In this paper, Dr. Shafer discussed whether history itself was more art or science, to which he concluded it was neither – rather, history is simply history. The same is true for investing – it’s an interesting combination of art and science that is heavily reliant on history, yet uniquely defined by each investor. There are millions of market participants transacting trillions of dollars on a daily basis, resulting in new data points each time a security is bought or sold – the output of which is readily available to the investable public. This makes it very difficult for the average investor to gain an informational edge, but many would argue that temporary market mispricings can be exploited with some combination of luck, skill and discipline. Research has shown that it is virtually impossible to consistently ‘beat the market’ (just ask Eugene Fama1), but as Burton Malkiel put it in A Random Walk Down Wall Street – “a blindfolded monkey throwing darts at a newspaper’s financial pages could select a portfolio that would do just as well as one carefully selected by experts.” Good investors understand that markets are efficient, but great investors realize that nothing works all the time. The sweet spot lies somewhere at the intersection of art and science – or said differently, math and common sense. Many investors fail to strike this balance of rational duality, but those that do earn a unique title that we like to call The Da Vinci Quant.
But in order to do so, one must first obtain a thorough understanding of quantitative (quant) investing. While the application of quant investment strategies is a relatively recent trend, the term has undoubtedly become overused. Quant investing emerged in the mid-1960s when Sam Eisenstadt created the first quantitative ranking system using 6-month trailing performance for a sample of stocks – quickly realizing that stocks in the top quartile were outperforming those in the bottom quartile. While his conclusion may seem relatively obvious, its applications have rapidly proliferated over the past several decades. Put simply, quant investing – often referred to as systematic investing – leverages historical data to develop rules-based methods for determining asset allocation decisions. Not only does this introduce a level of discipline and objectivity into portfolio construction, but more importantly, it removes emotional bias from the decision-making process – the consequences of which can be detrimental to long-term investors. Predicting markets is incredibly difficult (if not impossible), but markets do illustrate predictable patterns over time – something that quantitative models can exploit when designed properly. The key, however, is identifying that sweet spot between math and common sense – not becoming overly-reliant on said models, all the while avoiding the common pitfalls of our own emotional biases.
‘At Silicon Hills, we’ve invested considerable resources into identifying that very sweet spot – a rewarding journey that has taught us many interesting lessons. Understanding that nothing works all the time, we’ve come to the realization that math should inform when common sense can be relied upon – or said differently, what the balance of art and science should be at any given point in time. While objectivity often trumps subjectivity, there are instances when market environments lead to model imperfections, and when the human mind (despite its own imperfections) can add value that far exceeds even the most advanced quantitative models. As we turn the corner on 2022, we look forward to sharing with you these realizations in even greater detail – modeling our insights after those of Leonardo da Vinci and applying lessons learned to strike that delicate balance between math and common sense. So whether you’re a current client or simply a curious reader, we encourage you to join us as we explore The Da Vinci Quant. Welcome aboard.
1 Fama, Eugene, 1970. “Efficient Capital Markets: A Review of Theory and Empirical Work”. Journal of Finance.
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