Cornerstone Growth – Strategy Introduction and Out of Sample Results

Last week, I wrote about the cornerstone value strategy from James O’Shaughnessy’s first edition of What Works on Wall Street, published in 1996. This week I’ll introduce the cornerstone growth quantitative investing strategy and show out of sample backtesting results, net of taxes and fees. The strategy buys the top 50 stocks (by 12-month price performance), rebalanced annually, that have the following characteristics:

  • 5 consecutive years of earnings growth
  • Price-to-sales ratios of less than 1.5
  • Have a market cap larger than $150 M (in 1996 $, about $235 M today), which he calls “All Stocks”

As of October 23, 2017, 3645 of 6084 stocks in the Compustat database (~60%) are large enough to be considered.

Cornerstone Growth vs Large Stocks, Nominal Returns (12/31/51 - 12/31/94)
Cornerstone Growth vs Large Stocks, Nominal Returns (12/31/51 – 12/31/94)

Backtesting results were promising (and significantly better than the cornerstone value strategy), although these numbers do not reflect transaction costs, taxes, or slippage.

The out of sample backtest below includes 0.25% slippage for all transactions and are shown for various starting capitals, trade fees and tax brackets (with a 1-year rebalance period, we can sell (winning stocks) on day 366 and take advantage of lower long-term capital gains tax rates). For losing stocks, you typically want to sell them on day 364 to be able to use them as short-term capital losses, which can be used to offset any short-term capital gains you have, which are taxed at a higher rate (therefore, offsetting them is advantageous; this is one method of tax loss harvesting). Although this is a good idea for implementation of this strategy, tax loss harvesting was not utilized in the backtest below.

The backtest is also composited weekly, meaning each year’s return is the average return of 52 different portfolios. So if the return for the strategy in 1999 was 10%, that’s the average of 52 portfolios — 1/1/1999 to 1/1/2000, 1/8/1999 to 1/8/2000, 1/15/1999 to 1/15/2000, etc.

The benchmark this time is the SPDR S&P 500 ETF SPY (which includes dividends so the returns are total returns). Returns are shown as excess returns over buying and holding SPY.

Cornerstone Growth vs SPY, Excess Total Returns (01/09/99 - 10/23/17)
Cornerstone Growth vs SPY, Excess Total Returns (01/09/99 – 10/23/17)

With no transaction costs, the amount of starting capital doesn’t affect the excess return. If you’re paying $4.95 or $6.95 per trade, you would have needed to start with at least $10,000 to outperform buying and holding SPY. If you’re paying $19.95 a trade, you’d have needed $15,000 to start depending on your tax bracket.

The top excess return shown (for $0 trades and 10/15% tax bracket) resulted in an excess return of 413%, turning $15,000 into an astonishing ~$113,000 over ~18 years instead of ~$42,000 for buying and holding SPY.

Backtesting also indicates a slightly smaller drawdown (~50%) for the cornerstone value strategy than SPY (~55%) over this time period.

Cornerstone Growth vs SPY, Nominal Returns & Risk Statistics (01/02/99 - 10/23/17)
Cornerstone Growth vs SPY, Nominal Returns & Risk Statistics (01/02/99 – 10/23/17)

Based on the weekly composited portfolios, this strategy performs better during down markets (when SPY is decreasing) than up markets.

Cornerstone Growth vs SPY, Weekly Composited Portfolio Nominal Returns (01/02/99 - 10/23/17)
Cornerstone Growth vs SPY, Weekly Composited Portfolio Nominal Returns (01/02/99 – 10/23/17)

So the key takeaways:

  • Free trades are awesome (like with Robinhood).
  • For the most part (with the exception of low starting capital/high transaction fees/high tax bracket), cornerstone growth outperformed buying and holding SPY out of sample (1999 to 2017), and significantly outperformed the cornerstone value strategy.

As of 10/23/17, the top 5 stocks for the cornerstone growth strategy (highest 12-month price performance meeting the criteria from above) are LGIH, SKX, THO, CDW, & CBZ.

This strategy screen is available to my group on Portfolio123, where you can customize it or implement it as is. If you just want the stock picks, you can subscribe on Patreon below.

Cornerstone Value – Strategy Introduction and Out of Sample Results

In the first edition of What Works on Wall Street, published in 1996, James O’Shaughnessy outlines the framework for the cornerstone value quantitative investing strategy. The strategy buys the top 50 yielding stocks, rebalanced annually, among a universe of stocks he calls “market leaders.” (Although dividend yield is no longer a good value indicator for stocks).

Market leaders are defined by the following:

  • They’re large (their market cap is greater than the universe average). These stocks are also called “Large Stocks”, which he used as his benchmark for this strategy.
  • They have more common shares outstanding than the universe average.
  • They have cash flows that exceed the universe average.
  • They have sales that are >=1.5x the universe average.
  • They are not a utility.

His universe was simply the Compustat database (the same used by Portfolio123). As of October 10, 2017, 441 of 6088 stocks in the database (~7%) are “market leaders.”

From the 1st Edition of What Works on Wall Street
Cornerstone Value vs Large Stocks, Nominal Returns (12/31/51 – 12/31/94)

Backtesting results were promising, although these numbers do not reflect transaction costs, taxes, or slippage.

The out of sample backtest below includes 0.25% slippage for all transactions and are shown for various starting capitals, trade fees and tax brackets (with a 1-year rebalance period, we can sell (winning stocks) on day 366 and take advantage of lower long-term capital gains tax rates). For losing stocks, you typically want to sell them on day 364 to be able to use them as short-term capital losses, which can be used to offset any short-term capital gains you have, which are taxed at a higher rate (therefore, offsetting them is advantageous).

The backtest is also composited weekly, meaning each year’s return is the average return of 52 different portfolios. So if the return for the strategy in 1999 was 10%, that’s the average of 52 portfolios — 1/1/1999 to 1/1/2000, 1/8/1999 to 1/8/2000, 1/15/1999 to 1/15/2000, etc.

The benchmark this time is the SPDR S&P 500 ETF SPY (which includes dividends so the returns are total returns). Returns are shown as excess returns over buying and holding SPY.

Each color represents a different starting capital
Cornerstone Value vs SPY, Excess Total Return (01/09/99 – 10/14/17)

With no transaction costs, the amount of starting capital doesn’t affect the excess return. If you’re paying $4.95 or $6.95 per trade, you would have needed to start with at least $10,000 to outperform buying and holding SPY. If you’re paying $19.95 a trade, you’d have needed $15,000 or $20,000 to start depending on your tax bracket.

The top excess return shown (for $0 trades and 10/15% tax bracket) resulted in an excess return of 236%, turning $15,000 into ~$72,000 over ~18 years instead of ~$42,000 for buying and holding SPY.

Backtesting also indicates a slightly larger drawdown (~63%) for the cornerstone value strategy than SPY (~55%) over this time period, which brings into question whether someone would be able to psychologically tolerate this strategy.

Cornerstone Value vs SPY, Nominal Returns & Risk Statistics (01/09/99 - 10/14/17)
Cornerstone Value vs SPY, Nominal Returns & Risk Statistics (01/09/99 – 10/14/17)

Based on the weekly composited portfolios, this strategy performs better during down markets (when SPY is decreasing) than up markets…

Cornerstone Value vs SPY, Weekly Composited Portfolio Nominal Returns (01/09/99 - 10/14/17)
Cornerstone Value vs SPY, Weekly Composited Portfolio Nominal Returns (01/09/99 – 10/14/17)

…so using a market timer to exit the market improves the drawdown but erases the alpha.

Cornerstone Value vs SPY, Nominal Returns & Risk Statistics w/ 50/200 day SMA Cross Market Timer (Exit) (01/09/99 - 10/14/17)
Cornerstone Value vs SPY, Nominal Returns & Risk Statistics w/ 50/200 day SMA Cross Market Timer (Exit) (01/09/99 – 10/14/17)

However, using the same market timer to add a low beta filter (if the market timer is signaled at a rebalance date, only repurchase stocks if their 3-year beta is in the bottom 10% of stocks in the universe) may preserve alpha while decreasing drawdown.

Cornerstone Value vs SPY, Nominal Returns & Risk Statistics w/ 50/200 day SMA Cross Market Timer (Lowest Decile Beta Filter) (01/09/99 - 10/14/17)
Cornerstone Value vs SPY, Nominal Returns & Risk Statistics w/ 50/200 day SMA Cross Market Timer (Lowest Decile Beta Filter) (01/09/99 – 10/14/17)

So the key takeaways:

  • Free trades are awesome (like with Robinhood).
  • For the most part (with the exception of low starting capital/high transaction fees/high tax bracket), cornerstone value outperformed buying and holding SPY out of sample (1999 to 2017).
  • This strategy can be implemented on its own or in conjunction with other strategies I’ve discussed, such as market timing.

As of 10/14/17, the top 5 stocks for the cornerstone value strategy (highest yielding among market leaders) are ETP, CTL, MBT, EPD, & ETE.

This strategy screen is available to my group on Portfolio123, where you can customize it or implement it as is. If you just want the stock picks, you can subscribe on Patreon below.

Market Timing Beats Time in the Market (At Least Since 1871)

“Everyone agrees that it’s appropriate to divide the space of a portfolio between different asset classes,” i.e., dividing your portfolio between different asset classes (e.g., 60% stocks and 40% bonds). However, it remains controversial to divide the time of your portfolio between different asset classes as market conditions change, also known as market timing. It is an alternative to maintaining a singular asset allocation over time (or changing your asset allocation in response to non-market conditions, like your age). Despite its poor reputation, the data show that market timing beats time in the market (at least since 1871).

The premise of the strategies discussed in this post is simple: things that are trending down tend to keep trending down, and vice versa. I won’t get into why this is true (though as I’ve written about previously, investors aren’t rational, and tend to panic sell during a down trend and pile in during an up trend), just that following market trends has been beneficial since 1871. It’s the premise behind trend following, which has been practiced for hundreds of years. (Disclaimer: these strategies are not new and not my own).

The scope of this post is the US market, specifically the S&P 500 index and the 10-Year Treasury Bond (GS10). Historical data was obtained from Professor Shiller (most well known for CAPE). Total return (including dividends) is used for the S&P 500 and monthly returns for 10-Year Treasury Bonds (Long Government Bond pre-1953) are calculated using Professor Damodaran’s methodology (which isn’t 100% correct, but is close enough). The strategies are rebalanced at the first of every month. And by the way, I’m only calculating Sharpe ratio after 1934 because the risk free rate I’m using is the yield on the 3-month Treasury Bill.

The two strategies both use a simple moving average (SMA) and are described below:

  • SMA(10) – When the price of the S&P 500 index closes below it’s 10-month (200-day) SMA, sell the S&P 500 index (stocks) and buy the 10-Year Treasury Bond (bonds). Keep holding bonds until the price of the S&P closes above it’s 10-month SMA.
  • SMA Cross – When the 50-day (2.5-month) SMA of the S&P closes below it’s 200-day SMA (also called the death cross), sell stocks and buy bonds. Keep holding bonds until the 50-day SMA crosses above the 200-day SMA (also called the golden cross).

These two strategies will be compared against buying and holding each of the following asset allocations:

  • 100% stocks (again, total return of the S&P 500 index, including dividends)
  • 100% bonds (10-Year Treasury Bond)
  • 63% stocks & 37% bonds (it turns out that since 1871, both of the SMA strategies are in stocks about 63% of the time and in bonds about 37% of the time, so I thought this would be a more interesting comparison)
Market Timing Strategies – Statistical Comparison (1871 – 2017)
Marketing Timing Strategies – Growth of $100 (1871 – 2017)

Obviously, these strategies have worked very well historically. The returns by decade are shown below.

CAGR by Decade for Market Timing Strategies (1880s through 2000s)

The only decade where the S&P 500 Total Return outperformed both market timing strategies was the 1990s. Below are charts showing how the CAGR, Sharpe ratio, and maximum drawdowns (from starting year to 2017) change as the starting year changes.

CAGR vs Starting Year for Market Timing Strategies (Starting Year to 2017)
Sharpe Ratio vs Starting Year for Market Timing Strategies (Starting Year to 2017)
Max Drawdown vs Starting Year for Market Timing Strategies (Starting Year to 2017)

These charts tell the same story, with the market timing strategies leading the pack regardless of the starting year. The max drawdown chart is interesting because you can clearly see recessions and how each strategy performed by the magnitude of the drop.

Speaking of drawdowns, the 100 largest drawdowns of the S&P 500 Total Return and the corresponding drawdowns of the market timing strategies are shown below.

100 Largest Drawdowns of the S&P 500 Total Return and Corresponding Market Timing Strategy Drawdowns (1871 – 2017)

The data depicted was not organized for individual recessions, so there is some overlap and misrepresentation. The raw data with associated dates is here.

Saving the best for last, the base rates for the strategies are below.

Base Rate Comparison for Market Timing Strategies (1871 – 2017)

These base rates tell you what % of time each strategy has resulted in a positive (1-, 3-, 5-, or 10-year) CAGR. For example, for 3.4% of the rolling 10-year time periods between 1871 and 2017, you would have lost money (return less than 0%) by being invested in the S&P 500. Or, there was an 89.9% chance that you’d have a positive return on a 10-Year Treasury Bond during any given year between 1871 and 2017.

Base rates can be very helpful psychologically; knowing how often a 3-year losing streak has happened in the past can provide a frame of reference when trying to decide to change strategies or stick with it.

Using more powerful (albeit more limited in terms of historical data) tools like Portfolio123, these strategies can be tested and implemented with more recent data. Below are results for both strategies using SPY and IEF from 1999 to present. (I know that IEF was only introduced in 2002, but Portfolio123 has extended the price for this and many other ETFs backward to maximize usefulness in backtesting).

Detailed Statistics for the SMA(10) Market Timing Strategy Using SPY & IEF (Jan 1 99 – Sep 18 17)
Detailed Statistics for the SMA Cross Market Timing Strategy Using SPY & IEF (Jan 1 99 – Sep 18 17)

Switching out ETFs (e.g., SHY to short the market instead of IEF, just going to cash, using a more bullish ETF), using exponential moving averages instead of SMAs, combining market timing indicators, implementing these timing rules into other portfolios (stock or ETF), and many more permutations are possible with Portfolio123.

These two timing rules are published and automatically updated here. If the timer is indicating to be invested in stocks, it will say “Invested.” Otherwise, it will say “Cash.”

Future posts include more market indicators as well as economic indicators (such as unemployment rate, and how well it’s worked since 1948 when it was first published) that can be used to help inform your investment decisions.

I recommend further reading by people smarter than me if you’re interested.

Stock Picks Now Available on Patreon

For many investors, full-blown quantitative investing tools like Portfolio123, which starts at $35/month, is too much to jump into right away.

I’ve just launched a Patreon page (linked below this post and on the sidebar of the blog) to lower the barrier to entry to start investing in a quantitative investing strategy.

Starting at just $5 a month, you’ll have access to stock picks for various quantitative investing strategies discussed on this site. The initial launch includes 4 strategies from What Works on Wall Street (the statistics of which can be seen here), but will eventually include more strategies.

I hope this enables and empowers more ordinary investors to start thinking beyond the index.

Frequently Asked Questions About Quantitative Investing

I’ve recently published a frequently asked questions about quantitative investing (with answers). You can find it here for future reference. If you have ideas for future questions, please contact me.

What is quantitative investing?

Simply put, quantitative investing is a systematic way to choose investments based solely on quantitative measures. Unlike qualitative measures (how competent is management? what are the competitors? where is the industry headed?) that are difficult to judge (unless you’re Warren Buffett, work on Wall Street, or spend all day researching stocks), quantitative measures are available to anyone (historical price data, financial disclosures such as 10-Ks and 10-Qs, etc.). By compiling and analyzing these historical data, it is possible to identify quantitative measures (i.e., factors) that have been associated with excess returns.

But isn’t the market efficient?

The efficient-market hypothesis (EMH) argues that it is impossible to consistently beat the stock market because all stocks are always traded at their fair value and stock prices fully reflect all available information. EMH would have you believe the Warren Buffett’s (or any skilled investor’s) track record is merely luck (which he refutes).

If EMH were true, the performance of cheap stocks (low price to earnings (P/E) ratio) should match the performance of expensive stocks (high P/E) (since all available information is already reflected in the price, cheap stocks are cheap because they aren’t good stocks, not because they’re undervalued). Research has proved this wrong:

frequently asked questions about quantitative investing

If EMH were true, the dots would all fall on a horizontal line. Also add odds with EMH are economic bubbles (or individual stock bubbles), which manifest as a result of cognitive biases such as overconfidence, overreaction, representative bias, information bias, and various other predictable human errors in reasoning and information processing, such as irrational exuberance, when underlying value is ignored and prices can dramatically rise and frantically fall.

To a quantitative investor, holes in the EMH theory — inefficiencies — are opportunities for excess returns.

Aren’t you just curve fitting? Past performance is not an indication of future returns.

You’re absolutely right, past performance of a particular strategy is not an indication that the strategy will perform similarly in the future. However, what else do we have to go on other than past performance? Don’t millions of people invest in US equity indexes with the expectation that future performance will match or come close to historical performance of that index?

Is that such a good assumption? What’s to say the US market doesn’t go the direction of Japan?

frequently asked questions about quantitative investing
The Lost Decades – Japan Nikkei Index Monthly Chart

To combat the threat of overfitting, Portfolio123 offers numerous way to test robustness, including interchangeable universes and ranking systems, to ensure a strategy works across a wide range of assumptions. They’ve also published educational materials, including a strategy design class, that teach best practices in avoiding common pitfalls such as overfitting.

In defense of overfitting in general, O’Shaughnessy published several strategies that have returned above 15% annually since 1964 (limits of his data), and those strategies have continued to work since being published (out of sample).

If your strategies work so well, why aren’t hedge funds using them/hiring you?

I get this one a lot. Firstly, a lot of the strategies discussed on this site were not developed by me. Some were developed by James O’Shaughnessy and have been published in his What Works on Wall Street book. Others, like the methodology from Joel Greenblatt’s book, are built on the shoulders of Warren Buffett’s investing philosophy.

The underlying theme for most of the strategies discussed on this site are based on the two factors of value and momentum, which have been proven time and again to be associated with excess returns.

Assuming this is true, why aren’t all hedge funds investing this way/why aren’t you working for a hedge fund/why haven’t you sold this strategy to a hedge fund for millions of dollars?

The short answer is that they already invest this way. In fact, almost all of the largest hedge funds, including AQR, AHL, Bridgewater, and the Medallion Fund (the most profitable hedge fund ever), use quantitative analysis to make trades (flying in the face of the efficient market hypothesis).

The issue with hedge funds is their scale. They are forced to avoid small stocks, even if those stocks look the most attractive in terms of momentum or value, because the market impact of their large buy and sell orders would significantly affect the price of the stock and erode any excess return they had hoped to achieve. As a result, they are forced to focus on only larger stocks and be less concentrated in factors proven to generate excess return. This disadvantage, along with lack of long-term discipline, is a chief reason for consistent underperformance of the benchmark among hedge funds.

Their disadvantage is the individual investor’s advantage. Your small orders have much less market impact and you can buy the best value or momentum stocks no matter their size, providing much more exposure to proven factors and increasing excess returns.

What about indexing?

By now, you probably know how I feel about indexing. But indexing does have its place; if you don’t want to think about your portfolio at all, indexing is for you. But I argue that even a small amount of effort spent intelligently choosing your investments can be worthwhile.

Would you buy a bag of 10 apples if you were guaranteed that 5 of them were rotten? Probably not, but that’s what you’re doing when you index. An index fund buys all the stocks (winners, losers, and everything in between) in order to replicate the return of that index.

If you can eliminate even 5% of the losers by learning to identify and avoid characteristics often associated with losing stocks (poor earnings quality or financial strength, for example), you can tremendously improve the return of your portfolio.

Another characteristic of index funds that is often cited as their main advantage can be a drag the more your portfolio grows. Index funds like Vanguard’s VTSAX have an expense ratio of 0.05%. That’s 0.05% of your portfolio paid to Vanguard every year, regardless of how large your portfolio is.

Investing in a quantitative strategy like the ones published by O’Shaughnessy, which require rebalancing a portfolio of 25 stocks once a year is cheaper than indexing (assuming $10 per trade) once your portfolio eclipses $500,000. And if you take advantage of low or no-cost brokerages (like Robinhood which offers free trades or larger institutions that offer X free trades a month if you have a certain portfolio value), your cost goes to 0%. Meanwhile, Vanguard would still be taking 0.05%.

The Vanguard Group, a private company with over $3.8 trillion in assets under management, has never disclosed its executive compensation. With an average expense ratio of 0.19%, that $3.8 trillion in assets nets a cool $7.22 billion every year in fees. That’s enough for $515,000 for each of its 14,000 employees. Not that I’m trying to bash Vanguard or promote active managers (I’m not), but just pointing out that indexing is not the end-all, be-all, and that investing for yourself is the best course.

So what’s the catch? Why create this site?

The goal of Portfolio Perfection is to empower ordinary investors to think beyond the index. You do not have to be a hedge fund manager to be a successful investor. You do not have a degree in finance or spend all day reading quarterly earnings statements to implement a quantitative investing strategy.

The number one barrier for ordinary investors is the stigma associated with investing in individual equities. But when presented with evidence, logical minds can make rational conclusions: quantitative strategies have been proven to work. You, the ordinary investor, have an advantage over hedge funds because you can concentrate your holdings in the best stocks, small or large (as discussed in another FAQ).

Once you’ve made it past the first barrier, the second barrier is obtaining the data, organizing it in a way that’s useful, and quickly and easily developing robust strategies. That’s where Portfolio123 comes in.

And that’s where the catch also comes. The fact is, good financial market data is expensive. Free sources exist, but they’re mostly just historical prices (which is fine for technical analysis) and not historical earnings statement information (from which most strategies on this site are built). Portfolio123 not only has the data we need, but an extensive platform on which to build and test quantitative strategies.

This whole site may seem like a paid advertisement for Portflio123, but the truth is that its a tool that I discovered and it’s been tremendously helpful in jumpstarting my portfolio and my path to financial independence. I don’t work (nor have a degree) in finance, but I am passionate about it and in my opinion, portfolio return is the oft-ignored leg of the three-legged stool of financial independence (the other two being earnings rate and savings rate).

I do receive a paltry commission if you use my referral link and become a member of Portfolio123, but my main motivation is continuing to learn and write about quantitative investing and spread the word so that others may benefit as I have.

“A rooster crows only when it sees the light. Put him in the dark and he’ll never crow. I have seen the light and I’m crowing.” – Muhammad Ali

Health vs. Wealth: Drinking, Eating, Smoking, and Gambling Stocks

Over the past 17 years, investing in vice stocks (the S&P 500 stocks within the following GICS sub-industries) delivered almost triple the annual return than SPY (S&P 500 index fund):

  • Brewers – 30201010
  • Distillers & Vintners – 30201020
  • Soft Drinks – 30201030
  • Tobacco – 30203010
  • Casinos & Gaming – 25301010
  • Restaurants – 25301040

For this time period, the number of stocks in these sub-industries in the S&P 500 ranged between 14 and 20. I’ve abbreviated these as BeSToGaR (Beer, Spirits/Soft Drinks, Tobacco, Gaming, Restaurants).

Only investing in the top 3 of these stocks (as ranked by the 2 factors from a famous formula) and rebalancing every 3 months pushes the CAGR a few percentage points higher, though at the expense of a larger drawdown.

Widening the net to include stocks within the Russell 3000 (also top 3 with a 3 month rebalance) also improves performance.

Adding the drug retail (GICS 30101010) and food retail (GICS 30101030) is another iteration to consider. (BeSToGaRR; extra R for Retail).

Investing in vice stocks; Performance chart of the BeSToGaR/R quantitative investing strategy.
Performance chart of the BeSToGaR/R strategy.
Investing in vice stocks; Performance statistics of the BeSToGaR/R strategy.
Performance statistics of the BeSToGaR/R strategy

To replicate or refine this quantitative investing strategy, click here.

Taxes & Fees: Considerations for Short-Term Investing

Both the trending value and consumer staples strategies I’ve introduced work well with annual rebalancing, which is great because gains you make on stocks that were held for more than a year are subject to a lower capital gains tax rate. Gains from stocks that were held for less than a year are subject to your ordinary income tax rate.

Long Term vs Short Term Capital Gains Taxes
Long Term vs Short Term Capital Gains Taxes

There are real tax benefits to holding stocks for more than a year, but there are also benefits to trading more frequently:

Quantitative Strategy Performance for Various Trading Frequencies
Quantitative Strategy Performance for Various Trading Frequencies

To help determine if the increased return from trading more frequently is worth the higher tax rate, let’s review an example:

Long-term: I invest $1,000 in the trending value strategy on Day 1. I sell on day 366, my capital gain for the year being $1,000 * 16.1% = $161. Because I’m in the 25% tax bracket, I have to pay 15% of that gain in taxes, $161 * 15% = $24.15, so my end balance after tax is $1,000 + $161 – $24.15 = $1,136.85.

Short-term: I invest $1,000 in the trending value strategy on Day 1 and rebalance monthly. By day 366, my capital gain is $1,000 * 20.9% = $209. Because I’m in the 25% tax bracket and I traded more frequently, my capital gains are subject to my ordinary income tax rate, $209 * 25% = $52.25, so my end balance after tax is $1,000 + $209 – $52.25 = $1,156.75.

After taxes, the monthly version of the trending value finishes ahead of the annual version by $19.90. Trading these two strategies more frequently returns more before taxes, but after taxes it’s close to even. (It looks like the trending value strategy does not benefit beyond monthly, which makes sense since the ranking system uses 6 month % return).

There are, however, many strategies that benefit tremendously with more frequent trading because they are able to react more quickly to opportunities as they arise. Increased trading frequency also ensures that you aren’t stuck in a poor position because of a poor entry point (What Works on Wall Street demonstrates that the return of an annual strategy can vary widely depending on which month you enter, partly due to the ever-shifting January effect).

To help make this decision whether to trade once a year or more frequently without having to go through an example every time, I’ve created a table. It shows the nominal gain required if trading long-term or short term.

Comparing the two columns within each tax bracket indicates the additional return required to offset the tax implication.

Capital Gains Taxes vs Trading Frequency
Capital Gains Taxes vs Trading Frequency

One caveat to the table is that if your ordinary income is right below the next highest tax bracket, and adding short term capital gains on top of that pushes the total into the higher bracket, the portion of short term capital gains that fit into the higher tax bracket will be taxed at that higher tax rate (read more here). If this is the case, you can compare an LT column to the ST column in the next highest bracket.

If you’re trading a quantitative strategy inside of a 401(k) (more on how in an upcoming post), you don’t have capital gains taxes to worry about. But you do have trading commissions to worry about. Fidelity charges $7.95 a trade; some charge more, some less, but it’s typically around there.

Assuming $10 a trade and a 25 stock portfolio (to make the numbers more even), the trading costs (as a percentage of your portfolio) for different rebalance schedules and portfolio sizes are summarized below:

Trading Commissions as a % of Trading Frequency Portfolio Value
Trading Commissions as a % of Trading Frequency Portfolio Value

I’ve highlighted in green 2% and lower, which is about the most you would expect to be charged for an actively managed fund.

So if you’re not indexing, whatever your portfolio size and the trading frequency you choose to implement, make sure that your expected return above the benchmark index is greater than your taxes and fees.
Robinhood offers trades for $0, which can significantly lower the barrier to entry to short-term strategies (and trading in general) for ordinary investors without a lot of capital. There are valid criticisms (including on execution of trades, which matters more the more often you trade), but despite the criticisms $0/trade is hard to beat.

$DRYS: This is What Insanity Looks Like

Some of you may have heard about $DRYS, the dry bulk shipping company that had a massive spike and subsequent collapse in the last week.

The Spectacular Rise and Fall of $DRYS
The Spectacular Rise and Fall of $DRYS

Within one week, $DRYS went from under $5, to almost $100 before trading was halted by Nasdaq for a day and has now fallen to around $12.

And for no other reason than irrational exuberance.

If only we had learned from our friend Isaac Newton, who lost millions in the South Sea Company bubble and collapse.

Isaac Newton's and the South Sea Company Stock
Isaac Newton and the South Sea Company Stock
Takeaways:
  1. As long as people are irrational, irrationality (i.e., inefficiency) will exist in the stock market.
  2. Inefficiency breeds opportunity (i.e., the price of a security does not always reflect its intrinsic value).

Quantitative investing is one path to finding repeatable opportunities to exploit. Chasing greed is a path to getting burned, if not this time then maybe the next.

I’m hoping to have more new posts soon. I’ve been busy putting where money where my mouth is, experimenting with some new strategies with real money and not just words on a blog.

Death of a Value Factor? The Arbitrage of Price to Book

The trending value strategy buys the top 25 stocks by their 6 month price momentum among the top decile of stocks ranked by value composite 2 (VC2), a combination of price-to-earnings ratio, price-to-sales ratio, price to book ratio, earnings before interest tax depreciation and amortization to enterprise value ratio (EBITDA/EV), price-to-cash flow ratio, and shareholder yield.

Price to book was touted by Ben Graham, the father of value investing, in his book The Intelligent Investor (“By far the best book on investing ever written.” – Warren Buffet) as a cornerstone of his rules for investing. Eugene Fama and Ken French published their famous three-factor model in 1992, which identified price to book as one of three factors that can explain the performance of a portfolio. They created a growth portfolio, comprised of stocks with the highest (top 30%) price to book ratios, and a value portfolio comprised of the lowest (bottom 30%) price to book ratios.

Since its publication, low price-to-book ratio has become the cornerstone of value indices, including the Russell 1000 Value (see page 25), the MSCI US Prime Market Value Index, and others, which are now tracked by hundreds of billions of dollars of value ETFs and mutual funds.

Any factor that is widely identified risks the chance of arbitrage. Essentially, as investors become aware of an anomaly (low price-to-book stocks performing well, in this case) and start tilting their portfolios toward that anomaly, it tends to be eroded away. Below is the recent performance of the top decile of stocks ranked by the various value factors of VC2.

Nominal Return %, Top Decile of Various Value Factors (Jan 1999 - July 2016)
Nominal Return %, Top Decile of Various Value Factors (Jan 1999 – July 2016)

The first thing that stands out is how well each factor has performed against the S&P 500 over the last 16 years (it has be too good to be true, right?). The second thing that stands out is that price to book has been eroded as a value factor. It’s reasonable to believe that the erosion is due at least in part to its identification and wide use as a value factor.

This erosion, in addition to historical long periods of underperformance, is reason for concern for the trending value strategy. It’s reasonable to expect better performance if price-to-book was removed from VC2.

Nominal Return %, Trending Value with and without Price-to-Book (Jan 2010 – July 2016)

Removing price-to-book from VC2 does improve performance, though the trending value strategy has been pretty flat for the last 2 years, as stock prices continue to rise and the bull market continues.

In addition to dropping price-to-book from his value composite of evaluating stocks, it appears from his mutual funds’ fact sheets that James O’Shaughnessy has replaced price-to-cash flow ratio with free cash flow to enterprise value.

Nominal Return %, Top Decile of Various Value Factors (Jan 1999 - July 2016)
Nominal Return %, Top Decile of Various Value Factors (Jan 1999 – July 2016)

Free cash flow to enterprise value has been pretty much on par with price-to-sales and price-to-cash flow for the past 16 years, with price-to-cash flow actually outperforming free cash flow to enterprise value from about 2010 to 2015. But O’Shaughnessy has access to much larger datasets than I have available through Portfolio123, which may indicate larger advantages outside of the past 16 years. Additionally, free cash flow to enterprise value seems to have advantages over other traditional value factors, at least in theory.

Mimicking O’Shaughnessy by dropping price to book and replacing price to cash flow with free cash flow to enterprise value, value composite 4 (VC4) is born (VC3 was already named by O’Shaughnessy, as briefly mentioned here).

Nominal Return %, Trending Value (VC2, VC2 – PB, & VC4) (Jan 2010 – July 2016)

The trending value strategy that uses VC4 finishes a touch ahead of the benchmark for this time period, and has exhibited a much flatter trend over the last ~2 years than trending value using VC2 or VC2 without price-to-book.

How these perform moving forward remains to be seen, and I’ll continue to track both. But O’Shaughnessy himself altering the value composite from the one he published in 2011 is pretty strong evidence that he has acknowledged the arbitrage of price-to-book.

If you’d like to test price to book or hundreds of other factors for yourself, start here.

Consumer Staples Strategy – July 2016 Signals

The current stock signals for the consumer staples strategy introduced here are below. They are also published here. I used Portfolio123 to generate this screen.

HLF, Herbalife Ltd
NPD, China Nepstar Chain Drugstore Ltd
SENEA, Seneca Foods Corp.
CCE, Coca-Cola European Partners Plc
MED, Medifast Inc.
ADM, Archer-Daniels-Midland Co
GMCR^16, Keurig Green Mountain Inc
IMKTA, Ingles Markets Inc
MHG, Marine Harvest ASA
UVV, Universal Corp
USNA, USANA Health Sciences Inc
PM, Philip Morris International Inc
KMB, Kimberly-Clark Corp
AVP, Avon Products Inc.
NUS, Nu Skin Enterprises Inc.
ABEV, Ambev SA
GIS, General Mills Inc.
VGR, Vector Group Ltd
EPC, Edgewell Personal Care Co
PEP, PepsiCo Inc
CHD, Church & Dwight Co. Inc.
BTI, British American Tobacco PLC
WFM, Whole Foods Market Inc
FDP, Fresh Del Monte Produce Inc.
BG, Bunge Ltd