Does experience trading the markets provide an actual trading edge? Surely an investor who’s seen many bull and bear markets has an advantage compared to a novice just starting out in the field. The beginner is an alpha source for seasoned traders as the former pays their “tuition” associated with learning how to trade. The counterargument, using efficient markets logic, suggests that if a great number of portfolio managers (PMs) have significant experience, then the performance benefits of experience become arbitraged away as prices quickly incorporate the collective wisdom of the pros and experts.
Fund marketers ignore the efficient markets logic and advertise portfolio manager experience because it’s very convincing to most customers. I’ve traded asset classes for about 20 years. I’ve experienced two major and several minor bear markets, and seen a variety of bull markets. When I assess my current trading edges, I admit I’ve started putting “experience” as an edge – although at times with a question mark behind it.
Studies show that manager tenure and experience has little impact on mutual fund performance.1 I’ll speculate that the constraints associated with mutual fund portfolio management greatly inhibit the use of experience as an edge. Prospectus limitations on what securities can be purchased and the requirement to stick with an established investment discipline limit the flexibility to use experience to add value. Career risk can also drastically alter a PM’s personal risk-return profile, inhibiting the use of experience to benefit clients.
Seasoned relationship managers and investment advisors can be highly valuable to their clients since they draw on experience to help a client weigh the pros and cons of making a decision – especially in stressful moments. Of course, their job is not about adding alpha, although many claim they can. Just because a person has 20 to 30 years in the industry doesn’t mean they automatically have an “experience edge” that translates into superior performance. Many PMs are not intentional in how they learn from their experience. They’re lazy.
As an asset class trader, free to shift to any asset class and any investment style at any time, experience can become an impactful trading edge. Intentionally developing an “experience trading edge” requires a carefully planned personal mastery process. Much like a training program used by elite athletes, this mastery process is used to successfully play the asset class trading “meta-game” to better assess what approach works best at a particular moment in time. The mastery process is never-ending and consists of learning, practicing, recording results, reflecting and incorporating feedback to get better. To ignore the process is a huge missed opportunity to get better at this game, and perhaps develop an “experience” trading edge. (more…)
In the Market Tells – Part 1 blog, I introduced the concept of market tells as an enduring trading edge that can be used to enhance returns over the long term. Developing the ability to identify market tells and act on these signals takes practice. It’s important to keep track of trading results to get better and gain confidence to jump on these opportunities in size when they occur.
In this blog, I first clarify thoughts on a few instances when the market tell guidelines are not met. The most important guideline for identifying a market tell is the 80% rule, which states that to consider market action as unusual (and thus providing a market tell), the usual behavior must occur at least 80% of the time. Otherwise, it’s just too difficult to associate the unusual behavior with a market tell rather than normal market gyrations. Next, I’ll review three more market tell techniques that can be used to trade asset classes. I’ll close with some thoughts on crowded trades and implementation hints.
On some days, one sector may be up while the rest of the market is down. If the behavior is explained by news, then this price action is totally normal. Even without news, this sort of divergence occurs often enough to consider it normal activity. If a security is illiquid, such as a closed-end fund, then this action might be correcting a stale price from the previous day, which is not unusual at all.
To contrast, if all risky assets are aggressively moving higher (such as when the S&P 500 is up 2%+ for the day), and one sector is flat, then this behavior is much more unusual and may be the basis of a market tell, especially if no news explains the behavior. However, we need to remember that the time scale of this edge is on the order of a day or two.
Watching how a stock or asset class reacts to news associated with a known announcement date (such as an earnings call or a central bank news release) is very difficult to use as a market tell. There is usually an enormous amount of trader attention to these known release dates, which probably means there’s no trading edge. Much of the news and all the probabilistic outcomes have been priced in before the announcement, which is another way of saying the pre-announcement price is set such that there is no edge in buying or shorting the security ahead of the announcement, or in fading or chasing the post-news reaction.
Finally, watch out for interpreting short-term market action when there’s a lot of unwinding of positions. For instance, at the first of the year, it’s best to wait a week before interpreting market action as hedge funds unwind their end-of-year trades. This sort of behavior can occur during bear markets also. This is a very chaotic time when a lot of unwinding is occurring – it’s a very difficult time to interpret market action versus any sort of playbook. Wait until the dust settles and stocks start moving in unison before interpreting market action.
Divergences and Market Tells
In discussing market tells, I’ve used the term “divergence” to describe local price action where an asset class’s price or relative strength line is diverging from normal expectations. Searching for divergences is a staple of technical analysis.1,2 In the past I have falsely interpreted a divergence as a market tell. These misinterpretations generally occurred during relatively quiet times in the market, usually during bull markets, when the 80% rule did not apply.
Ask a market strategist about divergences, and the first thing that comes to their mind is the case where the stock market is making new highs while some other risky asset class is moving lower. For instance, from mid-2014 to mid-2015, the S&P 500 was moving higher, while junk bond markets where moving lower. This isn’t supposed to happen, and such a divergence is often interpreted as a warning of future weakness in the S&P 500, leading to a convergence of the two markets. (more…)
In any competitive field, the awareness of tells can provide a significant and enduring edge. Most people think of poker when they hear about tells.1 Is a player acting strong to encourage other players to fold? Does a player seem nervous when they throw their chips in the pot? Can any useful information about an opponent’s poker hand be gleaned from these actions?
Tells occur in many aspects of life and competition. For most sporting events, searching for tendencies in an opponent’s play is an integral part of game preparation. In the home arena, parents look for facial clues when interrogating a fidgety teenager as the youngster explains what she’s doing on a Saturday night.
Tells are an important source of feedback when trading the financial markets. I call these “market tells” to distinguish between the variety of tells that occur in other forms of competitive environments (more on this distinction later). A market tell is a powerful approach to sensing moments in time when market participants are not positioned correctly.
A simple example of a market tell is a stock that’s acting strong when it should be weak. Perhaps the stock is in an uptrend when extremely bearish news is released about the prospects of the company (such as a product recall). Unexpectedly, after a momentary dip on the news, the stock price continues to go higher. That’s a positive tell for future outperformance.
Properly identified, tells can get you out of a trade much sooner than waiting for a technical trend-following sell signal. Tells can help you identify future outperforming asset classes. They can provide positive feedback that a current trade is working. No matter what your trading discipline or time scale, searching for market tells is a great trading edge.
The more you clarify and develop your thoughts around market tells, the more confidence you’ll have to quickly jump on trades and to trade with high conviction and size. I began noticing market tells soon after I started trading in the late 1990s. In 2005, I decided to be more disciplined about it by keeping track of results. When I observed a tell, I printed out a chart and documented what I expected to happen, and then slipped the paper into my “market tells” folder. I also traded on this information, and over the next four years, avoided looking at the results.
In mid-2009, I checked to see how these predictions worked. The success rates for these trades at both the short and intermediate time scales were excellent. These results piqued my interest to further investigate and refine the use of market tells in my trading. In 2014, I performed yet another trade analysis, and in 2017, I find myself slightly reshaping my views while writing this blog.
Classification of Tells
To provide a framework for thinking about market tells, I’ll review various forms of tells. (more…)
Does momentum really work? Can you outperform benchmarks by simply holding top-performing securities? I have my doubts, but I’ve decided to be open-minded and revisit this idea.
A decade or two ago, we used momentum extensively to select equity asset classes and avoid the poor performers. I lost faith in momentum somewhere in the 2009 to 2010 time frame after the premium suffered a historically large drawdown in 2009.
We turn off momentum at the beginning of new bull markets, so we didn’t suffer from this drawdown, but hedge fund assets had grown exponentially to over $2 trillion at the time, and momentum mutual funds were being introduced. The strategy appeared to be very crowded.
Momentum was a great trading edge for many decades. Now it’s just too easy to do and too popular with an enormous amount of assets implementing momentum in various forms.
Let’s define the momentum effect. The idea is that top performing assets over a 3 to 12 month time frame tend to outperform over a similar time frame. Poor performing assets tend to underperform in the near future. Momentum is sometimes confused with trend following.1 Momentum ranks recent performance among peers, such as ranking U.S. stocks among each other, or U.S. sectors among each other, or individual country stock market indices among each other. Trend following looks at absolute prices and asks if prices are in an uptrend or downtrend, and shifts to cash or shorts an asset when prices are in a downtrend. Momentum indices remain exposed to falling prices, and thus can suffer large losses during bear markets. Long-short momentum portfolios can also suffer enormous equity-like drawdowns.
A typical momentum measure is 12-month performance, although the sweet spot look-back period can vary from 6 to 15 months. For really short time periods, one month or less, there’s a short-term reversal effect where top performers typically underperform during the next month. 2-4 As the look-back period extends beyond a few years, recent top performers begin to lag the averages in the future and the bad performers tend to do better as the latter are now underowned and better valued with higher expected returns. (more…)
The massive migration of assets from actively managed equity funds to index funds has attracted a lot of attention lately.1-6 The discussion varies, including worries about the future of active managers, concerns about market efficiency, and claims that active managers are about to outperform, and a variety of social impacts caused by this trend.
The flow of assets among U.S. equities is massive and may be a sea-change inflection point in the financial markets. The transition toward indexing has accelerated in the U.S. as more and more professional investment advisors have finally acknowledged the failure of active management. New Department of Labor fiduciary rules also make it more difficult for investment advisors to recommend active managers without exposing themselves to potential litigation risk. Figure 1 from a recent Wall Street Journal article, using data from Morningstar, shows the accelerating trend.1 Similar trends are occurring more slowly among bond funds and international equity funds.
Many articles, mostly wishful thinking from those selling active stock picking, suggest the “performance pendulum” will eventually swing back in favor of active managers outperforming. John Rogers, a highly respected portfolio manager for the Ariel Funds, recently captured active stock pickers’ common sentiment about active versus passive management.
“This is going to be the decade for stock pickers. … It’s been a long trend we’ve gone through where active managers have underperformed. We know in 30 years of doing this … we have gone through these waves. Things become very popular, very hot and everyone follows that concept. What’s worked yesterday gets everyone excited and people give up on what’s not working and often that’s where the opportunities are.” 6
Barron’s seems to be hot on this idea, with multiple articles suggesting active stock picking will make a comeback.7,8 Perhaps they’re nostalgic for the investing world of the 1980s and 1990s when stock pickers were the investment stars.
A much talked about research piece out of Sanford Bernstein, titled “The Silent Road to Serfdom: Why Passive Investing is Worse Than Marxism”, lamented a world where indexing dominated the markets.4 Figure 2 shows the parabolic rise in the number of indices used to track various slices of the markets. (more…)
Periods of high volatility, sharp sell-offs and bear markets create an environment rich in trading and investment opportunities for those that remain level-headed and prepared. As prices recover and the next equity bull market develops, all risk-on trades generally work. Eventually the bull market ages, and finding new trading ideas becomes a bit more challenging.
Developing successful trades in this environment requires creativity and hard work, searching for ideas that are good, yet not so well known as to be crowded, and therefore ineffective. This is easier said than done. Most traders tend to pile into similar mid- and late-cycle trades, which are often marginal with respect to an edge, or tend to be based on economic predictions and/or secular themes rather than exploiting another group of investors.
When too many traders are in the same trade, it becomes crowded. As you might expect, crowded trades lose their edge, and thus should be avoided. That is a good rule to live by, but even the best portfolio managers will occasionally join the crowd due to an extremely high conviction level associated with a trade. An asset class trader must then be aware that the price action associated with the trade will change when there are lots of fellow portfolio managers in the mix.
The concept of crowded trades has become standard trading lingo, much more so than it was 10 to 15 years ago, likely due to the growing popularity of hedge funds over that time period.1,2,3 Yet as far back as the 1960s, crowded shorts were a problem for hedge fund managers.4 In periods of limited opportunities, traders often read similar research reports, are attracted to the same outperforming assets, and in general, lack sufficient imagination to develop a new trade idea. In addition, larger hedge funds tend to implement global macro and asset class level trades due to capacity constraints.
Definition of a Crowded Trade
Whenever there’s too much money or too much attention showered on an asset type, you should expect lower returns, or losses in the case of a trade.
Pundits and fund managers throw around the crowded trade language all the time, often to justify their own positioning. (more…)
In this blog I’ll examine the old “sell in May and go away” seasonal pattern associated with risky assets. It’s timely to consider this pattern since the markets are now entering the seasonally weak period. Furthermore, stock market performance has been relatively weak in a number of recent “strong periods,” such as in January 2016, November 2015 through January 2016, and November 2015 through April 2016, which often provides a foreboding tell of additional weakness during the traditional seasonal weak period of May through October.
Seasonality as a trading edge is also worth considering because trend following has become very trendy these days, with billions of dollars flowing into this discipline every year via managed futures funds. The problem with these flows is that the effectiveness of trend following diminishes as more assets are devoted to the discipline, since trend following is naturally capacity constrained due to high turnover (>200%) and the liquid demanding nature of trading. It seems that trend following is crowded.
At this point in time, it may be interesting to examine other market timing signals as an alternative way to add and reduce risk exposure. One such approach is seasonality, which is probably underutilized by the asset class trading community and thus might be more effective than trend following over the near term.
The seasonal pattern has been well known for decades – the stock market’s best period is from November through April, and its poor-performing period is from May to October. This is not the case every year, but on average this seasonal pattern has held up really well with stock markets around the world for decades.
Academics call this pattern the Halloween effect since the buy signal is generated by buying at the close on October 31 every year and the sell signal is on every April 30. What’s amazing is that seasonality has not been arbitraged away, even though the cost of implementing a seasonal trading system has been low since the 1980s. The old adage of “sell in May and go away” still works! (more…)
A stale pricing edge occurs when a security or fund can be purchased or sold at a price that is stale with respect to current up-to-the-second information. This trading edge is as fleeting as a twenty dollar bill sitting on a busy sidewalk. For instance, if the price of a U.S. equity closed-end fund is sitting at bid $10, ask $10.10 for most of the day without trading activity, and the S&P 500 trades up 2% during the day, the ask price of $10.10 may be too low, and therefore, stale. Then buying at the bid can provide a risk-free profit (if hedged by an S&P 500 short) of at least 10 cents, since the fund should be trading at $10.20/$10.30.
You’ll never find stale prices with liquid large cap equities or ETFs. Only rarely do they occur with securities that don’t trade very often, such as closed-end funds. The stale price edge is not backtestable with price data since simulated orders would have affected prices at the time.
Generally, we’d expect stale price opportunities to occasionally occur with illiquid stocks that trade with a wide bid-ask spread, with very little size offered. Stale prices may also emerge when transaction costs are high (perhaps due to a financial transaction tax), or when markets are highly volatile. In the heat of a stock market crash, when 10% up and down days are the norm, we’d expect a few stale price opportunities to emerge. Getting back to the U.S. equity closed-end fund discussed above, if the S&P 500 ramped 5% in the last hour of the day, and the closed-end fund lagged up only 2%, then perhaps that’s an opportunity to buy at the ask, and expect its price to eventually catch up (possibly the next morning).
Funds are the bread-and-butter tools for asset class traders – ETFs, ETNs, closed-end funds, open-end funds, limited partnerships, etc. There are stale pricing opportunities that can occur with investment funds from time to time.
Most notable was the stale pricing associated with open-end mutual funds. This advantage is no longer available today, but it represented a structural edge from the 1980s to the early 2000s. In his book The New Market Wizards, Jack Schwager profiled one trader, Gil Blake,1 who used this edge in the 1980s to produce an annualized return of 45% per year over 12 years. Mr. Blake actively traded Fidelity sector funds to generate those returns, switching in and out of funds every day. Interestingly, he was unaware of why the strategy worked, when what he was doing was systematically exploiting stale pricing. At that time, mutual fund prices exhibited a daily serial correlation because many of the securities held in the fund had stale prices when used to calculate the daily close-of-business net asset value (NAV). (more…)
The beginning of the year marks the time when chief investment officers, market strategists and other chief prognosticators publish their top investment themes. Barron’s also publishes their much anticipated “round table” issues in mid to late January, which pull together top investors to discuss the state of the markets and where they see investment opportunities.
There are hundreds of firms producing research all the time, most of it free. The big Wall Street firms produce enormous amounts of content, but so do practically all buy-side investment management firms these days. If you’re not careful, combing through research reports for trading and investment ideas can absorb all of the day. There are also the independent firms such as Ned Davis Research, BCA Research and the Leuthold Group, which are very expensive, but provide lots of interesting and useful information for asset class traders.
Common sense suggests that you can’t just read the research, implement the trades suggested and expect to outperform the market. That would be too easy. This is the case even for costly research from the independents, because after all, tens of thousands of professional portfolio managers can afford these services.
For an asset class trader, reading research and market commentary is important, primarily so you understand what everyone else is thinking, become aware of crowded trades and estimate what information is already priced into the markets. Every once in a while, a new piece of information is gleaned or a new way of thinking about an asset class is found. If discovered ahead of most other market participants, this information can be the seed of a new trade.
At any point in time, there are industry thought leaders who seem to have a special knack for investing and trading the markets. When they speak, I pay careful attention because the probability of discovering a new trade idea from them is much higher than reading the everyday, run-of-the-mill content.
Generally, these thought leaders are very experienced, talented and rich. They’re not inhibited by career risk. Their motivations are aligned with us, because being right about an investment theme is the most important goal associated with speaking publically since the prediction is on the record. Contrast this motivation with that of research content providers and newsletter writers, where maximizing readership is the primary goal. (more…)
I’ve been delayed in posting the next blog entry due to my current heavy work load, which is intensified by recent market conditions. This will happen occasionally. Our clients come first, and this blog will have to take a back seat when my daily workload becomes too intense.
My target posting rate is one per month. There is no rush. The primary purpose of the blog is self-mastery, which is ultimately a long-term pursuit.
In addition, there are times when a blog post I’m working on just doesn’t come together well. As I write about a particular subject, inconsistencies cause me to rethink the premise. That is the purpose of the blog – to explore the nuances of what works in trading asset classes. I’m currently working on a half dozen blog posts in various forms, but with each I’ve hit stopping points where the logic is not complete.
Recently, Alex Golubev and I worked on the concept of divergences. Throughout my career I’ve taken notice of divergences when trading asset classes. When looking at past data we see a relationship between credit spreads and future S&P 500 returns, similar to what has been discussed in the news recently. I had hoped to generalize the concept to all sorts of risky asset classes, yet the data did not support that view. So perhaps, I may have to adjust my use of that concept in the future.
I also intended to write about using non-trend information to trade S&P 500 movements, such as valuation, sentiment, and cycles. In addition, I planned to write about back-testing issues associated with this sort of information. However, our work on divergences has caused me to reexamine the use of non-trend information. This is especially important because trend following has become so popular lately; there may be value in using these non-trend indicators in the future.
Due to the infrequency of blog posts, I highly encourage all readers to sign up for the automatic email notification system.
Have a prosperous 2016.