The crowds, momentum, and risk in markets

 "These heroes of finance are like beads on a string - when one slips off, all the rest follow." - Henrik Ibsen during the early phase of the Great Depression 

An insightful comment from someone not in finance. Ibsen was mainly a playwright; however, he was aware of the goings-on in Europe and the world. We have heroes of finance, but they are not the contrarians. They are the ones leading the crowd or banging the drum to move the crowd. Everyone likes the person who reflects their thinking. I follow Bob because Bob's thinking is consistent with my view of the world. Show me the person who is thinking differently. They are the people who will move me to think better. 

Global Capitalism - we may never go back the the early 20th century world

 

Jeffry Friedman is one of the leading economists on the history of the international global order. His book, Global Capitalism: Its Fall and Rise in the Twentieth Century, is now 20 years old, but it is a good read if you would like to know where we have been before the current globalization upheaval. Global capitalism is not natural. It was fought for by a few visionary bankers. These bankers made money, but they also saw a more connected world. Unfortunately, the Great War destroyed the high point in global trade and reset the world financial order. The depression forced new isolation. The Second World War again led to a new, more controlled international order under U.S. dollar hegemony. This system broke down, but we saw a new emergence of global capitalism following the fall of communism, cheap transportation, and a world willing to cooperate. The story ends before the Great Financial Crisis, which again led to a shift in global capitalism toward autarky, neo-mercantilism, and non-cooperation. We are observing a decline in trade volumes. We may never see coordinated global capitalism, but we do see bastardized forms of public-private coordination imposed on economies. 

Hedge funds taking on long equity exposure

Hedge fund strategies are often purchased because there are expectations that they will exhibit a beta similar to that of equity markets. Not all the betas will be the same, but they will generally run between .5 and .6 on the high side to zero or slightly negative on the low side. CTAs usually have the lowest beta but also likely have low alpha. 

These betas or correlations will change with market conditions. The hope is that if there is an increase in beta, it is not because hedge fund managers are chasing the equity market with momentum trades, but rather because they are showing some market-timing skill. Evidence on market-timing the overall market is weak, so there should be concern when hedge funds exhibit higher short-run correlations with equity markets. Of course, hedge fund managers may have timing skills, but this is not the reason why most managers are buying these strategies. 

Investors seek uncorrelated returns, so there is a general expectation that betas will be low and stable. Yes, that means that hedge funds will underperform versus the overall market on an absolute basis, but investors should not pay for unwanted beta.

 

The K-shaped economy - Wall Street versus Main Street

 

I normally hate the comments about the difference between Main Street and Wall Street. Usually, it is a false dichotomy, but the current environment suggests that the average consumer or wage-earner is having a harder time than wealth-holders. This is what happens when we are in a more inflationary environment. Yes, inflation is off its highs, but the average inflation for this century is closer to 4% than the 2% Fed target. 

The labor markets are looking weak and confidence is weak, yet the stock market is higher based on strong earnings from those economies that have network effects and represent the tech industry. Two-income households may be doing well, but the rest of the country is trying to hang on and deal with recurring price increases that do not appear in the CPI indexes. 

A strong stock market may pull the economy higher, but that is unlikely. The higher stock market is likely the result of too much money chasing existing assets. The good markets may not be seeing all of the inflation, but the financial markets are seeing "asset inflation.

Is there a story for small caps?

 

If you look at the Shiller CAPE P/E values, the large-cap market looks expensive. If you look at small-cap stocks excluding the largest Mag 7 stocks, the market seems better positioned. Now that the small-cap risk premium has fallen, some have questioned its validity, but the number suggests a closer look.

If we just look at the 493 SPX stocks outside the Mag & there seems to be more value broadening investor focus. Of course, earnings for the Mag 7 have been higher, which justifies holding these stocks. Additionally, small-cap value and growth have not been rewarded. To hold these smaller-cap names, you truly have to believe that current P/E valuations are a strong predictor of short-term stock performance. Perhaps that is the case over the next five years, but it is a stretch for the next year.

Gold central bank holdings - Saying no to fiat money

The perception of the gold market has changed radically since the Great Financial Crisis (GFC). During the first 10 years of the century, central banks sold gold. Who wants gold when you can have dollars, euros, or yen? Who wants a real asset with no yield when you can have a financial asset with a yield? Who wants a real asset when inflation was under control and less than 2%?  

Now, we know who wants these assets - central banks. These institutions create fiat money, and they don't want it from their peers. Of course, rates were set to zero and, in many cases, moved to negative values during the period of QE. If financial assets can yield negative returns, a hard asset with zero yield looks pretty good. If government debt reaches a new high well beyond GDP, we can suspect it will not be paid with taxes, and governments will have to default on the debt or inflate their economies. The pandemic suggested that governments will go to any lengths to boost the economy with new money. 

The central banks are acting rationally and not telling the public what they really think. Fiat money is out, and hard assets are in. 

JGB rates starting to matter to the rest of the world

Japanese 10-year JGB yields are now 2.34, the highest this century. An end to loose monetary policy, continued loose fiscal policy with the expectation of a tax cut, the "Takaichi Trade", and persistent inflation that is currently at 2.9% means that there is a strong reason to see yields move even higher. The rising JGB rate is having an impact worldwide as money starts to flow back to Japan. Now, it is hard to say this is a complete reversal when real rates are still negative, but the global financial landscape is changing, putting pressure on Treasuries and rates in other countries.

What if we have clarity on Treasury rate direction?

 

We have been strong believers in using volatility, whether the VIX or the MOVE index, as a strong indicator of fear and uncertainty. This is a nonlinear relationship. An increase does not necessarily mean a decline in prices, but once volatility exceeds a threshold, there will be a strong price reaction. Now, we can look at the decline in the MOVE index and infer that the term premium should decline. Since September 2004, short rates have declined by 175 bps, yet long-term yields have increased. This is not what should be expected. Lower volatility should reduce risk and lower yields. This is not happening. 

So what is the reason for the higher, longer-term yields? Well, if volatility measures uncertainty, perhaps there is no uncertainty at all, and bond investors are clear. Bond buyers believe there is greater risk in holding Treasuries, that inflation is rising, and that the safety of dollar Treasuries does not exist. In that case, volatility can be lower, and rates trend higher. Lower volatility and lower uncertainty do not mean clarity is good. 

A link between policy uncertainty and gold

 

"A bet on gold is really a bet that the people in charge don't know what they're doing."

- Matt O'Brien, 2015

'The monetary order is breaking down,' - Ray Dalio

Gold prices have reached uncharted territory amid US policy uncertainty and trade tensions. You could look at headlines and make some connections, but more importantly, we can examine objective measures of uncertainty as indicators of a change in the monetary order.

The trade policy uncertainty index has fallen from high levels, but remains at extreme levels. The global economic uncertainty is also high and at extremes. 

If the monetary order and policy framework is breaking down, there will be a search for safety. However, if the safety of holding dollars and Treasuries is no longer present, we will see a search for alternatives, and right now that is in precious metals. 

Gold allocations will rise, and even a small increase across many portfolios will create demand that current mining production cannot meet. This is fueled by increased demand from central banks. 

Combining volatility (fear indexes) - a strong indicator

 

We know that many investors use the VIX index as a fear gauge or just a measure of market risk. We also know that the same investors use the MOVE index to measure volatility and fear in the bond market. Some researchers decided to look at the divergence between these two indices as perhaps a stronger signal; see "Divergence of Fear Gauges and Stock Market Returns". The authors find that the divergence of fear indexes (regressing MOVE on VIX and using the residuals and the MOVE/VIX ratio) is a negative predictor of future equity market returns. This predictor does well for both in-sample and out-of-sample tests. 

Looking at the difference between MOVE and VIX indexes is a simple measure that can be followed by almost any investor. Simplcity may make this indicator obsolete if "everyone" is using it, but in the near term, we think this is a good, simple signal tool that I have been using for some time in different forms.

What does gold arbitrage tell us about globalization

 

Gold quality is the same worldwide. There are differences in purity which can be accounted for in price, but an oz of gold in Shanghai, London, or New York should fetch the same price within a range. The price range difference should reflect the cost of transporting physical gold from one location to another. The tightness of price differences around the world is a measure of the fissure of globalization and free trade. If the world is in a free trade environment, gold price differences should be within the range of transport costs. If there are large differences in locational prices, trade is disrupted. 

A close look at price differences across major gold markets suggests an arbitrage breakdown due to tariff uncertainty in 2025. There has also been a disconnect in physical markets due to the desire of major buyers to hold gold in their own domiciles. As gold has become in short supply in some locations, there has been a disconnect that cannot be solved by the usual form of transportation arbitrage. This is a sign of a bubble, but also a sign that investors and physical users do not want to have geographical uncertainty.

Fiscal versus moentary dominance - the real battle

Janet Yellen, who served as both Treasury Secretary and Fed chairman, presented "The Future of the Fed: Central Bank Independence and Fiscal Dominance" at the AEA convention earlier this month. She does a thoughtful job of describing the differences between these two forms of dominance, yet she misses the mark in her description of the current environment.

We cannot continue independence and monetary dominance if there is a fiscal crisis. Fiscal policy saw periods of deficit and then a return to something normal; however, in the last decade, or since the Great Financial Crisis, there has been a change in government debt dynamics, so that fiscal policy has a more dominant role in monetary policy. Dominant does not mean controlling. In this example, fiscal dominance means the issues with fiscal policy have a more dramatic impact on the economy than monetary policy.

The current debt levels cannot be sustained with a growing amount of tax revenue used to pay interest on debt. The Fed has ignored this fiscal crisis. They have refused to comment on rising debt-to-GDP ratios to avoid being political. Yet the ongoing QE process, coupled with Fed high Treasury balances, shows that the Fed has lost its monetary dominance and must deal with a debt crisis. 

Trump's desire to lower interest rates is just an extreme manifestation of the fiscal dominance needed to sustain current government policy. Let's not forget that inflation is one way of getting out of a fiscal bind. If there were controlled deficits, there would not be a need to discuss lower interest rates. The fiscal excesses of the past have to be addressed. Yet, who wants to say we have a debt problem?  

Once again, our foecasting skill is poor

We try and try, but the results are always the same. We are not good forecasters. Should we stop trying to predict? That would be the obvious answer, yet anyone who invests needs to make assumptions, which are forecasts. The easy answer is to diversify, but there are assumptions about what may happen in the future. The classic 60/40 stock/bond mix will not last forever. 

Turn the loser's game into one where you can limit downside. Provide ranges and not point forecasts. The key is knowing your limitations when it comes to forecasting. If you are likely to be wrong, think in terms of probabilities. Think about the impact of being wrong and how much exposure you would like to have.

 

Deep learning and asset management - it is here but requires significant work

The use of deep learning techniques has exploded in finance, but few papers summarize what has been achieved. That has changed with a new paper,  see Deep Learning in Asset Management: Architectures, Applications, and Challenges. This work provides a good survey of how to apply deep learning within asset management. While the authors suggest that the use of deep learning is promising, they also note significant challenges, including low signal-to-noise ratios, non-stationarity in financial time series, and the market's adaptive nature, which can face regime changes and adapt to patterns. Along with outlining the problems with CNNs, RNNs, and transformers, there are many practical challenges in using deep learning models, from data usage to the usual overfitting problems. While deep learning has focused on quality predictions, there may be useful ways to integrate deep learning into holistic approaches to asset management 

Supply - demand imbalance and commodities

 

Commodities have price cycles between supply and demand imbalances. Sometimes the imbalance is caused by a supply shock, such as a war or a weather event. Sometimes it is a demand shock caused by unexpected growth in a specific market sector. 

Beyond the shocks, there are natural imbalances that arise when demand increases, yet there has been underinvestment in supply, or supply production is too slow to address the immediate demand. This is likely to occur in metals markets, which need a long lead time to mine and process ore.

Mining is capital-intensive, and if the return from mining is uncertain or too low relative to other investments, capital will move to other sectors, leading to a supply shortfall. 

The supply and demand imbalance in metals is especially problematic because demand increases are hard to respond to when supply production has a long lead time. This issue gets worse when there are supply chain shocks. Mining supply change shocks occur when one supplier drives price, and there are limited alternatives for finding new supply. 

The current supply-demand imbalance is not just a gold problem, but a silver problem. In fact, there is a supply imbalance with nickel, cobalt, copper, palladium, rhodium, and aluminium, along with a rare earths supply change problem. Surprisingly, all of these imbalances have been documented yet are only now being recognized. 

Howard Marks on bubbles - thoughtful advice

It is good to get some perspective on the current bubble discussion across assets. The following comments from Howard Marks provide useful insights that are evenhanded yet focus thinking on the problem. These excerpts are from a longer letter with sage advice on how to think through the AI bubble and associated thinking on financing data centers.

I’ve concluded there are two different but interrelated bubble possibilities to think about: one in the behavior of companies within the industry, and the other in how investors are behaving with regard to the industry.

Newness plays a huge part in this. Because there’s no history to restrain the imagination, the future can appear limitless for the new thing. And futures that are perceived to be limitless can justify valuations that go well beyond past norms – leading to asset prices that aren’t justified on the basis of predictable earning power.

The key thing to note here is that the new thing understandably inspires great enthusiasm, but bubbles are what happen when the enthusiasm reaches irrational proportions.

Struggling with whether to apply the “bubble” label can bog you down and interfere with proper judgment; we can accomplish a great deal by merely assessing what’s going on around us and drawing inferences with regard to proper behavior. 

The key realization seems to be that if people remained patient, prudent, analytical, and value-insistent, novel technologies would take many years and perhaps decades to be built out. Instead, the hysteria of the bubble causes the process to be compressed into a very short period – with some of the money going into life-changing investment in the winners but a lot of it being incinerated.

Debt is neither a good thing nor a bad thing per se. Likewise, the use of leverage in the AI industry shouldn’t be applauded or feared. It all comes down to the proportion of debt in the capital structure; the quality of the assets or cash flows you’re lending against; the borrowers’ alternative sources of liquidity for repayment; and the adequacy of the safety margin obtained by lenders. We’ll see which lenders maintain discipline in today’s heady environment.

I know I don’t know enough to opine on AI. But I do know something about debt, and it’s this:

• It’s okay to supply debt financing for a venture where the outcome is uncertain.

• It’s not okay where the outcome is purely a matter of conjecture.

• Those who understand the difference still have to make the distinction correctly.

from Howard Marks Is It a Bubble? Oaktree Client Letter 

 

 

Nonlinear momentum - Increase positions with signal strength

 

Many firms are using trend-following. Their distinctions are often based on the length of trend signals, the set of markets used, and the risk management techniques employed. A recent paper, Nonlinear Time Series Momentum, measures the nonlinear relationship between trend and risk-adjusted returns using machine learning techniques. Using techniques to exploit nonlinear relationships within momentum outperforms simple linear methods. This nonlinear value-added is observed across all asset classes, frequencies, and horizons and lookback periods. This is especially true during market downturns. 

For modelers, this research concludes that simple nonlinear transformation of momentum signals will improve strategy performance, and signal strength interacts with predictability. However, as signals move to extremes, the extra risk from adding to position sizing diminishes, and at some point is not worth taking the extra risk. Hence, there is a complex nonlinear function between signal strength, sizing, and optimal risk-adjusted returns. Increase position exposure when signals are stronger, but reduce the signal exposure as you move to extremes.  

The foundation for decision-making - measurement

“Measure what is measurable, and make measurable what is not so.” - Galileo Galilei

"If you cannot measure it, you cannot manage it"  - Peter Drucker 

The one-two combination for quant decision-making is: first, measure everything because when something is placed on a scale, it can be compared, and second, remember that if it cannot be measured, it cannot be managed. There are no feelings in investing. There is no place for comments like, "I feel rates are going higher..." There is only a place for precision that can be compared with past predictions and future views. Yes, this is hard, but it is the only way to judge the quality of your decisions. 

Chaos and machine learning

 

"Chaos theory -the qualitative study of unstable aperiodic behavior in deterministic nonlinear dynamical systems" - Stephen Kellert 

That definition of chaos theory is all-inclusive, yet in a world before ML, it would be hard to model using simple linear regression techniques. ML is helpful because it can address the characteristics of chaotic systems. It also helps define the type of ML necessary to employ when faced with a chaotic system. Foremost, ML learning can address nonlinear relationships. All neural network ML can address nonlinear relationships. ML can also work with dynamic systems that have strong cross-asset relationships. ML can also address aperiodic behavior by examining time-series relationships using techniques such as long short-term memory (LSTM) recurrent neural networks (RNNs). What takes more work is dealing with unstable systems that change over time. This requires ML models that are compact and can be retrained regularly. 

Wet streets cause rain problem - the need for causal thinking

The "wet streets cause rain" problems, and the need for causal thinking. Reading the news causes noise in thinking about markets because newspapers can get causality wrong. If "A"  happens in the news, then that must have caused "B". Or, if I see the event "A", it is likely to result in "B", not because of past history, but just because they happen at the same time. It is vital to get the causality right before the narrative. Narrative can be easily written when you accept correlation as the driver, not causality. Causality is connected with what is probable by what may have happened in the past. 

Causality provides constraints on thinking. It can lead us to conclude that we don't know the reason for some events. It is harder to write news stories when you are forced to use reason and causality. It is harder to be an investor if you are constrained by the limits of what is possible. If you focus on causality and logic, you may have to say, "I don't know why the market moved." 

China tech sector showing surge

 

The Chinese stock indices have been strong performers, matching those of other major countries around the world. This increase is despite an overall economy that is not doing very well. The real estate markets remain morbid, but sentiment has changed regarding trade wars and geopolitical risks.

The strong China tech sector may be about relative valuation. Chinese tech is cheap relative to similar US firms. It is also associated with a government focus on growing the tech sector, both to avoid dependence on the US and to increase the opportunity to dominate this industry globally. Since the launch of Deepseek AI, there has clearly been more attention to this sector. Clearly, Chinese tech is being pulled along by the strong US moves. 

 

Risk means more things can happen than will happen  - Elroy Dimson 

I view risk as the dispersion of countable events. Uncertainty is the events that are not countable, yet the Dimson definition hits home as a key description. It is the range of what is possible, both good and bad. For volatility, the dispersion from the mean says more things can happen than will. For uncertainty, more possibilities are riskier than fewer. Although we focus on the bad, risk can also be a positive.

Back to basics with bubbles - the fire analogy

The fire analogy, or triangle, was developed by Quin and Turner in their book Boom and Bust to describe what is necessary for a bubble and thus also identifies the conditions under which a bubble may burst. The triangle states that an asset needs Oxygen, Fuel, and Heat. Oxygen is the marketability or ease of trading. Fuel is the money and credit availability. Heat is the speculation or momentum associated with an asset. 

Assets have to be easy to trade by a large set of investors. Bubbles need credit or money to borrow and leverage into larger positions. Heat is the speculation or momentum of assets, which creates a sense of FOMO and provides positive feedback on market views.

We have all three conditions in the tech AI space, and until there is a change in conditions, we will continue to see prices move higher. Rates are coming down. These assets remain marketable, and momentum remains favorable. 

Boom and Bust - A great book on bubbles, which is useful today

Qunat versus macro framework - One example of differences

 

Here is an example of how one macro hedge fund looks at the quant versus macro playbook. It is a good depiction of those managers who closely link quantity work with discretion. This is not better or worse than a fully quant or discretionary style, but it shows these two are connected. 

Quants usually hold more positions and rely on the law of large numbers and diversification. If you follow the fundamental law of active management, the goal is to make many "uncorrelated" bets. Note that each sees the world differently with respect to how they view the world, form bets, and build portfolios.

The rise of alternatives with pensions - the great hedge fund demand increase

 

The key development for the hedge fund industry was a change in demand from institutional investors. The shift in risk-taking was the overall driver for pension behavior in the last 35 years. This change in risk-taking reduced exposure to fixed income and increased the desire to seek higher-returning investments with a similar low overall risk. This shift is presented in the paper, The Rise of Alternatives. 

The shift in risk allocation by pensions and endowments led to a considerable increase in hedge fund demand across all strategies, with two effects. One, money flows into hedge funds increased as expected from any shift in demand. Two, the institutional investors demand greater oversight through their due diligence, which has increased the demand for legal, compliance, and risk management. Hedge funds grew larger, more sophisticated, and more complex in response to this greater demand.

What will be the impact from tariff in 2026

 

The standard trade world seemed to be coming to an end during the Liberation Day debacle, yet the US moved away from its strongest tariff rhetoric, and the world seemed to adapt to a higher-tariff environment. The worst tariff policies were not implemented, yet many policy effects take time to take effect. Firsm can adjust pricing, reduce profit margins, hold-off prcie increases, yet many responses are temporary.  Goods shipments increased, but now both importers and exporters will have to normalize in a world with a higher tariff. For Chinese manufacturers, this will mean tighter margins, but it will also likely lead to a shake-up of firms and industry-wide considerations. This will be a global effect. 

The end of the Great Globalization

 

Every analyst develops and markets year-end forecasts, but the most important part of forecasting is not knowing where we are going, but knowing where we are in the world economy. You cannot map the future if you do not know the current or past, and the critical issue is the end of globalization. Many discuss tariffs, but the real problem is what will happen to global trade and foreign direct investment. 

Global trade is showing flat growth, and FDI flows have fallen significantly. There is a change in relative trade; however, for most living standards, the overall trade flattening is the key driver of growth. 

The Great Globalization is over, and we will all suffer from this in 2026.

Distinction between US and Europe - the type of capitalism?

There is a growing distinction between the United States and Europe. Growth is slower in the EU. Productivity has been lower, especially in the last 25 years. Government is bigger. But, most important is the development of companies and technology. There is no substantial growth of start-up firms that may be developing new technology. 

Just look at the number of public companies that have gained scale from scratch over the last 50 years. This is the critical issue if you believe the EU stock market will grow faster than the US over the next decade. 

There needs to be introspection on why this is happening. A start was the Draghi report last year, but there has to be a willingness to incorporate these recommendations. There is a different view of capitalism that may be holding back firm development.