Using Multiple Value Metrics Simultaneously To Beat The Market Over Time - Introducing The Value Blend Strategy

by: Ryan Telford


James P. O’Shaughnessy wrote about the value composite strategy and found it to beat the market over time.

The Value Blend strategy is a combination of 8 separate value factors inspired by O’Shaughnessy that has also beaten the market over time.

We look at the performance over time and several risk metrics of the Value Blend strategy in different market cap universes.


In many early quantitative "buy and hold" investing strategies, one metric often defined the strategy. High dividend yield, the defining feature of the Dogs of the Dow strategy, is a good example. Of course you could use the low P/E ratio, low EV/EBIT or several other low price to fundamental metric ratios as a single factor in a strategy.

Other more recent strategies may include multiple metrics considered simultaneously. Joel Greenblatt's Magic Formula ranks firms for both quality and cheapness; there is also the Piotroski score, which ranks stocks based on 5 different fundamental value factors (stay tuned for a future article).

In his book, "What Works on Wall Street", James O'Shaughnessy profiles many different quantitative single factor investing strategies, and how they performed over time.

He also included a strategy that he referred to as a Value Composite, or a combination of different value factors. The idea was that for a stock to pass the screen, it needed to pass several low value metrics. For example, instead of just having a low price to earnings (P/E) ratio, it would also need to have a low price to book (P/B) ratio, low price to sales (P/S), among others.

O'Shaughnessy developed three versions of his Value Composite. The strategy referred to as Value Composite 2 is based on the following six (6) value factors:

  • Price to Book, P/B
  • Price to Earnings, P/E
  • Price to Sales, P/S
  • Enterprise Multiple, EV/EBITDA
  • Price to Free Cash Flow,
  • P/FCF Shareholder Yield, Dividend Yield plus Buyback Yield

(Source: What Works on Wall Street, 4th edition)

Most investors should be familiar with the first three factors, P/B, P/E and P/S. When these values are low, it suggests that the investor is purchasing the book value (assets), earnings, or revenue (sales) at a good price respectively.

The Enterprise Multiple is a refined version of the P/E ratio, which I discuss at length in my low EV/EBIT strategy profile. Like any value metric, a low EV/EBITDA may suggest that a stock is positioned for a gain in the future.

Free cash flow is a favorite metric of many investors, Warren Buffett included. While buying stocks at low price to book value, revenue or earnings can be a helpful value indicator, some may argue that they are only accounting values and may not truly reflect a firm's value to an investor. Free cash flow is cold hard cash that the firm has available; some investors are of the opinion that FCF is the only real indicator of the value of a firm. The lower the price to FCF, P/FCF, the cheaper an investor is paying for the free cash flows of the firm. FCF is also used in the Discounted Cash Flow analysis, which is a popular tool to value a company.

Finally, there is Shareholder yield. Most investors should be familiar with dividend yield. This is of course the return from dividends paid by the stock. Historically, higher dividend yields have been shown to be a potential indicator of an undervalued stock (as is the basis for the Dogs of the Dow strategy). In this situation, not only is the stock potentially undervalued, but the investor receives an automatic return from the dividend payment.

When firms have excess cash, they have several options. They can reinvest the cash in the company to earn a return, or pay out a dividend to investors. They can also use the cash to buy some of its own shares and take them off the market. This essentially reduces the number of shares outstanding, and increases the value per share for investors. As an example, assume a firm has a current market cap of $1B, and 100 million shares outstanding, resulting in each share's worth of $10 ($1B divided by 100 million shares). Should the firm decide to buy back 10 million of its own shares, the total number of shares outstanding would reduce to 90 million, and the share value would increase to $11.10 ($1B divided by 90 million shares) presuming the market cap remains the same. To an investor, this signifies a buyback yield of 11.1%.

Shareholder yield includes both the dividend yield and the buyback yield.

On their own, each of these six metrics may signify an undervalued stock ready for a gain sometime in the future. On the other hand, the stock may be cheap for a reason and could end up being the dreaded "value trap". The idea behind combining value factors is that if one, or two of the factors do not end up finding a winner, then the remaining factors may, or at the very least reduce portfolio volatility.

Each stock in the testing universe receives a score on each metric, which is composited into an overall score, and the top 30 stocks in the universe are selected.

Introducing the Value Blend Strategy

Inspired by O'Shaughnessy's methodology, I have developed my own composite value strategy, which I refer to the Value Blend strategy. Eight (8) different value factors are combined to give a stock a score, and the top stocks with the highest rank pass the screen for the given universe.

Buy and Hold

Similar to the Magic Formula and the Low EV/EBIT strategies, the Value Blend strategy is an equal weighted "buy and hold" strategy. The 30 stocks that pass the screen are purchased in equal dollar amounts. For example, for a portfolio value of $30,000, each position would be worth $1,000. It stock 'A' appears on the screen selling for $1 per share, then 1000 shares would be purchased. If stock 'B' is selling for $5 per share, then 200 shares would be purchased, and so on.

After one year, the portfolio is rebalanced with new stocks passing the screen. The objective again is to maintain equal weighted positions at the beginning of each rebalance period. Stocks no longer on the screen are sold, and new ones are purchased. Positions in stocks remaining on the screen from the previous year are adjusted to achieve equal weighting. Supposing in the first year the $30,000 portfolio earns 20%, then each holding would be $3,600 (less brokerage fees) for the next period.

Brokerage Fees & Taxes

Many investors new to quantitative buy and hold investing often ask about the impact of brokerage fees on returns. For investors who have little annual turnover in their portfolio(s), the buy and hold annual turnover range of anywhere from 25% to as high as 100% (the latter of which is rare) may seem high. I wrote about the impact of brokerage fees in buy and hold investing here. In summary, with brokerage fees at historically low levels, for portfolios worth at least $30,000 these fees have a minor impact on returns. Also bear in mind that all performance backtesting reported below also takes into account the costs of transactions, i.e. performance reported is less transaction fees.

In terms of taxes, each investor's tax situation is different, both in terms of tax brackets and in which types of accounts (tax free or taxable) they are invested in. For any tax implications, be sure to speak to your tax professional to assess any impacts.


O'Shaughnessy also found that a value strategy with a momentum component helped performance through additional return, and/or reduced volatility. I have also found this to be true in my own research; a future article on this will follow in the future.

Historical Performance

Let's take a look at the historical performance of the Value Blend strategy.

I have covered quantitative investing profiles in previous articles. For a background on backtesting and the various factors involved, I invite you to read my primer series on backtesting.

Part I, Constructing a Robust Backtest

Part II, Interpreting the Data

Part III, Universes

Part IV, Frictional Costs, Hedging & Benchmarks

The Universes

For the US version of this strategy, three (3) different universes are considered:

  • Large Cap stocks (largest 20% of the market by market cap)
  • Medium and Large Cap stocks (largest 50% of the market by market cap)
  • Small, Medium and Large (largest 75% of the market)

For all universes, over the counter stocks (OTC) are excluded.

The Large Cap Universe

The table below provides summary statistics on the Value Blend strategy in the Large Cap Universe. Data is from Jan. 1999 to Dec. 2015.

(Source: data, Author Calculations & Table)

This strategy has historically achieved returns in excess of 17% (arithmetic).

Volatility, or standard deviation, is just above 20%. What defines a "typical" volatility value depends on each investor's tolerance for volatility; generally higher return buy and hold strategies over one year have volatility periods in excess of 20%. Both Sharpe and Sortino ratios are relatively high, however we are taking on more volatility.

Note how the average maximum drawdown is less than the benchmark R1000TR. This could be attributed to the different value factors at work to minimize the negative impacts in downturns.

While average results over the entire period are helpful, let's take a look at the results over rolling periods.

Base Rates

The odds of beating its benchmark over all given rolling periods over the testing period is known as a base rate (for a primer read here). Let's check out the base rates for the LC Value Blend Strategy.

(Source: data, Author Table and Calculations)

These base rates are good; an investor would have had a nearly 88% chance of beating the benchmark over any 10 year period since 1999. This is an important consideration when working with longer time horizons.

Over shorter periods, the strategy was less able to beat the benchmark as often as it did over longer periods. That said, when the strategy did beat the benchmark, it did so by an average of at least 19.6% for any of the periods.

Min & Max Periods

Let's take a look at the best and worst case scenarios for each duration of rolling period.

(Source: data, Author Table and Calculations)

Longer holding periods of 7 and 10 years would have had all positive returns even in the worst years, compared to the benchmark which all had negative returns in their minimum scenarios.

Of interest is the maximum 10 year return for both the strategy and the benchmark. The period ending date occurred within 2 months of each other, yet the absolute dollar value of the strategy after the period was nearly 3X greater than benchmark. This is a reminder of just how different quantitative strategies can behave from their benchmarks.

On the opposite end, while not so dramatic an example, an investor would have lost more in one year with the strategy ending Oct 2008 compared to the benchmark loss 2 months later (-44.5% vs. -41.6%).

5 Year CAGR Trend vs. Benchmark

Our strategy had a rough start over our period trailing the benchmark's rolling 5 year CAGR until the end of 2004 (which should not be a surprise, as many value investing strategies had very poor returns during the dot-com bubble). The strategy outperformed dramatically in early 2005, and slowly declined but remained in positive territory until the beginning of 2011 where it fell behind the benchmark. It then soon regained the lead and hit its peak in 2013, only to relax and descend again until today. As one of my themes on Seeking Alpha, no strategy stays high forever, nor remains low forever.

(Source: data, Author Graph)

Stocks Passing the Screen

As of Jan. 01 of this year, the following US stocks passed the Large Cap version of the Value Blend screen:



Price per share, 01 Jan 2017

Market Cap ($M)






Life & Health Insurance


Anthem Inc



Managed Health Care


Bed Bath & Beyond Inc.



Homefurnishing Retail


Best Buy Co Inc



Computer & Electronics Retail


China Petroleum & Chemical Corp Sinopec



Integrated Oil & Gas


China Southern Airlines Co Ltd








Communications Equipment


Everest Re Group Ltd





Fiat Chrysler Automobiles NV



Automobile Manufacturers


First American Financial Corp



Property & Casualty Insurance


Ford Motor Co



Automobile Manufacturers


Gap Inc



Apparel Retail


General Motors Co



Automobile Manufacturers


Kohl's Corp



Department Stores


Korea Electric Power Corp



Electric Utilities


Lincoln National Corp



Life & Health Insurance


Metlife Inc.



Life & Health Insurance


Old Republic International Corp



Property & Casualty Insurance







Principal Financial Group Inc.



Life & Health Insurance


Prudential Financial Inc



Life & Health Insurance


Reinsurance Group of America Inc.





Sinopec Shanghai Petrochemical Co Ltd



Commodity Chemicals


SK Telecom Co Ltd



Wireless Telecommunication Services


Sun Life Financial Inc



Life & Health Insurance


Ternium SA





Unum Group



Life & Health Insurance


Valero Energy Corp



Oil & Gas Refining & Marketing


Woori Bank



Diversified Banks


Xerox Corp



Technology Hardware, Storage & Peripherals

(Source: data & Author table)

In any quant strategy, there can be occurrences of industry concentration. If the industry is on the up and up, the portfolio can benefit. On the flip side, if the given industry has been hit hard by a government regulation change or a commodity price slump, this can also drag down the portfolio performance. For this year, we shall see what the concentration in Life & Health Insurance does to the portfolio in the coming quarters.

Large & Mid Cap Universe

In some strategies, we find that returns can increase with smaller stocks (lower market capitalization). Whether or not the extra return is accompanied by extra volatility is the question. Let's see how the large and mid cap universe performed with the Value Blend.

Our testing universe is Medium and Large Cap stocks (largest 50% of the market by market cap).

Summary Statistics

(Source: data, Author Table and Calculations)

Return has improved in screening more stocks in our universe, suggesting that medium size stocks may be well suited for the value blend strategy. Both arithmetic and geometric averages have improved from just screening large cap stocks, with the arithmetic entering 20% annual return territory.

Volatility (standard deviation) has increased slightly from the Large Cap version (from 20.2 to 20.5%), however per unit return volatility has actually decreased. Recall that Sharpe and Sortino ratios are indications of how much return is achieved per unit of volatility (Sharpe is for positive and negative volatility, Sortino for downside volatility only). In this case both Sharpe and Sortino ratios have increased compared to the Large Cap version, from 0.67 to 0.68 and from 0.93 to 0.95 respectively).

Maximum drawdown in the strategy is slightly less than the Benchmark as well.

As with most strategies, there are seasonal variations in the performance depending on the time of year that one invests and re-balances. A seasonal standard deviation of 318% is about average.

Base Rates

Let's see how the strategy has performed during all rolling periods.

(Source: data, Author Table and Calculations)

As with many of quant value strategies, an investor's chances of beating the benchmark historically increases with longer holding periods. An investor had a 68.4% chance of beating the benchmark over any one year period, with a maximum of 86.7% chance of beating the benchmark over any 10 year period.

Base rates (odds of beating a benchmark over a given rolling period) vary slightly from the Large Cap version of the strategy; however when outperformance does occur, the investor would have enjoyed returns at least 24.5% greater than the benchmark for the respective period.

Minimum & Maximum Periods

Let's take a closer look at each of the periods to see how the strategy stood up.

(Source: data, Author Table and Calculations)

While minimum returns for 1, 3 and 5 years are less than for that of the benchmark, the maximum returns are also significantly higher than the corresponding benchmark maximums. This trend of maximum returns is the same for 7 and 10 year holding periods as well. Best case, an investor could have increased his/her initial investment of $10,000 by a factor of 10 in the period ending Jan 2010.

5 Year CAGR Trend vs. Benchmark

The 5 year CAGR in excess (or lagging) of the benchmark follows a similar trend to that of the Large Cap version of the strategy. After recovering after a rough patch in 2003 and 2004, the strategy peaked a couple of times in the following years, then gradually receding (but in positive territory) until 2010. 2012 and 2013 were very good years to end for the 5 year delta CAGR peaking at nearly 145%, then to drop off until the end of 2015.

(Source: data, Author Graph)

Largest 75% Market Cap Universe

Small cap stocks are an interesting animal. Some value strategies are well suited to them, while others not so much. Often more inefficiently priced than medium and large caps, there can be more potential returns. They can also change in price very quickly and potentially bring volatility to your portfolio.

The Largest 75% Universe includes Small, Medium and Large (largest 75% of the market). In screening out the bottom 25%, we attempt to weed out the low liquidity stocks (often associated with low market caps). Not that low liquidity stocks are to be avoided in absolute; there are several investing strategies that use low liquidity stocks to the investor's advantage. More on that in a future article.

Let's see how the Value Blend strategy has done in this universe.

Summary Statistics

(Source: data, Author Table and Calculations)

The first thing you will probably notice is the attractive returns. At nearly 25% arithmetic mean, this strategy has done quite well on one year average returns. While it has increased volatility from the Large and Large/Medium cap versions of the strategy, it achieved more return with less volatility. The Sharpe and Sortino ratios are the highest of the strategies, with the Sortino ratio in particular (measuring downside volatility) breaking the 1.0 point (recall the higher the better for these values). That said, at its worst drawdown an investor would have needed to stomach a nearly 70% decline, compared to nearly 56% of the R3000TR benchmark.

Base Rates

One year averages are helpful, but let's see how the strategy has beaten the benchmark over rolling periods.

(Source: data, Author Table and Calculations)

With the added volatility, the longer an investor can hold on the better. A base rate of 58% on one year returns is not that spectacular, however when it did beat the benchmark it did so by a significant margin of 38.1%. As with all strategies, the longer the period, base rates increase. At a holding period of 10 years an investor would have had nearly an 85% chance of beating the benchmark, and beating the margin by an average of 42.2%.

Max & Min Periods

Worst case, the strategy could have lost money in 1, 3, 5 or 7 year periods. With any 10 year period, all minimum returns were positive (although just barely at 1.8%).

Best case, these strategies have provided excellent returns in positive markets. The dollar value of a 5 year portfolio could have increased more than 10 fold; the maximum return for a 10 year period resulted in an increase of nearly 20 fold.

(Source: data, Author Table and Calculations)

5 Year CAGR Trend vs. Benchmark

The 5 year CAGR in excess (or lagging) of the benchmark follows a similar trend to that of the Large Cap version of the strategy. After recovering after a rough patch in 2003 and 2004, the strategy peaked a couple of times in the following years, then gradually receded (but in positive territory) until 2010. 2012 and 2013 were very good years to end for the 5 year CAGR peaking at nearly 145%, then to drop off until the end of 2015.

(Source: data, Author Graph)

This strategy follows a very similar trend line of 5 year CAGR difference from the delta to the Large and Large/Medium cap strategies, but with higher peaks and lower troughs.

To Conclude

The Value Blend strategy has performed with market beating returns over time since 1999, with varying degrees of volatility. While screening for multiple value factors, this strategy does not rely on a single metric. Returns are attractive and have increased with including smaller cap stocks, however volatility is also higher. That said, Sharpe and Sortino ratios are also relatively high for these strategies, so an investor would have achieved more return per unit of volatility.

If this strategy profile on the Value Blend piqued your interest in quantitative investing, check out my strategy profiles on the Magic Formula and the low EV/EBIT strategy.

Until next time, happy investing.

For more articles on quantitative investing, be sure to follow me on Seeking Alpha.

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

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