Most of you know by now that when it comes to investing I have an infrequently used approach, namely I get excited when markets correct, rattling on about opportunity and misdirected interference, but when they go up is when I become singularly annoyed. Not just because optimism diminishes with each record day, but because when markets rally for a long time, such as the last nine years, a pesky phenomenon occurs, namely, everybody starts to think they can do it. We’re at that time now, is it time to sell?
Investing result over the last 100 years is still driven by its primary fuel, luck. Keeping clients happy and distant has been a strategy nurtured by the industrial age and borrowed from many industries. However, what’s changed in recent years, in my opinion, has been a subtle transition from ambitious yet conflicted stock brokers to using discretionary institutional methodologies of investing, the kind used to manage corporate and municipal defined benefit assets. In short, science is finally taking over art in the race of discipline over decisiveness. And when I speak of science I’m not only talking about algorithmic voodoo and its army of fans empowered by the belief in its promise to unlock the secrets of investing. I’m talking about the science of behavior, of organization and of the math based systemization of monitoring of aggregate assets to grow in good times and protect in bad times. So what does all this mean for the armchair investor?
Algorithm is the buzzword of this century (not bitcoin) but does anyone really understand it? In fact most people seem content to having phones, watches and laptops concerned in only what they do and rarely for how they do it. In the book "Homo Deus: A Brief History of " by Yuval Noah Harari, the author devotes a few chapters to his characterization of the inner workings of the brain representing a better example of a working algorithmic model. We see something, our brain takes the object and runs it through our well stocked, albeit somewhat disorganized, legacy of facts and images and, aha! We have the answer to what we see. Add to that our dispositional influences and, maybe. It’s always been said that emotion is a disproportionate factor in the volatility of the stock markets. Recently, common behavioral traps (buy high, sell low) have found a home in behavioral science.
Within nature math has been applied to ideas, bringing them together with platforms to quantitatively measure their impact. However it doesn’t always work. In years past much of quantitative analysis had been used adversely not because the substantive aspects of the formulas don’t ’work but because in financial services outcome is too often seen as more important than the sanctity of the determinant, often just another small act of sequential omission. These days quantitative analysis is simply the way to alter the outcome of calculated, including some algorithmic, results allowing for variables created from typical dispositional influences, for example, judgmental bias, the number one hindrance to successful investing.
Technical analysis is essentially math based and designed to recognize (not predict) trend and momentum. In the end a technical indicator such as Relative Strength can tell you when a currency, commodity, equity or nearly everything in nature is a good investment however that's not the same as suggesting it worth paying attention to. This invited a scientific mind to find the lowest common denominator to best managing a portfolio of assets. Funny thing is the solutions were, as many scientists long agreed, easier than they were expected to be. The first premise in approaching to apply the new science to investing was accepting some parts of the concept of the efficient frontier. Namely that the global markets are reflective of all the information available. Therefore research can uncover much of said information, but no amount of research can give one a tool to consistently outperform the markets. Welcome, probability and its measurable fuel, risk. In my opinion risk is among the best ways to control and protect an asset. Simply put, if one fears that the market will go down they can buy (slow down) an ETF such as SPY (S&P 500). No one knows when the markets will correct, but probability can be applied better to when the markets will recover after they correct. At that time we can speed up if we sell SPY and buy AMZN (Amazon). The ability to systematically allocate and monitor accounts in this way introduces a level of control that armchair speculation will never need, nor accomplish.