The Plight of the Average Investor and Why Algorithms Can Help
Investing in the stock market a notoriously hard endeavor. There are vast quantities of research, both from Wall Street and academia, attesting to its difficulty and proposing competing theories as to the best approach.
On Wall Street, analysts favor two main strategies: fundamental analysis and technical analysis. Fundamental analysis is a ground up approach; these analysts attempt to examine various qualities of companies and invest when they believe the strengths, management, and prospects of the company warrant a higher price.
Technical analysts invest based on price fluctuations. Technical analysts rely heavily on “charts”, or graphs showing the short-term price movements of a stock. These practitioners analyze trends and patterns in price movement across immense quantities of data to discern opportunities to buy and sell stocks for a profit.
Academia, on the other hand, favors the “Efficient Market Hypothesis” (EMF) which states that all new information is quickly reflected in stock prices, and thus there are few opportunities to profit above the average market return. EMF proponents believe that both technical and fundamental analysis are unable to produce superior returns while minimizing risk in the long run. Thus, they advocate for “indexing” to the return of the market, that is investing in a broad array of stocks through vehicles such as mutual funds to obtain the stock market average return.
Interesting, though these three schools notoriously and vehemently disagree on optimal investing strategy, they do agree on a crucial detail; sticking to a strategy, through thick and thin, is vitally important. This is where the everyday investor often stumbles, and this is where algorithms can help.
So, what is an algorithm? Found in many contexts from optimizing computer code to solving Rubik’s cubes, algorithms are a set of rules and patterns used to solve a complex problem. Many large quantitative investing firms utilize highly complex algorithms to make split-second investment decisions with large amounts of capital. However, algorithms need not be arcane, complex, or scary.
We see, time and time again, that the biggest plight of the everyday investor is the inability to stick to a strategy over long periods.
It is estimated that investors lose 2–10% of their potential returns annually due to overtrading and attempting to “time” the market by investing when they believe opportunity is striking.
For reference, the average return of the S&P 500 (the “market”) is between 7–10% over the long term. So, it’s clear that overtrading and failing to maintain a consistent strategy can compromise investment returns dramatically. So why do people continue to actively trade? The answer, largely, is because active trading is fun.
We all love to feel smart, and who doesn’t want to pick the next Apple, Google, or Facebook? However, even seasoned investing professionals often fail to outdo the average market returns while searching for such holy grails.
It is because of this failing of everyday investors and investing professionals alike that largely academic proponents of indexing to the market have a strong position in this debate. However, indexing, especially if you do not believe in the EMF, leaves a lot of value on the table. Indexing will obtain the market average return over the long term. Additionally, it is a strategy that is often hard to maintain; it turns investing into a humdrum process of investing in a few mutual funds that track this market average, and holding those investments forever. This reduction of investing plays directly into our desire to trade, and thus causes us to stray from the path, not stick to the strategy, and compromise our returns. Here, again, algorithms can help.
Instead of boiling the process down into two options, sticking to a simple mutual fund buy-and-hold strategy or trading actively for fun at the expense of returns, algorithms could allow everyday investors to allocate funds and let a computerized set of rules invest on their behalf. If the algorithmic strategy has been thoroughly tested and its risk analyzed, everyday investors can combine the joy of discovering the “next big thing” (in this case, the algorithmic strategy of their choosing) with the predictability, ease, and consistency of index mutual fund returns. In this way, investing in algorithmic strategies can be thought of as “super indexing”; the power and thrill of active investing with mitigated pitfalls.
Overall, algorithms can help all types of investors, from seasoned investing professionals to novice investors. However, one benefit above all others is constant across all groups; algorithms help us overcome our tendencies, pick a winning strategy, and stick to it. And that itself is powerful.
