Those interested in managerial ownership and firm value may be interested in a study by Morck, Shleifer, Vishny. The abstract is below, but in summary, firm value increases as management ownership, and therefore alignment, increases from 0% to 5%. As ownership increases form 5% to 25% management entrenchment reduces firm value. Finally above 25% the alignment effect reasserts and firm value increases. It would be interesting to see any difference in how ownership was achieved, i.e. founders vs option thieves.

The convergence-of-interest hypothesis suggests that a firm’s market valuation should rise as its management owns an increasingly large portion of the firm. On the other hand, the entrenchment hypothesis suggests that as management increases its ownership, the incentive to maximize value declines as market discipline becomes less effective against a larger shareholding manager. The authors attempt to reconcile these competing theoretical predictions by examining empirical data of firm management ownership and Tobin’s Q. The latter variable, equal to the ratio of the firm’s market value to the replacement cost of its physical assets, is used as a proxy for market valuation of the firm’s assets. A piecewise linear regression reveals a positive correlation between management ownership and Tobin’s Q in the 0% to 5% ownership range. From 5% to 25% management ownership, the relationship is negative, but at levels greater than 25% the relationship again is positive. The authors put forward a theory that the convergence-of-interest effect operates over the whole range of ownership, whereas the entrenchment effect reaches a maximum value at some less than 100% management ownership mark. Thus, at low levels, the convergence effect is predominant. At somewhat higher levels, the entrenchment effect becomes predominant. Finally, having reached a maximum value, the still-increasing convergence effect again becomes the predominant factor. Management Ownership and Market Valuation: An Empirical Analysis Morck, Shleifer, Vishny

Look beyond the numbers.

I’d like to thank Mike for commenting on this post on ROE. By focusing on the actual calculations, Mike made me realise that I failed to explain the main idea behind the post. ROE is an excellent ratio, it’s one of the first financial ratios you should put in your tool-belt. The next step is to understand the three business levers that underpin ROE; profitability, asset turnover and leverage. The product of those levers is ROE, they are the three main tools management have to enhance the return to owners.

Watching management control those levers is a glimpse into the quality of management.

It’s best to analyse both through time and across similar companies. Take a look at Westfarmers’ ratios. Data from ApsectHuntley. Click Image to enlarge.

Were Westfarmers managing for ROE when they bought Coles? Comparatively, Woolworths have doubled their profit margin, improved their inventory turnover and reduced their leverage to keep their ROE in a healthy mid twenty and up band, over the same timeframe. Woolworths is currently managed better, and according to MyClime selling at a good margin of safety.

Until now we’ve been looking in the rear view mirror, it’s time to look forward to consider what is coming. Westfarmers have the opportunity to improve, unfortunately the current price seems to assume they’re going to. Can WES improve their net margins, or at least stop the slide. I’ll leave you considering the future.

In this article on the five financial ratios you need, the author picks five decent ratios, yet combined they don’t even allow you to construct the basic accounting equation of assets, liabilities and equity. They chose; gross profit margin, net profit margin, current ratio, inventory turnover, return on owner’s equity. By replacing ROE by financial leverage (Assets/Equity), you can easily calculate ROE and determine liabilities. Drop gross profit as well, you’ve already got COGS and Sales in the other metrics. It’s a good list, yet fails to consider the importance of cash flow.

If you want to learn more about financial analysis then I recommend Analysis for Financial Management by Robert Higgins. Amazon has 92 used copied from $0.01. 

Disclosure: No position in WES or WOW. Amazon link; I wonder what my cut of $0.01 would be?

Mike was kind enough to tell me why he thought I went wrong with Neptune. His points were valid and I look forward to his post on Neptune. Here is my reply.

Thanks for your comment. You’re right, cash flow or more correctly owners earnings (shortcut by operating plus investing cash flows, CFO+CFI) showed the big shortfall that the shareholders funded. Cash flashed warning signs as I said on my watch list page and here on this post. “Neptune is not self funding, but they are creating value. It is not an appropriate investment for a defensive shareholder, but enterprising investors may consider it. Buying Neptune requires investors to buy into a growth by acquisition story” and “While I prefer to invest in companies that are both self funding and creating value from a Hewitt Heiserman’s It’s Earnings That Count perspective, I occasionally forsake my better judgment and dip my toe into unabashed growth stories like Neptune.”

As I come from a business background I look at companies as businesses with opportunities and risks and try to understand why their solvency, liquidity, profitability, valuation and activity ratios are as they are. To do that you need the story.

Owner’s earning is good, but you’ll seldom find it in high growth or acquisitive companies. Growth consumes cash in working capital and fixed assets, unless the company has Dell or Blue Nile like negative working capital. Cash flow, ROE, P/E and the plow back ratio are all handy ratios to have in your toolkit and sustainable growth is a great investing criteria.

If I come across a free copy of Value.Able I’ll read it. Roger is a good writer, but I prefer Damodaran, Heiserman, Greenwald, Klarman, Shiller, Fabozzi, Lynch, Tharp, Fisher, Greenblatt, Schwager, Neff, Pabrai, Montier, Grantham, CFA Level 1 Study Sessions, which Investopedia has good notes for, and more.  Ratios are good as filters and for a quick glance, but it’s good to go beyond them to know the business and current story. If you want a really good book to read then check out Hewitt Heiserman’s It’s Earnings That Count.

Mike or others please correct me if I’m wrong, but isn’t Montgomery’s formula simply another version of the dividend growth model, which Damodaran has a good page on. I know Roger uses a multiplier like most valuation models have since Graham published his earnings growth multiplier model. I do like Roger’s A1 style methodology and wonder if he got the idea from time management, as that’s where I first encountered the effectively of two criteria decision rules.  A1 rules work.

• Forward P/E equals payout ratio /cost of equity and growth rate.
• Payout ratio equals 1 – Dividend Growth Rate(g)/ROE or if you prefer g = Retention rate (rr)* ROE, you only need to know one of these as deriving the other is simple.
• From memory Clime do something similar. I’ll refresh my memory tomorrow when I attend two sessions of their’s at Melbourne seminars.
• Is Roger’s equity * payout ratio * multiplier + equity * plow back ratio * another multiplier?

Yes intrinsic value matters, yes you need to be able to work it out. Though keep in mind Buffett does it in his head, it’s not hard to get a close enough number. Writing off book value limits your stock universe as price/book is a great quick screen for value among insurers. I wrote about the power of book to market and how it has delivered Buffett like returns.

As Neptune was risky I only dipped my toe in with a 4% investment. While I should have sold when it was up 60% in two months or anytime on the way down I didn’t. That’s the hidden cost in exchange for a H1 average in my Masters and sitting the CFA Level 1 exams in the same year.

Mike’s post was timely as now that I’m on summer break I have a lot of catching up to do.

Neptune is currently suspended, Christian has gone, his roll-up strategy over extended the company and transferred wealth from investors to rolled-up company owners. Here’s the Chairman’s recap speech at the AGM. I should have limited risk to a 1.5% loss of the portfolio’s value, then it would have only been .75% annual drag. As it is even if Neptune fails it will only be a 1.5% annual drag for the last two years. That downside compares to the opportunity of 15-20% portfolio upside that a well executed roll-up can enjoy. Take a look at Middleby Corp for the type of returns that investors can get if it works well. MIDD is also a great example of why some acquisitive strategies work better than others. The human capital roll-up of buying out small entrepreneurial companies, like Christian pursued at Neptune, seldom works out and at most deserve a small investment kept on short lease. I got it right, except for the short lease.

It’s not over yet, I still own a call option on the new management team returning Neptune to profitability. With Gorgon coming in 2012 and a new experienced CEO I’d haven’t written off Neptune yet. Look for extreme bargain prices for a really cheap call on the turnaround, or keep it on your watch-list and watch the announcements, metrics and news flow closely.

Wikipedia quotes Buffett’s version of owner’s earnings as representing

reported earnings plus (b) depreciation, depletion, amortization, and certain other non-cash charges…less (c) the average annual amount of capitalized expenditures for plant and equipment, etc. that the business requires to fully maintain its long-term competitive position and its unit volume….Our owner-earnings equation does not yield the deceptively precise figures provided by GAAP, since (c) must be a guess – and one sometimes very difficult to make. Despite this problem, we consider the owner earnings figure, not the GAAP figure, to be the relevant item for valuation purposes…All of this points up the absurdity of the ‘cash flow’ numbers that are often set forth in Wall Street reports. These numbers routinely include (a) plus (b) – but do not subtract (c)

Here is what I said just after my Neptune investment.

I still don’t feel comfortable investing in NMS and am breaking a personal rule by investing when I have doubts. One of my favourite quotes is capital is scarce and investment opportunities are plentiful. Warren Buffett expressed the same sentiment when he compared investing to baseball without strikes. He said you can wait for the right pitch all day and there is no penalty other than lost opportunity. So, when the fielders are asleep, step up and hit the pitch.

NMS is not in my hitting zone, it’s a fast curve ball with the potential to strike me out.

Disclosure: Long Neptune Marine. Mistakes were made, lessons reinforced. Amazon affiliate payments on book links.

UPDATE:

Via trading markets Neptune Marine Services plans to raise $A80 million to improve its poor financial position. A share placement will raise $A28.3 million, with $A51.7 million from a renounceable rights issue. The capital raising will be priced at $A0.08 a share, while the stock last traded at $A0.205.

Discussion on Skewness

What is Skewness

Skewness is a measure of the asymmetry of probability distributions. Negative skew or left skew has fewer low values and a longer left tail, while positive skew has fewer right values and a longer right tail.

Image 1: Skewed Distributions.
Image source http://en.wikipedia.org/wiki/File:Skewness_Statistics.svg reprinted here under creative commons attribution CC-BY-SA-3.0

Why skewness in returns is important in asset pricing

Modern finance is heavily based on the unrealistic assumption of normal distribution. This discussion aims to highlight the importance of skewness in asset pricing. The primary reason skew is important is that analysis based on normal distributions incorrectly estimates expected returns and risk.

Harvey (2000) and Bekaert and Harvey (2002) respectively found that skewness is an important factor of risk in both developed and emerging markets. Harvey (2000) concluded “Risk measures implied by asset pricing theory, in particular world beta and coskewness work reasonably well in capturing the cross-section of average returns in world markets.”

Nassim Nicholas Taleb in Fooled by Randomness provides an excellent example of the importance of skewness.

In an analyst meeting Taleb predicted the market had a 70% of going up the following week. Fellow employees were confused as Taleb had a very large short position on SP500 futures and was betting the market would go down. To which Taleb replied, “my opinion was that the market was more likely to go up…, but that it was preferable to short it…, because, in the event of its going down, it could go down a lot.” Taleb (2007 p101).

Knowing that the market has a 70% probability of going up and a 30% probability of going down may appear helpful if you rely on normal distributions. However, if you were told that if the market goes up, it will go up 2% and if it goes down, it will go down 10%, then you could see the skewed returns and make a better informed decision.

E(r) = 0.7*0.02 + 0.3*-0.1 = -0.014

Investors must look beyond simple probabilities, mean and standard deviation and think in terms of uncertainty, expectation and magnitude of the outcome. Taleb (2007 p103) states “rare events are not fairly valued, and that the rarer the event, the more undervalued it will be in price”.

Research on Skewness

There is a large body of literature on skewness across various markets and asset classes. Evidence of skewness in assets has existed for more than three decades (Beedles, 1979; Alles and Kling, 1994; Chen, Hong and Stein, 1999) to name a few. More recently, Harvey and Siddique (2000) suggested that investors require payment for negative skew and expected return increases with negative skewness. Their results showed skewness exists in asset prices and that a pricing model incorporating skewness helps explain expected returns in assets beyond beta, size and book to market. They concluded, “systematic skewness is economically important and commands a risk premium, on average, of 3.60 percent per year.” As mentioned above Harvey (2000) found that the majority of developed markets have negative skew.

Damodaran (1985) was the first to highlight that negative skewness can result from the distribution of good and bad news from companies. Companies’ release more good news than bad news and bad news tends to be released in clumps.

Hong and Stein (1999) proposed another reason for skewness. Analysing the implications of short sale constraints they developed the following intuition. As the price of a share falls more information is unveiled; specifically the price at which market participants with differing valuations see value. Their differing views were not previously available to the market due to short sale constraints. This led to Chen, Hong and Stein (1999), to test whether shares which investors disagree more about as shown by increases in turnover, have higher skewness. Montier (2002)

Chen, Hong and Stein (1999) documented three major conclusions in their study into conditional skewness in stock prices.

In the cross-section, negative skewness is greater in stocks that: 1) have experienced an increase in trading volume relative to trend over the prior six months; 2) have had positive returns over the prior thirty-six months; and 3) are larger in terms of market capitalization.

Behavioural Finance Explanations of Skewness

While the existence of skewness is well documented the reasons for the skewness are less certain. Alles (2004) using simulated data concluded a combination of Alles and Kling (1994) hypothesis derived from Kahneman and Tversky’s (1979) prospect theory in conjunction with Brown, Harlow, and Tinic’s (1988) “uncertain information hypothesis” (UIH)  can explain negative skewness. Importantly their finding also showed a simple version of the geometric random walk model could not generate negative skewness.

Alles also provided two explanations for the tendency of skewness to be less negative than normal during market downturns and more negative during upturns, as observed by Alles and Kling (1994). As per prospect theory, investors’ perspectives are subjectively based on their reference point. They become less risk lovers when losing and risk averse when winning. According to UIH investors categorise uncertain news as either good or bad. It can be assumed investors’ categorisation is subjective based on their reference point. So in bad times, bad news is viewed less negatively while positive news is viewed skeptically.

Investors’ preference for positive skewness and aversion to negative skewness

Financial theory says that rational investors should prefer positive skew; however, evidence exists showing that investors also prefer negative skew.

The longshot bias illustrated by the popularity of lotteries, gaming machines and researched in horse racing is used to show investors’ preference for positive skew. Hodges, Tompkins and Ziemba (working paper), show that the longshot bias does exist in some options markets, but I have found no evidence that a broad cross-section of investors ‘suffer’ from this bias. The bias may be no more than the behaviour of risk lovers.

Harvey and Siddique (2000) found negative skew receives higher returns. They assumed that investors require payment for negative skew; however they did not prove that investors both correctly assessed and required payment for that skew. The excess returns could be market inefficiency at pricing improbable events.

There is evidence that highlights the inability of individuals to correctly assess probabilities. As Taleb (2004) points out “agents underestimate the extreme values of a distribution in a surprising manner; violations are far more excessive than one would expect: events that are estimated to occur less than 2% of the time will take place up to 49%.”

Behavioural finance suggests investors have a preference for numerous small wins and a single large loss over numerous small losses and a large win. A negatively skewed distribution provides the necessary environment for many small wins, as the majority of incidences are to the right. The reasons for this can be explained by prospect theory, which hypothesises that investors receive decreasing reward for further gains.

Lakonishok, Lee, Pearson and Poteshman (2007) noted covered call writing is the most popular option strategy. Covered calls entail capping upside returns while taking on downside risk, a negatively skewed strategy. Shefrin and Statman (1993) and Hoffman and Fisher (2010), found framing and risk aversion can explain investors’ predilection for covered writes.

Kahneman and Tversky’s (1984) suggested one way to reconcile a co-preference for negative and positive skew. They argued that “black swans” are neglected whereas longshots are overweighted. I suggest risk lovers overweight the longshots while the majority of investors prefer negative skew, i.e. a preponderance of small wins while neglecting the risk of the skewed distribution.

Asset pricing factors such as firm size and book-to-market ratio may be acting as a proxy for skewness

Harvey and Siddique (2000) recognised that book to market (HML) and size (SMB) effects may act as a proxy for skewness in asset returns. They found partial evidence for that when adding skewness alone or in conjunction with HML and SMB to industry portfolios produced similar results. Ajili (2004) in a study on the French Stock Market found “co-skewness and co-kurtosis don’t subsume the SMB and HML factors.”

Chung, Johnson and Schill (2004) found “it is conceivable that the SMB and HML loadings are such good proxies for the higher-order co-moments that, given problems of estimating higher-order co-moments, the Fama-French factors could be superior in actual use.”

Distinction between skewness in returns and co-skewness in returns

Skewness refers to the distribution of returns of a single asset while co-skewness compares the returns of the asset to the market, i.e. is the asset’s returns more (positively) or less (negatively) skewed than the market’s returns. Finance theory suggests investors generally prefer both positive skewness and positive co-skewness.

Conclusion

Skewness exists in most financial markets and is an important measure of risk most likely not subsumed by HML or SMB. It is still unclear why skewness exists though several compelling arguments have been made; including, good/bad news asymmetry, price discovery, prospect theory and uncertainty of information. Negative skew had been shown to receive higher expected returns. It is generally believed that investors have a preference for positive skew, though evidence supporting a predilection for negative skew also exists.

References

• Ajili, Souad, Size and Book to Market Effects vs. Co-skewness and Co-kurtosis in Explaining Stock Returns (December 2004). Available at SSRN: http://ssrn.com/abstract=634684
• Alles, L,. “Time-Varying Skewness in Stock Returns: An Information-Based Explanation,” Quarterly Journal of Business and Economics, Thursday, January 1 2004, http://www.allbusiness.com/public-administration/administration-economic-programs/941260-1.html, Accessed 16 July 2010
• Alles, L., and J. Kling, “Regularities in the Variation of Skewness in Stock Index Returns,” Journal of Financial Research (Fall 1994), pp. 427-438.
• Beedles, W.L., “Return, Dispersion and Skewness: Synthesis and Investment Strategy,” Journal of Financial Research (Spring 1979), pp. 71-80
• Bekaert, Geert and Harvey, Campbell R., Research in Emerging Markets Finance: Looking to the Future (September 11, 2002). Available at SSRN: http://ssrn.com/abstract=795364
• Brown, K., W. Harlow, and S. Tinic, “Risk Aversion, Uncertain Information and Market Efficiency,” Journal of Financial Economics ( 1988), pp.355-385.
• Chen, Joseph S., Hong, Harrison G. and Stein, Jeremy C., Forecasting Crashes: Trading Volume, Past Returns and Conditional Skewness in Stock Prices (December 1999). Available at SSRN: http://ssrn.com/abstract=194948 or doi:10.2139/ssrn.194948
• Chung, Y. Peter Peter, Johnson, Herb E. and Schill, Michael J., Asset Pricing When Returns Are Nonnormal: Fama-French Factors vs. Higher-Order Systematic Co-Moments. Journal of Business, Forthcoming. Available at SSRN: http://ssrn.com/abstract=503122
• Damodaran, A., “Economic Events, Information Structure, and the Return-Generating Process,” Journal of Financial and Quantitative Analysis (December 1985), pp. 423-434.
• Harvey, Campbell R., The Drivers of Expected Returns in International Markets (July 25, 2000). Available at SSRN: http://ssrn.com/abstract=795385
• Harvey, C. and A. Siddique, (2000), ‘Conditional Skewness in Asset Pricing Tests’, Journal of Finance, Vol. 55, No. 3, June, pp. 1263 –1296.
• Hodges, Stewart D., Tompkins, Robert George and Ziemba, William T., The Favorite/Long-Shot Bias in S&P 500 and Ftse 100 Index Futures Options: The Return to Bets and the Cost of Insurance. EFA 2003 Annual Conference Paper No. 135; Sauder School of Business Working Paper. Available at SSRN: http://ssrn.com/abstract=424421 or doi:10.2139/ssrn.424421
• Hoffmann, A. O. I. and Fischer, Tobi , Behavioral Aspects of Covered Call Writing: An Empirical Investigation (May 25, 2010). Available at SSRN: http://ssrn.com/abstract=1615405
• Kahneman, D., and A. Tversky, “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica, 47 (1979), pp.263-291.
• Kahneman, D., and A. Tversky,   “Choices,Values and Frames,” American Psychologist (1994), 39:4, p341-50 Accessed via http://www.poldracklab.org/teaching/neuroeconomics/kahneman-tversky_1984.pdf 14 July 2010
• Lakonishok, J., Lee,  I. Pearson, N. D. & Poteshman, A. M. (2007). Option Market Activity.
• The Review of Financial Studies, Vol. 20, No. 3, 813-857.
• Shefrin,  H.  &  Statman,  M.  (1993). “Behavioral  Aspects  of  the  Design  and  Marketing  of Financial Products. Financial Management”, Vol. 22, Issue 2 (Summer), 123-134.
• Statman, Mier, “How Many Stocks Make a Diversified Portfolio”, Journal of Financial and Quantitative Analysis, Vol.22 No.3 1987
• Taleb, Nassim N., ( 2007),”Fooled by randomness : the hidden role of chance in life and in the markets,” 2nd ed, Penguin Books

This post on biggest investors mistakes by the usually brilliant Jeff Miller at A Dash of Insight pushed my buttons. Jeff normally takes great care with his assertions and goes to great lengths to back them up with credible evidence, but in this case he leads off with an apples to oranges example.

Many studies have shown that individual investors, managing their own accounts, do about 5% worse than they would if simply buying an index fund.  The top investment managers have regularly beaten the averages, so it is a big spread.

I commented

You’re comparing average investors with top managers. While I know that was merely an introduction and not central to your point, I found it incongruent from a guy who normally takes such care with his assertions. On top of which there is plenty of evidence to show that agency costs are a permanent feature of fund managers and as Patrick said that the average manager under-performs.

The average investor contains all investors. Do you know of any evidence that analyses experienced investors, say 10 years plus experience and accounts over say $300k, or any other figures?

Another commenter replied,

“CXO Advisory in March cited an SSRN paper that showed Chinese investors with large accounts (presumably more experienced) had higher returns than those with small accounts. The difference was quite significant, and apparently due to reasons that might be expected – buying value stocks and trading less frequently. They also have links to additional studies.”

I decided it was time to pull together some of the literature on individual investors.

Can Individual Investors Beat the Market?

We document strong persistence in the performance of trades of individual investors. The correlation of the risk-adjusted performance of an individual across sample periods is about 10 percent. Investors classified in the top performance decile in the first half of our sample subsequently outperform those in the bottom decile by about 8 percent per year. Strategies long in firms purchased by previously successful investors and short in firms purchased by previously unsuccessful investors earn abnormal returns of 5 basis points per day. These returns are not confined to small stocks nor to stocks in which the investors are likely to have inside information. Our results suggest that skillful individual investors exploit market inefficiencies to earn abnormal profits, above and beyond any profits available from well-known strategies based upon size, value, or momentum. read more

Wow! Not a bad starter, but let’s keep digging. If you know of any research highlighting how individual investors can outperform then please leave a comment.

In general individual investors tilt the scales in their favour by looking to exploit the excess returns offered by small caps, value stocks and momentum stocks. There is plethora of research on all three.

Momentum

Research into momentum in stocks shows stocks do exhibit positive momentum over 3 – 12 months. While over the longer term of multiple years a negative correlation is found, i.e. momentum stocks eventually fall back to the pack.

• Momentum studies like this, Momentum Strategies by Louis Chan, Narasimhan Jegadeesh and Josef Lakonishok, reveal how past earnings surprises predict large drifts in future returns as analysts and the market are slow to respond.
• Momentum Profits, Non-Normality Risks and the Business Cycle by Ana-Maria Fuertes, Joelle Miffre and Wooi Hou Tan. This paper show that momentum profits are not normally distributed and, relatedly, that the momentum profitability is partly a compensation for systematic negative skewness risk in line with market efficiency.
• For more on momentum.

I’ll post on value vs growth and small vs large caps in separate posts, but for anyone unfamiliar with the large body of evidence the basic story is both value and small caps have in the past outperformed growth and large caps by significant margins. I do mean to imply that all investors should manage their own investors. I actually believe the contrary, most investors should invest in index funds with long term perspectives, investing more when markets are historically undervalued and less when they are overvalued. Alternatively they should seek out small cap value focused fund managers with a record of outperformance. As with everything there are exceptions. Self directed investing takes a lot of time and skill and is an art that few truly master.

Advantages of Individual Investors over Institutions

The following is a quick incomplete list, primarily to get some thoughts down for further investigation.

• Jack be nimble jack be quick. Individual investors can react faster to opportunities and change their asset allocation much faster than institutions.
• Patience. Institutional imperatives force most funds into high turnover strategies. Individual investors can wait for fat pitches.
• Small caps outperform. Large institutions can’t take meaningful positions in small caps. Even small institutions find it hard to take meaningful positions in small caps without pushing the price up on entry and down on exit.
• Management costs. With active fund managers charging between 1-2.5% and sometime with an added 20% of excess returns, individual investors are given a head start.
• Agency costs.  On top of fees are agency costs. Institutions’ primary aim is to make themselves money, while they’d like to make their investors money too, conflicts can and do arise.
• Flexible guidelines. Most funds are constrained by their strategies, which for the most part are designed to fit in with particular themes, e.g. large cap growth. Plus many are mandated to be 100% invested all the time. Talk about a handicap. Individual investors can invest in the best opportunities regardless of style, market cap or any other arbitrary restriction and they can move to cash when markets are overvalued.
• Comparison.  Most funds ‘need’ to follow relative return investing styles. The smartest investors all say absolute returns should be focused on, with relative returns a mere point of interest. Individual investors can focus on their absolute returns without fear of being fired for underperforming.

In fairness, institutions have many advantages over individual investors, but as they also have large marketing departments I’m sure you;ve already heard their side of the story. Finally I in no way mean to slate Jeff Miller and encourage readers to add his blog, A Dash of Insight, to their regular reading list.

Price Earnings Ratios as Forecasters of Returns

Robert Shiller and John Campbell initially published on P/E Ratio predicting later real returns in this 1988 paper.  In 1996 Shiller followed up the research with this easily digestible paper on the topic. In summary he found that the random walk theory does not look right and that current index valuations have a strong predictive ability for future long term returns. Over the short term noise prevents prediction, whereas over the long term, ten years, current valuation levels are predictive of future returns.

As I have said before the voting machine should be used for the short term, the weighing machine for the long term.

In this 2004 paper, Proxying for Expected Returns with Price Earnings Ratios, Charlotte Strunk Hansen and Bjorn Tuypens expanded on the work of Campbell and Shiller.

Long-run regression models using trailing earnings over price ratio to predict future returns suggested by Campbell and Shiller (1988, 2001) work quite well. However, in this note we show that this variable might result in a downward biased proxy for expected future returns. Instead we suggest using a moving average of the log of 1 plus the earnings price ratio when forecasting long-run returns. The empirical results for the S&P 500 show the superiority of our approach to existing ones.

So rather than only looking ahead and wringing our hands about the economy, the recovery, PIIGS and all the other noise, let’s try ignoring that for a moment and practice driving using our rear view mirrors.

The average ratio shown over Shiller’s data was 18.28. From 1811 to now the average cyclically adjusted P/E10, CAPE, is 16.36 and from 1950 on the average is 18.59. The current ratio is 20.31 indicating a slightly overvalued market which will return slightly less than average over the next ten years. Too many numbers? OK then, time for a graph.

Anyone paying attention to Shiller ten years ago would have been saved a lot of pain. As he and Campbell suggested the high CAPE ratio correctly predicting poor returns during the naughties. Shiller’s detractors point out that he said the high CAPE in 1996 would lead to poor returns over succeeding decade. Whereas the actual real returns over the next decade were a respectable 47%. I’d suggest that is simply a matter of trends going further and for longer than anyone can ever predict. Plus Shiller never claimed a perfect correlation. Like any stock market tool, it should not be used in isolation but in context.

Let’s look at some averages.

This chart shows the 10 year returns based on ranges of P/E10 CAPE (blue squares). The red diamonds are the number of occurrences. Keep in mind these are real returns adjusted for inflation, hence they appear considerably lower than the usually referenced nominal returns.

Now let’s drill down into a more relevant subset for the current ratio. This chart shows the average ten year returns for CAPE ratios around the current P/E10 CAPE of 20.31.

Naturally the more detailed I drill down into the data the less reliable any assumptions are. However, in general the trend is pretty obvious. The higher the P/E10 the lower the expected returns. P/E10 ratios around the current 20.31 deliver considerable less than the average return of 57% across the period used, 1950-2010.

Let me leave you with a similar warning to Shiller’s warning in his 1996 paper with a small adjunct. Driving by only looking in your rear view measure is hazardous and great caution must be used. Driving without rear view mirrors is also hazardous and great caution should be used. So look forward, but be mindful of the past.

The following article comes from one of the best discussion board posts I’ve read. The post is republished below with the permission of the author. This enriching and entraining article exemplifies a style of fusion investing, the fusion of business momentum and value. I hope you enjoy reading and thinking about this article as much as I did. It’s a fantastic example of looking at things from a new angle.

What did we do right in 2009?

One year of good return may be just a result of high tide lifting all boats or simply mean-reversion from a terrible year. Nevertheless, my biggest take-away from 2009 was a subtle but important change to my investment philosophy – I have changed my focus from “good and cheap” to “better and cheap”. I care more about change in fundamentals – I prefer a bad company that is getting better over a good company with no change in story. This new philosophy has led to solid stock picking, which generally out-performed the market with what I believe to be lower risk (“permanent loss of capital”). Equally important, this new framework gives me better guidelines to size my bets, especially betting heavily in situations where both the story is getting better and stock is cheap.

When I started investing a few years ago, I was firmly in the value investing school – concepts like “intrinsic value” and “Mr. Market”, coined by Ben Graham and popularized by Warren Buffett, clicked for me instantly. I spent time studying company fundamentals, coming up with an estimate of the intrinsic value, and trying to buy at a cheap or discounted price. In short, I was trying to buy “good and cheap”, and results were satisfactory.

However, I have come to realize the quality of the company and absolute discount to intrinsic value are not everything – one has also to consider the time and factors it takes for the discount to narrow, which typically depend on the business cycle. Thus my new approach comes down to balancing between value and momentum. Value refers to the price paid for the business. Momentum, not to be confused with price momentum in quant and technical analysis, refers to business momentum, i.e. how well the business is doing. Improving momentum can come in the form of higher margin, accelerating topline growth, or improving ROIC. With the exception of select great companies in their growth phase, most companies’ stock price and business momentum move in cycles/curves similar to sine waves with peaks and troughs.

These two curves are closely related – when business momentum is good, stock price tends to go up, and vice versa. However, there is often a lag between the two curves, and depending on the part of the cycle, stock price will react to the change in business momentum very differently. I believe this is the crux of investing – how you identify which part of the cycle the company is in, which drivers to watch for and which valuation metrics to use. For example, earning revision is a powerful factor but completely useless at business peaks and troughs. P/E may be a good valuation metric in general, but unadjusted for margins, it is useless or even dangerous at extremes. [I stopped highlighting here as it's all so good the entire article should be highlighted!]

For example, assume a retailer’s intrinsic value is $20, and buying at $15 may give an expected return of 33%. However, the same $15 price may correspond to two points on the momentum curve – one where the curve is turning up (story getting better) and the other where the curve is trending down. In the former case, you will probably get to $20 in 6-12 months. In the latter case, you may have to wait 18-24 months before the retailer corrects excess inventory and produces positive SSS (curve turning up again) to reach the $20 intrinsic value.

There are two obvious problems with buying at the latter point. First, time adjusted return is obviously inferior. Second, the stock price may first plunge to $6 before recovering. While a pure value investor may think a lower price makes it a better buy (even more margin of safety), reality is that an adverse price movement will slowly but surely inject doubt into my mind. Have I made a mistake? Is this a value trap? Very seldom does stock price move down without some deterioration of business fundamentals and some changes to the initial investment thesis. So unless one has an iron stomach (I don’t), it is very tough to keep calm during the price downdraft and continue to average down. There is an even bigger issue – if you are prepared to average down, chances are that you will not buy a full position initially, and inevitably you will end up establishing similar-sized partial positions for all new ideas. Yet some of those ideas will have good business momentum and they are your surer bets, so you lose potential profits in positions that actually have the best risk/time adjusted return.

So doesn’t quant investing capture “better and cheap”, as preached by the noted quant investor Cliff Asness? Yes and no. I believe there are two problems with quant investing. First, it mistakes cause with effect – price momentum is the result of business momentum, and while the two will resemble each other at certain part of the cycle, they will diverge significantly at critical turning points. Second, the effectiveness of various factors differs significantly from industry to industry as well as at different parts of the business cycle. Quite simply, quant investors lack the domain knowledge of each industry and use the same factors or same weightings across sectors during different points of the cycle.

For example, quant investors will universally use factors such as earning revision, revenue/EPS surprise/breadth to capture business momentum. While this does a satisfactory job overall, it will not capture key drivers for each industry, which often cannot be retrieved from standardized financial statements, such as inventory/store for retailers, or asset inflows for asset managers. Often changes in these key drivers will long precede actual changes in earnings, so generalized quant investing could easily miss the turn. As another example, six months ago, both KIRK and ARO got the highest rating in our internal quant system, yet the two retailers could not be more different in terms of where they were in the business and margin cycle, and the subsequent divergence in stock performance illustrated the flaw in the quant investing approach.

I certainly do not want to leave the impression that other investing approaches are inferior. Indeed, there are many ways to achieve success in investing, and everyone needs to find approaches to fit his or her own traits. I believe I have found mine by balancing between value and momentum. Put simply, I aim to invest in situations where fundamentals are about to turn or have turned while valuation is reasonable. I am certainly not reinventing wheels here, as this is the approach advocated by both Peter Lynch (“catching the turn”) and Warren Buffett (“What we really like to see in situations is a condition where the company is making substantial progress in terms of improving earnings, increasing asset values, etc., but where the market price of the stock is doing very little while we continue to acquire it”).

Well, this approach may sound good on paper, but how many of these “perfect” situations exist, given how efficient market is with so many hungry and smart investors poring over every corner of the market? I believe these opportunities happen more often than one may think, especially if one can invest in small-cap or micro-cap land. For example, I monitor about 50 names closely in the retail industry (which I shamelessly consider to be my circle of competence). This year alone, I identified 4 separate names that fit the criteria. They respectively returned 50%, 70%, 100% and 900%.

One may counter that retail stocks have done very well in general this year and question whether throwing darts randomly would have generated similar if not better results. I would argue that much of the return (especially the out-sized ones) was hope-based, and rational investors could not have predicted those returns ex-ante with any confidence to place a big bet, as some of those names could easily turn out to be zeros. Yet in all four names I identified, I was reasonably certain of the business momentum and earning surprise, and could accordingly place out-sized bets (10%+), with confidence that even if it did not play out according to plan, I would suffer very small losses due to valuation. While hindsight is 20/20, I could also identify at least two retail names annually over the last few years that fit my “better and cheap” criteria. So they definitely occur, and one just needs to have the patience and courage to bet big when they do come along, usually when market is bad. Those situations can occur in large-cap stocks as well, such as FDX throughout this year. FDX had over $20B market cap, was followed by 25 analysts, yet the stock was at trough EV/sales, even though earnings had bottomed and was poised to recover through cost cuts and market share gains. Earning estimates have moved up 60% in 6 months and stock went up over 150%.

As with anything in investing, there are also drawbacks to my approach. One is depth vs. width – I need to be able to identify and evaluate key drivers for the companies and industries, and this takes significant amount of time. The rarity of these “perfect” situations forces me to turn over a lot of rocks. To date, I am reasonably comfortable with retail industry, and to a much lesser degree with software, asset managers and transport industries. I may soon reach (if not already) a point where I can not physically monitor more names. The other problem is scalability – most of my top ideas are in small to micro-cap land, so it is questionable whether my approach can really handle more than say $50-100M of assets. But that will be a nice problem to have, and I suspect I will just have to make the trade-off between absolute performance and AUM.