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.
Cut your losses short, and let your profits run.
For centuries, that’s probably the number one trader’s adage. This is exactly what the trend following (TF) investment discipline does – using simple rules to be long markets in uptrends and short markets in downtrends. The mathematical rules used to identify uptrends and downtrends are predefined and mechanically implemented to eliminate human emotions in deciding when to be in or out of a market.
The most common way trend following is implemented is with managed futures funds, which are typically placed in the “alternatives bucket” of an investment portfolio, perhaps making up 5% of the total. A good example of such a fund is the AQR Managed Futures Strategy Fund (Symbol: AQMIX). Managed futures funds apply the trend-following discipline to various equity and fixed income markets, along with currency pairs and commodity futures.
In this blog, I’ll examine the trend-following approach applied to asset classes that have a positive risk premium above inflation and T-bills, which are stocks and bonds. In addition, the focus will be to shift into cash when stock and bond trends are down, rather than more aggressively shorting downtrends. This is traditionally the realm of market timing, hence my distinction using the title “Trend-Following Market Timing”.
It’s an open question for me about whether managed futures funds add value over the long term, and this is worthy of a future blog piece. Generally, shorting stocks on a TF signal doesn’t work very well in the long term, but it does reduce managed futures fund correlation to stocks, and provides “bear market insurance” for a diversified portfolio of stocks and bonds. If you believe, as I do, that currency pairs and commodity futures do not deliver a long-term return above T-bills, then what kind of return can we expect from applying a trend-following overlay to these markets? I’m not sure what the answer is, so I won’t be addressing that question in this blog.
Everything I write beyond this point about trend following applies to the market-timing view of shifting between stocks and cash, and asking whether such an approach provides any sort of trading edge. (more…)
An essential component of successful trading is having a good sense of timing. The standard industry tool for getting the timing right is technical analysis, so we need to examine its effectiveness. I’ll assume the reader is familiar with technical analysis, and as an asset class trader, I’ll also assume we control a limited amount of assets such that positions can be bought and liquidated with minimal trading impact costs.
For extremely large asset management firms and hedge funds, technical analysis is not an available tool because all-in trading costs are prohibitively expensive with this approach. These firms are forced to use other approaches such as value investing, which is, ironically, a horrible tool for timing price moves. Even large trend following CTAs are forced to use the most liquid futures contracts to minimize trading impact costs. So perhaps being small and using technical analysis is an advantage for us.
In a nutshell, technical analysis is the analysis of price and trading volume patterns to identify current and future price uptrends, downtrends and trend turning points. Practically all trading “how-to” books rely almost exclusively on technical analysis. Technical analysis techniques and patterns appear to be valid on all time scales and with any tradable security.
I gravitated to TA rather quickly when I first started trading, because as a full-time engineer, trading was a hobby. I didn’t have the time or expertise to dig through how the “fundamentals” influenced prices. I read many books on technical analysis and trading. The classic books are still the best,1-4 while most others fail to provide additional insight. In the next few blog posts I’ll talk about technical analysis, trend following and backtesting issues associated with mechanical trading models. Nowadays, I still look at charts and use basic technical analysis, but I don’t expect too much from it. (more…)
In the last blog, I discussed the trading edge associated with predicting investment flows. In this blog, I’ll provide examples using this trading edge to pick outperforming asset classes. Of course, this is a hypothetical exercise with the full benefit of hindsight, and as is often the case, flows may not be the only cause of the observed performance.
1995 to 1999
This era is widely recognized as the culmination of the 1982 to 1999 secular bull market in U.S. equities. As shown in Figure 1, it was a time when retail investors were obsessed with stocks as illustrated by the meteoric rise in CNBC viewership.1 Index investing was also becoming very popular, but at that time indexing solely meant investing in the S&P 500. Mutual fund managers were the investment stars of the era.
Figure 1. CNBC viewership history.1
Figure 2 from Ned Davis Research shows equity mutual fund net flows as a percentage of U.S. market capitalization from 1960 to present.2 These flows can be attributed primarily to retail investors, who throughout the 1990s powered a strong equity bull market. (more…)
In a nutshell, I want to own securities held by asset classes receiving large inflows of cash over an intermediate 6 to 12 month time scale, and avoid asset classes facing large intermediate-to-long term outflows.
Large intermediate-term inflows create essentially continuous daily net-demand that tends to bid up the price of the associated securities over time. Outflows do the opposite. We want to jump ahead of the buying and selling as long as the flows are significant and expected to continue over time. Inflows leading to outperformance can also be self-reinforcing as many investors are susceptible to performance chasing (flows lead to more flows).
Who’s on the other side of this trade? Long-term flows tend to be strategic allocation decisions made by large institutional investors, foreign investors, investment advisors/brokers and retail investors, in a manner that is typically price-insensitive. Nowadays the vast majority of investors spend their time deciding what investment manager to hire rather than what securities to purchase. When fund managers receive new money, they tend to buy what they already own. Not all funds do this, but most do, and index funds in particular must buy securities in exact proportion to the current portfolio.
While there are many excellent and talented investors in all the above groups, allocation decisions tend to be herd-like and heavily correlated with each other. These allocation waves can last for years as hundreds of institutional investors, millions of advisors/brokers and tens of millions of retail investors implement the latest fashionable portfolio allocation approach. The faster an asset class trader can jump on these trends, the more profitable this edge may become. (more…)
Providing liquidity to motivated buyers and sellers has worked throughout history. It’s an enduring trading edge that I expect to work forever – both in and out of the trading arena. In life, a person highly motivated to purchase a specific house, a specific car or the latest consumer gadget pays a price that’s higher than a reasonable substitute. A person forced to sell a house will likely concede a not-so-small financial penalty because of the need to sell immediately.
Consistently being a motivated buyer of things will act as a drag on the personal balance sheet. Taking advantage of sales or the occasional motivated seller provides a little alpha in the growth of personal wealth.
Similar opportunities occur in the financial markets. With respect to the motivated buying/selling (MB/MS) edge, we’re searching for moments in time when the price action is affected by a large amount of buying/selling that is price-insensitive AND is occurring due to reasons unrelated to enhancing portfolio risk-adjusted returns.
We distinguish MB/MS from everyday price volatility by understanding the motivations and techniques used by other market participants, and identifying instances when a price is perhaps being pushed away from equilibrium value for non-economic reasons. We sell into the price strength or buy into price weakness created by the MB/MS and then wait for prices to snap back when done.
This trading edge takes experience and educated guesswork. You might ask if there’s too much competition in this space from market makers, Wall Street trading desks, high frequency traders, statistical arbitrage hedge funds and others. The answer is absolutely yes, and I’m not asking you to compete with these pros. The goal is to be on the lookout for when market makers and other arbs need some help pushing prices back to the equilibrium value. (more…)
In a previous blog I discussed the efficient market hypothesis (EMH), which can be summed up with the following statement by recent Nobel Prize winner Eugene Fama.
An efficient capital market is one in which security prices fully reflect all available information.1
I presented the following three arguments in favor of pragmatically adopting an efficient markets view when investing.
- The logic of hyper-competition in a fair trading arena – any trading edge will quickly attract competition and be arbitraged away.
- The mathematical fact that investors as a whole cannot beat the market, and since professional investors manage the majority of assets, aggregate professional alpha must be close to zero before fees.
- While acknowledging that there can be long-term skilled winners, the empirical evidence suggests it’s very difficult to distinguish luck and skill when evaluating past performance, even when judging your own trading ability.
In this blog I’ll go one level deeper to discuss the fundamental foundations for an efficient market. Why review these? Mainly to develop a better understanding of “the enemy,” and to identify weaknesses in the EMH assumptions that may lead to trading edges. Rather than argue about whether a market is efficient, let’s search for nuanced moments in time and securities-space when the EMH assumptions may not hold – leading to an exploitable trading edge for us. (more…)