Momentum: the value investor’s best friend

Quantitave Value Investing in Europe: What Works for Achieving Alpha

There is a growing literature on statistical methods to beat the market inspired by the original classics, Dremen‘s Contrarian Investment Strategies and Greenblatt‘s Little Book, but a new report uniquely focuses on Europe and adds momentum to the mix of factors tested. Like other studies, it shows simple value measures beat the market, but with a surprising twist.

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EBIT explained

A new series on frequently used terms starts

Where you’ll see EBIT

EBIT is used in Joel Greenblatt’s Magic Formula as the profit measure and numerator of the Return on Capital and Earnings Yield calculations. It’s part of the Return on Total Assets component of the Magic Formula inspired Nifty Thrifty screen developed for my Money Observer column of the same name.

EBIT Defined

Typically a company’s income statement, which explains how much profit a company retained over a period of time, is laid out as follows:

Revenue
- Cost of goods sold
Gross profit
- Operating expenses
Operating profit
+ Non operating income
- Interest payable
Profit before tax
- Tax payable
Profit after tax
- Dividend payable
Retained profit

At each stage, the amount of profit is reduced by costs grouped together into categories. To calculate gross profit, accountants take the company’s revenues for the period and deduct the direct costs of raw materials and stock sold. To calculate operating profit they deduct indirect costs like wages, for example, depreciation, and marketing.

After operating profit they add non-operating income, for example income from investments, and deduct debt interest to calculate profit before tax. Then they deduct tax to calculate profit after tax. The dividend is deducted from profit after tax, and the remainder, retained profit, is added to the balance sheet increasing (if it’s positive) the book value of the company.

EBIT stands for Earnings Before Interest and Tax. It’s the profit figure after operating costs but before interest and tax are deducted. Usually it‘s synonymous with operating profit, although technically EBIT includes non-operating income and, according to some definitions, exceptional items, for example income from the disposal of assets, subsidiaries say, or buildings. Where these are significant, the two measures will differ substantially.

EBIT = operating profit + non operating income

How I use EBIT

EBIT divided by Total Assets and expressed as a percentage gives ROTA, one of the fundamental profitability ratios, used in the Nifty Thrifty formula.

EBIT divided by Enterprise Value, the sum of a company’s market value and net debt, gives the Earnings Yield, the Nifty Thrifty’s measure of value.

I’ll explain these terms in future posts.

I only use EBIT in the computer generated Nifty Thrifty portfolio so I don’t calculate it for individual companies. If I did, I’d probably include investment income and exclude exceptional items since, by definition, exceptional items are unusual or one-off, they could make a company look more profitable or less profitable than it actually was.

Sharelockholmes, the database I use to generate the Nifty Thrifty recommendations calculates EBIT by taking Profit before tax and adding the interest back to it. This calculation includes both investment income and exceptional items.

Value-Investing.eu uses operating profit. This measure excludes both investment income and exceptional items.

In The Little Book that Still Beats the Market, Joel Greenblatt doesn’t explain the EBIT calculation in detail, although references to “actual operating earnings before interest expense and taxes” implies (to me) the Magic Formula uses the operating profit measure.

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Thanks to blippy, @brenmcl, @BryceElder, @mcturra200, @gpietersz, @SajKarsan and @V4Value, for helping me untangle EBIT, mostly in 140 characters!

More grist to the Magic Formula mill

More evidence Greenblatt’s formula works in the UK, and a suggestion to improve it

The briefest of recaps: The Magic Formula is a simple quantitative strategy developed by US hedge fund manager and value investor Joel Greenblatt that, according to his tests and other researchers, beats the market handsomely. You can read all my notes on the Magic Formula here, preferably starting with the last post. Using and testing the strategy in the UK is tricky though, because we don’t have a proprietary database like Greenblatt’s and testing by individual investors, mine particularly, probably lacks the rigour of the best in academia or the City. That hasn’t stopped us trying though…

Reader Tom Norman has back-tested the of Magic Formula since 2001 in the UK that corroborates my results. Like me, he used Return on Assets (ROA) instead of Return on Capital (ROC), see this post for why, and he used the same data source (Sharelockholmes.com), so I’m relieved, but not altogether surprised, he got a similar result.

Rather wonderfully, he also tested the combination of the Magic Formula and the F_Score, which I use to run the Nifty Thrifty portfolio for Money Observer magazine, an idea we both got from a research note written by Morgan Stanley’s Edmund Ng. The F_Score, which discriminates between companies getting into trouble and companies getting out of it, should weed out some potentially big losers and reduce the risk inherent in the Magic Formula.

The Nifty Thrifty has not performed well so far, so these results, clipped from Tom’s email, come at a good time:

Average return greater than FTSE 100 for MF after 1 year (using ROTA rather than Greenblatt’s method): 9.62%

Average return greater than FTSE 100 for MF after 1 year (as above) but F_score >= 7 : 8.27%

BUT

Standard deviation of MF only: 13.18
Standard deviation of MF/F_Score: 8.74

So filtering for F_Score should considerably reduce the volatility of one’s portfolio and the 8.27% surplus return is much more statistically reliable than the 9.62% from MF only portfolios.

Also of interest was what happens if we look at surplus return / standard deviation further than one year out:

MF only holding for:

2yr 15.68% / 17.29
3yr 16.94% / 19.93
4yr 19.90% / 23.73

MF/F_Score

2yr 16.77% / 10.73
3yr 21.05% / 15.07
4yr 24.27% / 16.93

My conclusions are:

1. Contrary to popular opinion the F_Score provides useful information about large companies – particularly their ability to provide superior risk adjusted returns over an extended period of time.
2. With even small transaction costs it’s worth holding on to shares for two or even three years rather than the one year advocated by the MF.

Sharp-eyed readers will notice Tom’s average returns for the Magic Formula are a few percent below mine, but we think that’s because he started his tests a year later, missing an exceptional year for the Magic Formula.

Mines flood Nifty Thrifty screen

Last night I ran a mechanical screen, the Nifty Thrifty, in preparation for a quarterly article I write for Money Observer magazine. Since I’d tweaked the screen, I wanted to see what kind of results it came up with before running it for real at the end of the month.

I don’t talk about the Nifty Thrifty much here because this blog is about the Thrifty 30 portfolio, which is in some ways similar, but in others completely different.

Thrifty 30 holdings are picked by a human (me) and not an algorithm and while I tend to favour obscure companies when picking them myself, only the UKs biggest companies with market capitalisations over £500m are allowed in the Nifty Thrifty.

Nevertheless the two portfolios follow the same principle; I’m looking for good companies at cheap prices that are not likely to be getting into serious trouble.

Over the long-run I’m very interested to see which portfolio does better. The Nifty Thrifty is a lot less effort to run, but the Thrifty 30 is my baby.

The Nifty Thrifty is inspired by Joel Greenblatt’s ‘magic formula’, which I have discussed at length here. I use Earnings Yield as a measure of value, Return on Assets as a measure of quality, and Piotroski’s F_Score to improve the algorithm’s timing. Each qualifying company is ranked by these three measures (the higher the better) and an overall ranking derived. Here are yesterday’s top fourteen:

I’m still in shock. Six are miners and three are energy companies. If I include all of them, as I should according to the formula, one third of the Nifty Thrifty portfolio will be in natural resources. And if next year is a bad year for resource companies, it will be a bad year for the Nifty Thrifty.

As a value investor, I’m leery about resource companies. Their performance depends on the high prices of the commodities they mine. When prices fall, their profit margins collapse and suddenly they don’t look like such good investments.

Maybe I made a mistake including miners in the first place. Greenblatt excludes financial companies and utilities from his formula. I think his measures of value and quality aren’t appropriate for banks and insurance companies and he doesn’t like utilities because their profits are constrained by regulators.

But magic formula portfolios are often rich in energy and resource companies even though one of the basic principles of the magic formula is that companies earning a high return on capital or assets one year will reinvest those returns in money making enterprise in forthcoming years. That may not be true of resource companies, if the price of copper, or oil has fallen down a deep shaft.

Most companies are sensitive to outside forces to some degree, and I suppose you can’t be too fussy or you might as well hand pick the companies from a very small pool. But resource companies are surely among the most extreme cases.

Looking back at the credit crunch, the top three companies in my list performed very badly. Rio Tinto shares fell 85% top to bottom, Eurasian Natural Resources fell 87%, and Antofagasta fell 65%.

You’d have to be very unlucky to suffer the full extent of those drops, but my testing of the magic formula showed that three of the portfolios formed in the year leading up to the crisis did worse than the FTSE-100. That only happened one other time in a decade of testing forty individual portfolios.

Miners did feature in those losing portfolios. Kazakhmys was ranked fourth in the portfolio formed on 31 December 2007 and became its biggest loser, falling 75%. Vedanta, ranked sixth fell 56%, and Antofagasta, ranked second lost 14%. BHP Billiton, ranked 26, actually gained 22% though.

Most of these companies have recovered from the credit crunch and resumed their apparently remorseless rises on the back of a commodities boom that has lasted more than a decade now, but it’s too late for the one year magic formula portfolios that invested in them in the months before they dropped in price.

In the latest version of his book, The Little Book That Still Beats the Market, Greenblatt expresses surprise, and perhaps a bit of a disappointment than the magic formula beats bull markets, and loses to bear markets when you might expect a value portfolio to do better in a bear market.

It’s just a theory but maybe the energy and resource companies in magic formula selections explain some of the puzzle.

Anyway, the preponderance of miners and oil producers in this months table leaves me with a conundrum if, as is likely, it’s replicated in a few days time when I repeat the exercise for the magazine. Do I ignore the miners, or include them? Or do I exercise some common sense, and include the highest ranked, but replace the lower ranked resource companies with the next most highly ranked company that isn’t a resource company to achieve a more diverse portfolio. You’ll have to check the Money Observer website in a week or so to find out.

I see fellow magic formula investor Mark Carter likes BHP Billiton because it’s a magic formula company, but if you read his write up, and the update below it, you’ll see the kind of handwringing a value guy does when adding a resource company to his portfolio.

Those magic formula loose ends…

Too good to be true?

The loose end that bothers me most about last week’s magic formula testing is the incredible success of the formula in the first three years of the test. It’s visible to the naked eye:

The 12 portfolios formed between 31 March 2000 and 31 December 2002 beat the FTSE by an average of about 23%.

It wasn’t just the margin by which the formula beat the market in those years that surprised me, it was the fact that the formula beat the market at all considering between 2000 and 2002 the market was crashing.

In his book, The Little Book That Still Beats The Market, Joel Greenblatt says:

…it turns out that much of the outperformance of our portfolios comes during the up months. On average during this 22-year period, the magic formula portfolios "captured" 95% of the S&P 500′s performance during down months and 140% of its performance during up months.

In other words, in his experience the formula performs badly in bear markets. In mine it did particularly well in one bear market.

I emailed Steve Lewis, the architect of Sharelockholmes, to ask him whether he could think of any reason why the data might be less trustworthy in the earlier period.

He replied with three possibilities, all stemming from the fact that the data in Sharelockholmes comes straight from company results and is not adjusted for:

1. Changes to the accounting of pension deficits, which would affect the value used in the ROA calculation.
2. The introduction of IFRS in 2005, which replaced the old GAAP accounting standards.
3. Delisted companies before 2003. Steve set up Sharelockholmes in 2003, using data going back to 1999 for companies still active in 2003. So Sharelockholmes doesn’t contain records for companies that delisted between 2000 and 2003, although it does thereafter.

Don’t be critical Sharelockholmes, it’s the only affordable database I know that has the data I need, albeit unadjusted.

Instinct tells me that none of these factors should radically influence the results. Pension accounting wouldn’t affect the ROC calculation yet it exhibits a very similar pattern to the ROA version of the magic formula, and delistings in the 2003-2010 data didn’t affect the average performance of the magic formula strategies.

But instinct is basically worthless, and reconstructing the database to include companies that delisted is beyond me.

Maybe we should ignore the data from 2000-2003. This would knock out most of the ‘dirty data’ at the expense of reducing the total number of portfolios from 40 to 28, reducing the time period of the test from eleven years to eight, and perhaps exclude a period of genuinely outstanding performance for the magic formula.

In the most recent eight years of the test, the marginally superior ROA version of the magic formula beat the FTSE 100 by about 8% a year as opposed to 13% for the whole period of the test, and the ROC version 7% as opposed to 12%.

Which is closer to the truth? I honestly don’t know, but either way, the magic formula has worked in the UK.

That’s good news.