Academic Research

Our team has years of both academic and industry experience and we have developed extensive research that has lead us to develop our proprietary custom portfolios in order to maximize returns.*

Market Insights

The regression analysis displayed to the right is an example of, what we believe, is the Achilles’ heel of a capitalization-weighted index.  The Y-axis, or vertical line of the chart, is the 10-year forward-looking return of the S&P 500. The X-axis, or horizontal line, is the price-to-sales ratio of the S&P 500 over multiple periods going back to January 1985.  Simply put, as the S&P 500 increases due to investor optimism, its price-to-sales ratio also increases while the forward 10-year expected return goes down. If the price-to-sales ratio remains low, the forward 10-year expected return goes up.

We believe that by aligning systematic exposure by price-to-sales ratios, an investor creates optimal exposure, particularly in the current uncertain market.  Based on our research, we believe that investors who avoid portfolios with higher price-to-sales ratios, increase their probability of higher forward-looking returns.

The current S&P 500 is an example of a higher price-to-sales portfolio.  Based on the chart, there is a high probability of lower forward-looking returns for the capitalization-weighted index. We designed our multi-factor price-to-sales ratio management strategy to manage within a corridor of normal price-to-sales ratios.  Based on our research, the result is a higher forward-looking return expectation for our client’s equity portfolios while maintaining efficient exposure to the entire U.S. equity market.

We believe proper index portfolio analysis provides guidance into factors contributing to the average return of the index.  We believe systemically positioning the portfolio using factors that provide returns above the average return of the broader market is key.  As indicated through the Fama-French Three Factor Model, value quality stocks and smaller sized stocks can provide above average returns over time.  Additionally, our findings shows that factors such as momentum and low volatility are meaningful in affecting returns. Global Beta Advisors portfolio team has more than 50 years of combined experience developing investment strategies for investors.  The Global Beta Advisors team, in conjunction with S&P analysts, has developed several factor portfolios to that seeks to provide complete beta exposure to the US equity market that exposes client portfolios to factors that seek lower valuations than historical capitalization indexes.

Data sourced from Factset

The above chart is a monthly series of price to sales data points of the S&P 500 from January 1985 through December 2017 and the corresponding 10 year forward return from each data point to display a linear correlation between price to sales and forward looking returns. Please see important disclosures.

FAANG Analysis (03/31/2019)

Facebook Price to Sales Hypothetical Chart

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["FB P\/S: 14.16: {y}","FB P\/S: 10.12: {y}","FB P\/S: 7.23: {y}"]
  • The blue line represents a hypothetical of Facebook’s price to sales multiple that is 40% higher than the current price to sales multiple that is represented by the red line.
  • The red line represents a hypothetical of Facebook’s current price to sales as of the end of the prior month.
  • The green line represents a hypothetical of Facebook’s price to sales multiple that is 40% lower than the current price to sales multiple that is represented by the red line.

Data sourced from Factset  Please see important disclosures.

Amazon P/S Hypothetical Chart

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["AMZN P\/S: 5.79: {y}","AMZN P\/S: 4.14: {y}","AMZN P\/S: 2.95: {y}"]
  • The blue line represents a hypothetical of Amazon’s price to sales multiple that is 40% higher than the current price to sales multiple that is represented by the red line.
  • The red line represents a hypothetical of Amazon’s current price to sales as of the end of the prior month.
  • The green line represents a hypothetical of Amazon’s price to sales multiple that is 40% lower than the current price to sales multiple that is represented by the red line.

Data sourced from Factset  Please see important disclosures.

Netflix Price to Sales Hypothetical Chart

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["NFLX P\/S: 14.82: {y}","NFLX P\/S: 10.59: {y}","NFLX P\/S: 7.56: {y}"]
  • The blue line represents a hypothetical of Netflix’s price to sales multiple that is 40% higher than the current price to sales multiple that is represented by the red line.
  • The red line represents a hypothetical of Netflix’s current price to sales as of the end of the prior month.
  • The green line represents a hypothetical of Netflix’s price to sales multiple that is 40% lower than the current price to sales multiple that is represented by the red line.

Data sourced from Factset  Please see important disclosures.

Apple P/S Hypothetical Chart

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["APPL P\/S: 5.12: {y}","APPL P\/S: 3.66: {y}","APPL P\/S: 2.61: {y}"]
  • The blue line represents a hypothetical of Apple’s price to sales multiple that is 40% higher than the current price to sales multiple that is represented by the red line.
  • The red line represents a hypothetical of Apple’s current price to sales as of the end of the prior month.
  • The green line represents a hypothetical of Apple’s price to sales multiple that is 40% lower than the current price to sales multiple that is represented by the red line.

Data sourced from Factset  Please see important disclosures.

Alphabet P/S Hypothetical Chart

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["GOOGL P\/S: 8.62: {y}","GOOGL P\/S: 6.16: {y}","GOOGL P\/S: 4.40: {y}"]
  • The blue line represents a hypothetical of Alphabet’s price to sales multiple that is 40% higher than the current price to sales multiple that is represented by the red line.
  • The red line represents a hypothetical of Alphabet’s current price to sales as of the end of the prior month.
  • The green line represents a hypothetical of Alphabet’s price to sales multiple that is 40% lower than the current price to sales multiple that is represented by the red line.

Data sourced from Factset  Please see important disclosures.

The Achilles’ Heel

Modern Portfolio Theory has taught us the value of diversifying portfolios.  The concept of diversification is to assemble a portfolio that captures both the average return of the broad market as well as capturing the average volatility of the market.

By way of example, many market participants use the S&P 500 index as a proxy for both the average return and the average volatility of the market.  Therefore, when investors seek equity returns, they are advised by many investment advisors to seek low cost equity strategies with a “beta” that is close to 1, relative to the equity market.  As a result, the S&P 500 index is generally the benchmark chosen to measure “beta” against.  We believe this tautology provides an unstable foundation of misunderstood market theory.

Most of the broad indexes utilize a capitalization methodology
This leads to factor distortions caused by investor sentiment
A too optimistic or too pessimistic sentiment may result in miss-priced share prices

During the past 10 years, the unparalleled access to long term capital market data, as well as processing this data, has uncovered long existing capital market subsets that provide greater than average returns of the equity market, as measured by traditional equity market indexes such as the S&P 500.

Through our research, the Global Beta team believes to have identified untapped benefits by fine tuning broad indexes, which is the foundation of Global Beta’s portfolio construction process. Academic and capital market participants, Gene Fama and Ken French, have utilized their access to historical capital markets data.  We have reviewed this research and discovered subsets to the broad market.  These subsets are generally classified as: value, momentum, low volatility, profitability, investment and size (lower capitalization).  Their research set forth a five-factor model which describes all the above factors, except momentum.  We believe that value, quality (also seen as profitability), momentum, growth, low volatility/beta and size are the most imperative factors that drive equity market return.  Additionally, some participants are able to segment into even smaller subsets of those factors, such as earnings yield and dividend yield.  We believe continuous research will provide more valuable data concerning the capital markets.

Global Beta develops our own research through our vast network of data providers.  Our research focuses on the false ability of the research being presented as new investment concepts.  We then create portfolios that we believe incorporate the most current and unfalsifiable data.  We believe our clients will benefit from our extensive experience of actually constructing global portfolios for more than 35 years.