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.

A dirty secret in private equity

 

The private equity markets have been a darling for many pensions and endowments because of their strong long-term returns. Of course, investors should expect a premium over public markets because money is locked up for a long period of time. The problem is that many funds are nearing the end of their lives, yet many of the underlying investments have not been sold to other firms or IPOed. The money invested 10 years ago has not met expectations for a sale. The funds will have to extend their life. The IPO market has improved in 2026, but the numbers are deceiving since there have been a few very large deals. 

This private market environment suggests that investing in more liquid public markets should be viewed as a more viable option.

A warning sign of a bubble? stock issuance

 

One of the key signs of a stock bubble is the issuance of new stock. Simply put, when the stock market is overvalued, smart firms will issue new stock to take advantage of these high values. The increase in supply will flood the market and generate downward pressure on stocks. We are seeing more issuance in the market this year, but it is not at extreme levels. The large increases in 2020-2021 were associated with SPACs, which cratered the following year. 

On the other hand, stock buybacks will occur when the stock market looks cheap, and CFOs want to increase their companies' stock prices. We are still seeing the numbers increase, although at a slower pace. 

What is important, and what we have not done here, is to adjust buybacks and issuance to the total stock market valuation, which may provide a better relative measure. In that case, buybacks may not seem as excessive, nor does issuance seem to be exceptional. There are still mixed signals on these CFO signals of overvalued stocks.

Say yes to corporate transparency

The SEC proposed that firms would only have to provide information on a semiannual basis. Yes, it is a burden on companies to provide quarterly reports, but what makes US markets so liquid and effective for all investors is that corporate information is readily available. Now, there may be some companies that pause about going public because of SEC regulations, but the solution is not to cut the amount of information provided to investors. Could there be a tightening of rules on what information is made available? Yes, this may be helpful, but cutting out core information by half is not the solution.

Goldman Sachs, based on survey data, found that almost 100% of respondents oppose the move to semi-annual reporting. The four leading reasons include transparency, timeliness, and protection. Do not change what most seem to believe is not broken. What are the people at the SEC thinking?

Warsh and the star-studded task forces

 

Chairman Warsh has announced the members of his task forces for review of Fed policies. I am impressed by the choices and the seriousness with which you are trying to provide fresh perspectives on some key problems. This is not an internal review but includes both business and academic thinkers with key knowledge for each task. I am thinking this may be one of the most extensive and far-reaching reviews ever attempted by the Fed. 

Task Force	Objective / Focus Area	Members (Co-Leaders)
Communications	Reviewing how the Federal Reserve conveys policy deliberations, decisions, forward guidance, and economic projections.	* Peter R. Fisher (Professor of Practice, Foster School of Business, University of Washington)* Arminio Fraga (Founder/Chairman, Gávea Investimentos; former President, Central Bank of Brazil)* Mervyn King (Former Governor, Bank of England)
Balance Sheet Policy	Examining the costs, benefits, and institutional implications of the Fed’s balance sheet regime.	* Karen Dynan (Professor of Economics, Harvard University)* Raghuram Rajan (Professor of Finance, University of Chicago Booth; former Governor, Reserve Bank of India)* Jeremy Stein (Professor of Economics, Harvard University; former Federal Reserve Governor)
Data	Improving the quality, speed, and timeliness of real economic signals to inform monetary policy decisions.	* Raj Chetty (Professor of Economics, Harvard University)* Doug McMillon (Former President and CEO, Walmart Inc.)* Kevin Murphy (Professor of Economics, University of Chicago)
Productivity and Jobs	Assessing the economic impact of general-purpose technologies, particularly artificial intelligence (AI), on labor and productivity.	* Marc Andreessen (Cofounder and General Partner, Andreessen Horowitz)* Charles I. Jones (Professor of Economics, Stanford University / Anthropic)* Asha Sharma (Executive Vice President & Xbox CEO, Microsoft Corp.)
Inflation Frameworks	Evaluating the effectiveness and design of the Federal Reserve’s framework for price stability and inflation targeting.	* Greg Mankiw (Professor of Economics, Harvard University; former Chair, Council of Economic Advisers)* Thomas Sargent (Professor of Economics, NYU; Nobel Laureate)* William White (Senior Fellow, C.D. Howe Institute; former Economic Adviser, BIS)

Warsh and forward guidance - NOT

 

What is clear from the first Warsh press conference is that he will not provide forward guidance on the Fed’s actions. Chairman Warsh is not going to tell the market anything about what the Fed may be doing in the immediate future.

Governor Waller, on the other hand, provided a spirited defense of forward guidance that serves a specific purpose, albeit flexible to meet policy needs. Perhaps forward guidance was useful when we were close to the zero bound, and the Fed wanted markets to know that rates would be lower for longer. It may not be appropriate today, given that we are in a different regime.

Nevertheless, forward guidance is supposed to signal policy to the makrets os they will bend to the desires of the Fed. If the Fed is unsure which direction to take, any forward guidance may be wrong-footed. 

In any case, less forward guidance should lead to more bond market volatility and more discussion on what the Fed is thinking. Fed watch is back in vogue.

Hidden Dissent and the Fed

 

The markets were in an uproar over the dissents at the last FOMC meeting, and they may be expecting further dissent at the July meeting. Yes, dissent announces and measures dissent, but there are other ways to measure dissent that may be more useful. Researchers can look at the comments of Fed governors and bank presidents to measure dissent. The use of NLP and LLMs can count words, form sentiment indices, and use context to measure differences of opinion over time. 

Two researchers have examined transcripts and minutes from Fed officials to measure what they call “hidden dissent.” Their index is available through their website, digitecon.org. Their index correlates with dispersion in SEP forecasts. Markets respond to dissent across the Fed.  

Jevons paradox and AI

Jevons’ paradox was developed in the 1800’s to explain dynamics in the coal market. Jevons found that technological improvements can increase resource efficiency, but that will often lead to an increase in total resource consumption. The increase in efficiency leads to lower effective cost, and with costs lower, demand will increase. 

We see this effect across many industries undergoing technological improvements, but it may be best exemplified by the AI market, where efficiency keeps improving while demand still grows. The electricity demand will increase. The demand for chips will increase. The demand for data will increase. Yes, there is efficiency, yet consumption of the core product and associated supplies will also increase. 

There is nothing special about the AI industry. It is following the same behavior we have seen countless times over centuries. 

Data dependence as "constrained discretion"

Ben Bernanke and Rick Mishkin called the use of data dependence in monetary policy "constrained discretion". We are in another of those periods of constrained discretion regarding inflation and monetary policy. 

Robert Hetzel, a former Fed economist, said that policymaking has a flavor of "guess and correct." There are forecasts and guesses of what should be the key target variables, inflation and growth, and then policy is corrected to move toward the target. 

The data dependence is an important part of the guess-and-correct view. There is no set rule, but a set of adjustments toward the expected target. 

Going broke and not taking profits

Good investors focus on risk. Risk is the downside. Bernard Baruch said it well when he said nobody ever went broke taking a profit. - Sam Zell

This is a classic rule in trading, yet it has little meaning. Anytime you have a positive profit, you can take it, yet you are likely leaving money on the table. Simply put, do you take profits on what could be noise or price variation due to current volatility? 

Perhaps better to take profits against valuation. This requires some valuation statement that may be a mistake, but it is a better requirement for profit-taking. 

Nobody ever went broke selling at or above fair value. 

Dispersion - what does it mean?

 From the newsletter, Owenomics, we see that stock dispersion is at levels not achieved since 2008 and 2000. Now, this may not be an indicator of a market top, but it does tell us something about market behavior. 

Dispersion is not the same as volatility. Volatility measures deviations from the mean over a given time period. Dispersion measures the deviation of returns across a set of assets. It is a cross-sectional measure. Higher dispersion means there is a greater variation in the winners and losers relative to the mean return. This could mean there is a disruption in the current market regime or a rotation between industries and firms. It could mean there are a few very strong winners or losers.

Past periods of strong dispersion include the bursting of the tech bubble in 2000, the bursting of the housing bubble in 2008, and the Great Financial Crisis. Disruption leads to dispersion, but greater dispersion does not necessarily imply a general market decline.

The end of being drunk on AI?

 

There is an interesting index on token expenditures called the Silicon Data LLM Expenditure Index that measures the price of tokenization for AI. It is showing a decline as users move to cheaper models. If the price of tokens increases, demand will respond. 

Companies are now monitoring token usage and prices and ensuring that employees don’t treat tokens as a free good. This is natural behavior on the part of firms, but it also means that revenue growth for AI providers will likely slow and fall short of market expectations. 

The wealth gap - the economics of envy

One of the most dramatic changes in the US over the last 35 years has been the distribution of wealth between the middle class and the top 1%. From a gap clearly in favor of the middle class, the number now puts the top 1% at more than 25% of total wealth. 

The cause of this gap reversal may be twofold. Low interest rates support those with higher wealth tied to equity risk. Low interest rates harm those with cash deposits rather than risky assets. Second, significant wealth has been created through innovation, benefiting entrepreneurs and venture capital investors. The first is based on the choice of monetary policy. The second is based on capitalism. It is not obvious that tax policy can reverse this if the impact is to drive down the price of risky assets. 

Any adjustment to this distribution should be driven solely by what will best increase economic growth through risk-taking.

One reason for the rise in US socialism

Should we be surprised by the rise of socialism in the US? No, if you look at the numbers. This is important because changes in the regulatory or tax environment will affect the return on capital, which will, in turn, impact the valuations of all companies. In an already overvalued world, a change in the government regime can be a catalyst for a downturn.

The evidence is in the changing percentages of profit to GDP and of employment compensation relative to GDP. Historically, employment compensation was always higher than profits, but that shifted in the post-2008 period. What happened? The cost of capital fell as rates approached zero. This allowed profits to increase as capital costs fell. Coupled with a change in industry structure, with the most profitable companies in the tech sector and not as heavily concentrated in labor-sensitive companies, the dynamics of profits to employee compensation flipped. There was no diabolical plot driven by greed, except to say that monetary policy assumed a trickle-down effect from lower rates that may not have worked as expected. Now we will have to live with the consequences.

Death of despair and macroeocnomics

 

There has been a change in society since 2000. Drug deaths, suicides, and alcohol deaths are up significantly. Society is different, and this is being reflected in consumer confidence numbers and views on economic optimism. This has been documented in the book Deaths of Despair and the Future of Capitalism, by economists Case and Deaton, which focuses on the crisis in the American working class. Their argument is that the loss of manufacturing jobs in the US, especially after China joined the WTO and the pandemic, was a major driver for this change. We may be seeing a reversal in the pandemic effect, but the loss of manufacturing is unlikely to be reversed. 

The optimism in the country has a lot to do with spending and with policy choices, and the current despair is not helping to solve any gap between the optimism of the lower middle class and wealthier individuals.

The single most important driver of the current housing market

The housing market has stalled, with sellers not finding buyers at current prices. There is a logjam, with sellers unwilling to lower prices after the steep run-up during the COVID pandemic. Buyers are being selective because not only do they have to pay a high price, but the financing is at levels above 6%.

Perhaps this is the market just finding an equilibrium price, but the real reason for any gridlock may be past mortgage interest rates.  Right now, 50% of mortgages are below 4%. 20% is at rates below 3%, and 30% is between 3 and 3.99%. There has always been a gap between current mortgage rates and the rates that homeowners have locked in. Unfortunately, the gap may be larger than normal. In real terms, homeowners in the post-pandemic period have negative real mortgage rates. There can be a gap of over 500 bps between those low mortgages and current real rates. 

Homeowners are not stupid. They know they got a great deal and do not want to move to a home that may not be significantly better and financed at a much higher rate. They will stay in their existing home or only leave if there is a significant gain from switching.

 

Equities at the half year mark

 

Even with the Iran War, the equity markets are generally up double digits for the year, with the only laggard being the S&P top 50 firms. June seems to be seeing a notable rotation out of information technology and communication services and into other sectors, such as industrials and health care. There also seems to be a rotation from growth into quality, although momentum was still a market leader. The rotation theme also seems to indicate a shift from large-cap stocks to small caps. In fact, small caps have been the best-performing sector. 

US stocks are still performing well versus the rest of the world in June and for the year. Nevertheless, international equities showed strong performance for the quarter. Fixed income showed slight gains for the month and quarter, but commodities have continued to slide. 

Our biggest concern for the second half of the year is the risk of a correction from high valuations. A rise in rates or a slowdown in credit growth to stop an inflation surprise is a likely downside surprise. 

Hedge funds and AI

 

HedgeWeek with Arcesium published a survey titled "Age of AI II: Understanding Hedge Funds' AI Capability versus Usage." It is a short piece, but what stood out for me was the interesting fact that smaller firms are embracing AI use more than large firms. The smaller firms are not only more active users but also seem willing to train employees in AI use. They are usng technology to potentially punch above their weight. The large firms do not seem as agile in using AI and are more concerned with data and output integrity. Will this translate into better returns? Harder to measure, but AI may be a disruptor to hedge funds.