Financial Decoder: Episode 3

by: Charles Schwab

Calculating the performance of your portfolio can be tricky. If you don’t get it right, you might not be able to answer the pivotal question of whether or not you need adjust your mix of investments. How do your built-in biases affect the way you think about your investments’ performance? In this episode, Mark Riepe looks at some ways to mitigate common errors and avoid miscalculating past performance.

What’s an acceptable level of performance for your portfolio? Before you can answer that question, you first have to be sure you’re calculating your returns correctly. Even professionals get it wrong sometimes. In this episode, Mark examines how the availability heuristic and confirmation bias could affect your ability to make the best decisions with your portfolio.

Mark Riepe: I’m Mark Riepe and welcome to Financial Decoder, an original podcast from Charles Schwab. This is a show about how cognitive and emotional biases can have a big impact on your financial life.

It’s a companion to Schwab’s Choiceology podcast, which looks at how the decisions we make impact our lives and examines the subtle biases at play that drive those decisions.

This podcast decodes the biases that influence specific financial decisions that we all face and offers strategies to help us mitigate these biases and improve our decision making.

Every time you review your investments I, suspect that the first thing you ask is whether you made money or lost money.

After that you ask another question: whether your portfolio is performing well enough. That simple question opens a Pandora’s box of emotional and cognitive processes and their associated biases.

But before we get into all of that, let’s go back to over 20 years ago and share a story about a publishing sensation that tells us a lot about why decisions involving investment performance are deceptively complex. The story centers around The Beardstown Ladies Investment Club. If you’re of a certain age, like I am, you may remember that for 12 years the club had generated amazing performance. Its market-beating returns, plus the compelling personal stories of its members, led to widespread fame, many media appearances and a best-selling book1 that spawned four follow-on titles2.

After a couple of years, all of this attention inspired a journalist to look into and ultimately question the club’s track record.3 More formal investigations followed, and the final conclusion was that the club didn’t generate returns that were double those of the average equity mutual fund, and the club didn’t beat the S&P 500 by 8 percentage points per year. In fact, they underperformed the market by almost 6 percentage points per year.4

Now, there was no nefarious intent on the part of the club. According to the club’s treasurer, it was a computer input error. They simply miscalculated.5

This story matters, because you can’t make a decision about whether your level of investing performance is acceptable unless you actually know what your investment performance is. And that calculation is more complex than you might imagine when you start taking into account multiple trades, multiple flows of cash into and out of the portfolio, and multiple positions.

So, my first tip is the most obvious tip imaginable: Calculate your performance, and make sure you do it right.

My second tip is this: Don’t ignore the first tip by winging it and making a guess. Because when you’re imprecise or calculate incorrectly, you’re likely to err in a particular, harmful direction, thanks to the overconfidence and confirmation biases that we tend to have.

Confirmation bias is our tendency to cherry-pick information. We gravitate towards information that supports our point of view, and we ignore or discount information that doesn’t.

And overconfidence is our tendency to think we’re better at a task then we actually are.6 When we consider past investing performance in the abstract, overconfidence may lead us to think highly of our investing prowess - and assume that our performance has been good.

Then, when we start thinking more about it and try to make an estimate, confirmation bias can lead us to remember mostly good investments and conveniently forget the ones that didn’t work out so well.

This isn’t just a theoretical risk. In one study, individual investors were asked what they thought their performance was over the previous four years. The performance investors reported bore no resemblance to reality.

What’s more interesting to me is that the investors estimated that their average return over the period was +15% per year7, but the actual average return was +3.4% per year. In other words, these investors, on average, thought they were performing nearly 12 percentage points better than they actually were.8

In past episodes I’ve mentioned that experience can help solve some of these biases, but it doesn’t appear to work all that well in this case. In this same study, investors with 5 or more years of experience were only slightly more accurate in estimating their past performance.9

So, if experience doesn’t solve the problem, what can you do? Here’s a simple three-step process that does.

  1. Don’t guess,
  2. Do the math, and
  3. Do it correctly.

Let’s regroup for a second. The point of the first segment is to make sure we look at past performance through clear glasses and not rose-colored ones.

But there’s another dimension to performance that comes into play when deciding whether the performance of your portfolio is adequate. That dimension is whether or not the performance is living up to your expectations.

Some individuals compare the performance they experience to what they expect. They’re unhappy when returns don’t meet their expectations. That’s perfectly valid as long as the expectations are reasonable. But biases come into play in the formation of those expectations.

For example, one study looked at six different surveys of how investors expected the stock market to perform over the next year or so and tracked those expectations over time. The expected returns from those surveys moved directly opposite to what financial theory suggests. It appears that investors tend to take recent performance and extrapolate that into the future.10 If things are going well, they think they will continue going well, and if things are down, they think there is no way back up.

Other studies have found that investors form expectations based on their own experiences.11 One study found that the personal experience of an investor over the past 12 months influenced their expectations for returns over the next 12 months. When past returns were high, their expectations for the next 12 months rose, and vice versa.

As with many aspects of financial markets and efforts to predict performance, there’s a lot going on that contributes to these behavioral patterns. But I’m just going to focus on one piece of the puzzle, and that is how we make use of the availability heuristic. This was discussed in the “Swimming with Sharks” episode of Choiceology.

The essence of the heuristic is that when we need to make a decision, we use evidence to help us decide. One method is to use the evidence that’s most readily available. The easy availability of evidence isn’t always a problem, but when used uncritically, it can lead to systematic biases.

Think about this in the context of setting performance expectations. When imagining what might happen next, the most available evidence is what happened most recently. It’s natural to treat recent performance as normal, and therefore, it becomes part of your expectations. If we combine that tendency with the fact that we tend to extrapolate recent events too far into the future, then we have two biases both pushing up your forecast. But this can be costly.

We’ve already seen that there are documented tendencies for people to overestimate their past performance. And because of the availability heuristic, people are likely to then use this faulty information to form their expectations for the future.

Now, none of this matters if the investor doesn’t act upon the expectations. But that’s not the case.

Studies have shown that investor expectations are strongly correlated with the flows into and out of mutual funds. In other words, when expected returns were high, money flowed into funds, and vice versa.12

Given that expected returns don’t predict actual returns very well, we see a source of underperformance by individuals. For example, for the 10 years ending in 2017, the average individual who invested in mutual funds underperformed the actual funds’ return by nearly 1% per year.13 This is solely because of bad timing as to when they put money in or took money out. In other words, they tended to put money into the market when it was high or took it out when it was low.

But there are other problems as well when expectations get too high. One example of this is the case of California state pensions. Realized returns were great in the late 1990s, thanks to the booming stock market. State officials acted as if they thought those high returns were going to continue for an extended period of time. As a result of this assumption, benefits were made more generous, and the belief was that an increase in contributions to the plan wouldn’t be necessary.

While those expected returns never materialized, the boosting of benefits was real. The result is that today state pensions funds are severely underfunded compared to the promised payments.

It’s easy to see this same situation playing out with individuals. An individual has some investing success, then projects that success will continue and begins to spend more now and save less because he thinks the performance of his portfolio will do the saving for him. That’s not a sustainable strategy.

So how do you fix this? Here are two ideas.

First, get comfortable with uncertainty. Accept the fact that the absolute level of performance for your investments is extremely hard to predict over short periods of time and also pretty hard over long periods of time. Take projections of the future, even from smart professionals, with a healthy grain of salt. Focus more on a range of possible outcomes and make sure that you’re set up to be successful even if the actual results are closer to the lower part of the range.

Now, we started this podcast with a question: Whether or not the performance of your portfolio was acceptable. The answer helps to determine whether you’ll decide to make adjustments. And that leads to the second idea. It’s important to think about investing alternatives and how they would have performed or will perform in the future.

The process is called benchmarking. Sometimes that alternative is an index fund. Sometimes it’s the performance relative to other financial products that are doing the same thing. Pick a benchmark or peer group that makes sense.

But be careful, because here’s where the availability heuristic comes into play again. The media talk about how stock averages do on a daily basis. That performance is the most available when you think about how your portfolio is doing. But that’s a problem, given that most investors aren’t 100% invested in stocks.

If your portfolio is half stocks and half bonds and the stock market is up 10% for the year, it is highly unlikely that your portfolio will be up 10%, because you’re taking on much less risk, thanks to the bond half of your portfolio. Create a relevant benchmark and then compare your performance to that benchmark before you make any final decisions as to whether your performance is acceptable.

One final thought: At the end of the day, the market determines the performance of a portfolio. The investor is like the coach who puts the players out on the field, but the players have to make it happen. The coach can only do so much.

That’s why it’s so important to have a financial plan and to think hard about how much to save. The financial plan forces you to think about the possibility of bad performance, and savings is the most reliable way to build up your portfolio. It’s the one thing you can most control. And it’s also the topic of our next episode.

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1 Beardstown Ladies’ Investment Club, The Beardstown Ladies’ Common Sense Investment Guide: How We Beat the Stock Market—and How You Can Too, Hyperion, 1995.

2 Beardstown Ladies’ Investment Club, The Beardstown Ladies’ Guide to Smart Spending for Big Savings: How to Save for a Rainy Day Without Sacrificing Your Lifestyle, Hyperion, 1997; Beardstown Ladies’ Investment Club, The Beardstown Ladies’ Stitch-in-Time Guide to Growing Your Nest Egg: Step-by-Step Planning for a Comfortable Financial Future, Hyperion, 1996; Beardstown Ladies’ Investment Club Staff, The Beardstown Ladies’ Little Book of Investment Wisdom, Hyperion, 1997; and The Beardstown Ladies’ Pocketbook Guide to Picking Stocks, Hyperion, 1997.

3 Shane Tritsch, “Bull Marketing,” Chicago, March 1998.

4 Walter Hamilton, “Returns Come Back to Haunt ‘Ladies’, Los Angeles Times, March 18, 1998.

5 Hamilton, ibid.

6 Markus Glaser and Martin Weber, “Why Inexperienced Investors Do Not Learn: They Do Not Know Their Past Performance,” Finance Research Letters, December 2007, p. 204.

7 Glaser and Weber, ibid, p. 207.

8 Glaser and Weber, ibid, p. 208.

9 Glaser and Weber, ibid, p. 203.

10 Robin Greenwood and Andrei Shleifer, “Expectations of Returns and Expected Returns,” Review of Financial Studies, 2014.

11 Ulrike M. Malmendier and Stefan Nagel, “Depression Babies: Do Macroeconomic Experiences Affect Risk Taking?” Quarterly Journal of Economics, February 2011.

12 Greenwood and Shleifer, ibid,

13 Schwab Center for Financial Research with data from Morningstar, Inc. Fund return is the weighted average time-weighted return of all active funds in the Morningstar domestic equity, specialty, and international stock categories. Each fund is represented by its oldest share class. Investor return for each fund is calculated by Morningstar and reflects the average return on all dollars invested based on estimated monthly net fund flows. The aggregate investor return and fund return are averages weighted by the size of each fund. Only funds with both the fund return and the investor return are included in the analysis.