Illiquidity premium and asset allocation - Mistakes in management even when paid a premium

If you have an asset that has an illiquidity premium, an optimizer will love it as a choice. An illiquidity premium is a dangerous area for investing. First, are you getting paid enough for illiquid? Second, is there a good way to measure illiquidity? Third, do you really know your liquidity needs?

There are two assets, one liquid and the other illiquid, for consideration. The illiquid asset has a slightly higher yield, a premium for less liquidity. An optimizer will choose the illiquid investment, all else being equal. The illiquid asset will have a smoother volatility. The optimizer will again prefer it versus a liquid alternative. An illiquid investment will not react as quickly to new information and will be less correlated to other assets. The optimizer will again prefer it. An optimizer will love an illiquid asset, and that is a problem.

Now, we have been extreme with our view of a two assets, but the point should be clear. There will be allocation distortions if you do not properly account for illiquidity.  Liquidity will never be available when you want it. Illiquidity will never improve from what was sold to you.

So how do you address the illiquidity problem? There are two simple solutions. First, rough-up the data by using a volatility that may be closer to the volatility of the asset class associated with the illiquid asset. This will downgrade the smoothness of the illiquid asset and will also increase the correlation with other assets in the asset class. Second, cut the returns that will represent the risk premia associated with liquidity. An optimizer does not think about liquidity. An investor should account for these factors.

What is PMI telling us about the stock/bond mix?

A big problem with macro fundamental investing is getting timely data on the economy and then translating that information to effective investment signals. Government issued data generally are out of date and old information for forward looking forecasts. Hence, there is greater value on macro data that is current and prospective. 

The PMI forecasts, which are announced monthly, are a good macro candidate given they are measured across a broad number of countries, have significant history, and are forward-looking expectations of economic activity. 

A graphically analysis of PMI explains the major sell-off in European equities relative to the US. It can also explain the decline in EM equity valuation. Similarly the PMI forecasts can tell us something about the broad trends in bonds. If the PMI is declining (increasing), there should be a bond rally (decline). Rates fall during declines economic activity. Investors just have to get an early signal. The current reading suggest that any switch away form equities should focus on bonds not cash. 

Categorization and classification - Fundamental to finance and investing

"Categorization is not a matter to be taken lightly. There is nothing more basic than categorization to our thought, perception, action, and speech. Every time we see something as a kind of thing... we are categorizing."   
- linguist George Lakoff
from The Geometry of Wealth by Brian Portnoy

Most investment work is about forming categories. We divide securities into asset classes. We make subcategories within an asset classes. We make industry classifications. We divide risks into different types of premia. There are value classifications. There are categories and classifications based on macro factors like inflation. Investors like to group. All scientists like to make groups and form clusters of similar things to find commonality.

The basics of science have always been about categorization. Whether animals, plants, or rocks, the basic work of observation and classification has been a core principle of observational science. Before there is theory, there is observation and the observation lends itself to systems of classification. Before we can offer explanation, there is a need for categorization and search for sameness and differences. Providing taxonomies helps us understand the world around us. 

Categorization is part of the narrative of science and a normal part of the storytelling of finance. The categorization is a heuristic for finding or describing correlation and similarity. The theory of finance attempts to look beyond classifications or categorization and in order to find first principles of what is an asset separate from any naming convention.

There can be a distinction between categorization and classification because most things do not fit into simple boxes. Classification is more formal and systematic based on rules. Categorization uses boundaries for similarities. See Elin K Jacob "Classification and Categorization: A Difference That Makes A Difference". The structure of categories and classification are not often discussed and just assumed, yet the choices are important.

By grouping stuff, we can find commonality and outliers that may suggest opportunity. The convention of what is a stock or bond or the distinction of what is an industry matters. The classification of an asset, which at times is seemingly arbitrary, has structural implications. Whether rated investment grade or high yield or whether included in an index or excluded, the category placement will have real return impact. How a manager is classified by the Morningstar service, or even how a hedge fund identifies itself matters for relative performance. 

So when building a portfolio and discussing allocation decisions across asset class, managers or risk premia styles, think about the implication of your categorization. The breakdown of asset classes or subclasses will impact investment decisions and return.

Equity risk allocation - No change in exposure reduction view

It not a matter of like or dislike the fundamentals of equities in the current environment. When sentiment changes and volatility increases, reassessment of current exposures is warranted. However, concern about the macro environment should be increasing. Maintaining lower market risk exposure by more than half of core allocation from 60% to 30% or half equity beta exposure is appropriate. (The darker red signifies a stronger trend.)

Growth - While recession risks are still limited by any probability-based model, economic growth will be tempered in 2019 both in the US and rest of world. Earnings have not yet been significantly affected by growth, but forward expectations are now slightly biased downward.

Liquidity - Continued Fed tightening and expectations of tightening around the rest of the world serve as a negative for fixed income. High rates are starting to impact higher levered firms and lending. Make no mistake this is what central banks want. 

Risk Appetite - Higher volatility with changes in sentiment suggest market is moving to risk-off environment. Financial condition trends are pointed lower. With risk-off, harder to buy on dips so more downside follow-through.

Structural - Gridlock in government will negatively affect further tax reform and regulatory changes. Fiscal deficit is now pro-cyclical which will further affect rates.

Technical - There have been some key periods of divergence between equity style sectors. International and EM have actually been a place to hide in the near-term 

Looking over year-to-date, six-month, and three-month returns shows three distinct differences: The divergence between US and rest of world, the dislocation between large-cap and value, growth, and small cap equities, and the reversal of the earlier international underperformance.