Unlike other measures, this is comprehensive; it leaves nothing out. Further, it is not something that has to be calculated: Once you have reformulated balance sheets and income statements as all good value investors have, it just falls out from the OI number and beginning and ending NOA for the period.

An alternative calculation through the financing activities is:

That also drops out from reformulated statements.

A factor model projects the expected return from holding a stock by multiplying the expected return for a set of “factors” by the stock’s sensitivity to that factor return and summing for all factors:

The ellipse (…) indicates their can be additional factors, 4, 5, 6, and so on. The factors are   usually seen as risk factors with the  the sensitivity of the given investment to that risk. For a factor model to be legitimate, Alpha is zero if the market is efficiently pricing factors or non-zero if mispricing risk. So a factor model is both a model to capture the risk of investing but also for identifying abnormal returns (alphas).

The Capital Asset Pricing Model (CAPM) is such a model (with only one factor):

where the sole Factor₁ is the expected return on the market. That model was shown to not work^2M“beta is dead”^2Mthus an attempt to identify additional factors in a “multifactor model.”

Now, what are these additional factors? There have been attempts to identify them with theory but the main approach has been empirical: Search in the data for measures that predict returns, then form factors with these measures. This is data mining with no theory or even explanation of why these are the factors we want. At last count, over 400 potential factors have been “discovered” by dredging data! When factors are “discovered,” there remains the additional task to get the expected return to the factors. The tricky matter of getting the expected return on the CAPM is compounded with additional factors. Then there remains the problem of estimating the betas on the factors.

The book has been quite skeptical about the CAPM, and your authors are more so with multifactor models. They appear to work well in explaining returns in the cross section. However, applying them out of sample (in real time) they don’t work and it’s in real time that the investor works. So, when you see an investment firm touting returns from factor investing, remain skeptical. They are probably working with doubtful models or doubtful estimates of factor returns. They may be reporting in-sample correlations or reporting real-time returns in periods when the strategy just got lucky Or, if the model happens to capture some aspect of risk, the returns are just reward for risk exposures.

The most ubiquitous model is the Fama and French model in its various forms. Here is an excerpt from an interview with Fama:

WALKER: In your opinion, what’s the current best asset pricing model? Is it the five-factor model?

FAMA: I don’t know. I wouldn’t claim that. It does well on the things it was designed to explain, both nationally and internationally. But there are contradictions of it. So it’s like every other model. There are things that it can’t explain. So I would say it explains the things it was designed to explain, and they’re really important. A lot of money is managed based on those things. But is it the best model? I hope not.

I would like to see… I don’t want more factors. I want less. I want simpler models that work, not more complicated models. So I’m still hoping that it’ll last—I will last—to the point where something good comes along that says, “I don’t need five; here are two that’ll do the trick.”

WALKER: Yeah. More parsimonious.

FAMA: Yeah, right, exactly.

WALKER: Because the models with more factors feel like you’re just overfitting to the data.

FAMA: Right. You’re just data dredging.

WALKER: Data dredging, yeah. Have you developed any theories behind any of the factors that you added to the CAPM?

FAMA: Well, so the three-factor model basically added a size factor, small stocks versus big stocks, and a value-growth factor, value versus growth being the second factor. And there was a little bit of intuition in those, in the sense that everybody would think that small stocks are more risky than big stocks. Everybody would kind of agree that value stocks tend to be poorly performing companies. Maybe the market requires higher expected returns for those.

But multifactor asset pricing requires something in people’s tastes that make them have negative attitudes that will persist. So if you tell me that after this discovery of these things, value factor, small stock factor, people pile into them because they’re really not concerned that the stocks are small or that they’re poorly performing companies, they only care about the expected return. Well, then I get a problem because I think that will erase it. I think that’ll nullify the model on its own.

The problem is you won’t know if that happened or not. So those models have not done as well in the last 15 to 20 years of data. But that’s a drop in the bucket as far as model testing goes. That’s the reality of it. You basically need a lifetime of data to test an asset pricing model

https://josephnoelwalker.com/eugene-fama-156/?ref=the-joe-walker-podcast-newsletter

For reasons why factor investing fails, see

Arnott, R., C. Harvey, V. Kalesnik, and J. Linnainmaa. 2019. Alices’s Adventures in Factorland: Three Blunders that Plague Factor Investing. Journal of Portfolio Management 45 (4), 18-36.

As both earnings and book value are determined by the accounting, so it book rate of return, ROE. That is the key to understanding the trap in value vs. growth investing, as chapter 12 explains.

Here is an example of the new presentation from the proposal:

This book stresses that growth is risky, so higher estimated growth typically requires a higher discount rate. But the study reported that, for 45% of analysts, the higher the discount rate estimated, the lower the growth rate in the terminal values in their valuation models,

See Décaire, P., D. Sosyura, and M. Wittry. 2024. Resolving Estimation Ambiguity. At https://ssrn.com/abstract=4953859.

See Wahlen, J. and M. Wieland. 2011. Can Financial Statement Analysis Beat Consensus Analysts’ Recommendations? Review of Accounting Studies 16, 89-115.

Source: Thesis by Jing Yuan for the Masters Program in Accounting and Fundamental Analysis, Columbia Business School, Effectiveness of Security Analysts’ Recommendations: Insights from China and the U.S., December 2024.

Distribution of Ratings

The following graphs summarizes the distribution of ratings. The ratings use the I/B/E/S standardized IRECCD ratings ranging from 1 to 5, where 1 represents Strong Buy, 2 represents Buy, 3 represents Hold, 4 represents Sell, and 5 represents Strong Sell. CSMAR applies standardized 1-5 recommendation ratings for Chinese analysts: 1 represents Buy (买入), 2 represents Overweight (增持), 3 represents Hold (持有), 4 represents Underweight (减持), and 5 represents Sell (卖出). The 1-5 rankings are broadly comparable.

Ratings in both countries are shewed towards BUY, more so in China.

Sell recommendations are relatively rare. Is this because the firms that analysts select to cover are those with better prospects? Is it because analysts tend to have in upward bias? Is it because analysts associated with brokerage houses are inducing us to trade?

Recommendation revisions are viewed as the most informative feature of analysts’ reports. The following gives the frequency of recommendation upgrades and downgrades from one level to another.

The U.S:

China:

Do analysts’ recommendations revisions predict stock returns? Here are the findings from this sample period. The tables give the average return for holding periods following the revision. Returns are excess (abnormal) returns over the market return, that is, the return for the target stock minus the return on the market for the period. *** indicates statistical significance at the 1% level, ** at the 5% level.

U.S.:

China:

Forecasting score = w₁A₁ + w₂A₂ + w₃A₃ + ……

where the A numbers are the drivers and the w are the weights given to the drivers.

This has been attempted, though can probably be improved upon. (Get to work!) The score is called an S-score (an income sustainability score) with a high S-score indicating a higher probability of RNOA increasing and a low S-score indicating a higher probability of RNOA decreasing.

Here is how the S-score tracks RNOA:

Year 0 is the year that the financial statement analysis to calculate the S-score is carried out. A high S-score is one in the top 1/3 of scores, a low S-score is one in the lowest 1/3. In the plot, the S-scores forecasts the actual RNOA five years ahead. The difference between the future RNOAs for low and high S-scores is quite significant, about 4%.

The S-score also predicts stock returns. Here are the average difference between returns from investing in high S-scores versus low S-scores, adjusted for firm size:

These are returns from going long on high S-scores and short on low S-scores. They are positive almost every year. The graphs need to be updated for post-2002 years.

See Penman, S., and X. Zhang. 2006. Modeling Sustainable Earnings and P/E ratios with financial Statement Information. At https://ssrn.com/abstract=318967.