Hedge fund start-ups - a thing of the past

 

A CAIA post shows that private capital firms now outnumber hedge funds. In fact, the number of hedge funds has declined over the past decade. Hedge funds are not a growth business. This can be associated with the rise of multistrategy firms that will pick up talent in pods, as opposed to investing in individual firms. This has not happened in other areas of alternative investing. The performance across hedge funds is tighter than other alternatives, even though there are significant differences in strategies. 

The hedge fund industry is undergoing consolidation, with higher start-up costs and increased operating expenses. You have to ask whether someone wanting to start a hedge fund is a little crazy. 

The Fed always to the rescue?

There has been considerable discussion about the need for the Fed to lower interest rates. I do not want to get involved in that battle at this time. Instead, I have been shocked by the chart below concerning the "lender of last resort" Fed behavior. The numbers are outstanding. We thought that the Fed was active during the GFC; yet, if we look at the post-2020 period, we will see the Fed going to almost any length to support stability. The COVID response was overkill, but the First Republic response was on a different level. This may be internalized in the minds of investors, yet we cannot know with certainty what the Fed will do next time. There is moral hazard uncertainty, and this can be a problem. 

 from @greg_martis 

Innovation and change impacts quant results

I found this table one of the most interesting for the month. Corporate behavior is all about change, innovation, and adapting to market demands. This is a constant, yet many of the models we build to describe equities are based on assumptions of stability. The first products offered by many firms are long gone, and most investors do not remember or have never encountered these products. Lego - wooden toys? McDonalds - hot dogs? Who would have guessed?

We believe that betas are stable; however, if you examine this list, firm betas will be pretty dynamic. Now it may be stable over a short-term three-year period, but if a firm is transitioning strategy, these short-term betas will also be under attack and changing radically. Modelers cannot escape the news about firms.

from @stats_feed 

 

Corporate bond stress falling - what will it take to change spreads?

Despite all of the noise in politics, policy uncertainty, and geopolitical tensions, corporate bond spreads have fallen to levels seen at the beginning of the year. While there was a significant increase in stress in April, overall stress levels remain below the peak during the COVID-19 pandemic. Indeed, the market is sensitive to changes in policies that will impact earnings; yet, corporate buyers have overlooked these risk and uncertainty issues. The next test will be softness in the real economy, yet the link between economic uncertainty and spread risk does not seem strong.

 

Gold and central banks - Do what we do not what we say

 

I thought at one time that central banks believed that gold was a "barbarous relic". Those times are long gone. The latest survey on central bank behavior and gold shows that 3/4ths will increase their gold portion of total reserves. It is higher simply because the price of gold is higher; yet, we are seeing more central banks increasing their gold exposure. Where is it coming from? Survey results suggest that dollar exposure is expected to decline. Reduce dollar exposure and hold more gold. 

43% of central banks expect to increase their gold exposure in the next 12 months. This is up from 8% in 2019. Gold, the hard asset, is back with central banks. No, this comes even as inflation has fallen, so central banks may be arriving late to the table, although central bank gold holdings have been on the rise for years. 

The explosive increase in gold prices is simply. There is a shortage of this "safe asset" relative to supply. There have been pockets of lower demand, but central banks are price-insensitive 800-lb gorillas in the gold marketplace. This is more than an inflation hedge, but for many EM central banks, a sanction hedge. 

It is hard to see a substantial price reversal in gold when central banks are key buyers. Gold is not at attractive prices for accumulation but central banks are telling the market you should hold this diversifier. 

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

Causal discovery and trading

 

Causal discovery techniques can help any quantitative hedge fund, but may be especially helpful for enhancements to trend-following through finding causal links with other markets. The basic structure for a trend-following model is to use past values of a variable to extrapolate ot the future. Look for the trend, yet it would add significant value if you could learn whether other markets may have some causal impact on another variable. 

The standard approach to time series causality is to use Granger causality tests, which simply determine whether some time series Y causes or has an impact on the prediction of X. However, a growing number of alternative techniques are available to aid in causal discovery, thereby improving trading, such as time series data causal inference, vector autoregressive linear non-Gaussian acyclic models, and time-varying interactions models for nonlinear observations. The code for these algorithms is already written, so it is relatively easy to implement for a set of assets.

We are not planning to explore all of these techniques, but there are ways to support better causal discovery that can be used to improve the inputs in investment strategy. See "Trading with Time Series Causal Discovery: An Empirical Study" for a simple application of causal discovery for long-short equity portfolios. Now, these algorithms are not easy to implement due to the time required for computation; however, this seems to be a fruitful area for further research, especially given the growing interest in causal reasoning in finance.

Choose your correlation carefully - Kendall's Tau

Portfolio construction is fundamentally based on the correlation between assets. The lack of correlation creates diversification, yet limited work has been conducted in testing alternative forms of correlation. Most construction work is based on Pearson correlation, which looks at linear dependence across assets. There are limitations with Pearson correlation, so sometimes an alternative is used, Spearman's rho correlation, which is based on rank ordering. Spearman's correlation can adjust for non-normality and outliers, but there is a problem with the assumption of monotonicity. The third alternative, Kendall's Tau is based on measuring the concordance between asset pairs through counting the sign of movements across assets. 

A relatively simple paper looks at the portfolio construction of daily foreign exchange pairs using all of the same parameters except for the correlation matrix. See "Beyond Correlation: Enhancing currency portfolio construction through Kendall's Tau and Correspondence Analysis". I was surprised by the results. Yes, volatility is higher, but the overall portfolio performs better. There are clear benefits from using Kendall's Tau. The numbers are compelling enough to ask a simple question: "Why not try this alternative?"

 

The impact of narrative: The power of Fed speak

 

We expect that the Fed speeches have an impact; however, the measure of their effect on equity and bonds has not been precise. A paper, "Mind your language: Market responses to central bank speeches," shows that from the speeches, there are forecast revisions that can then explain volatility and tail risk in major asset  classes. Fed chairman speeches will have more impact than others through larger forecast revisions, but the Fed chairman can also calm markets with the right speech. 

The paper utilizes NLP, or natural language processing, to aid in identifying information that causes changes in macroeconomic forecasts. The critical point is analyzing the continuous flow of central bank communication, not just an isolated speech. There are clear regimes in Fed speak, and it is good to identify these trends. 

Quants focus on what is countable, yet the non-countable, like we see in speeches, is essential. If you can turn the non-countable from narrative into something measurable, there is an opportunity to form probabilities and make better trades.

I have taken the view that you want to avoid FOMC and major Fed speeches because there is too much uncertainty; however, if we decompose what is said, investors may be able to tilt their positions to their advantage. 

The power and gravitation pull of doing nothing in asset management

There is value in doing nothing in asset management. One, doing nothing reduces transaction costs. Two, doing nothing allows for the power of compounding. Three, doing nothing reduces the emotional biases associated with trying to take action. Four, doing nothing helps to clarify the distinction between effort and work. Showing that you are doing something is not the same as doing work. 

By having a long-term view, there should be less trading. Long-term investing is a do-nothing management approach because the long-term decisions should not be swayed by short-term changes in markets. There is more value in doing less. 

These are all good reasons, but there is also a pull to doing nothing that has a negative effect. Nevertheless, no action at the wrong time will be costly. Fear of making mistakes and emotional regret may stop an investor from taking needed action. Aversion to regret will reduce action. You cannot regret the action not taken. You could, but generally, regret is about what was done, not what was missed. Lack of knowledge or ignorance.

So, there needs to be a checklist for action. Do I have a valid reason for action? Do I have the right time perspective? Have I accounted for the cost of trading? Will I regret this decision if the market direction changes?  Be careful with action. From a model perspective, what is the action beyond a prediction of noise. 

Sophisticated investors and market efficiency

 

Market efficiency will vary by the type of investor. There are different levels of efficiency based on your structural advantage. Market efficiency is based on the behavior of a given market and not on the profitability of a given trader. Hence, you can declare a market as efficient, yet there could still be profitable investors. Similarly, market efficiency could be rejected, yet that does not ensure an investor can make money in that market. 

For retail investors, the market is very efficient. You cannot get an edge if you are slow to react, have less information than other investors, process the information poorly, and have high transaction costs. If you are an institutional trader, your sense of efficiency is different. You may have a slight edge on reaction time, trading efficiency, and information processing. If you are a hedge fund, you may have an even greater edge; however, being declared a hedge fund does not necessarily confer a lower efficiency level. 

The old argument by Friedman on the efficiency of speculation is that reasonable speculation will drive out poor speculators and thus make the market efficient. The counterargument is that noise traders are more prevalent than shrewd speculators and can keep the markets inefficient. A corollary to the Friedman argument is that there are different classes of investors with varying levels of capital that can exploit opportunities, so while efficiency may exist on average, that is not the same as saying the markets are efficient for everyone.

A sophisticated investor has an edge and creates an opportunity to exploit inefficiencies. Hence, the job of any due diligence is to identify sophistication and the chance for the edge that can be exploited. 

Stan Fischer - A great influencer on macroeconomics

 

Stan Fischer is one of the great macroeconomist of the last 50 years, and as shown by the figure above he influenced many of the other leading economists of this time. He was one of the key pillars of MIT macroeconomics and was clearly one of the strong influencers of monetary policy choices around the globe. You cannot talk about macroeconomics or international macrofinance without looking at some of his papers. I cannot say that I always agree with his research work, but that does not change his substantial impact on macro thinking. 

He will be missed, yet we must ask what would have happened to macroeconomic thinking if Fischer had not existed. Would we be better or worse off with our thinking?  Would someone else have filled the void? More so than any one piece of research, Fischer was a teacher, whether at MIT, the IMF, the World Bank, or the Fed, who set the agenda for many other researchers. In this case, he could not be replicated.

Riding bubbles is a strategy - but more than one way to do it

Jarrow and Kwok, in their new paper "Riding A Bubble: A Study of Market-timing Trading Strategies," identify when there are Q-bubbles based on local martingale properties. The idea is that a bubble will exhibit extreme values over different time horizons, and an investor should hold a significant bubble move until it reaches a set barrier. At this time, the investor should exit. Ride the bubble until the returns reach an extreme and then walk away. The basic story seems easy enough, yet the key is to determine the bubble component, which is based on the tail probabilities. Bubbles have a fat-tailed Pareto distribution. If an investor sets an upper bound on the price of the asset and it is reached before a certain time, then exit. 

The idea is relatively simple, yet despite the simulations run in the paper, this point of exit is harder to find in practice. It will encourage getting out of positions early, even if there is an optimization and an accounting for risk aversion. 

The trend-follower will generally not follow this type of strategy. The trend-follower will always hold the position until there is a reversal and a stop is hit. You will sacrifice some of the return in exchange for carrying any position as long as possible. Yes, there will be losses in the end when the market turns, but the ability to maintain a position in a bubble will generally be worth the added risk and the likelihood of some give-back. 

Financial innovation is a virus!

"Financial innovation is like a virus, finding weaknesses in existing inventive schemes and regulations. When something is growing very fast, that suggests they have found a weakness." - Jeremy Stein Harvard University. 

This is one way to think about financial innovation, but it is not very appealing. It argues that innovation is just an attempt to evade regulation. There is no doubt that some goals of innovation are evasion, but there are also other reasons, such as market efficiency. Nonetheless, one can argue that regulation reduces efficiency, and innovation attempts to address the problem. If the problem is corrected, there will be more growth in innovation. Securitization, derivatives, and ETFs are all significant innovations that make the markets more efficient, while also addressing regulatory concerns.

Knowledge and wisdom for picking your financial facts

 

"Knowledge is a process of picking up facts, wisdom lies in their simplification" - Martin H. Fischer 

from story on Jane Street's traders:

Jane Street software engineer Ian Henry said the firm's traders all need "fighter pilot eyes" to deal with "extremely high information density" while making trading decisions. Henry said that, when making tools for these traders, he has to fine tune their size by a matter of pixels, in order for traders to maximize what's on the screen.

Henry says one of two main categories of applications built at Jane Street is focused on "managing traders' attention," ensuring they're alerted to interesting things amid that sea of information. He says the challenge for engineers is around "balancing noisiness" and stopping those tools from annoying traders with unnecessary information. 

Is the problem for Jane Street the acquisition of knowledge or its simplification? I want more information because I never know what will be helpful, but then I have to be selective to focus my attention. 

The trend trader will say that I focus my attention on only a limited number of issues—the trend in price. All other information is unimportant. The discretionary trader will argue that all information is essential, and I don't want to be constrained by limits on what I can review. 

Where is the trade-off, and how much information is enough, is one of the key issues for any investor

Bond and equity expectations are different

 

A recent paper by AQR, "Why are bond investors contrarian while equity investors extrapolate," makes an interesting observation. I have always thought that bond investors were mena reverting based on their conservative nature. There are limits to where yields can go. Equity investors are optimists, which means that returns can always move higher based on unlimited possibilities. Overoptimism will lead to the extrapolation of good news. Of course, this does not explain what happens to markets when they start to move negatively. The pessimism of bond investors forms beliefs about limitations and the notion that good news cannot last.

AQR states that the cause is information salience, the attention -grabbing qualities of certain information. This, however, does not focus on why there is salience that is different across markets and why it may persist. Nonetheless, it is essential to think about differences in how expectations are formed in major asset classes. 

CBOE dispersion index mean reverting

 

The CBOE dispersion index, DSPX, measures the difference in volatility of individual stocks versus the volatility of the S&P 500 index. It peaked during the period of maximum trade uncertainty because the market could not determine the impact of trade tariffs on individual firms. Now that trade risk has declined, the DSPX has also declined, as the market now focuses on other factors that are more likely to impact all stocks. The key question now is whether we will have a recession. 

Fama-French factors and economic drivers - Watch macro

 

The Fama-French factors have been used extensively to describe any set of returns; however, further work is needed to explain the economic drivers behind these factors. The paper "Understanding Asset Pricing Factors" takes a novel approach to analyzing the connection between economic events and factor moves. The authors analyze days with significant factor returns and then classify them by linking them to new articles the following day. Macroeconomic news, monetary policy, and corporate earnings reports are the main drivers of returns for factors, as should be expected. 

The Fama-French factors include four major factors beyond market risk: SMB (small minus big size effect), HML (high minus low value effect), RMW (robust minus weak profitability effect), and CMA (conservative minus aggressive investment effect). It is found that HML value premium is related to the macro factor. CMA is related to the commodity factor. SMB is correlated with the exchange rate factor and the unknown factor. RMW is related to shocks in individual companies. The authors use AI and human coders to classify news events that were tied to the FF risk factors.  

The categorization shows that humans and AI do a similar job and there is a clear distinction between the events that drive factor returns. Unsurprisingly, macro is the dominant driver of large moves. This resurrects the issue that even equity investors who may focus on factors RV should follow what is happening in the macroeconomy.

Trend-following returns during S&P 500 drawdowns are not all the same

We are learning more about the qualities of trend-following as a hedge or safe asset. A hedge asset is one with low correlation to a risky asset. A safe asset has low correlation overall, but will have a negative correlation during a drawdown for a traditional risky asset. Trend-following will have the characteristics of a safe asset, but only if an extended drawdown exists. If there is a short-term drawdown, it is less likely that trend-following will generate the desired return pattern. This is a key risk of holding a trend-following manager.

There may be some diversification gains, but a safety effect may not exist. We have seen Treasury yields during the current crisis, so it is hard to say whether there is a true safe asset. Therefore, it may make sense to return to first principles. A safe strategy is one that can hold either long or short positions and adjust those positions based on the direction of trends. It is not a structural safe asset but a behavioral safe asset based on the dynamic actions of market participants. 

 

Trend-follower dispersion in return performance

SG Prime Serves has provided their assessment of trend-following managers over the past 25 years since the inception of their index. There is dispersion in performance, which should be expected since there are many variations in trend-following. The trend indicator, a simple trend model for comparison, shows that the correlation between managers and a generic fund will deviate when there is a strong macro event.

The data suggests that holding just one manager may not be optimal and that a portfolio will reduce the potential for regret. Now, is there a correct number of managers to hold? That is a more difficult question. One is too low, yet 6 may be too many if the trend allocation for an overall portfolio is 2%. That said, a large pension may want to include a trend index and then add some satellite managers who have specialized skills. 

 

Get your financial stylized facts right - The Bayesian foundation for empricial finance

Economists and financial professionals often use the term "stylized facts" as an alternative to a set of descriptive statistics or just data. Formally,  a stylized fact can be a simplified representation or an empirical regularity that serves as the foundation for building theory. It may not be a simple piece of information, but a formal empirical regularity. In a recent paper, "International Financial Markets Through 150 Years: Evaluating Stylized Facts", the authors test a set of well-known stylized facts across a broad set of markets and a long time. This is the most exhaustive analysis of well-known stylized facts ever undertaken. We will not present all of the findings, but will show the summary table of what was tested.

Why is this so important? The stylized facts should be thought of as the Bayesian prior for any future analysis. Start with the stylized facts of what should appear in the data. You should not find something different, but you should argue that this is the basis for any future work. Before you pass judgment on a theory or set of data, look for the stylzied facts that already exist.

 

Co-occurrence versus correlation- Still learning about diversification

 

There is a need to deepen our understanding of diversification, but is there a limit to how much we can extract given the basic principles of covariance and correlation? Some in finance are starting to discuss the concept of co-occurrence, which, in the case of asset management, measures the cumulative co-movement of asset pairs during a specific horizon. If, over a particular horizon, the cumulative returns converge to the other returns in the portfolio, you may not have the diversification expected. If we have two assets that, over a one-year horizon, generate similar returns, then you are not diversified. The paths are different, but the two assets end in the same place. 

You can measure the co-occurrence by multiplying the z-scores of two assets and dividing by the average of their squared z-scores. The co-occurrence will fall between -1 and 1, much like the correlation. If the z-score of an asset is zero, then the co-occurrence will be zero. If the z-scores are highly aligned for the tested time period, the co-occurrence will tend toward 1. The determinant of the co-occurrence is the joint informativeness of the two variables, which is essential because the correlation of any two assets is the weighted average of the informativeness with the co-occurrence. So, the co-occurrence tells us something about correlation when we align the return horizon, which we do not do with traditional correlation measures. 

Correlation is useful, but we also want to see how assets move over longer horizons to determine whether there is a pattern of behavior that tells us whether performance diverges or is similar. 

Evaluating trading strategies - Harder than you think

Most investors often rely on a simple search and ranking across managers to find the best trading strategies. For example, sort by the highest Sharpe ratio and choose the manager with the highest number; then you have a winner. Alternatively, search for a manager with a minimum Sharpe ratio and then conduct due diligence as a two-step process. 

Unfortunately, life is more complicated. There is a distribution of Sharpe ratios across managers and over time, based on the strategy type and analysis used. If there is an average Sharpe for a strategy, then outliers on the high side may not be following the strategy named, or they may be subject to mean-reversion. The Sharpe ratio for any period may differ, so the last three years may not be representative of the manager's performance over the long run.  There is a reversion to the mean when you sample the Sharpe ratio for a set of managers over a specific timeframe. Time and context matter.

If you look at enough managers, you will find some that are perceived to have an edge based on the sample data collected, yet this analysis may generate a false signal. For managers, you need to examine the sample set that has been analyzed. For quantitative strategies, you need to consider the backtesting performance and the length of time reviewed for the strategy. Again, sample size matters.

Additionally, evaluation is usually not concluded with the numbers but through extensive discussion with the manager to find their edge, yet the edge narrative or the manager's story-telling is inherently subjective. Is the choice of the manager a function of their skill at describing what they do or what they actually do? 

Perhaps a critical selection skill is reviewing why you chose a manager who failed as well as looking at the managers that you rejected who then went on to perform better than your existing portfolio. What your selection impacted by bad luck, and where the managers you rejected subject to good luck, or in either case did you miss something that is not in your due diligence analysis.

Sharpe on thinking about risk

 

"If you're not thinking about risk, then you're not thinking." William Sharpe

"Portfolio management is risk management" is a mantra that I follow. Risk cannot be separated from return. If I can lower my risk, I can increase the Sharpe ratio without focusing on finding a better-returning asset. Of course, lowering risk will impact return. There is a cost to risk management. The secret is trying to manage the risk at a low cost. The critical component is portfolio construction because diversification is the only free lunch in finance.

A pet peeve of mine is that managers often discuss the return generation process before jumping to the risk management discussion. I understand that it is easier to separate the two, yet the link between return and risk is critical.  

Hofstadter's Law - applies to macro events

 

Hofstadter's Law: It always takes longer than you expect, even when you take into account Hofstadter's Law. — Douglas Hofstadter

We have written about this, yet it keeps coming up in project management. It can also come up with crises. We may identify the key issues that may cause a crisis, yet it often takes longer to reach the actual event. What has been found with Hofstadter's Law is that while the median time to get a project done is usually within expectations, there are often very long tails beyond what is typically expected.

We know that uncertainty is high and impacts investment and consumer behavior. Still, the manifestation of this uncertainty may take months to show up in asset prices and actual data. Hence, identifying an event and seeing the impact of these expectations may not coincide. Being first when making a forecast is not the same as profiting from a forecast. 

See The planning fallacy and Hofstadter's Law

What is on risk radar for family offices?

What is most interesting about future risks is that they are often not countable events. They are events with little past precedent and are hard to handicap. They are subjective probabilities, not objective measures. A major geopolitical event will be adverse for risky assets, but what is the severity of this event? When we use the term 62% probability, does that mean that 62% of the respondents think there will be a geopolitical event, or does it mean there is a 60% chance of it happening? Sometimes, when we present numbers, we do not ask what they really mean. When we use small phrases we are not clear what that phrase could mean. For example, what is a food crisis?

We may love survey work. It provides a foundation for further discussion, but that does not always mean the survey generates clarity of the risks faced. 

Machine learinng and currency trading - still driven by momentum and carry

Traders are still dealing with the puzzle of currency prediction. For decades there has been a simple challenge and research conclusion. Most models cannot beat the random walk at predicting currency movements. The machine learning explosion allows traders to explore different methods for looking at the same data. In a new paper, "Machine Learning in Foreign Exchange", the author looks a number of prediction techniques including neural networks and tree-based models. It finds that a neural network approach outperforms linear and tree-based models and can do a good job of predicting cross-sectional excess returns. It can beat the random walk but the predictive power declines at longer-term horizons. 

A non-linear approach does much better than simple linear models; however, many avoid using ML techniques given their more difficult explainability. The author uses local interpretability techniques like DeepLIFT and Layer-wise Relevance Propagation (LRP) as well as Shapley values for global interpretability. What should not be surprising is that the key factors we always know as important for explaining currency returns still hold. Momentum and carry are still the most important factors for making currency predictions, yet  it is how and when they interact with currencies that matter. The non-linear influences dominate currency production. Use the same features but link them together in new ways.

Now, when looking at the set of models. It is not always clear why one approach does better than another. There clearly may be overfitting with some models. The feature importance also are complex. There are many carry features that can provide forecast value which seems odd. Nevertheless, there is a lot of good work her which needs further testing to further support currency predictions.

Business and financial cycles are different but an important indicator

The idea of a countercyclical risk premium tied to consumption asset pricing models is the standard view in macrofinance, but there is also a growing view that there is a financial or credit cycle with booms peaking before macroeconomic real behavior. Equity and factor premium are tied to credit availability and balance sheet constraints. Hence, investors should not just look at business cycles, which are intermittent, but also at the financial environment to capture poor financial environments. See the paper "Financial and Business Cycle Risk Premia". 

Using a Markov switching model, the author identifies financial and business cycle regimes. Recession states suggest that there will be higher equity excess returns the following quarter, but this equity premium will be even greater if it is consistent with a financial crisis. This impact is even greater if the poor regimes are matched with deteriorating macro conditions. There will be higher unconditional equity returns if there are favorable financial conditions. 

Regardless of the model being used, knowing where you are positioned relative to the business and financial cycles is critical. While the extremes in the business and financial cycle are not frequent, tracking these regime changes will have an appreciable impact on asset allocation.

A pivot to Europe requires an understanding of history

 

The uncertainty concerning US policy, as well as the hegemony of China, at odds with Western thinking, means there is a new interest and pivot to Europe. We are seeing the pivot to Europe in the stock returns for the year. This move is at the beginning of a change, but worth following closely.

One of the best books on the topic is Rethinking Europe's Future by David Calleo. It is not a book of Europe's future but a history of its past, as it tells how Europe got to its current state of a partial European state. While many consider Europe a third alternative to the current bipolar hegemony, it is still far from a finished political product. Europe is a state of mind, an ideal, but still not a reality. There is monetary policy integration and movement to some form of political and economic union. However, the idea of nation-states still burns in the heart of most Europeans. Citizens still identify as their country of origin and not as "European". The transition will still take time, and as of yet, has not ensured success. Europe is a work in progress, which means that economies of scale, common regulation, universal financing, and speaking with one voice for international affairs are still in the distant future. 

We can be excited by a Europe of strong investment opportunities, but the chance to outshine the US equity markets will still take time. Investing in Europe is a good tactical trade, yet it may not yet be a strategic re-weight.