The types of trend-following systems - choose what fits

 

A recent paper tries to develop a unified theory of trend-following by classifying trend-following into three groups; see “The science and practice of trend-following systems”. 

The authors break trend-following into three types: European, American, and Time Series Momentum. 

European trend-following is based on continuous weights using an EWMA filter. In this system, position sizes are proportional to the signal strength, with the number of contracts changing day to day.

American trend-following uses channel or breakout systems with binary position sizes, so exposure is fully allocated when a signal is on. 

Time series momentum is based on the momentum of returns adjusted for volatility. 

There are parameter specifications that will deliver similar risk-adjusted returns close to the SG trend index. 

All of these trend-following systems are strongly correlated, yet there are differences in position-taking and signaling. The success of trend-following is based on the serial dependence of the underlying price series. Differences in performance will be related to differences in short- and long-term variance, and signals are related to an autocorrelation and drift term. Profits are related to an autocorrelation term even if drift is zero. 

A benign inflation number - now what?

Inflation numbers were within expectations, yet this is not something to celebrate. Core inflation is still above the 2% target, and headline inflation is still above 3%. Has the Fed been successful with its policy? The answer is no. However, the current inflation numbers suggest that no action will be taken at the September FOMC meeting. Policy changes are unlikely before an election, and that will take us to the end of the year to see any policy rate change. 

The important monetary policy information will come from the Jackson Hole conference and any headway from the five task forces reviewing policy. Chairman Warsh, if current behavior is a guide, is unlikely to tip his hand about policy at the Kansas City Fed confab, so the market will have to make decisions for itself on the direction of rates.

TIPS yields continue to move higher

 

It is not just nominal yields that are moving higher. Real yields have also been on a steep ascent, with levels at the highest in ten years. In fact, to get to these real yields, investors will have to look at data prior to the GFC. We are in a strong situation where real yields are telling investors that we have tight monetary conditions, yet inflation and nominal yields suggest that concerns about inflation are real. This places the Fed in a difficult policy environment and clearly is a reason for FOMC voting disagreement. 

Yen intervention can buy time not a solution

Always bet against intervention if there is no change in policy. Now, this does not mean you should fight a central bank in the short-term. It does mean that intervention has to continue if it is to work. Central banks are much savvier with their intervention. It will occur when there is limited liquidity. It may come through markets not expected, like EUR/JPY instead of USD/JPY. It will be followed by rhetoric to reinforce resolve. It will occur through selected banks to ensure not all are harmed. 

It can buy time but it cannot buy a solution.

AI investment follows other boom and bust cycles

Booms or bubbles are often associated with excessive investments. The euphoria associated with a new technology or meme leads to significant money flows. But there is a marginal return on the capital that falls as more investment dollars flow into the theme. The excess investment leads to a large capital stock in the new technology that may have significant positives for an economy, yet that does not make it a good investment, especially for those that come late to the investment cycle. Is new money late to the cycle? There is not a definitive answer, yet it is clear that all of the capital chasing returns will not be rewarded and likely will be a bust. 

Canals, railroads, new tech in the 20s, and the dotcom boom all saw significant capital investment, yet for many the rewards were limited.

 

Consumer sentiment not supporting market sentiment

 

University of Michigan Consumer Sentiment is now at the lowest level in 40 years. I could go back further. It is the lowest level ever recorded, as listed in the FRED database. Worse than the Volcker recession and the double-digit recession. Sentiment has fallen since the pandemic. The consumer confidence survey data, which goes back even further to 1959, shows the same pattern. There is a confidence problem in the US. 

The Conference Board numbers are better, yet the overall direction is the same. So, even if you choose the best survey, there is still a problem. This will carry over to economic behavior, yet it is not clear when or how, but the general trend is negative. Who or what is going to provide a jolt to sentiment? It does not seem to be on the horizon, and because of that, it is hard to see strong economic growth over the next year.

Yen intervention will not change fundamentals

 

The coordinated Yen intervention continues as we see the currency has continued to improve. Short-term intervention can reduce volatility, but this is not a volatility problem. This is a policy problem, and nothing has changed in policy. The Bank of Japan has moved its target rate to 1% and has provided forward guidance that it expects rates will continue to move higher. If this is the policy, then the yen carry trade will be reversed, and there will be further upward pressure on US Treasury rates.

The expectation is that short-term intervention will stop the yen slide and reduce pressure on global rates, but that is not how markets work in the longer run. There is no change in BOJ policy, no change in Fed policy, and no change in global imbalances. Hence, the markets will readjust their positions and currency rates will have to adjust. The process can be slowed but not reversed. 

What makes me worry about the markets - August 2026

 

The markets are connected, and the message is not good:

1. The Korean tech bubble is bursting, and some return to normality; however, it is not done yet. US retail investors are net sellers. We just saw a US fund blow-up with Situational Awareness. It is a one-off, but investors need to realize that investor euphoria led to the excessive money flows.  

2. Coordinated intervention in the yen market to stop a massive currency slide. Japan rates moving higher, so global fixed income is being reset. 

3. Long-term Treasuries at levels not seen since 2007. Real rates continue to move higher. Housing market declines will continue given high mortgage rates. Cost of borrowing for all AI projects is going higher. 

4. Consumer sentiment continues to move lower. 

5. Continued war in Ukraine and the Middle East, which impacts the oil market. Oil inventories are reaching low levels. 

What can LLMs do and more importantly, not do?

 

A very interesting position paper worth a read for any follower of AI: “LLMs Can't Jump: Why the Abductive Leap is the Final Frontier of AI Discovery.” The thought is simple. AI may be good at some forms of inference, like deduction and induction, but it is not able to do the third type: abduction. “While AI can compress data (Induction) and prove theorems (Deduction), it cannot yet recreate the intuitive leap Albert Einstein took to formulate the axioms of General Relativity—a process rooted in embodied physical simulation rather than symbolic manipulation.”

The Three Pillars of Inference

The paper adopts the framework of Charles Sanders Peirce to categorize AI’s missing link:

• Deduction (Rule + Case → Result): Applying a known law to a specific situation. (AI status: SOTA - Achieved)
• Induction (Case + Result → Rule): Spotting patterns in data to find a general rule. (AI status: SOTA - Achieved)
• Abduction (Rule + Result → Case/New Rule): Inventing a hypothesis to explain a surprising or singular phenomenon. (AI status: The Missing Jump)

There is still room for importing thinking by analysts. The market has to understand which skills are important for analysts and what can be done by quants.  

More than a Korean bubble problem - US retail is selling

 

The bubble in Korea has been viewed by some as an isolated event. There is no question that Korean-specific regulation and behavior were a strong contributor to this bubble, yet we should look at what is happening in the US. One, the semiconductor sector is showing strong declines. Not like Korea, but the pattern is similar. Two, the move in the AI and IT sectors has been driven by retail, and retail within the US is reversing. The market is changing, and the marginal retail investor is a strong seller who is moving to more protective assets. 

Market sentiment is changing, and the positive forecasts in the last quarter are being revised. 

Yields at levets not seen in a decade

 

Since the pandemic, 10- and 30-year Treasury yields have been on a steady march higher. We have seen continued inflation above target, continued budget deficits, the shock of pandemic QE, and no strong policy moves to stop the ascent. We are now seeing rates that will take us back up to pre-GFC levels. Could this be considered normalization of rates? This is hard to argue when you look at the combination of inflation and budget deficits. We are moving into a new period of rate behavior, and levels are unlikely to move back to the 3 percent range unless we have a large economic slowdown.  

The equity maket sectors are getting more disperse

Using the v-Lab data, we are seeing that correlations across sectors in the US are falling and showing more dispersion. If the stock market, like all markets, can be viewed as a network, we are seeing the network expanding after a period of strong connection in the first quarter of 2025. We are seeing a disconnect in the IT sector but also across real market sectors. This can be viewed as a market for stock pickers.

 

Gold taking up the slack of lower dollar central bank demand

At some point, the fiat money producers will believe that fiat money is losing its value. There will not be an announcement, but there will be a slow adjustment. Look at the reserve assets held. Central banks are expected to reduce their exposure in dollars. They have already seen an increase in gold holdings. Now you have to be careful when looking at gold, because a large percentage of the increase in reserve exposure is not from more tonnes of exposure but through an increase in price. 

That said, most central banks as of mid-June expect that gold will move moderately higher even after the poor second quarter. Will we see the highs from January and February this year? Unlikely, but there is more upside than downside for gold in the second half of the year. 

 

Monetary policy uncertainty relatively stable

Everyone in the bond markets has been talking about Fed Chair Warsh and his changing views on forward guidance. His policy stance is clear. He does not want to give forward guidance. He will reduce guidance to the minimum. Markets have reacted to this with much wailing, yet we need to focus on the actual impact on markets. One way to look at this is through market-based measures of monetary policy uncertainty. We have a history through an index created by the San Francisco Fed. The data suggests there was a spike when Chairman Warsh was appointed but has been relatively stable for the last few months. Overall, the Warsh regime is showing higher uncertainty, but the levels are not at extremes. Nevertheless, there should be concern not for the short run but for numbers that are closer to 24 months. Longer-term uncertainty is always higher than shorter forecasts; however, there needs to be focus on what investors are thinking beyond 2026. 

 

Korean KOSPI - the bubble market has burst

Sometimes the bubble burst will sneak up on you even if it is in plain sight. The Korean KOSPI index is down over 44% in approximately 40 days, with over 300,000 individual leveraged accounts liquidated and over 1.2 million accounts receiving margin calls this month. 

The index was about 45-50 percent information technology, with the largest exposure in Samsung and SK Hynix. The problem for the US is that the IT sector is globally integrated, so events in South Korea are not just a local event. Clearly, there has already been some spillover, but the real fear is a contagion that would require valuations for this sector around the globe to be reset. 

You can already see the spillover when we compare the one-month returns.

 

Central bank behavior and gold trading

 

Gold has had a complete turnaround after reaching highs well above $5,000 an ounce. So, what was going on? One clear indicator is that central banks have cut their gold purchases. Central banks have not been viewed as profit maximizers, but the high prices in the first quarter may have been too much, and they took a step back from the market. The other profit-maximizers may have had the same view, and the result has been an over 25% decline from the highs in just over one quarter. This has occurred with both a Middle East war and continued inflation. 

While real-time buying by central banks is hard to obtain, the demand for gold by different agents is still an important way to track gold dynamics.  

The failure of forward guidance or what should we expect

 

Analysts are calling it a failure of the Fed's new forward guidance policy. The policy, of course, is no forward guidance. Given Fed Chairman Warsh is not going to tell the market much about the intention of Fed policy other than "it will not waver" from trying to fight inflation, the market will have to decide on the efficacy of current policy. The Fed, instead of driving policy, can now learn what the markets are discounting. The market is now providing information to the Fed instead of the Fed trying to manipulate the direction of rates. 

Prices and markets are acting as signals, not as something that is controlled by central bank guidance. Now, the Fed and the market may not like the signal,  but they are signals nevertheless. The signals are now very clear. The market does not believe that the Fed can control inflation, especially over the longer run. Long rates are reaching year highs, and the yield curve is steepening. This signal, especially with the increase in real rates, is tightening the credit markets. 

The bond markets are now once again a signaling market. There will be more uncertainty in these markets, but they are now telling the Fed what they think about policy, both monetary and fiscal. You may not like what the markets is telling us, but the message is clear.

How to measure slippage - look for negative surprises

 

The paper by Ilija I. Zovko, "Realtime price impact detection"  provides an interesting solution to the slippage monitoring problem. 

Traditional methods for managing market impact typically rely on monitoring post-fill price slippage. This approach fails in real-time environments for two key reasons:

Statistically Slow - Estimating price slippage requires hundreds of fills before the signal can be statistically distinguished from background market volatility. By the time a trader detects impact through slippage, the order execution is usually finished.

Causally Ambiguous - Slippage measures correlation, not causality. An adverse price movement could be caused by the trader’s own actions (information leakage) or by an unrelated participant/market alpha. The required response is opposite in each case: slow down trading if caused by self-impact, or speed up trading if competing against outside alpha.

Instead of tracking price level changes over time, Zovko proposes tracking timing synchronicity.

The model tests for statistical surprise in how quickly an adverse market event occurs immediately following a trader's action (a fill or limit placement). The underlying assumption is that when a trader's actions leak information or cause market impact, the market reacts unusually fast. Unusually fast adverse market prints serve as a signature of causal impact.

To turn timing surprise into an actionable real-time metric, the research establishes a two-step statistical approach:

First, the background stream of market events is modeled as a Poisson process with a locally estimated, time-varying intensity. Each post-action event yields a $p$-value quantifying how surprising it is to observe an adverse event so rapidly.

Second, because a single fast event is insufficient proof, the p-values across successive fills are aggregated using Fisher's method. This allows statistically significant evidence to accumulate rapidly.

The resulting statistic reacts within a handful of fills (e.g., 3–5 actions) rather than requiring hundreds of fills. Because it operates in real time and detects causality, traders can dynamically throttle exposure to specific venues, counterparties, or execution styles mid-order if impact is detected.

Momentum and network learning

The paper “Network Momentum across Asset Classes” introduces a multi-asset quantitative trading strategy based on network momentum—a trading signal derived from momentum spillover across different asset classes. Momentum spillover occurs when past performance in one asset predicts future returns in linked assets. While traditionally studied in pairwise connections (e.g., stock-bond pairs or supply chains), this paper expands the concept to a systemic network across four distinct asset classes.

The study analyzes daily price data from 1990 to 2022 across 64 continuous futures contracts covering Commodities, Equities, Fixed Income (FI), and Foreign Exchange (FX).

Methodology

Graph Learning - Uses a linear and interpretable graph learning model to infer dynamic asset networks strictly from historical price data, overcoming the lack of explicit fundamental links between disparate asset classes.

Momentum Features - Leverages 8 individual momentum features per asset, including volatility-scaled returns over multiple horizons (1-day to 1-year) and normalized Moving Average Convergence Divergence (MACD) indicators.

Portfolio Construction - Combines learned graph adjacency matrices with a linear regression model to forecast 1-day volatility-scaled returns across all assets.

Out-of-sample backtesting from 2000 to 2022 yields an annualized return of 22% and a Sharpe ratio of 1.51 after volatility scaling. The strategy exhibits low downside risk and low correlation with traditional individual momentum strategies. Inter-class (cross-asset) connections significantly enhance return predictability and diversification.

Treasury convenience yield and inflation

 

The convenience yield associated with Treasury securities is dynamic, meaning the price of safety associated with this safe asset is constantly changing with the macro environment. This important paper, "Inflation and Treasury Convenience,” on the macro dynamics of the convenience yield finds that inflationary supply shocks raise the opportunity cost of holding money and money-like assets, increasing convenience yields. Exogenous liquidity demand shocks will also elevate convenience but depress consumption and inflation. Given the difference between supply and demand shocks, there will be a weaker convenience-inflation link in the post-2000 period, which saw more liquidity demand shocks.  

This shows that convenience yield will be associated with macro dynamics and not just the demand for safety. My view is that this work makes it more difficult to discuss when there is a change in safety for Treasury assets. Yes, we can say it will be linked with macro dynamics, but ultimately most are interested in the price of safety based on some form of risk.

No momentum factor after accounting for cross-sectional liquidity

What causes momentum, or what is associated with momentum? A new paper suggests that there is a strong link between liquidity and momentum, and that changes in liquidity precede momentum gains.

The paper, titled “Momentum Returns and the Role of Liquidity Improvements” by Jeppe Bro, demonstrates that the traditional stock market momentum anomaly is actually driven by cross-sectional liquidity dynamics rather than representing an independent risk premium. 

The novel idea is that past winners systematically see their trading liquidity improve before a portfolio is formed, while losers see liquidity deterioration. The momentum price drift is the market adjusting to these new liquidity states. The author calls this the Liquidity Improvement Factor, and when including this factor, there is no momentum alpha. The data shows mixed results pre-2000 data relative to more recent data. Any momentum effect is drift toward high-liquidity stocks and is a byproduct of liquidity dynamics. This is the most recent paper that attempts to explain the momentum factor. 

This is a very interesting thesis. Investors should track or follow liquidity changes to enhance any measure of momentum. I have some issues with the Amihud measure of liquidity, which looks at absolute return divided by vol and is manipulated to form liquidity differences. Still, I do not have a better alternative at this time. 

Periods of financial stress - the long history

 

A paper that has not received much attention focuses on measures of systemic risk in "Systemic Risk Measures: From the Panics of 1907 to the Banking Stress of 2023". Much of the data from this paper is available from V-lab. The work suggests that there have been more stress periods recently, but their duration has been shorter. There has not been a stress period since the banking crisis of 2023. More importantly, the work examines what happens cross-sectionally to firms when they enter periods of stress and identifies specific institutions that face risk. 

The stress measures focus on US financial firms' stock return comovements and can predict market outcomes like realized volatility and returns, balance sheet outcomes, and bank failures. The stress periods focus on the contribution and exposure versions of CoVaR, marginal expected shortfall MES, and SRISK. 

Stress periods are identified through a two-step process based on narrative analysis, with start and end periods associated with the GZ credit spreads for more recent periods. 

The key finding is that market-based indicators offer distinct information that complements traditional balance sheet metrics for risk assessment. 

SRISK around the globe - Look at China

 

There is concern about the risk in the current markets given the high valuations. In a perfect world, investors would like early warnings of market stress as an indicator that it is time to rebalance portfolios. Identifying different stress indicators will allow investors to triangulate on the true market environment. One that I have been recently focused on is SRISK from the website V-Lab. 

SRISK measures the capital shortfall of a firm conditional on a severe market decline, and is a function of its size, leverage, and risk. The current numbers show that the world SRISK is falling. There has been a significant decline in developed markets, but emerging markets are moving to high levels specifically because of the large increase in China risk. As a large economy, China's risk is passing through to the rest of the world.

The concern is spillover risk from China to the rest of the world.