Market macrostructure matters

 

I found a recent paper, "Market Macrostructure: Institutions and Asset Prices", an interesting research piece that opens up new thinking about markets. The concept is simple. The market macrostructure, or the combination of key players in the marketplace with different objectives, will impact the return-generating process; however, further work is needed in this area to develop empirical tests for changes in macrostructure.

The premise for examining market macrostructure is straightforward. In market microstructure, the focus is on the dynamics of transacting, whereas macrostructure states that asset returns are influenced by changes in the behavior of key traders in the marketplace. The change in the behavior of central banks, pensions, and other financial intermediaries will translate into changes in return patterns. For example, changes in the behavior of central banks through quantitative easing (QE) or quantitative tightening (QT) policies will impact the return pattern of markets. Their size and influence will impact how returns are generated. For example, a central bank's asset purchase program based on policy considerations will differ from the behavior of profit-maximizing traders. Hence, the macrostructure will change. The macrostructure will change again when the central bank becomes a net seller or refrains from engaging in active buying and selling.  

The authors do not explain how they plan to thoroughly test this modeling. Regime changes focus on changes in return patterns through observing time series. Still, these return patterns are influenced by the market's macrostructure, which encompasses policy changes, regulatory changes, and financial innovations. The cause of regime shifts is shifts in the market macrostructure. If you can identify the shifts in macrostructure

Causal discovery and trading

 

Causal discovery techniques can help any quantitative hedge fund, but may be especially helpful for enhancements to trend-following through finding causal links with other markets. The basic structure for a trend-following model is to use past values of a variable to extrapolate ot the future. Look for the trend, yet it would add significant value if you could learn whether other markets may have some causal impact on another variable. 

The standard approach to time series causality is to use Granger causality tests, which simply determine whether some time series Y causes or has an impact on the prediction of X. However, a growing number of alternative techniques are available to aid in causal discovery, thereby improving trading, such as time series data causal inference, vector autoregressive linear non-Gaussian acyclic models, and time-varying interactions models for nonlinear observations. The code for these algorithms is already written, so it is relatively easy to implement for a set of assets.

We are not planning to explore all of these techniques, but there are ways to support better causal discovery that can be used to improve the inputs in investment strategy. See "Trading with Time Series Causal Discovery: An Empirical Study" for a simple application of causal discovery for long-short equity portfolios. Now, these algorithms are not easy to implement due to the time required for computation; however, this seems to be a fruitful area for further research, especially given the growing interest in causal reasoning in finance.

Choose your correlation carefully - Kendall's Tau

Portfolio construction is fundamentally based on the correlation between assets. The lack of correlation creates diversification, yet limited work has been conducted in testing alternative forms of correlation. Most construction work is based on Pearson correlation, which looks at linear dependence across assets. There are limitations with Pearson correlation, so sometimes an alternative is used, Spearman's rho correlation, which is based on rank ordering. Spearman's correlation can adjust for non-normality and outliers, but there is a problem with the assumption of monotonicity. The third alternative, Kendall's Tau is based on measuring the concordance between asset pairs through counting the sign of movements across assets. 

A relatively simple paper looks at the portfolio construction of daily foreign exchange pairs using all of the same parameters except for the correlation matrix. See "Beyond Correlation: Enhancing currency portfolio construction through Kendall's Tau and Correspondence Analysis". I was surprised by the results. Yes, volatility is higher, but the overall portfolio performs better. There are clear benefits from using Kendall's Tau. The numbers are compelling enough to ask a simple question: "Why not try this alternative?"

 

The impact of narrative: The power of Fed speak

 

We expect that the Fed speeches have an impact; however, the measure of their effect on equity and bonds has not been precise. A paper, "Mind your language: Market responses to central bank speeches," shows that from the speeches, there are forecast revisions that can then explain volatility and tail risk in major asset  classes. Fed chairman speeches will have more impact than others through larger forecast revisions, but the Fed chairman can also calm markets with the right speech. 

The paper utilizes NLP, or natural language processing, to aid in identifying information that causes changes in macroeconomic forecasts. The critical point is analyzing the continuous flow of central bank communication, not just an isolated speech. There are clear regimes in Fed speak, and it is good to identify these trends. 

Quants focus on what is countable, yet the non-countable, like we see in speeches, is essential. If you can turn the non-countable from narrative into something measurable, there is an opportunity to form probabilities and make better trades.

I have taken the view that you want to avoid FOMC and major Fed speeches because there is too much uncertainty; however, if we decompose what is said, investors may be able to tilt their positions to their advantage. 

The power and gravitation pull of doing nothing in asset management

There is value in doing nothing in asset management. One, doing nothing reduces transaction costs. Two, doing nothing allows for the power of compounding. Three, doing nothing reduces the emotional biases associated with trying to take action. Four, doing nothing helps to clarify the distinction between effort and work. Showing that you are doing something is not the same as doing work. 

By having a long-term view, there should be less trading. Long-term investing is a do-nothing management approach because the long-term decisions should not be swayed by short-term changes in markets. There is more value in doing less. 

These are all good reasons, but there is also a pull to doing nothing that has a negative effect. Nevertheless, no action at the wrong time will be costly. Fear of making mistakes and emotional regret may stop an investor from taking needed action. Aversion to regret will reduce action. You cannot regret the action not taken. You could, but generally, regret is about what was done, not what was missed. Lack of knowledge or ignorance.

So, there needs to be a checklist for action. Do I have a valid reason for action? Do I have the right time perspective? Have I accounted for the cost of trading? Will I regret this decision if the market direction changes?  Be careful with action. From a model perspective, what is the action beyond a prediction of noise. 

Sophisticated investors and market efficiency

 

Market efficiency will vary by the type of investor. There are different levels of efficiency based on your structural advantage. Market efficiency is based on the behavior of a given market and not on the profitability of a given trader. Hence, you can declare a market as efficient, yet there could still be profitable investors. Similarly, market efficiency could be rejected, yet that does not ensure an investor can make money in that market. 

For retail investors, the market is very efficient. You cannot get an edge if you are slow to react, have less information than other investors, process the information poorly, and have high transaction costs. If you are an institutional trader, your sense of efficiency is different. You may have a slight edge on reaction time, trading efficiency, and information processing. If you are a hedge fund, you may have an even greater edge; however, being declared a hedge fund does not necessarily confer a lower efficiency level. 

The old argument by Friedman on the efficiency of speculation is that reasonable speculation will drive out poor speculators and thus make the market efficient. The counterargument is that noise traders are more prevalent than shrewd speculators and can keep the markets inefficient. A corollary to the Friedman argument is that there are different classes of investors with varying levels of capital that can exploit opportunities, so while efficiency may exist on average, that is not the same as saying the markets are efficient for everyone.

A sophisticated investor has an edge and creates an opportunity to exploit inefficiencies. Hence, the job of any due diligence is to identify sophistication and the chance for the edge that can be exploited. 

Stan Fischer - A great influencer on macroeconomics

 

Stan Fischer is one of the great macroeconomist of the last 50 years, and as shown by the figure above he influenced many of the other leading economists of this time. He was one of the key pillars of MIT macroeconomics and was clearly one of the strong influencers of monetary policy choices around the globe. You cannot talk about macroeconomics or international macrofinance without looking at some of his papers. I cannot say that I always agree with his research work, but that does not change his substantial impact on macro thinking. 

He will be missed, yet we must ask what would have happened to macroeconomic thinking if Fischer had not existed. Would we be better or worse off with our thinking?  Would someone else have filled the void? More so than any one piece of research, Fischer was a teacher, whether at MIT, the IMF, the World Bank, or the Fed, who set the agenda for many other researchers. In this case, he could not be replicated.

Riding bubbles is a strategy - but more than one way to do it

Jarrow and Kwok, in their new paper "Riding A Bubble: A Study of Market-timing Trading Strategies," identify when there are Q-bubbles based on local martingale properties. The idea is that a bubble will exhibit extreme values over different time horizons, and an investor should hold a significant bubble move until it reaches a set barrier. At this time, the investor should exit. Ride the bubble until the returns reach an extreme and then walk away. The basic story seems easy enough, yet the key is to determine the bubble component, which is based on the tail probabilities. Bubbles have a fat-tailed Pareto distribution. If an investor sets an upper bound on the price of the asset and it is reached before a certain time, then exit. 

The idea is relatively simple, yet despite the simulations run in the paper, this point of exit is harder to find in practice. It will encourage getting out of positions early, even if there is an optimization and an accounting for risk aversion. 

The trend-follower will generally not follow this type of strategy. The trend-follower will always hold the position until there is a reversal and a stop is hit. You will sacrifice some of the return in exchange for carrying any position as long as possible. Yes, there will be losses in the end when the market turns, but the ability to maintain a position in a bubble will generally be worth the added risk and the likelihood of some give-back. 

Financial innovation is a virus!

"Financial innovation is like a virus, finding weaknesses in existing inventive schemes and regulations. When something is growing very fast, that suggests they have found a weakness." - Jeremy Stein Harvard University. 

This is one way to think about financial innovation, but it is not very appealing. It argues that innovation is just an attempt to evade regulation. There is no doubt that some goals of innovation are evasion, but there are also other reasons, such as market efficiency. Nonetheless, one can argue that regulation reduces efficiency, and innovation attempts to address the problem. If the problem is corrected, there will be more growth in innovation. Securitization, derivatives, and ETFs are all significant innovations that make the markets more efficient, while also addressing regulatory concerns.

Knowledge and wisdom for picking your financial facts

 

"Knowledge is a process of picking up facts, wisdom lies in their simplification" - Martin H. Fischer 

from story on Jane Street's traders:

Jane Street software engineer Ian Henry said the firm's traders all need "fighter pilot eyes" to deal with "extremely high information density" while making trading decisions. Henry said that, when making tools for these traders, he has to fine tune their size by a matter of pixels, in order for traders to maximize what's on the screen.

Henry says one of two main categories of applications built at Jane Street is focused on "managing traders' attention," ensuring they're alerted to interesting things amid that sea of information. He says the challenge for engineers is around "balancing noisiness" and stopping those tools from annoying traders with unnecessary information. 

Is the problem for Jane Street the acquisition of knowledge or its simplification? I want more information because I never know what will be helpful, but then I have to be selective to focus my attention. 

The trend trader will say that I focus my attention on only a limited number of issues—the trend in price. All other information is unimportant. The discretionary trader will argue that all information is essential, and I don't want to be constrained by limits on what I can review. 

Where is the trade-off, and how much information is enough, is one of the key issues for any investor

Bond and equity expectations are different

 

A recent paper by AQR, "Why are bond investors contrarian while equity investors extrapolate," makes an interesting observation. I have always thought that bond investors were mena reverting based on their conservative nature. There are limits to where yields can go. Equity investors are optimists, which means that returns can always move higher based on unlimited possibilities. Overoptimism will lead to the extrapolation of good news. Of course, this does not explain what happens to markets when they start to move negatively. The pessimism of bond investors forms beliefs about limitations and the notion that good news cannot last.

AQR states that the cause is information salience, the attention -grabbing qualities of certain information. This, however, does not focus on why there is salience that is different across markets and why it may persist. Nonetheless, it is essential to think about differences in how expectations are formed in major asset classes. 

CBOE dispersion index mean reverting

 

The CBOE dispersion index, DSPX, measures the difference in volatility of individual stocks versus the volatility of the S&P 500 index. It peaked during the period of maximum trade uncertainty because the market could not determine the impact of trade tariffs on individual firms. Now that trade risk has declined, the DSPX has also declined, as the market now focuses on other factors that are more likely to impact all stocks. The key question now is whether we will have a recession.