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.
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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…)
Liquid alternative funds are the new hip product sold by investment management companies. Liquid alt funds offer strategies that have been used by hedge funds, managed futures funds and private partnerships for many years with the potential to earn a higher risk-adjusted return than stocks and bonds often combined with a low correlation. Previously this space was largely off limits to small investors due to institutional-sized minimums or the need to be an accredited investor. Now these strategies are accessible to all investors via ETF and open-ended fund structures, which offer daily liquidity.
The trend towards liquid alt funds is motivated by the desire for enhanced portfolio diversification and the need to do something about low bond yields. These were the same motivations that led to the massive growth of hedge fund assets over the past 10 years as pension funds allocated to this space following the 2000 to 2002 bear market.
The efficient market hypothesis (EMH) can be summed up with the following statement:
An efficient capital market is one in which security prices fully reflect all available information.1
What does this statement mean? It implies that all information that is commonly used to make investment and trading decisions is already accounted for, without bias, in current prices. It implies that technical and fundamental analysis have no value in beating the market. It implies that luck is the primary factor in determining investment manager winners and losers. It implies that buying and holding the market over the long term is the most logical approach to participating in the markets.
The EMH can never be proven either empirically or mathematically. However, this is one economic idealization that is actually pretty useful in practice. There is an enormous amount of academic evidence that is consistent with the EMH. The efficient markets logic is also very compelling. As a trader, we need to acknowledge that dealing with the mechanisms that make markets efficient is part of the game.
Trading books never talk about efficient markets or its implications. If they do, it’s done quickly and disparagingly. Why is that? (more…)
I gravitated to asset classes fairly quickly when I first started trading. I had a 60 hour/week engineering job, and didn’t have the time to perform any kind of individual security analysis or sort through hundreds of individual stock charts each day. There were substantially fewer asset classes to keep track of, and that was manageable for me. This was in the early 1990s before ETFs were available, so I used individual mutual funds.
Most practitioners in the field define asset classes very broadly, such as stocks, bonds and cash. From these broad asset classes, there are sub-asset categories among stocks and bonds. For instance, Ned Davis Research1 divides the U.S. stock market into nine stock sectors and about a 100 industry groups. Morningstar splits stocks into nine style boxes with growth versus value on one axis, and small versus large capitalization stocks on the other. (more…)