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.

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.

 

Periods of Stagflation - there have always been with us

Despite strong performance in equity markets, there is still considerable talk of stagflation. The stagflation story is not just a 70's problem. It can happen in other countries and almost any time. It is more likely that we have a supply shock that can affect both prices and growth. The longer the oil crisis in the Middle East lasts, the greater the likelihood that we will see stagflation. Stagflation has generally been short-lived because the underlying cause changes. An energy crisis is averted through a new supply or a solution to the initial problem. 

Right now, we are not close to the 2% inflation target, and the cost of higher energy is just starting to bite. The likelihood of a stagflationary period in the second half of the year is increasing.

Narrative and macro investing

 

An exciting area of macro research is the use of narrative to help explain the weekly movements in equity markets. This work is still in its infancy and seems to be taking several different directions. This work on narratives started with Robert Shiller and his research on narrative memes that may create bubbles. Another analytic approach has been the development of indices that attempt to measure risk by counting mentions of news events. It has expanded with the development of NLP and LLM models.

An interesting application has been developed using the GDELT database to create different narratives. These narratives are then used as input to a macro model that uses key economic data from FRED. See the paper, “Monitoring Narratives: An Applicaiton to the Equity Market" Using a set of key narratives developed around news themes, the authors find that added narrative information will increase R-squared and reduce error for a model trying to explain equity returns. 

This paper scratches the surface, but it does provide an interesting link between narratives and fundamental data to help explain equity returns. For all the focus on macro quant data, story-telling is still an important driver of markets.

Meaaurement uncertainty is real

 

Macro investors have to deal with measurement error within government data. This is the second year in which we have seen major revisions to labor data. Last year, a major seasonal adjustment affected employment numbers. This year, there has been a significant change due to benchmark revisions. The two charts below provide evidence of what has happened to the labor data. The first chart shows that nonfarm payrolls have been revised down by approximately 1 million jobs. In the second chart, you can see the monthly change.

This new data provides further evidence of the K-shaped economy. Some of the revisions are based on demographics, so they do not translate into higher unemployment, but they do create a more confusing picture of the macroeconomic environment, which will impact bond returns in particular. 

I use macro signals to improve my trend- and price-based signals, but when macro data are noisier, the value added by macro analysis diminishes.

“Low-hire, Low-fire” labor environment - Labor gridlock

In a K-shaped economy, we are seeing labor market gridlock. Job openings are falling, and separations are low. Quit rates are also at the lowest levels since the pandemic. Workers do not want to leave their jobs. Firms do not want to hire workers, nor do they want to fire them, because they don’t think they can find better workers. Turnover is a good sign for a labor market, and we are not seeing this in the US economy. The economy is gridlocked on uncertainty. If you don’t know what the future may hold, you don’t want ot make new investment decisions in labor or capital. 

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.

Macrodrivers of stocks and bonds

 

The determination of the macroeconomic drivers of stocks and bands is a topic that has received much attention. If you can crack the holy grail of what drivers the major asset classes, you will be in a great position to manage any diversified stock bond portfolio. 

We know that the corrrelaiton between these two assets follows wide regime changes from positive to negative and then back again, so it is critical to find the key relationships. The recent CFA Institute brief, "Macroeconomic Drivers of Stocks and Bonds," utilizes an extensive dataset of macroeconomic variables to identify the key drivers. 

The focus of the paper is on factor selection using LASSO regression, which facilitates the stability selection of variables, and out-of-sample random forest regressions to enhance the prediction of key variables. 

The brief finds that LASSO regression can help narrow the set of factors used in finding these key relationships, and the use of random forests can lead to a significant reduction in the root mean squared error of the forest. Both are valuable tools with for helping any macro analyst improve their predictions. 

Nevertheless, the most interesting result is that you don't need many factors to explain a large amount of the variation in stock and bond returns. A keep-it-simple approach, combined with some stronger machine learning tools, will add significant value to any model-building. 

Market macrostructure matters

 

I found a recent paper, "Market Macrostructure: Institutions and Asset Prices", an interesting research piece that opens up new thinking about markets. The concept is simple. The market macrostructure, or the combination of key players in the marketplace with different objectives, will impact the return-generating process; however, further work is needed in this area to develop empirical tests for changes in macrostructure.

The premise for examining market macrostructure is straightforward. In market microstructure, the focus is on the dynamics of transacting, whereas macrostructure states that asset returns are influenced by changes in the behavior of key traders in the marketplace. The change in the behavior of central banks, pensions, and other financial intermediaries will translate into changes in return patterns. For example, changes in the behavior of central banks through quantitative easing (QE) or quantitative tightening (QT) policies will impact the return pattern of markets. Their size and influence will impact how returns are generated. For example, a central bank's asset purchase program based on policy considerations will differ from the behavior of profit-maximizing traders. Hence, the macrostructure will change. The macrostructure will change again when the central bank becomes a net seller or refrains from engaging in active buying and selling.  

The authors do not explain how they plan to thoroughly test this modeling. Regime changes focus on changes in return patterns through observing time series. Still, these return patterns are influenced by the market's macrostructure, which encompasses policy changes, regulatory changes, and financial innovations. The cause of regime shifts is shifts in the market macrostructure. If you can identify the shifts in macrostructure

The explosion in housing wealth - what does it mean?

 

Forget about the stock market as a wealth creator. The housing market is generating a huge increase in household wealth, which should make everyone happy. However, this gain may come from the huge housing bubble, which has exceeded the bubble from before the GFC. So there is a bubble, and households may feel wealthier, but there is a catch. You cannot convert that wealth easily into cash unless you sell the home. The result is that households may have more wealth, but they cannot consume that wealth, and there is a likelihood that the value of that wealth will fall when you want to monetize it.

GDP forecast and expectations all point in the same direction - down

 

Financial markets are in upheaval, albeit stocks are off their lows. Bonds reflect a substantial risk premium to hold both US Treasuries and corporates. Most importantly, expectations for future growth are falling fast. Even the adjusted GDP now is signaling zero growth. This is an analytic nowcast measure for growth that is below the Blue Chip forecasts. Private investment growth has fallen about one point since the election, and the consensus is that we will have a hard landing in the next 12 months.

Forecasts have been wrong in the past, yet the strong consensus should impact spending and investment patterns. Hence, we are facing a self-fulfilling prophecy.   

There are limits to the value from a crowd of economists

Is there a wisdom of crowds effect for macro forecasts? The answer is yes, per the new paper "On the wisdom of crowds (of economists)", but the impact of looking at more economists diminishes quickly. Whether the MSE, the change in the MSE from adding another economist, or looking at the relative improvement, the answer is all the same.  Check or average a few economists but the marginal impact of looking at a large group is minimal. Most economists seem to come up with similar forecasts which is not surprising. No economist wants to be an outlier relative to their peers, and most economists use the same models or frameworks which means they are likely to derive the same result. There is no value from looking at a big crowd of economists on the big macro questions. 

Recoveries will age but look for bad behavior first

An old paper that I forgot about but still relevant today is "Will the Economic Recovery Die of Old Age?". Recoveries can get long in the tooth, but most recessions are man-made. The cause a recession can be Minsky speculative excess, poor policies, or just old age. They are created from bad policy choices or the accumulation of excesses, yet like mortality tables there is a life span for any recovery. Of course, through good policies, the length can be extended. We can expect that current recoveries will have a longer life past on history, but the likelihood of failure will still go up as we age. We are better at getting policy right, but we are not perfect. The current recovery can still continue and the likelihood of a recession is still likely below double digits, but watch for signs. 

Asymmetric financial shocks - always greater downside risks

Financial shocks are asymmetric, that is, the impact of a downward financial shock is significantly greater than a positive shock. The asymmetry is not with the size of the shock but the sign of the shock. A negative shock creates a stronger reaction than a positive shock. The size shows a symmetry. See the paper "Machine learning the macroeconomic effects of financial shocks". Using Bayesian Neural networks (BNN) based on excess bond premium for inflation, industrial production, and employment. This is a simple straightforward approach for a machine learning model. Perhaps many think that the result is obvious, but it reinforces protecting the downside and preparing for bad news. You will not get this result from a simple regression model, so it is important to see how non-linear thinking can be incorporated in trading.  

The dampened business cycle - Will this continue?

 

We present two charts: one showing the dampened business cycle and the second on current growth forecasts. First, we are in abnormal period based on the low percentage of the time in recession. The data clearly shows that the business cycle has been dampened, yet the post-GFC period has been extremely mild. The question is whether this is the new normal. Have we eliminated economic downside? That conclusion is unlikely which leads to our second chart.

The Atlanta Fed GDPNow forecast is showing a significant decline in growth for the first quarter. Interest rates are higher. The projected growth expectations have been lower albeit the Blue Chip consensus is still at 2+ percent. Nevertheless, policy and trade uncertainty are causing many businesses to turn conservative. The GDPNow estimates provide a strong warning sign that if current trends continue, we will have a recession sooner than when most think. 

Using generative AI for economic forecasting

 

Generative AI can be used to enhance economic forecasts through simple aggregation from corporate conference calls. We know that corporate CEO's provide economic outlooks on their earnings conference calls. It is part of the job, and it is critical that they get their forecasts right. The cost of being wrong high. Firm profits may suffer.

These conference calls provide a unique and special insight on the direction of the economy when all this information is aggregated. It is possible to aggregate all the verbal comments from transcripts using generative AI. All the information in conference call transcripts can be aggregated to form an index through highlighting key words and phrases like many other LLM models. An AI Economy Score can be employed to improve forecasts of GDP. 

Researchers have found that there is positive incremental value from using the score from the conference call transcripts. See "Harnessing Generative AI for Economic Insights". This paper scratches the surface on using AI to help with macro forecasting, but it provides a good foundation of how new tools can support better global macro decisions.

Risk cycles and the business cycle - Form low risk comes growth but also greater credit risk

There is a business cycle, a credit cycle, and a risk cycle. These are all connected. In the paper, "The impact of risk cycles on business cycles; a historical view" the identification of the risk cycle is used to generate statement on growth, risk-taking, and the potential for crises. 

The authors show that a perceived low risk environment will encourage risk-taking which will support stronger growth, but with this growth comes greater financial vulnerabilities, that is, there will be a reach for more risk and for higher leverage. The duration of low-risk impacts growth.

Increasing financial vulnerability will lead to a reversal of growth when these risks are realized. From a low-risk cycle comes a growth upturn which creates excess credit growth. If this happens on a. global the impact on growth will be stronger because there will be grater movement in global capital flows. 

This is another way of saying that Minsky behavior can be measured through a risk cycle.

Anxiety about downside risk not the same as uncertainty

Investor market behavior is often filled with anxiety - the negative emotion from the anticipation of future risk, specifically downside risk. Anxiety will be displayed with the behavior of investors through their exposure to negative news. "Anxiety is the response to future uncertainty about payoffs in a risky asset, resulting in strategic uncertainty driven by multiple narratives", see "Anxiety, Investment Behavior and Asset Market Volatility".

Anxiety focuses on downside risks and its increase in magnitude and can be measured through news stories and survey information. These measures of risk, a focus on the downside, are different from economic policy uncertainty or uncertainty indices in general which serve as proxies for subjective uncertainty and not downside risks. 

This research finds that there is a countercyclical relationship between anxiety and volatility. An increase in anxiety which driven by news of downside will be linked to lower volatility. Volatility and anxiety are not the same thing. 

It is not the inversion - it is the switch back to normal that signals a recession

 

There has been a lot of discussion about inverted yield curves indicating the likelihood of a recession. The curve inverts and the recession will be coming, yet this time has been different. The size of the inversion has been large and the time inverted has been long, yet there has not been a recession. 

The story has now changed. Yes, you need an inversion, but the real indicator is that the inversion moves to positive. We are now at the point of having a positive yield curve, so the recession is now coming. If you look at the data, the inversion and then a switch before a recession is all true; however, it is not clear what is the underlying story for this combination. A recession may be coming, but the inverted yield curve story just does not hold the same water as before.

How long is long enough with a inverted yield curve signal

 

I have been disappointed by the inverted yield. I truly like the simplicity of yield curve inversion as a key recession indicator. It makes perfect sense that an inversion increases the cost of short-term money and is a good reflection of tight monetary policy. Given the lag of around, at most, nine months for a monetary shock to impact the real economy, we should have seen the slowdown anticipated, yet nothing points to a recession, and we are now beyond 400 days.

Do a throw-out this recession indicator? No. First, I must understand why it does not work. What am I missing? A good signal has a strong link between signal and market response. A highly variable response timing is an ineffective signal. Second, I must recalibrate thinking so this signal loses power for any allocation decision. Markets change. Signals fail. New work is necessary.