithms simply don’t feel and react the way humans do.
