by Kirsty Peev, CFP®, Portfolio Manager
I recently attended an investment conference where I learned a rather baffling fact. According to IBM, over 90% of the data in the world today has been created in the last two years. It’s not hard to see how that happened if you look at the infographic below.
What is hard is making sense of that data when you’re trying to pick one stock over another. There is so much data out there about companies and brands that no individual or professional investor could possibly absorb it all before new information arrives.
Traditionally, an active stock picker would do detailed analysis on all available financial data, and even with just that to contend with, it was hard enough to consistently know what and when to buy and sell. Now, with the proliferation of data, a modern stock analyst has to go even further!
Here are just a few of the areas a “Big Data” stock analyst might research:
- Computerized analysis of word frequency and context in hundreds of thousands of corporate quarterly conference calls, central bank calls, international news articles and online forums to gauge sentiment on everything from corn futures to the performance of PepsiCo.
- Text analysis of Twitter and other social media to predict the likelihood of major events, like whether Greece would remain in the Eurozone.
- Smartphone beacon apps tracking how long you linger in a store can be used to project sales figures or sentiment.
- The number of likes/views on Facebook to gauge interest in products or companies.
- Seasonal searching for products and companies (for example, tracking interest in Ugg boots around the holidays in 2015 versus 2014)
- Investor sentiment from financial online forums (actually more useful in predictions than retail sentiment)
- Employee reviews on Glassdoor.com, new job postings, and the skill level of those job postings are used as local economic indicators.
Professional active fund managers are doing all of this research in addition to more traditional means of quantitative and fundamental stock analysis. So unless you have the time, interest, and ability to do this scale of research, you’re at a serious disadvantage when it comes to picking individual stocks. (And even if you wanted to, much of the data is not available unless you are a mammoth-sized institutional investor.)
This isn’t to say that you should stick your head in the sand and give up. The theory of efficient markets states that the market “prices in” all of the available data as investors act upon that data. So when you invest in an individual stock, the price you pay is based on all of the information buyers and sellers have about that stock up to the time you bought it. The same is true when you buy an index fund or mutual fund.
Pretty cool when you think about the ever-increasing amount of data that goes into investment prices as they change by the second…and even cooler when you realize that you really don’t need to worry about all of that data (much less pay through the nose for an active stock picker to research it for you). If you’re a long-term investor, better to focus on the aspects of investments that are actually within your control.
At Halpern Financial, we are not individual stock pickers, and our clients know why: research has shown time and time again that it’s not the most significant factor in your portfolio. (One study even showed that stock picking underperformed a random coin flip.) A 2014 analysis of a major Norwegian government pension fund ran the numbers to show that a whopping 99.3% of returns came from factors other than investment selection.
It all comes down to common sense. Spend less so you keep more. Low expense ratios, low turnover, and tax efficiency provide more reliable results than trying to sift through the noise of big data—which is why I make those factors my focus as a Portfolio Manager!
The Case for Index Fund Investing. Christopher B. Philips, CFA; Francis M. Kinniry Jr., CFA; David J. Walker, CFA; Todd Schlanger, CFA; Joshua M. Hirt. Vanguard, March 2015.
False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas. LAURENT BARRAS, OLIVIER SCAILLET, and RUSS WERMERS. The Journal of Finance, February 2010.
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