The microeconomics of leverage on aggregate demand

One of the best written books on the impact of leverage and debt on aggregate demand. There is a lot of talk on the wealth effect, income inequality, balance sheet recession, and consumer behavior, but this one book touches all of these issues in a thoughtful manner. The authors do a great job of writing about leverage and wealth effects on consumers with clarity. They also effectively use disaggregated data across regions and income groups to provide a persuasive case for their arguments. They show the fallacies of looking at aggregate data to make generalization about the state of the macro economy. 

Reading this book will have a profound impact on how you look at the ravages of leverage on marginal consumers who cannot afford a balance sheet shock.

Labor scoreboard - add the numbers

If we use a simple labor scoreboard, we can see why the Fed is being careful but sketchy on what it wants to do with policy. This scoreboard  of key variables is based on work presented in Institutional Investor. It gives a good simple picture of the labor market. All the measures have improved over the last two years, but they may not be above pre-recession levels. If we give a (+/-), the levels are close to the pre-recession levels.

The problem is with labor confidence and utilization which are more forward looking measures. How these are weighed by the Fed is unclear, but if you want a clear sign for when rates will be raised look to the battery of labor stats.  If there are some permanent changes to the labor market, the Fed may not have the ability to get to the magical pre-recession levels. This could be the real problem.

Labor market: (+) better than pre-recession peak; (-) worse than pre-recession peak

Leading indicators
1. Temporary help employment; +
2. Companies unable to fill job openings; +
3. Initial claims: +

Employer behavior 
1. Payroll employment: +
2. Job openings; +
3. Hires: +/-

Confidence
1. Hiring plans; +
2. Job availability; +/-
3 Quits: +/-

Utilization
1. Unemployment; -
2. Marginally attached workers; -
3. Job finding rate: -
4. Work part-time for economic reasons; -

Complexity and reductionism in finance

The love of complexity without reductionism makes art. The love of complexity with reductionism, makes science.  - E.O. Wilson Consilience 

Wilson makes a beautiful observation about complexity and reductionism. The world is filled with complexities and seemingly little connection. Celebrating this complexity gives us the uniqueness found in any art. Looking at complexity and being able to find commonality is the process of science. Science is the process of taking unique cases and observations and generalizing to find patterns.

Finance and economics attempts to find the primal parts in market complexity and reduce it to as simple terms as possible. This process of reductionism is the essence of good modeling. A good model should not focus on complexity but look for ways to find commonality and reduce the moving parts to as few as possible.

Portfolio decision pyramid -

The portfolio structure employed by a manager can be based on the amount of information available to the manager and his skill. If you are at the top of this pyramid and you have an information edge, then you should use it and try to maximize the Sharpe ratio of the portfolio. This means you have some idea what expected returns will be for assets included in the portfolio. If you cannot determine the direction of the market, but you can forecast or tell something about volatility and correlation between markets, then a risk parity approach or minimum variance approach may make sense. You can use the covariance matrix to help with allocation decisions. If you do not have have any idea what the correlations across markets may look like, you may want to only focus on volatility through a inverse volatility rule. This states that the correlations are all the same across assets. If you have no idea what returns or risk may look like, the best approach is to use an equal weighting strategy. Of course, if you equally weight assets from the same asset class, you will have a problem. The 1/n rule states that you have no information advantage across any part of the returns distribution.

This is a good simple way to think about portfolio construction and information advantage. Start with no knowledge and work your way up to the strong knowledge on risk and return place if you have an edge. Often times managers start at the Sharpe end of the pyramid when they belong at the equal allocation end.