Tactical Asset Allocation Research Archives

The Robust Asset Allocation (RAA) Index

Robust asset allocation solutions should be relatively simple, minimize complexity, and be robust across different market regimes. Simultaneous to these requirements, the solution must be affordable, liquid, simple, tax-efficient, and transparent, otherwise, many of the benefits of the solution will flow to the croupiers and Uncle Sam. We recommend that investors explore our robust asset allocation framework and go for the do-it-yourself solution. You'll be paying yourself 1%+ a year via saved RIA fees. Is this the only solution? No. But any solution must be robust, simple, tax-manageable, and low-cost. This is our best effort to develop a simple model. Developing a complicated model is easy; simple is difficult.

Face-ripping Drawdowns and the Magic Formula

Due to a surge of interest from our "Long Cheap; Short Expensive. Buyer Beware" post, we decided to analyze the impact of changing the net exposure of the Magic [...]

Harry Markowitz: An Equal-Weight Investor?

Jason Zweig's book, "Your Money and Your Brain" highlights an interesting conversation with Harry Markowitz. Dr. Markowitz is a Nobel Prize winner and his work [...]

Morningstar 2014 ETF Conference Slides

Many of you have asked for the slides I presented at the ETF conference on tactical asset allocation. Note, the 2nd slide is an animation [...]

Tactical Asset Allocation: Are Uber-Simple Systems Robust?

Simple and Effective Market Timing with Tactical Asset Allocation Lewis A. Glenn A version of the paper can be found here. Want a summary of [...]

Flexible Asset Allocation: Dethroning Moving Average Rules?

Strategy Summary The flexible asset allocation strategy was first proposed by Keller and Putten (2012), in their paper "Generalized Momentum and Flexible Asset Allocation (FAA): An [...]

Valuation Spreads Over Time: A Unique Market Timing Signal?

The blogosphere is spammed with commentary related to the current high market valuations and the inevitable crash that "must" ensue. We've even been involved in [...]

How many stocks should you own? The costs and benefits of Diversification

In this post we explore the trade-off between diversification and alpha generation. Here is a high level summary of the situation: Owning more stocks in a portfolio lowers "idiosyncratic" risk, or risk that can be eliminated through diversification...however...Owning more stocks dilutes performance of an "alpha" generating process. (e.g., forcing Warren Buffett to hold a 500 stock equal-weighted portfolio would dampen his alpha). In summary, fewer stocks in a portfolio imply more expected alpha and more idiosyncratic risk; more stocks in a portfolio imply less expected alpha and less idiosyncratic risk. But what is the optimal trade-off between alpha and idiosyncratic risk? Do we want to own a 1 stock portfolio? A 50 stock portfolio? A 1000 stock portfolio?

A Surprising Way to time Value and Momentum: Updated Analysis

Exploiting Factor Autocorrelation to Improve Risk Adjusted Returns K Oversby A version of the paper can be found here. Want a summary of academic papers with [...]

Tactical Asset Allocation During Cheap Markets

In our last post, we looked at tactical allocation using valuation metrics and trend-following measures. Our conclusion from the analysis is that discerning robust trading [...]

Old School Academics on Moving Average Rules: Remarkable.

Simple Technical Trading Rules and the Stochastic Properties of Stock Returns Brock, Lakonishok, and LeBaron A version of the paper can be found here. Want a [...]

Using Utilities to Time the Market

Strategy Background Beta Rotation strategy (BRS) is discussed by Charles Bilello and Michael Gayed in their new paper, “An International Approach to Beta Rotation: The [...]

A Simulation Study on Simple Moving Average Rules

The mention of technical analysis in the halls of academia can cause serious angst. The disdain for technical analysis likely stems from a firm belief that [...]

Timing the Market with Mean Reversion Indicators

Mean Reversion, Momentum and Return Predictability Huang, Jiang, Tu, Zhou A version of the paper can be found here. Want a summary of academic papers with [...]

How to Build Expected Return Forecasting Models

Investors are enamored with various investment houses and personalities who claim insight into the prospects for long-term expected market returns. Some classic examples include Nouriel Roubini, John Hussman, David Rosenberg, or Jeremy Grantham. All really smart people. But have you ever asked "How" these folks came to their conclusions? In most cases, the answer is probably "No" and the reason is because there is a lack of transparency from the author(s) and/or a lack of knowledge/understanding on behalf of the reader. We also want to highlight that one can develop incredibly complex return forecasting models -- super sexy, super interesting, super compelling, etc. -- but that still doesn't mean they are any good at forecasting much of anything.

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