TINA vs TARA vs TAPAS and 2023

We can think about the year through three acronyms: TINA, TARA, and TAPAS.

TINA - There is no alternative. 

TARA - There are real alternatives (Goldman Sachs).

TAPAS - There are plenty of alternatives (Deutsche Bank).

We were living in a TINA when rates were at zero. Wow then moved into the TARA world with nominal short rates close to 5.5% and real rates turning positive. We are now entering into the TAPAS world where there are plenty of alternatives. Investors can think about cash, bonds, or play equities based on current momentum. 

Of course, plenty of alternatives at the beginning of the year will turn into TARA and then TINA as we move through the year and find out the rosy assessment for all assets will not be the reality that we live in. We start with choices and then end with view options.

Robert Solow - One of the great economists of the 20th century died

 

Robert Solow was a one of the great economists of the 20th century. He drove thinking about growth theory with the importance of technical change over the value of labor and capital as the key input for economic advancement. Everyone who studied growth theory and technological change has started with the Solow growth model. 

He was a great teacher who influenced generations of MIT economists. He was an economist's economist who helped many future Nobel prize winners with their theories and models.

His insights were across the board but there is one that stick in my mind when thinking about communication and noise. 

"If you make communications cost less, you’ll get messages of zero value." 

Multipolar world and investing money - ambiguity drives down correlations

The key issue for international investing over the long run is whether the world is a single pole, bipolar, or multipolar set of interests and country alignment. Clearly, we moved from a bipolar world after the collapse of the Soviet Union to something closer to a singular pole. Some will say that we have a new bipolar world between the US and China, but the close interconnection between these two countries makes it hard to argue from a trade perspective regardless of rhetoric. Nevertheless, a bipolar world seems more likely as Russia and China have moved closer through trade and politics There is also a strong view that the world has turned multipolar with a broader set of competing interests across a diverse set of countries. 

Is India in a single center of interest or is it something unique? Is the Middle East in two centers of influence or is it more ambiguous? Is Latin America a separate sphere of influence? The world is more complex and less clear-cut in 2023 than 1993 or 2013. 

Trade lines especially with emerging markets are moving closer to a China-Asia focus than an US-EU focus. Financial flows are still dominated by the dollar, but these capital flows have been disrupted by political drama through growing sanctions. Countries want to become less dollar dependent even though it may be harder to do in practice because being on the wrong side of US policy can be harmful.

While we may not have insight on the changing tectonic shifts in geopolitics, we can say with certainty that the higher ambiguity of political relationships will place downward pressure on correlations across countries. A multipolar world will also create greater shock risk from alignment adjustments. EM risk higher in a multipolar world. 

Investment, speculation, and gambling - quick definitions

 

An interesting take on the difference between investment, speculation, and gambling.  

If you know the distribution, it is investing. If you don't know the distribution, it is speculation. If it just a passing of money between players, then it is gambling.

This can be compared with the dynamic Minsky lending description of hedge, speculative, and Ponzi. Bankers lend money with the payback based on whether there is a likelihood that the distribution of cash flows will cover the loan.

Brad Delong:

1. Investment: You can construct a reasonable Bayesian distribution of likely future outcomes (or you can borrow the one revealed by market prices), and with reasonable confidence commit your wealth to support activities that will yield in the future produce valued goods and services in excess of their production cost, and hence are likely to profit.

2. Speculation: You cannot construct any reasonable Bayesian distribution of likely future outcomes for new technologies. Why not? Because, as Friedrich von Hayek wisely observed, if we could do so we would already be much further along in deploying those technologies than we are. Hence we are in Knightian uncertainty rather than Bayesian risk. There may be activities that will yield in the future produce valued goods and services in excess of their production cost, and hence they may be future profit. But do not ask the odds. Nobody knows, and nobody has any business claiming that they know.

3. Gambling: This is easy: No money is coming into the system. So what one participant wins, another must lose: zero sum. You can do this, if it gives you a dopamine thrill. But don’t think that you are even speculating. And never think you are doing anything that might be rightly termed “investing”.

Growth stocks dominates value stocks for 2023

 

The growth versus value binge was a key story for 2023. While 2022 was a value year, 2023 proved to be driven by growth names over value as measured by the ratio of RLG/RLV (Russell 1000 growth over value ratio). It is notable that this was a short value play and the ratio of growth to value is near all-time highs for the last 10 years. The ratio is also on a monthly basis above previous highs from the dot-com era. This growth switch came despite higher interest rates. The growth firms were able to pass higher costs onto consumers as not the case for value stocks.

2023 - Nothing was what was expected - Forecast failures as usual

 

Once again, all we know for certain is that most major forecast will be wrong. If we look at the forecasts for 2023 at the end of the 2022, we would have seen private economics expecting a shallow recession with overall real growth for 2023 coming in below 1%. The Fed forecast was even lower for 2023. The actual real growth for 2023 will be above 2.5% for the year. What happened?

We started the year with a major banking crisis, the largest since the GFC. This was far from just a few regional banks getting into some deposit trouble. It required a large amount of extra liquidity from the Fed and was a complete surprise to the market.

Inflation followed a transitory story path so that by the end of the year we are looking at something in the range of 2-3% inflation for the key PCE measure yet we are at a Fed funds that is just under 5.5%. Neither path was predicted by the consensus. 

Logistics seems to be returning to normal and labor markets seem to be ending the "Big reshuffling" or the "Great Resignation" from the COVID period. We are still not at a normal labor market, but it is getting close. 

Median wealth for households is hitting record highs, but if you don't have a house, you are unlikely to be able to afford one as the home market is in gridlock. Mortgage rates are high, yet we are not seeing a large decline in home prices.

Bonds are still in the "Big Bond Drawdown", but equities are seeing a great year as investors are looking beyond any recession and forecasting lower rates in 2024. 

Wars and geopolitical uncertainty are still high, but that does not mean markets care.

So, once again do not make a new year's forecast which you know will not likely come true. 

How many recessions in a lifetime - in a career?

 

A very simple question? How many recessions will an investment professional experience in a lifetime or a career? The answer is not many. If you start in finance right out of college, you would have experienced two if you are in your late 30's. You have to be in your mid-40's to go through three with the dot-com bubble not a strong economic recession. You must be in your mid-50's to experience 4, and your late 50's or early 60's to have seen 5 recessions. The numbers are just very low, so most have not had much or any experience navigating against these headwinds. Do investors and managers have the mental toughness to deal with recessions, I don't think so given we have had bailouts in most cases and nothing that has been prolonged even when we count the Great Financial Crisis. 

SHAP values useful for machine learning

What features are most important for a machine learning model? The best measures are the SHAP values (Shapley Additive Explanations) which is based on cooperative game theory through finding what is the impact or value from a single feature relative to all combination of features. It is not the same as a coefficient in a regression so we cannot say what is the impact from a change in the exogenous variable. It does tell us the importance of one feature over others within a model, so it can provide focus on the most relevant features. The SHAP tells us the importance of each feature on the prediction of the model, but it does not provide any information on the quality of the prediction.

The cooperative game approach assumes that features are like players and measures the contribution of the feature on the overall prediction for each observation. The sum of the SHAP values will be 1, so this is the percentage contribution of a given feature. 

If there is an observation X, it will have a prediction f(x) with E[f(x)] the expected value of the target variable. The sum of the SHAP will be f(x) which for any observation X will vary against the average E[f(x)]. The SHAP will change with each observation which is an interesting characteristic because that will tell whether a given feature observation will be impactful on the model output. High (low) values of a feature observation may have high (low) positive contributions which provides useful information on the non-linear sensitivity of given features. 

There are several ways to look at the SHAP value. Features can be just rank ordered by their SHAP over a given period, so we know what the mean SHAP value for a given feature relative to all other features. What is also useful is a beeswarm or violin graph which shows the feature value (high or low) as color for each observation versus the SHAP value, the impact on model output. This can be done for each feature which will tell us the observation importance on model prediction. The data for given set of observation can also be looked as a waterfall graph. 

These types of techniques provide a richness of output from a ML model that can generate insights that may not be possible through standard regression techniques.

Dollar tells the easing story for now

 

The dollar is always a good barometer of Fed easing or tightening relative to other major central banks. The current story is very clear. The market perception is that the Fed will be easier earlier and more often than other major central banks. Now, the latest comments from a Fed official, Chicago Fed president Goolsbee, state that the market may be ahead of itself and can explain the dollar move higher, but the story is that the markets should expect a Fed that is done tightening.

The big Fed pivot to lower rates

 

from Bondsavvy.com

December has marked the big Fed pivot to lower rates. It may not happen immediately, but the direction is clear. We have peaked in rates as of this fed meeting and the direction is lower. Whether because we beat inflation, or there is a need to lower rates to support the economy, the answer is still the same. The Fed, through the dot plot forecasts, is expected rates to decline by 75 bps in 2024 with more declines in 2025 and 2026 to get back to a neutral rate of about 2.5%. There is no guarantee that this will occur since the SEP forecasts are just forecasts from different Fed officials. Nevertheless, by this measure, we can expect a strong headwind for stocks in the new year. 

The destruction in purchasing power drives negative sentiment

 

“You never regain the purchasing power you have lost to inflation. There’s no such thing as deflation in modern monetary life…The important thing is the loss of purchasing power…that is not going to be recaptured, and that’s I think, the political significance of this number.” - Jim Grant   

The core problem with inflation in a modern era is the fact that no deflation will occur. What you lost in purchasing power will not come back. Even if your wages increase, your savings will not be able to buy the same basket as two years ago.

Bank risks - Not understanding risks

 “Banks don’t go out of business taking risk, they go out of business levering the things that they’re told aren’t risky.” - David Dredge

Bank risk is always about the surprise risk. There was no risk with holding mortgages until there was. There was not risk with holding longer-dated Treasuries until there was. The bank conduct significant due diligence on their loans, yet mistakes are made. One mistake or an isolated mistake is expected, but systemic risk across a section of the entire portfolio is what hurts banks. Systemic risk with leverage matters. 

What is the reason for cutting rates? Not clear

Everyone says that rate cuts are coming although the Fed has certainly not argued that they will be seen soon. Its position is that rates will be higher for longer. Any rate cut issue should be focused on the "why" question. Is there a case for rate cuts based on a recession? No. Is there a reason for rate cuts based on inflation? Maybe, but not likely. To get the answers, all we have to do is focus on labor markets as a starting place.

If there is an expectation for a recession, then the Fed should cut rates; however, a look at the current labor market suggests that there is no reason for cut. Labor markets are still tight and the recent data do not provide evidence of a softening. Hence, no reason to cut based on recession story. 

If inflation has been beaten, then there is a reason for a cut; however, the evidence is not strong. Inflation has come down but labor and service costs are still above the 2% target and they are not showing the softening price movement seen in the goods markets. There is no reason for a cut.

For those focused on markets, any Fed cut has to be looked at conditionally. No recession and we will have a nice rally. If there is a recession, the Fed signal just reinforces the idea that monetary policy is necessary to stem the decline.

Let's not forget the debt problem - What may hold up rates

 

High public debt does not matter. That has been the mantra from both academics, policymakers, and investors and for a long period they have been right. It was the foundation of modern monetary theory. It has been a research question that has driven many researchers a little crazy. 

The premise for debt matters is simple. If the supply of debt increases too quickly, the price of debt will fall, and rates will rise. Of course, the demand side must be addressed to determine the equilibrium rate. This is where there are complexities. 

Yet, the conclusion for many is that at some level of debt relative to GDP, there will be a problem with financing which leads to higher rates or at the extreme the inability to pay. While government debt can be paid with taxes, there is a limit on what can be raised. Debt can be monetized, yet this will lead to higher inflation. 

Where is the point of no return? There is no clear answer. All we know is that fiscal deficits are not sustainable. See "Living with High Public Debt"; however, a quick look at the current debt levels suggest that there will be a reckoning. The US has the special feature of being funded only in dollars, yet a debt crisis will impact the price of the dollar.

Looking at current US government financing suggest that the degrees of fiscal freedom are falling if there is a recession. Rates will fall as money flows to bonds, but the supply will continue to grow. Without monetary help, the rates markets will not behave the same as the last recession.

A humble Fed and policy slowdown

 

Is the Fed right with holding rates at the current level? Hard to say. They have gotten the inflation forecasts wrong for years, so it any argument based on an inflation forecast is likely to be wrong. Similarly, any policy change based on whether there will be a recession is also likely to be wrong. The Fed is not a good forecaster, so any action should be based on humility. Should the Fed provide liquidity in a crisis? Absolutely. However, will the size and timing likely to be wrong? Indeed.

The argument in response to these wrong forecasts is that the Fed should be gradual, and the Fed should be cautious; however, this means that the Fed will likely be late with any policy action and will likely overdo it when liquidity is needed and under act when policy excess needs to be curbed. 

The result is that investors should make two key assumptions with Fed modeling. One, the Fed will make wrong forecasts. Policy mistakes should be expected. Two, the Fed will also error on caution so it will always be slow to react. This means that asset markets will also be slow to react to any shock. The major surprise will occur when the Fed does decide to act and not follow a cautious policy.

Sentiment and expressive responding in economic data

There has been an ongoing issue with the difference between what American consumers are feeling about the economy versus what is showing up in the economic numbers. By the numbers, the US economy is doing well. Ask the consumers and you will get a different story; a very different story. The view from consumers when you get into specifics is very wrong as seen from this FT survey.

What is driving this large divergence? There is now the argument that consumers are engaging in expressive responding. Expressive responding has been noted in political science research. Some respondents will provide false answers as an expression of political views to strengthen their point of view. For example, there is a large difference between Republican and Democrat consumers. Why is this happening now? And, why is this just happening with US economic data? Is expressive responding misinformation? How do you identify this behavior in a survey? Should economic surveys ask about politics and then eliminate any partisan participants. 

How much weight should you now place on survey data? Unfortunately, survey information or sentiment plays a key role in forming market expectations. This is not going to be solved in the near-term.

Back to monetary policy basics with the Taylor Rule

 

Let's go back to basics from an earlier time. The Taylor Rule. The Taylor Rule still is a useful framework for looking at where monetary policy may be going. First, the rule would have raised rates higher and earlier than the Fed. Second, the Fed would already be cutting rates and bringing them below the current level.

The Taylor Rule suggests that the Fed was late to raising and is late to cutting. The Taylor rule has issue, but it provides a point for discussion. Fed monetary policy through bad timing has contributed to global economic instability.

Different volatilities have different meanings

 

To many one volatility is the same as the other. You look at different time frames and then standardize to say annual values and you have a good risk measure for a stock. Take daily and convert to annual. Take weekly and annualize. Take 5-minute intervals and annualize. Granted there may be differences in autocorrelation and mean-reversion, but the story is very much the same. 

A new piece of research suggests that the time interval can provide different information on a stock. The short-term volatility interval can tell us something about idiosyncratic risk while a daily or longer volatility can tell us something about systematic risk. The shorter the time interval, the more the focus is on idiosyncratic risk. If this is the case, then you can decompose risk into idiosyncratic and systematic to provide more information on stock behavior. This can be done through some simple analysis. See "A tale of two risks: the role of time in the decomposition of total risk into systematic and idiosyncratic risks" 

The key piece of work is looking at the differences in mean-reversion rates for volatility. By looking at the mean reversion of the idio and systematic risk we can learn about how different stock returns evolve. Understanding the volatility evolution process can provide some insights on the role of different investor groups like retail traders.  

Rational inattention - the foundation for trends

We have seen more papers refer to rational inattention models as the basis for why there are trend in prices and why expectations may be slow to adjust. This is a critical model that moves beyond the thinking of behavioral biases. Rational inattention is not a cognitive bias but the reality of how humans may process information. 

Information is costly. It costs money, processing time, and most importantly, the attention we will give it. If there is more information, it will be harder to process any one piece. This applies to individuals and the market as a whole. If there is limited attention on the set of all information that can drive prices, then there will be a problem with processing. Critical information that can drive prices may not impact buyers and sellers immediately.

We have referenced this critical thinking in an earlier post:  

Rational inattention - An important concept for investors

There are now laboratory experiments that test rational inattention. The analysis has moved beyond just testing for slow adjustment but posing specific experiments on behavior that will tease out inattention and how it is addressed in a controlled manner, see "Experimental Tests of Rational Inattention". These are not easy tests to explain in a simple manner, yet the conclusions are important. Subjects adjust their attention based on incentives, but these adjustments are more than just a function of costs of information. Information impacts or changes prior beliefs, so the form of how information is collected and processed affects beliefs. Additionally, the feedback of information will matter with we give it our attention. 

How investors use and process information, that is, how we give information our attention is critical. At its core, this is a simple process, but it helps drive key thinking about how markets respond to data releases. 

R-star and how to think about interest rates

As we think about interest rates and the continuing great bond debacle, even with the recent decline in rates, we should think about R-star as guide for handicapping the long-term direction of rates.

First, a definition, "The neutral rate of interest (also called the long-run equilibrium interest rate, the natural rate and, to insiders, r-star or r*) is the short-term interest rate that would prevail when the economy is at full employment and stable inflation: the rate at which monetary policy is neither contractionary nor expansionary", from the Brookings institute.

If R-star continues to stay low, the current inflation declines should lead to further decreases in nominal rates. I am not a pure believer in this framework, but it provides context on how the market thinks about rates. For more details on the global r-star, see the bank underground paper from the Bank of England.

The Global r-star value through to 2020 has been close to zero, so the big question is whether the current r-star is moving back to 2%. A move to even 1% since 2020 will make for a very different rate environment for long-term investors. The authors looked at what were the drivers for r-star, and they conclude that it driven by demographics which will not change in the short-run. Nevertheless, even with this headwind, there are other factors which may grow in importance relative to what has been seen in the past. For example, productivity is still a critical driver. 

From a trading perspective, r-star has little value, yet if large investors use r-star as an indicator for their bond allocation, fixed income flows may see significant adjustments. 

Charlie Munger - Wit, brains, and an eye for value

If you don’t see the world the way it is, it’s like judging something through a distorted lens.

All I want to know is where I’m going to die, so I’ll never go there. And a related thought: Early on, write your desired obituary — and then behave accordingly.

If you don’t care whether you are rational or not, you won’t work on it. Then you will stay irrational and get lousy results.

Patience can be learned. Having a long attention span and the ability to concentrate on one thing for a long time is a huge advantage.

What is the best quote from Charlie Munger? There are just so many, but Warren Buffett says it best. We need thinkers that can clarify not mystify.

Charlie and I think pretty much alike. But what it takes me a page to explain, he sums up in a sentence. His version, moreover, is always more clearly reasoned and also more artfully — some might add bluntly — stated.

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 our explicit knowledge. It can provide a link as well as a bias 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.