I replicated another tactical ETF strategy in QuantConnect that I think is somewhat interesting. The guys over at AllocateSmartly do a lot of replication on these types of strategies (and do a great job). Rather than rephrase everything in my own words, you are probably better served reading the write up they did on the strategy here. Results are pretty good for what it is.
Lately I have been allocating most of my free time to an open-source Quant platform called QuantConnect. It is a really great platform that allows you to construct your own trading strategies using Python or C# and backtest before implementing the strategies live. It give you a ton of flexibility to program any features and test for significance while not being limited to a predefined set of indicators or signals.
I will be sharing my backtests that utilize ETFs with the code for my readers over the coming months and figured I would start with one of the more popular tactical ETF strategies. The first one I will share is: “Protective Asset Allocation (PAA): A simple momentum-based alternative for term deposits” based on Keller and Keuning (April 25, 2016)
– Average, unleveraged return better than SP500
– Significantly reduced drawdown vs SP500
Subject to constraints:
– Monthly rebalancing
PAA strategy summary
1. consider a set of N assets (ETFs)
2. select a protection factor (see below) and maximum number of assets to hold (TopN)
3. count the number (n) of the risky assets with positive prior month MOM (see MOM definition below)
4. compute the bond fraction (BF): BF = (N-n)/(N-n1). (see n1 definition below)
5. Invest a fraction BF of the portfolio into the safe set (bonds)
6. From a set of equities invest the remaining fraction (1-BF) in the top n_eq equities sorted on MOM
7. Hold for one month and then repeat to rebalance
Definition of terms used by Keller and Keunig
– momentum (MOM): to be MOM = (last month’s close)/(SMA over lookback period) – 1
– lookback period (L): L is measured in months
– protection factor (a): a = [0, 1, or 2] is used to adjust the BF gain: n1 = a*N/4
– number of equities to be purchased (n_eq): n_eq = min(n,topM)
If you have any questions or comments, please don’t hesitate to send me an email.
I was at a conference last week in Los Angeles for Citywire magazine and they had a speaker, Ben Hammersley, a technologist, journalist, and author based in London, England. Normally I don’t take notes during speaking engagements, but given the fact that such a large part of what I do now involves technology, he had my attention. A lot of what he said resonated with me, so I wanted to share my thoughts on his presentation with my readers.
He had a quote on the projector that seems simple, however, after examination it was extremely powerful:
In this industry, robo-advisors and the like have created a mad scramble of professionals that share the same anxiety-fueled thought: financial advisors and wealth managers will soon be replaced by robots. I personally believe that any redundant or repetitive task should be done with a script to automate the process. If you are not adding value other than completing the task and checking a box, there’s absolutely no reason you should be wasting brain power on it. Instead, they can focus on their clients and important day-to-day activities that utilize their experience. I believe the above quote could also read “Robots don’t steal careers. Robots steal tasks.”
The ‘5 Whys’
Ben then spoke about a concept introduced by Toyota in Japan, called the ‘5 Whys’. I believe this can be used to answer the question above and discover why you’re doing tasks in the first place.
“5 Whys is an iterative interrogative technique used to explore the cause-and-effect relationships underlying a particular problem. The primary goal of the technique is to determine the root cause of a defect or problem by repeating the question “Why?” Each answer forms the basis of the next question. The “5” in the name derives from an anecdotal observation on the number of iterations needed to resolve the problem.”
I believe many organizations in our industry would find that a good number of the tasks we perform are born from old ways and outdated mindsets. Maybe we should be more like a little kid that repeatedly asks “Why daddy?” or “Why mommy?” Although I’m not a parent, I’m sure that the repetition can actually push a parent to think, “Yeah, good question; why do we do that?” (maybe not out loud, but you get the point).
Break Free of Anchoring
He used this concept to dovetail into another interesting point about the mindset needed to succeed in today’s technology-driven environment with a simple concept: ‘Constant Legacy-Free Reinvention’.
I believe this is the core to many of the issues in our space. Anchoring to past technology and processes is a death nail in a forward-thinking firm. One must cultivate a fresh mindset every day and avoid clinging to the past.
Ben brought up a great story that can help advisors get into the robots-for-tasks mindset using chess as the analogy. He explained that in the 1980’s IBM created a chess-playing computer dubbed Deep Blue that won its first game against Garry Kasparov, a Russian and Croatian chess grandmaster, in 1996. Mr. Hammersley’s point was clear – everyone at that time predicted that this would be the end of chess as we know it. They thought people would lose interest knowing that computers had mastered the game, so why bother? However, the actual effect was quite the opposite. Instead of giving up, the quality and quantity of chess players substantially increased in a short period of time.
Why Did This Occur?
Well, for the chess-playing prodigy in an emerging market country, their abilities no longer plateaued as a result of competing against only local players. These underprivileged chess players now had powerful AI competitors they could play against from the convenience of their home. They even created AI that played to the ability of their opponent and slightly increased in skill as the player improved.
While the global chess world was experiencing a renaissance, local chess clubs faced new challenges. Players were found cheating constantly, as they could simply get up to go to the bathroom during a match and check their phones for optimal moves. The chess clubs wised up and, instead of countering the machines, embraced the change into a new form of chess dubbed ‘Centaur Chess’. Here’s an excerpt from Wikipedia:
“Advanced Chess (sometimes called cyborg chess, centaur chess or Ivanov chess) was first introduced by grandmaster Garry Kasparov, with the objective of a human player and a computer chess program playing as a team against other such pairs.
Many Advanced Chess proponents have stressed that Advanced Chess has merits in:
- increasing the level of play to heights never before seen in chess;
- producing blunder-free games with the qualities and the beauty of both perfect tactical play and highly meaningful strategic plans;
- giving the viewing audience an insight into the thought processes of strong human chess players and strong chess computers, and the combination thereof.”
Ben’s point was that everyone in the professional world should look to become ‘Centaur Chess Players’. As it relates to financial advisors, this means that we should embrace technology to supplement and increase our abilities by offloading redundant tasks to machines. By doing so, we can focus on strengthening our more human features that will never be replaceable by computers. As a side note, I am fascinated that Centaur Chess was first introduced by the same chess grandmaster that IBM’s super computer beat in 1996 for the first time. I think that is the best example of the mindset we should all look to emulate. Think about it: instead of taking the easy route and writing off all technology after Kasparov was beat, he embraced and actually used it to his advantage. This is painful and requires a lot of humility, but is a lesson worth respecting.
Moore’s law states that computers become roughly 2X as powerful from one year to the next. When building technology these days, you must account for the fact that what you are building could be obsolete in a few years. Therefore, the design of your technology stack should be modular with as little captive technology as possible.
Ben ended the presentation with some valuable technology that can be used to streamline your firm’s operations and unload many of your redundant tasks to new technology. For instance, you could use LawGeex for contract review, Amy for appointment and scheduling, and Textio for job description optimization. You can then use a device like Muse™ to optimize your mindset and WHOOP™ to optimize your heart rate and sleep pattern. Now that is a true, real-world example of fusing yourself with technology and becoming a modern-day cyborg (well, close enough).
Again, the point here was to embrace technological change and look forward to removing the monotony from your lives. Many careers and jobs these days have aspects that are never going to be accomplished with technology, so focus on what that is and offload the rest to technology (I know, easier said than done!).
According to Wikipedia, heuristics are defined as: any approach to problem solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for the immediate goals. Where finding an optimal solution is impossible or impractical, heuristic methods may be used to speed up the process of finding a satisfactory solution. Examples of this method include using a rule of thumb, an educated guess, an intuitive judgment, guesstimate, stereotyping, profiling, or common sense.”
From my experience investing and dealing directly with investors for over a decade now, I have come to realize that the majority of novice investors, whether they realize it or not, utilize heuristics in their investing process with widely varying degrees of success. For example, the stock market has been in a bull market for a prolonged period of time, and an investor decides to de-risk their portfolio due to the gut feeling that the market is due for a correction. Aside from the obvious flaws in this method of asset allocation, an investor that employs this method will most likely have a streak of success during certain market environments, and will incorrectly attribute any success gained from this method to its efficacy, all further reinforcing negative behaviors.
The Biggest Problem
The main problem I see with this simplified example is that when using heuristics or emotion-based decision (gut feelings), the investor must have similar experiences to benchmark their emotional decision making against in order for this method to have any chance at success. What do I mean by that? I guess what I am saying is gut feelings are only potentially useful when you have a decent sample size and the emotional intelligence to be able to recognize and journal emotional decisions at points in time. If you have a gut feeling that the stock market may be at a potential top but have never experienced a market top previously and instead rely on historical recounts of other people’s emotions during market tops, that gut feeling is just a random emotion-based decision that has no relevance to the stock markets’ behavior.
The Way to Succeed
In my opinion, the way to possibly successfully utilize rules-of-thumb is by chronicling one’s emotion-based decision making over the course of several market cycles. Forcing yourself to record how you feel during various points in both bull and bear markets not only helps you understand yourself, but provides you with a recount as to whether certain emotion-based decision making is positively, negatively (contrarian), or completely uncorrelated from future market performance. Due to the long-term nature of this exercise, it is unattractive to the market participants that lack patience or dedication, and most importantly it requires a high degree of self-introspection which is painful for many. These road blocks provide an opportunity for those willing to focus on the long term and getting to know themselves, both the good and the bad. This humbling process will help you gain humility, which is an invaluable asset when investing.
Rather than relying on something as volatile and prone to external forces as emotions to drive investment decisions, the majority of investors would benefit greatly from developing systematic investing processes that are repeatable and data driven. This doesn’t mean that you have to write complex algorithms to achieve this, but any good investment methodology should be able to be broken down into logical steps and understood by anyone with at least an intermediate knowledge of investing.
My Personal Preference
I personally gravitate towards quantitative methods, but am constantly reminding myself that simplicity is better than complexity. In the infamous “Quant Quake” of 2007, three quarters of Goldman Sachs’s assets destroyed a five-year span following sudden liquidation by a multi-strategy fund or proprietary-trading desk. This event likely caused algos to run awry and ignite fire sales in similar quantitatively contracted portfolios. My belief is that during times of panic, if you cannot dissect your investment process to understand why it may or may not be working, the majority of people would bail. Small mistakes such as falling victim to the allure of complexity, can and will destroy any success you may have had up to that point. Keep it simple out there, folks.
Over the last four years, there has been a widening market for global exchange-traded products (ETPs). In fact, according to the BlackRock Global ETP Landscape Report, it has grown from $598 billion in 2006 to $3.5 trillion in 2016.
As they grow, the waters might get muddy for the average investor, leading them to pick the first ETP that comes their way. Unbeknownst to most investors, each product reacts differently depending on the state of the market. Most ETPs are one of the following:
- Exchange-Traded Funds (open-end funds or ETFs)
- Unit Investment Trusts (UIT)
- Grantor Trusts
- Limited Partnerships (LPs)
- Exchange-Traded Notes (ETNs)
Now that we’ve identified the most common ETPs, it’s time to dive into the pros and cons of each investment type and the effects they can have on an investor’s portfolio.
Types and Sub-Types
Often referred to as open-end funds, ETFs pass all income, capital gains and losses, and dividends through to its shareholders. Dividends from these funds are usually reinvested until they’re distributed to investors on a quarterly or annual basis. Through their unique structure, they can use portfolio sampling, derivatives, and securities lending to increase returns to investors. In most cases, these products simply keep pace with the S&P 500, but some companies, like Pimco, have deployed more active strategies through ETFs.
Actively managed ETFs give investors similar exposure to mutual funds with intraday liquidity and minimal trading costs. To put it in context, mutual funds are only bought and sold at the end of the day after price fluctuations have been calculated.
UITs aren’t a far cry from open-end ETFs, barring a few minor differences. First, UITs are not permitted to loan securities, which is a common tactic used by fund managers to pump up returns. This limitation often means UITs copycat basic indices.
Secondly, this type of investment vehicle doesn’t reinvest dividends. Instead, they hold on until it’s time to pay investors. As a result, “dividend drag” comes into play, which means returns are not realized from dividend proceeds while they’re waiting to be disbursed. This can ultimately result in a reduction in returns relative to other investment structures.
Lastly, UITs feature termination dates that are established at the onset of the fund – these range from a few years to decades. In the case of equity UITs, they typically expire over 50 years from the start date. Alternatively, fixed-income UIT expiration corresponds with the maturity date of the bond investments held in the trust.
These are investment options that typically hold futures and physical commodities. Taxation is treated as if an investor were holding onto the underlying security and a pro-rata share of income and trust expenses are taken out.
Trusts holding physical commodities have their gains taxed as regular income – in many cases at 28 percent. Trusts holding futures are taxed every year even if the position has been sold. In this scenario, 40 percent of the capital gains are taxed as short-term, while 60 percent are taxed as long-term.
LPs are very similar to grantor trusts in that they take a pro-rata share of the expenses and income of the partnership. All LP owners are sent a K1 form toward the end of the year for tax purposes. Just as with grantor trusts, futures held by the partnership yield taxes at the end of the year even if the position isn’t sold. Gains are taxed at the hybrid rate shared by grantor trusts – 40 percent at short-term capital gains and 60 percent at the long-term capital gains.
Unfortunately, transparency isn’t as strong with LPs when comparing them to open-end ETFs, grantor trusts, or UITs. If you’re looking for examples, look no further than United States Natural Gas (UNG) and United States Oil (USO).
Exchange-traded notes have a completely different set of attributes with regards to the above. These vehicles produce no income distributions or dividends to speak of. Capital gains aren’t realized until the sale, maturity, or redemption of the ETNs, which means they may help with tax efficiency. They are typically unsecured, senior, unsubordinated debt securities that are meant to reflect returns linked to the performance of a market index, sans investor fees.
The reason for the lack of transparency in ETNs is related to the structure of the portfolio. There aren’t typically securities for investors to research or have any recourse with. With an ETN, investors hand over their cash to the issuer and receive compensation linked to an underlying index (less fees) identified in the prospectus. To ensure they get the best deal, investors are expected to analyze the credit quality of the issuer. If the credit quality of the issuer suffers, it could have a negative impact on the value of the ETN.
Investors can always opt into asset classes with complex tax characteristics by investing in an ETN that offers synthetic exposure to those assets. Some ETNs provide investors exposure to master limited partnerships without holding the underlying assets. In an effort to try to avoid creating a complex tax situation, these products seek to help investors replicate the returns in a portfolio.
ETPs have very subtle differences, but they all serve a very unique purpose. In attempting to maximize portfolio efficiency and avoid unwanted risk or exposure, it’s absolutely necessary for financial advisors to understand the difference before investing client money. Know what you’re selling.
The opinions voiced in this material are for general information only and are not intended to provide specific advice or recommendations for any individual. All indices are unmanaged and may not be invested into directly.
Investing involves risks, including possible loss of principal. No strategy assures a profit or protects against loss.
This information is not intended to be a substitute for specific individualized tax advice. We suggest that you discuss your specific tax issues with a qualified tax advisor.
Bonds are subject to market and interest rate risk if sold prior to maturity. Bond values will decline as interest rates rise and bonds are subject to availability and change in price.
Limited partnerships are subject to special risks, such as potential illiquidity, and may not be suitable for all investors.
Unit Investment Trusts (UITS) are a fixed portfolio of securities with a set term. Strategies are long term, therefore investors should consider their ability to pursue investing in successive trusts and the tax consequences.
An investment in Exchange Traded Funds (ETF), structured as a mutual fund or unit investment trust, involves the risk of losing money and should be considered as part of an overall program, not a complete investment program. An investment in ETFs involves additional risks such as not diversified, price volatility, competitive industry pressure, international political and economic developments, possible trading halts, and index tracking errors.
The fast price swings in commodities and currencies will result in significant volatility in an investor’s holdings.
Structured products typically have two components; a note and a derivative and a fixed maturity. They are complicated investments intended for a “buy and hold” strategy and offer protection from downside risk in exchange for forgoing some upside potential to achieve that protection. Principal protection may vary from partial to 100 percent.
Investing in structured notes is not equivalent to investing directly in the underlying securities or index and carry risks such as loss of principal and the possibility that you may own the referenced asset at a lower price, due to economic and market factors that my either offset or magnify each other. At maturity, if the derivative turns out to be valuable, the investor can gain exposure to the upside of that index.
The market for exchange-traded funds is rapidly expanding, which is why advisors need to perform proper due diligence before investing.
The First Step
We can start by selecting an ETF by size and/or market capitalization (anything from micro to large-cap), then choose from domestic or foreign equities at various levels of economic development. In fact, advisors and their clients can get as granular as choosing individual countries, regions, or sectors. Delineating the asset class by style, including value stocks, growth stocks, or a combination of the two is an option as well.
Assuming all the above, an often-overlooked aspect when researching exchange-traded products is the distribution of weights within the fund.
A Cass Consulting study, published in March 2013, inspected a number of alternative weighting schemes formed using the largest 1,000 equities in the U.S. stock market from 1969-2011. This study was meant to show the effects that weighting methods potentially have on volatility and performance.
In today’s investing world, weighting by market cap is one of the most common methods. This means that the weight of each stock is equal to its market capitalization divided by the sum of the market capitalization of all stocks in the index.
Cass went even further. They formulated the dividend-weighted index by dividing the five-year average total dividend payout for each stock by the five-year average total dividend payout of all stocks to obtain the weight for each company. For reasons of consistency, their research used the same process for total annual cash flow, total annual sales, and book value to calculate those indices.
Lastly, they drafted a “fundamentals” composite-weighted index, which factors the dividend weight, average cash flow weight, sales weight, and book value weight of all companies in the index. These four metrics are an average of the composite index weight.
Alternative Equity Indices
|Weighting Methodology||Return||Standard Deviation|
|Based upon a data set (utilized in Cass study, linked above) that comprises the largest 1,000 U.S. stocks for each year from 1969-2011|
Based upon a data set (utilized in Cass study, linked above) that comprises the largest 1,000 U.S. stocks for each year from 1969-2011
The ever-popular market cap-weighted strategy deployed by one of the largest ETFs in the industry, SPDR S&P 500 ETF (SPY), was rated as the absolute worst performer, as noted above. Market cap-weighted indices focus more on investing in overvalued companies than the underdogs, leaving less room for large extensions of growth.
As indicated by the Cass study, a weighting system based on fundamental factors tied to accounting measures, such as sales or earnings, could potentially provide above average returns.
For your convenience, here’s a list of alternative-weighted ETFs below:
- Beta-weighted: PowerShares S&P 500 High Beta (SPHB)
- Beta-weighted: Russell 2000 Low Beta ETF (SLBT)
- Dividend-weighted: WisdomTree Total Dividend Fund (DTD)
- Dividend-weighted: WisdomTree U.S. Dividend Growth Fund (DGRW)
- Equal-weighted: Guggenheim S&P 500 Equal Weight ETF (RSP)
- Earnings-weighted: WisdomTree Earnings 500 Fund (EPS)
- Fundamentals composite-weighted: First Trust Large Cap Core AlphaDEX Fund (FEX)
- Fundamentals composite-weighted: PowerShares FTSE RAFI US 1000 (PRF)
- Momentum and Trend-weighted: PowerShares DWA Technical Leaders (PDP)
- Revenue-weighted: RevenueShares Navellier Overall A-100 Fund ETF (RWV)
- Volatility-weighted: PowerShares Low Volatility (SPLV)
- Volatility-weighted: iShares All Country Minimum Volatility (ACWV)
Taking the time to understand all the nuances of the ETF you are about to invest in is of vital importance. Understanding the intricacies of your investment option will better equip you to adapt to various market conditions and adjust the portfolio as necessary. While using a weighting system based on fundamental factors has shown the potential for above-average returns over time, past performance is no guarantee of future success.
We like to think that issues are so easily solved with simplistic, watered down answers. To use an old cliché, the truth of an argument usually lies somewhere in the middle. For instance, when I see my fellow advisors arguing for or against active and passive fund managers, their biases come out instead of rational debate on the topic. Their supporting arguments always miss a couple of key points that could potentially alter the results and therefore the conclusions drawn from them. Let’s take a look at some of the flaws in conventional wisdom centered around desired characteristics when selecting fund managers.
Old Isn’t Better
Although many might believe otherwise, as age and size increases in a fund and fund family, the level of risk can actually increase rather than decrease. Why’s that, you ask? Before I tell you, you need to understand that the risk I’m talking about is the perceived risk of not achieving investment goals or the risk of destroying capital.
Alright, now that you’ve got that, know that strength in numbers only works if we’re talking about the reduction of costs through gained efficiency. However, after a certain point (let’s call it the optimal asset level), the bloated asset levels from all that baggage that potentially outweighs the benefits from scale.
People tend to cling to familiarity and old, well-established mutual fund companies are the natural choice for this majority. Unfortunately, familiarity doesn’t help much with your chances of outperforming a benchmark. As a perfect reinforcement to my point, a 2002 study by Otten and Bams found that younger funds performed better than older funds and fund age was negatively related to fund performance.
eVestment Alliance performed a study using investment data obtained between 2003 and 2014. The study grouped funds into age groups consisting of young (less than 2 years), mid-age (between 2 to 5 years), and tenured (greater than 5 years) as well as by assets under management (AUM) consisting of small (less than $250 million), medium (between $250 million and $999 million), and large (greater than $1 billion). Let’s take a look at their results.
Performance of Young, Mid-Age, and Tenured Funds
Performance of Small, Medium, and Large Funds
Source: eVestment Alliance, LLC
Among the portfolios of funds grouped by age, the youngest funds had the highest cumulative return from January 2003 to December 2014, at 250.25%. The mid-age index came in second at 144.04%, followed by the tenured index at 137.24%. Among the indices organized by size, the small index had the highest cumulative return from January 2003 to December 2014, at 139.86%. The medium index came in second at 106.49% and the large index third at 96.30%. Of course, past performance is not indicative of future results.
What could potentially be driving this irrational behavior despite evidence to the contrary? In behavioral finance, there is a negative effect known as the familiarity bias and it acts as a subconscious gravitational pull towards familiar or well-known investments. If I may be so bold, I believe this phenomenon is the predominant acting force behind this counterproductive investor behavior. There’s other behavioral forces at force here, such as herding (aka comfort in crowds), but the most pronounced in my experience has been the familiarity bias.
A New Risk
Okay, so we’ve established that many people have a natural bias that pushes them to older funds. However, no one is talking about the significant new risks that arise from this strategy. One such risk is auto-flow and I, like many of you in this industry, have witnessed it firsthand.
For those that don’t know what it is, here’s a definition:
Auto-flow is when the quality of the money into an investment product declines while its volume increases. Depending on the asset class that needs to be invested, these funds are considered an auto include. If there’s ever a hiccup in the auto-flow, there’s bound to be cascading outflows. Quality of the fund could be decreasing due to one of two reasons:
- Client servicing becomes more difficult with a rapid increase in volume or fund capacity becomes an issue.
- There’s a disconnect between the reality and investor expectations.
The aforementioned volume increases can be attributed to, as you might expect, increased popularity and/or great performance in the last few years. Check out the charts below from Propinquity to see a visual representation of auto-flow in the wild.
Marketing is (Sometimes) Everything
A second type of risk is marketing. I won’t name names here, but a fund company’s marketing can be very convincing especially around hot strategies that have just recently blown up in size. Once this cycle begins, people throw money into the strategies without even really understanding what they are investing in, let alone the underlying drivers of performance and whether they are sustainable sources of alpha going forward. With expectations at a peak, one stumble by the fund causes an “awakening” amongst these uninformed late stage investors and they then begin to learn about the strategy.
Something as small as one quarter of underperformance can cause investors with flawless expectations to flee. At this point, panic takes hold and they frantically flip through a prospectus and find something they should have known all along and immediately redeem their investments. At that point, it’s a fully-formed feedback loop that is vicious, if not impossible, to halt.
Propinequity also provides a nice illustration of two real life examples, with the fund names removed for anonymity (shouldn’t be too hard to find with a little research):
Running Down a Profit
It’s common for managers to lag their peers in the first half the year, only to chase extra risk toward the end of the year to make up for their losses. This brings us to our third type of risk.
In a study by Kempf and Ruenzi in 2008, they disputed this strategy by examining the role of tournaments within mutual fund families and how that can drive increased levels of risk taking during the second half of the year. First of all, let’s be honest with ourselves; all funds and fund managers are not considered equal within their fund families so there may be rivalry within firms, which leads to tournament-like behavior. Again, this may be mitigated by going with a lesser known fund company but, you know, that line of thinking may be a little too rational for some individuals in this industry.
We mentioned herding earlier, but let me elaborate a bit on this mentality and why it’s included as a risk factor. Most people find comfort in numbers, but from a risk standpoint I don’t see how. Not only does herding have the potential to ruin things for market participants, but they may also mess everything up for investors as well, which might detrimentally effect the management of a fund.
A study by Bär, Kempf, and Ruenzi in 2011 found that decisions made by a team rather than by an individual has a dampening effect on returns. They concluded that teams have a moderating influence on each other and consequently have less extreme investment styles, less concentrated portfolios, and therefore have less extreme performance outcomes. Although you could argue that you avoid extremes to the downside, I’d counter with this: why not just invest in an index fund?
In another study along the same lines, it was discovered that investors experience a deterioration in performance when switching from a single manager to a team approach, while finding an improvement in performance when switching from a team to a single manager. Older funds run by long-serving managers were also shown to underperform their counterparts.
The top decile portfolio of funds with the highest herding tendency underperformed the bottom decile portfolio of anti-herding funds by about 2.28% on an annualized basis, both before and after expenses. They also obtain similar results when we adjust the fund returns to account for their risk exposures: the underperformance of herding funds is 1.92% based on Carhart (1997) four-factor alphas. Their regression results show that the predictive ability of fund herding is distinct from the effect of past performance and other fund characteristics such as size, age, turnover, expense ratio, and net flows.
In Solomon, Soltes, and Sosyura (2014), they found that not only are investors selecting managers by chasing performance, but they are only doing so when it is trendy and fun to talk about:
Investors reward funds that hold stocks with high past returns, but only if these stocks recently received media coverage. We argue that media coverage of firms increases the salience of their stock returns and attracts investor attention. When faced with a long list of fund holdings, investors appear to respond only to those companies that were recently featured in the news. As a result, funds holding high- visibility winners attract greater capital flows than their counterparts holding less visible winners. Conversely, funds holding high-visibility losers experience a greater attrition of flows than their counterparts holding losers with similarly poor performance but no media coverage. In absolute terms, the effect on fund flows is larger for media-covered winners than for media-covered losers.
At least they are being picky with their performance chasing!
The Bigger Idea
This article isn’t meant to be an exhaustive list, but I think you get the point. Let’s recap.
- Passive Investing Vehicles – When my objective is to be at or around the index or I want exposure to an entire basket of securities for beta.
- Active Investing Vehicles – When I want to attempt to add value in the form of alpha.
In my research, I have found and partially demonstrated (via all that writing above) that you can at least tilt the odds of selecting a value-adding manager in your favor when you limit the scope of your analysis to a subset of the population and a specific set of criteria.
There are several ways to explain the outperformance achieved by funds that are newer and smaller. To survive, new asset managers must outperform their peer group to attract assets and build their businesses; thus, they work harder and take more significant positions in their high conviction ideas. Additionally, newer asset managers tend to be nimbler, making investment decisions faster by avoiding the more complicated and bureaucratic approval procedures inherent in many large competitor firms.
Let me illustrate this with a hypothetical, real-life example of events. Imagine you are a star manager for a fund that is two times the size of your closest competitor. Frequently, you try to differentiate and add value to your fund to justify your fees by employing unique investment opportunities. When you go to implement the idea, you discover that your fund would have to purchase the entire market’s supply of that security to gain a measly one percent.
You really wanted to add value to your fund, but you simply cannot due to capacity constraints. Over the course of a few years, you find dozens of no-brainer investment opportunities, but are met with the same capacity issues. You finally get fed up with these constraints and decide to start fresh with a new fund at a new firm (or by starting your own). That wish list of investment opportunities you were too big to take a piece of are now within reach and represent low hanging fruit you can invest in right out of the gate. Do you think you will outperform your old, gargantuan predecessor fund during the first couple of years?
This phenomenon is also evident in mutual funds. An analysis by Vantage Consulting Group on mutual fund data shows that small funds take roughly one percent more active risk.
The bottom line is, just as startups and venture capital can offer above market returns, the same can be extracted from investing in managers by simply reframing the way we evaluate them. New asset management firms, much like any other new business, are generally able to start off with the latest technology and best practices and are unburdened by legacy operational inefficiencies. Smaller and newer asset management firms can enjoy many of the same benefits that startups enjoy in other industries. By learning from past mistakes, managers can use their industry knowledge along with research and innovation to establish a firm using best practices.
If the goal is to outperform a benchmark, you must take career risk and no one likes being the unpopular person. If not, just consider buying low-cost index funds.
It’s often very easy to recommend an investment based on popularity, but if most investors could see the possible negative effects that these short-term decisions have on their long-term wealth, they might be less inclined to make a snap decision. Just as a credit card can be used now for a potential loss down the line, clients need to understand that this short-term happiness could negatively affect their portfolios for years to come. Most portfolio optimizers don’t account for regret minimization and a quality of life reduction later in life.
Do your clients make knee jerk investment decisions? Have you ever turned down a client’s request to shift focus to a less-than-exemplary fund? Email me at [email protected] with your thoughts.
Investing in mutual funds involves risk, including possible loss of principal. The opinions voiced in this material are for general information only and are not intended to provide specific advice or recommendations for any individual. All performance referenced is historical and is no guarantee of future results. Alpha is a measure of a fund’s performance compared to a benchmark. Beta is a measure of a fund’s volatility compared to the market as a whole.