I'm sure everyone has read things on Seeking Alpha or the wider financial media that has annoyed them. Maybe a writer was bearish on your Tesla (NASDAQ:TSLA) stock. Maybe an author criticized your favorite investing method. Maybe an expert disagreed with your overall outlook on the U.S. economy.
For myself, I generally have an agnostic view on the direction of markets, so the above examples don't bother me too much. As a scientist, what bugs is when data are misused.
I've seen several examples of this on Seeking Alpha, but for obvious reasons I will not link to them, because this is in no way meant to be construed as an attack on fellow authors. Rather, it is merely a scientist's perspective on how I think data should be properly used.
I will also be the first to say that I am not a professional in the finance industry. Investing is simply my passion and and my hobby. I am sure that financial professionals can find fault in my own articles, and I gladly welcome all critique.
Without further ado...
1. Misuse of exponential data
I've tried to debunk the notion that dividends make up the majority of total returns in an article "Dividends Do Not Make Up The Majority Of Long-Term Total Return" and then in a slightly satirical follow-up, "Capital Appreciation As The Source Of Long-Term Returns". There, exponential data were misused to erroneously imply that dividends were more important than capital appreciation for total return.
Another, related example is when examining the effect of fees. Consider a fund that compounds at a gross rate of 10% p.a., but has a 1% expense ratio, so the net compounding rate is 9% p.a. After a certain number of years, $100 compounds to $483,002 if fees are not taken into account, but only $214,268 (less than half!) if fees are included. Some might then conclude that the 1% in fees is the dominant factor affecting returns, implying that this 1% is somehow more important than remaining 9% of returns.
A more appropriate way to visualize this is to plot the returns on a logarithmic scale. Here we can see that the effect of fees is much less exaggerated than in the first chart.
Of course, I'm not saying that 1% of fees doesn't matter - there's no question that the lower the fees the better. Just that it's not as important as the first chart would have you think, and it's definitely not more important than the remaining 9% of annual returns.
2. Calculating performance ratios when the denominator is small
Over the past 5 years, Amazon's (NASDAQ:AMZN) total return of 241.7% is more than 3 times the total return of the S&P 500 (NYSEARCA:SPY) (75.35%). So, in my view it is appropriate to claim that AMZN has beat the market by 221%, because both numbers are reasonably large.
However, it not very appropriate to calculate performance ratios when the denominator is very small. Consider the total return performance of Utilities Select Sector SPDR ETF (NYSEARCA:XLU) and SPY from Aug. 1, 2015 to Apr. 27, 2016, a period of about 9 months.
XLU recorded a +12.00% gain, while SPY recorded a +1.43% gain, so utilities beat the market by 739% over the indicated time period. Does that sound meaningful to you?
Let's go forward one day. Now XLU's performance is +11.84%, and SPY's is +0.50%, so XLU beat the market by 2,268%! Let's stop and think for a minute. Does it make sense to you that by adjusting the end date forward by 1 day, a day in which both securities declined by less than 1%, the degree of reported outperformance would more than triple, from 739% to 2,268%?
Some commenters have stated that it's fine to do this as long as you're consistent. But consider what happens just one more day later, when SPY's return turns negative. With a +12.51% gain for XLU versus a -0.04% loss for SPY, the ratio, if calculated in the same manner, becomes a negative number, -31,275% (?).
So here we have XLU beating the market by 739% over 9 months, then by 2,268% one day later, and then by -31,275% on the third day, even though the three charts above look pretty much the same.
If SPY had been exactly flat over the time period, then the degree of outperformance of XLU would have become infinity percent. Clearly, being consistent is not a sufficient condition for being meaningful.
When numbers are small, it makes more sense to compare the differences in percentage in terms of percentage points, rather than as a ratio.
Thus, we could instead say that XLU led the market by 10.57 percentage points on day 1, by 11.34 percentage points on day 2 and by 12.55 percentage points on day 3. This also makes it much easier to gauge the relative performance of XLU over the three day period. An alternative is to use basis points.
3. Mistaking correlation for causation
This one is a classic science mistake - to assume that when two variables are correlated, one is the causative agent of another. Is it really plausible that there is a causative relationship between the number of Facebook (NASDAQ:FB) users and Greek sovereign bond yields?
Of course, not all relationships are as obviously implausible as that one. But a simple purview of the financial media will reveal that even just a few short years after the Great Recession, pundits were already predicting that the next bear market was imminent based on correlations between stock market performance with historical chart patterns, or with margin debt, or with ratio to GDP, or with central bank intervention, or with myriad other factors. While those indicators may well have their uses, the implication by some that the appearance of one will cause the occurrence of another event has been found to be false, much to the chagrin of investors who have been out of the markets all this time.
There you have it, my top 3 investing pet peeves. Do you have any investing pet peeves? Feel free to discuss in the comments section below!
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