Fed Up - a reread on the politics of the Fed

 

Is the Fed political? Yes, to the extent that it is driven by its own self-interest and preservation. It is political in that there is significant inbreeding of thinking and culture. This is especially true of research. All well-trained economists who often think alike and are focused on publishing research on their chosen topics, rather than on driving better decision-making by the Fed. It is not that they do not want good policy. It is the fact that they cannot think of how to do policy that veers from the status quo. One could say that QE and zero-interest rates did not establish the status quo, but they were a solution that fit the macroeconomic narrative at the time. 

I reread the Book Fed Up to hear one insider’s view of the Fed. This is not written by the Chairman or a leading economist but by a Fed bank advisor. It was a little long and could have been more directed in its arguments. Still, anyone who reads this will appreciate that having well over 1,000 economists and multiple regional district banks is crazy and does not serve the country’s good or its policies. 

I am not arguing for lower rates, but I will argue that the Fed could use a good shake-up. 

Signal filtering for mean reversion trading

 

If you aren't a trend follower within the quant price space universe, then you are a mean reverter. A paper discusses how to filter these reversion signals, "Advanced signal filtering for mean reversion trading." Mean reversion is based on the simple concept that an asste's price will converge to some fair value. This fair value could be as simple as a moving average. The spot price may fall above or below this fair value price. To solve this problem, the authors develop what they call the local average filtering objective (LAFO), a low-pass filter that operates across different frequencies. LAFO examines the average residuals over a moving window to capture moving-average characteristics. This information can be used to measure or describe mean reversion. LAFO is an extension of the mean squared error. Machine learning can help process data to identify the method of reversion to the mean and mispricing in a time series. 

Can there still be dislocations that will cause mean-reversion not to occur? Yes, but by examining different rolling windows of residuals, there is a good chance of finding revision opportunities.  

The Investor, The Chef, and The Recipe Book

 

I have once again discussed with friends what is proprietary regarding strategy details. Should a manager give up details on how they model and generate returns? I understand that in a competitive market, no one wants to give up their edge or reveal how they generate returns. However, investors still need to understand how returns are derived because backtests and past track records offer only limited insight into the return-generating process. There needs to be a framework for understanding how returns are generated. 

I often use the analogy of a cookbook. There are many great cookbooks available in a bookstore. Great chefs write cookbooks and share their “secrets” in their recipes, often with detailed instructions for preparing the meal. Measurements are given. Ingredients are detailed, and step-by-step instructions are often provided for preparing the meal at home. You may follow all the instructions, yet you will often not get anything close to what you experience in the restaurant. You are frustrated and feel the effort was not worth it. So, what do you do? You go to the fine restaurant and pay the price. You are happy and appreciate all of the work that the chef has done to prepare the meal. All are pleased. Information has been provided with high transparency, resulting in a willingness to pay the price for a great meal. There is transparency, which makes paying the meal’s price more worthwhile.

The core of this story is that there is knowledge from books and theory, and then there is practical knowledge from doing. When you invest with a special strategy, the manager is providing their specialized practical knowledge that cannot be easily learned. The devil is in the details, and that is often the secret sauce. I appreciate the practical wisdom on how to gather the right data, manipulate it, and use specialized techniques to extract signals. That is proprietary, but the overall concept should be discussed. Practical knowledge is contextual and situation-specific, and not easily learned just by being told how something is done.

Unified framework for anomalies - all in the past month of return behavior

 

The daily return information factor (DRIF) is a new concept that helps explain many of the anomalies we see in financial markets. Instead of imposing or seeking a new risk premium, the authors of this paper, “A unified framework for anomalies based on daily returns”, examine the overall mapping of returns over the last month to make predictions about next month’s returns. The authors examine both the time ordering and the magnitude of returns to develop a forecasting framework.

A chronological vector preserves time ordering and captures short-term reversal dynamics, while a ranked vector accounts for magnitude effects. The DRI variable will combine these two vectors so that next month's returns are based on the beta of the time and magnitude vectors. A chronology dimension captures price pressure and liquidity effects, while ranking reflects investors' focus on extreme outcomes. These effects remain after controlling for other risk factors.