Bond trading volume exploding in anticipation of a big change?

We have seen a near-term positive reversal for TLT, the long bond ETF, but what is surprising is the large increase in trading volume in October and early November. TLT was exceeding volume levels from the March 2020 debacle. Everyone wants to trade the long bond and get in on what some may expect is the big reversal. 

Perhaps the market is normalizing but there is still significant upside potential in bonds if a recession materializes and the Fed decides to lower rates. Those are big ifs, but that is the bet many have been making. 

Hat tip "Rudy Havenstein from A Havenstein Moment." <rudy@substack.com>

So where is the recession? The PMI and LEI say watch-out

There are two classic leading indicators for recession from real economic data. One, the NAPM PMI reading suggests a recession and/or a significant slowdown if the value of the index is below 50. Two, the Conference Board LEI index is falling consistently on a year-over-year basis. 

In the case of the PMI, we have been below 50 for over a year with the current reading again turning lower at 46.7. The Conference Board leading economic indicator index topped in early 2022 and has been falling ever since. By these measures, we should see a marked decline in risk-on assets, yet 2023 has been a positive year. Being early to a recession trade has not been helpful. 

Measure the direction of stocks and you can create value

Sometimes simple is the best approach to generating returns. Forget complex models and just focus on the direction of markets. In the paper "Directional Information in Equity Returns", researchers shows that there is sign predictability in equity returns. Look at some sequence of equity returns and you will be able to capture investor optimism and pessimism which can tell us something about stock direction. 

A set of stocks can be ranked based on the likelihood of direction to form a long-short portfolio. Buy the stocks that have a high likelihood of a positive move and sell those that have a high likelihood of a decline. 

Just sorting by directional likelihood may be able to do a better job than a traditional momentum portfolio. In fact, over the period 1991-2022, a directional portfolio will don better than a traditional momentum sorted portfolio. Over the longer-term 1932-2022, these two portfolios will give similar results. Just focus on direction and the rest may take care of itself. 

Alternative risk premium - Offers diversification if you pick the right mix

 

Alternative risk premia (ARP) strategies can be divided into offensive and defensive categories. The offensive strategies will perform better in risk-on environment and defensive strategies will do better during risk-off environments. In other words, offensive strategies will do better when equities are higher while defensive strategies will do better in bond positive environments. There is more to holding ARPs than looking at the unconditional correlations.

The paper "Does Alternative Risk Premia Diversify? New Evidence for the Post-Pandemic Era" analyzes the value of a diversified pool of ARPs to show their value in the post pandemic period. It finds that trend and commodities do a very good job of providing defensive diversification.

The key finding is that all ARPs are not the same. A simple unconditional or linear approach to analyzing ARPs will miss the value-added in up and down-market environments. Of course, investors have to form a view on the future direction of the markets to fully take advantage of the conditional behavior in ARPs. 

Earnings expectations, prices and slow reaction - The opportunity for momentum

 

What is now a current theme in finance to explain the slow reaction in prices and the opportunity for momentum profits is the idea of inattention. Investors are slow to react to news and thus slow to adjust prices which leads to trends. There is something missing about this argument. Investors seems to be inattentive, yet it begs the question of why would you be so inattentive and why don't machines just do the watching? 

We will forgo this discussion and focus on some interesting research, "Earnings Expectations and Asset Prices". The researcher's focus on analysts' earnings expectations and find that they generally under-react to news, but the under-reaction declines during high volatility periods. However, the degree of under-reaction has fallen over time. These stylized facts on earnings, assumed to be caused by inattention, can spillover to behavior of markets. 

If earnings expectations are slow to adjust there will be momentum in markets. When the adjustments do occur, especially in higher volatility environments, there is the potential for momentum crashes. Hence, there is a reason for switching in momentum look-backs based on the volatility regime. Use longer loopbacks under normal times, and short loopbacks during more volatility times. A mixed momentum strategy makes sense. Of course, this paper provides many more insights, but it does a good job of linking the inattention to new information on expectations which creates an environment where momentum works. However, the profits from momentum trading will be linked with the adjustments of inattention by market participants. 

Don't just follow history act on it

 

Everyone says you should study financial history, yet what does that mean? It means that you have a sense of market extremes and their causes. It means that you have a sense of what can surprise markets and what creates uncertainty. Understanding history means that you appreciate that policymakers will make mistakes. It means that you understand the dynamics of the business and credit cycle. Without history there is no context. Knowing history is not the same as acting on history. Use history as a tool not just a lesson. 

Smart money is dumb money with selling decisions

 

There have been countless studies about the poor decision-making of retail investors, but there has been less work about the quality of supposed smart money, institutional asset managers who are running relatively large portfolios. A recent paper well documents the poor behavior of smart money see "Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors". 

The researchers find that the smart money is not as smart as we think especially for on side of their decisions. The market experts seem to do a good job with their being decisions. There is a lot of care with what and when to buy. However, that same caution does not seem to be in place with sell decisions. Sell decisions truly underperform such that it can be no better than a random strategy. There is a lot of cognitive activity associated with buying assets, but there is not the same attention to selling decision.  

Even smart money managers can improve their investment decisions by just focusing more time and attention on their sell decisions. This is not trivial nor a throw away action. The sell decisions are often made hastily. Get me out. I am done with this investment. 

These poor selling patterns is especially present for winners. You guessed it, smart money managers we more likely to sell winner over losers without regard to the same focus shown when the purchase was made. Heuristics or simple rules will drive selling action in a way not seen with buying decisions. 

I would like to believe that smart money will do a good job with both buy and sell decisions, but the data does not point to that conclusion. I can say that I have often seen good managers through in the towel with sell decisions or just decide to get out without strong exist plan. What this research tells us is that investors have to careful with every decision there is no time to be lax. 

More ways to find price trends

 

There are many ways to find a price trend. Some may think that the methods for finding trends have been exhausted, but as seen with some new research that is not true. In the paper, "(Re-) Image(in)ing Price Trends" in the Journal of Finance, the researchers have used focused pattern recognition techniques to make price trend predictions. They have found that pattern recognition, through convolutional neutral networks (CNN), which uses images to make predictions different from traditional time series analysis, can be effective. Behavior creates patters that can serve as the foundation for finding price trends. Humans digest a lot of information in pictures which are used to make decision about trends. 

In this research, the authors focus on the images created from open, high, low, and close (OHLC). The set of information from this range data can be create images that can be standardized and compare with the past to form predictions. When these CNN models are compared with classic trend, momentum, and reversal models, it is found that imaged based models will have significantly higher Sharpe ratios and do especially well in the extreme deciles. Now, calibrated this type of model takes time and effort, but the size of the gain suggests that trying to offset the barrier to entry is worth it. Perhaps the technicians who focus on charts are not the crazy alchemists portrayed by finance professors. A technical or visual approach to investment decision-making may have merit, albeit its success may be due to diligent systematic measurement of patterns.

There is momentum in options

 

Momentum is everywhere. It exists across all asset classes and across long timeframes. It may also exist with options. A recent paper finds that there is option momentum. First, they create delta zero straddles for options and then measure whether there is unique momentum embedded in the option markets. Other approaches are also tested with similar results. See "Option Momentum" in the Journal of Finance. 

In fact, the momentum found in. option markets is stronger than what is seen in the underlying primal markets, but like stocks the one-month momentum will reverse over the following month. This option momentum exists even after accounting for other risk factors. This momentum does not have the same crash risk seen in stocks nor is there the strong reversal effect.

Overall, trading momentum through options can be profitable. The reason for these profits seems to be associated with market under-reaction. This study provides useful direction for traders to focus on centering momentum bets through trading options.

Attention spillover and asset prices

 

A novel research study finds that stocks displayed next to those that have higher returns over the past two weeks are associated with higher returns in the future week and then revert in the long run. Wow, just being displayed closer to winner may make other stocks winners for traders. The explanation is simple. There is a strong overconfidence bias coupled with limited attention.  Investors will trade more based on positive investment experience and will pay attention to the display of neighboring assets. This is all based on listing codes. See "Attention Spillover in Asset Prices" in the Journal of Finance.

I tread lightly with these types of tests which may be hard to replicate, but it is suggestive for why momentum continues to work in so many different markets, asset classes, and venues.

ML in finance needs explainability or will fail

JP Morgan has phased out a model that leverages machine learning technology for foreign exchange algorithmic execution, citing issues with data interpretation and the complexity involved.

The US bank had implemented what it calls a deep neural network for algo execution (DNA), which uses a machine learning framework to optimise order placement and execution styles to minimise market impact. Launched in 2019, JP Morgan said at the time that the move would replicate reinforcement learning

FX market news 

Interesting story that JP Morgan is taking a step back from machine learning. The reason is not that it did not work, but that it was too complex and too hard to interpret. This is a big issue with machine learning given the strong non-linear relationship that are not always apparent. How do you explain the results? Is there a simple narrative that explain the solution generated? Do we know what are the key features that drive results? 

There has been a movement to increase explainable AI, yet it is a big problem that has to be faced and addressed especially in finance. It starts with explaining the feature inputs that are used by the model. There needs to be a clear explanation of the technique used, and finally there needs to be a clear interpretation of the output. I have not seen the JP Morgan output, but I can tell you that explaining any ML model is not easy. Complexity must be addressed, and it takes a lot of work to make any investor comfortable with techniques that are not familiar. The burden on explainability is on the builder. Investors need to trust and verify.

Tacit knowledge - the reason for investment decision uniqueness

 

There is the knowledge you learn and there is the knowledge that is acquired. There is the explicit knowledge that we gain from books and learn from rules and there is tacit knowledge which is gained from experience or is associated with intuition and cannot be easily explained. Tacit knowledge is hard to express or explain and is gained through personal experience and observations. Tacit knowledge is a lens for viewing or explicit knowledge. It can provide a link as well as Bia in our thinking. It is what separates us from others when we make a decision. 

 Some argue that there is a further distinction between explicit and implicit knowledge and then tacit knowledge. Implicit is easy to communicate but difficult to document. We will take a two-part approach of explicit and tacit knowledge as a starting point for discussion. 

The philosopher, Michael Polanyi, who developed this dichotomy, bases his understanding of ‘’tacit knowing’’ on the principle ‘’that we can know more than we can tell.’’ There is knowledge we develop in context or through our set of experiences that is not easy to explain or transfer in the same way as a rule, formula, or book. 

from: https://www.institute4learning.com/2020/02/10/the-mystery-and-magic-of-tacit-knowing/

It is important to think about tacit knowledge because it explains why many investors will make different decisions when faced with the same information. Some of the difference in decisions is based on relative difference in risk aversion, but it is also based on our tacit knowledge of how we use our set of experiences to process information. For a quant, differences in tacit knowledge will lead to different models. Tacit knowledge leads to uniqueness.  When investors pick managers, it is often based on the investor's assessment of their tacit knowledge.

Moody's makes statement on US debt and it is not good

 

Moody's rating agency lowered its US debt rating to negative from stable. It is still triple-A although S&P has rated the US as AA+ since 2011 and Fitch downgraded the US to AA+ in August.  In its view, downside fiscal weakness has increased from continued political polarization. Deficits have continued and the interest costs now exceed $1 trillion annually, so there is less room to maneuver. With the continued debt ceiling cap, the political process will continue to have uncertainty. Eliminating the debt ceiling cap will not solve anything other than causing any debt management to be pushed further into the future without any fiscal responsibility.

Moody's expects federal interest payments relative to revenue and GDP to rise to around 26% and 4.5% by 2033, respectively, from 9.7% and 1.9% in 2022. These projections factor in Moody's expectation of higher-for-longer interest rates, with the average annual 10-year Treasury yield peaking at around 4.5% in 2024 and ultimately settling at around 4% over the medium term. The debt affordability forecasts also take into account Moody's expectations that, absent significant policy changes, the federal government will continue to run wide fiscal deficits of around 6% of GDP near term and to around 8% by 2033, the widening being driven by higher interest payments and aging-related entitlement spending. By comparison, deficits averaged around 3.5% of GDP from 2015-2019. Such deficits will raise the US federal government's debt burden to around 120% of GDP by 2033 from 96% in 2022. In turn, a higher debt burden will inflate the interest bill.

Of course, there is still the fundamental question - do deficits matter? The answer is that they do, except we don't know when or at what level. We may not know it until we hit that critical level but at that time it will be clearly too late. We do know that we are on a path that will take us to that critical level. Just because it has not mattered, does not mean it will not matter.

The divergence between economic expectations and reality - which is right?

 

Economists are failing with their forecasts and one of the reasons is that what is expected form the data is not matching consumer sentiment from surveys. Usually, pre-pandemic, the survey data gave us a good look at what might happen in the future. Now, we have more pessimism from surveys. The correlation or direction seems right but there is a level of caution that suggests something is wrong with consumer thinking. There is spending but it has not caused a sense of optimism. You could say that this is a carryover from the pandemic, which is true. However, saying this does not help explain the root cause or how this gap may be closed. The survey to hard data gap is something that is adding to market uncertainty. Who is to say which is right?

The Kolb Learning Model and Investments

 

The Kolb Learning model is a simple four-point approach to improving the learning experience. Start with some specific experience, which should lead to some reflective observations. What did you feel? Then, what did you see? 

The observing must be then placed in context which is conceptualized thinking. The thinking finally must be converted into action, or active experimentation which will, of course, lead to a new experiences. This is often referred to as Experiential Learning Theory (ELT). People learn by direct experience and is an effective way to teach young students. It is a learning style.

This is not different from following any scientific hypothesis testing. See, observe, conceptualize or hypothesize, and then test. Yet, it is useful to form simple frameworks for thinking based on our direct experience. 

The Kolb learning model or ELT can also be used with making investment decisions through linking a specific observation or experience to forming a hypothesis. 

Turning wrong into right

 

Investing is about failure. You are more likely to be wrong than right. Some will say that a great investor is one who is at most 60% right. It seems natural that we should spend more time on faults, errors, and failure. Change failure into success or at least a learning opportunity may make all the difference in the world. 

This topic is addressed in Amy Edmondson's Right Kind of Wrong: The Science of Failing. Written by an HBS professor, there are a lot of good stories of failure and what can be learned, but the core work is based on the serious research that all failures are not the same. Classify failure and we can do a better job of avoiding.

Failure is an outcome that deviates from desired results. Errors or mistakes are unintended deviations from prespecified standards. Violations are intentional deviations from rules.  We want to avoid violations, minimize errors, and learn from failure.

All failures are not the same. There can be good and bad failures. Good failures are necessary for progress. From good failures come discoveries. Basic failures are caused by mistakes that can be avoided, but there are also complex failures that are driven by multiple causes. 

Because we have aversion to failure and mistakes we try to avoid them and hide them under the rug. Let's not talk about mistakes. Hence, there is little opportunity for learning. 

Effective teams may see more mistakes because they are more willing to report failures and learn from them. They are open to learning from mistakes. The important point is to try and classify failures and focus on the why and how it can be avoided in the future. Is the failure systemic to the procedure used? Failures occur because rules are not followed, or the wrong rules are in place. Other failures can be associated unexpected events, which require a different mindset. There are also failures associated with new discovery, intelligent failure from dealing with high uncertainty allows for strong learning. 

We will all make mistakes. The question is whether we can stop basic failures and learning from complex and intelligent failures. 

"How Big Things Get Done" - They Don't

 

How Big Things Get Done is a thought-provoking book on why big projects fail. They are never done on time, cost more than budgeted, and do not meet the expectations. There are similarities across all these projects and after reading the introduction, you will want to halt all big projects before failure. But there is hope. If we change our thinking and how we frame projects, we might have a chance of getting it right. For anyone who is managing or planning a big project, this is a book that should be a must read.

The core premise is that project managers should think slow and then act fast. Spend more time on determine what is the goal or objective and then work hard to move forward toward that goal.  Use right to left thinking. Do not commit to a single way of thinking and find those that have the right experience to run a project. When reading these tips, it seems simple, but changing your thinking and framework is never easy. 

Thinking from right to left - a simple structure for any investment

Start with the conclusion - what do you want to get done? Do not start with a single solution but with an objective that will lead to a set of questions that may lead to alternatives. This is right to left thinking. 

There are several paths to achieve a goal, but the conclusion must be clearly identified. Amazon will often start with what will be in the press release for a project and what would be the frequently asked questions associated with a product or project. The end pitch comes first.  Get what you want to conclusion done first and then determine how the project will need to be managed to get that end result. You are working backwards. 

A writer must start with what he want the reader to take away first and then fill-in the chapters with the details. The story board needs clarity on what is the goal from a narrative.

For the money manager, start with what you want to deliver to the investor first. Research is about learning from data, but it should not be done in a vacuum. There needs to be clarity on what is the purpose of the research. Once you have the product, you can then work on building the models that will generate the desired conclusion. If you want a low volatility and uncorrelated fund, then start with that conclusion and all of the research has to be focused on delivering this type of product. 

Repetito est mater studiorum - The foundation for quant trading

 

Repetito est mater studiorum - Repetition is the mother of all learning.  Quant modeling is about doing the same thing when faced with the same set of information, but the repetition is not the end of modeling but a point of beginning. From the repetition, the modeler can find new patterns and focus on the outliers to create marginal improvement. Repeat, stop, learn, repeat, stop, learn. The process of assessing the repletion creates new value.

The planning fallacy and Hofstadter's Law

Hofstadter's Law: It always takes longer than you expect, even when you take into account Hofstadter's Law. You can plan all you want, yet you will unlikely finish your task on time. Accept it. Own it. Solving complexity takes time, so any modeling should start simple and then slowly add complexity. Get a simple model that works and then look for marginal enhancements. This is especially relevant for quant models. You want to make it perfect, but then the planning fallacy kicks in. 

The illusion of explanatory depth - an investment problem

 

There is the illusion of explanatory depth, the perception that we think we know about how things work better than we actually do. A classic study done around 2006 gave people an image of bike and asked the test-takers to fill-in the rest of the frame, the chain, and petals. 40% of the test-takers could not properly place these items on the bike. You can just image how a test of the illusion of explanatory depth may fare when asked about what drives the stock or bond market. If we are asked a set of repeated "why?" about an investment it is not clear what kind of answers we will get.

We have greater confident in our understanding of how things work versus our ability to explain how things work. When asked to explain even simple gadgets, many will fail. We also give ourselves more credit for understanding complex tasks just by watching other undertake the task. after watching some hit a baseball, we will usually conclude that it is relatively easy, and we can do it. Guess again. Our powers of observation concerning even repeatable task is poor. It is no wonder that many asset managers cannot explain their investment process or what may drive markets. 

Learning is not easy nor is understanding any activity. Accept that all investors suffer from the illusion of explanatory depth and a key part of making better investments is stripping away this illusion and focusing on creating clear simple stories for what the is relation of cause and effect, and accepting that sometimes, perhaps many times, there is no good explanation for why things happen. 

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The commitment bias and sunk costs

You hear the term in your introductory microeconomics course, sunk costs are sunk. But do you internalize this key concept? Unlikely. Sunk costs are what others suffer from. You are not a quitter. You will muscle through to success. Unfortunately, the sunk cost argument is also present as a bias in our thinking, the commitment bias. We commit to a specific behavior or thinking and do not easy change our behavior. We want to stay committed and not thought as someone who is always changing their point of view.  We want to save face and not be thought as being wrong. If we have a specific mental model in our head that we believe is correct, we stay committed because we believe luck or reality will turn in our favor. 

It is more likely that we will stick with a decision in the face of negative outcomes. We want to be proved correct. Of course, if there are commitment biases in others, we can be rewarded by taking advantage of it. Stickiness with investor commitment will lead to slower speed of adjustment in prices. The result will be trends which can be exploited. Trend-following exploits commitment biases in many forms.

So, the decision rule is simple, exploit the commitment bias of others and ensure that you question your own commitment to any one way of thinking. Avoiding the commitment bias is not the same as being willing to change willy-nilly. Fighting a commitment bias is a willingness to accept what is not working and changing for something better if dictated by the environment.  

BOJ creates a new world for bonds.. sort of

The Bank of Japan has adjusted its Yield Curve Controls (YCC) policy. The YCC used to be a 1% cap that was applied strictly. Now 1% is a reference point without a cap which means that rates can move higher as determined by the market albeit with the possibility of BOJ intervention to control the extreme. 

Of course, the BOJ now owns over 50% of Japanese bonds outstanding so it is the 800-pound gorilla in the JGB market. YCC has been in place since September 2016 and can be considered the last of the monetary easing policies of the major central banks. The BOJ is inching to some sort of policy normalization, but we are not there yet. The reaction was a sell-off in yen because the market still views this as underwhelming. The impact on global bonds markets will be felt albeit not immediately. The gap between Japan and the rest of world rates will close, yet rising rates will have balance sheet impact for the large yen holders.