Guerrilla Approaches to Finding and Evaluating Stocks Marc H. - - PowerPoint PPT Presentation

Guerrilla Approaches to Finding and Evaluating Stocks

Marc H. Gerstein mgerstein@yahoo.com Portfolio123.com AAII/Better Investing, Washington DC – 4/18/2015

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Marc H. Gerstein spent his career analyzing stocks, educating investors, and helping to develop stock screening platforms at Value Line and various web

• sites. He is presently Director of Research at Portfolio123 and Editor of Forbes

Low-Priced Stock Report. His commentary can be found on SeekingAlpha.com, Forbes.com and Harvest (hvst.com). He has authored three books, Screening the Market (Wiley, 2002), The Value Connection (Wiley 2003), Atlas Upgrades: Objectivism 2.0 (Create Space, 2013). He is presently working on a novel with a Wall Street setting, and a book with Stanford’s Dr. Charles Lee based on the latter’s course in “Alphanomics.”

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The Agenda

Recalibrating the Investment Mindset Case Studies: Developing Strategies Easy-to-Use Excel Valuation Tools

Organizing and Summarizing your analysis of a stock consistent with the approach to be discussed today Measuring the relative roles of “Noise” and “Value” in a stock price A very simple, practical, usable discounted cash flow type model based on “Residual Income”

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Recalibrating the Investment Mindset

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The Starting Point

Own great companies whose shares can be purchased at great (cheap!) prices Own companies with sustainable competitive advantages Own companies with wide economic moats Own companies with great management teams Don’t chase a lot of stocks but be patient and wait to swing at a “fat pitch” Research the daylights out of prospective investments and put the lion’s share of your money into just a few “best ideas” Etc., etc., etc.

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Oops

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It Sounds Wonderful On Paper, But in the Real World . . .

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The Problems . . .

Traditional guru advice is so sound and so well disseminated, that today everybody and anybody is a Buffett wannabe, a Graham follower, a Magic Formula devotee, etc. Really, truly, genuinely great companies are scarce and their shares tend to get popular and, hence, expensive

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Act Like an Investor, Not a Foodie Judge

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If there’s a teeny-bit too much seasoning, live with it!

My Investment Hero: Monroe Milstein (Founder of Burlington Coat Factory)

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The “Milstein Moment”

• The Bloodletting

On a circa 1990s conference call, Wall Street analyst were taking the Burlington Coat Factory CFO to task, and rather harshly, for disappointing sales numbers The CFO was having trouble articulating a reason that was considered satisfactory by the analysts

• The Rescue

CEO Monroe Milstein, who typically said little during these calls, broke in: “Gentlemen,” he said. “I’ve been in this business for many years. We have good months and we have bad months. This was a bad month.”

• The outcome

Prolonged silence The analysts were rendered speechless The call then proceeded to an uneventful conclusion

• Epilogue

Milstein cashed in huge when the company was sold to Bain Capital in 2006 for $2.06 billion

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Don’t Rush to Judgment

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Life is Harsh: Learn to Accept Corporate Warts

Too many investors are devoting too many hours to quests for perfection, and finding themselves disappointed by when

Subsequent events reveal that the supposedly great company was more human than they had realized, and/or The price they paid for the great-company shares turned out to be fart less of a bargain than they had originally supposed

On the other hand, too few investors appreciate the

• pportunities available in companies that are plain-vanilla,

mundane, ordinary, mediocre or downright lousy

The shares can benefit as the companies improve (i.e. from horrendous up to mundane) The shares tend to be cheaper

It can be great to buy a company with 25 units of Pathetic if the stock is priced as if the company had 40 units of Pathetic

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Divine Revelation

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On Predictions

Disciple (a/k/a John Denver): “God, can you predict the future?” God (a/k/a George Burns): “Of course I can predict the future; as soon as it becomes the past.”

• Source: “Oh God!” (Warner Bros. 1977)

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Human Insight - Aristotle

“[T]he habits of perfected Self-Mastery and Courage are spoiled by the excess and defect, but by the mean state are preserved.” “At all events thus much is plain, that the mean state is in all things praiseworthy, and that practically we must deflect sometimes towards excess sometimes towards defect, because this will be the easiest method of hitting on the mean, that is, on what is right.”

From Nicomachean Ethics, Book II (D.P. Chase Translation)

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Human Insight – Lao Tzu

Holding a cup and overfilling it – Cannot be as good as stopping short – Pounding a blade and sharpening it — Cannot be kept for long Gold and jade fill up the room – No one is able to protect them – Wealth and position bring arrogance – And leave disasters upon oneself When achievement is completed, fame is attained – Withdraw

• neself – This is the Tao of Heaven

Lao Tzu, Tao Te Ching, Ch. 9 (Derek Lin Translation)

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Relevant Buzz Words

• Buzz Words for Aristotle and Lao Tzu

Virtue The Mean Withholding Stopping Short

• Buzz Word for Us

Mean Reversion Corrections Overbought and Oversold Business Cycles Economic Excesses Supply and Demand (market forces correct excesses and deficiencies in pricing and units) Company Transition (as growth companies mature) The overpowering of economic moats

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The Destination

• Be an Investor, not a foodie judge

Don’t obsess over finding perfection

What looks to some “gurus” like a fat pitch is more likely a slow-moving knuckle ball that breaks erratically and flutters frustratingly past your frantic swing

Don’t be judgmental

Accept normal warts

Recognize that extremes are not sustainable

Embrace the ordinary, or worse

Embrace the ordinary

• Invest like a human

Recognize and even embrace our inability to know the future

Diversify, diversify an diversify

Focused portfolios (most money in a few best ideas) are nonsense. They presume too much about our ability to know the future, and if we could know the future, there’d be no reason not to put 1200% of assets into one’s single favorite idea

Accept that it’s better to be “vaguely right than precisely wrong”

British Philosopher-Logician Carveth Read

Don’t fret about being wrong, just try to be less wrong than others

• Dr. Aswath Damodaran, NYU Stern School of Business

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Case Study: Developing a Strategy

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Introduction to Strategy Development

All of these strategies were developed on Portfolio123 using screens and ranks If you subscribe to Portfolio123, you will be able to replicate tem and modify as you wish If you are not on Portfolio123, you can work with these ideas based on financial data you can find in Stock Investor Pro and on several web sites (e.g., Morningstar, MSN Money, Yahoo! Finance)

You’ll see as we go along that it’s not about precise formulas It’s about broad ideas regarding what to look for A very important theme will emphasize the “generalist” idea rather than the “specialist”

i.e., a stock that is decent in a lot of respects (and may or may not be excellent in some and/or dreadful in others); as opposed to a stock that seems excellent under a particular formulation You can apply this approach whether or not you use screeners, etc.

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How We’re Going to Implement Our New Mindset

• We’re going to seek adequacy or OK, as opposed to excellence

And we’ll be open to inadequate, or bad, if we can get indications of improvement

• We’re going to seek “fundamental generalist” situations rather than

specialists

Example: Prefer company A (score of 70 of better under, perhaps 8 out of 10 factors and no factor worse than 50) over company B (scores of 90+ in two factors and below 30 in all others)

• We’re going to tolerate redundancy

Data oddities are ubiquitous and unavoidable, so we’ll use “factor diversification” to diminish the impact of oddball items, such as:

• the 643% EPS gain that would have been 3% but for a non-recurring gain
• r a sales decline of 45% that would have been seen as 12% growth but for a major

divestiture

• We’re going to embrace and celebrate stylistic diversification

Even if you’re a committed value investor, would it pain you to take a big profit by selling to an algorithmic day trader?

• So why not allow your models strategies to include ideas that might make stocks

attractive to the latter?

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An Idea Library

• We’ll start by looking at some ranking systems I created and use on

Portfolio123

Unlike a screen, which seeks a small number of companies that pass all of a set of yes/no filters, ranking systems assign best-to-worst scores to all companies A multifactor ranking system is one based on more than one factor, as

• pposed to a simple sort based on a single factor

Other well-known ranking systems: Investors’ Business Daily (Stock Checkup), Value Line, Morningstar, Schwab, MSN, The Street.com, etc.

• They’re all sensible. It’s not about specific formulas but in how you sue the information
• If you want, especially if you don’t use a quant platform, you can make an

checklist based on these factors

Don’t get too precise Sample score for each item

• Great (5 points), or
• OK (4 point), or
• Bad (-2 point)

Choose a scoring system that will

• Allow generalist companies to wind up with good grades
• Make it hard for specialists to dominate
• And which won’t get carried away by negative unless there are too darn many of them

See next slide for sample spreadsheet

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The Portfolio123 Comprehensive: QVGM Ranking System

Basic: Quality Ranking System (25%) Basic: Value Ranking System (25%) Basic: Growth Ranking System (25%) Basic: Momentum Ranking System (25%)

The Portfolio123 Basic: Quality Ranking System

• Margins (25%)

Trailing 12 Month (TTM) Operating Margin (75% - higher is better) 5 yr. average Operating Margin (25% - higher is better)

• Turnover (25%)

TTM Asset Turnover (100% - higher is better)

• Returns on Capital (25%)

TTM Return on Investment (30% - higher is better) 5 yr. average Return on Investment (40% - higher is better) TTM Return on Equity (10% - higher is better) 5 yr. average Return on Equity (20% - higher is better)

• Finances (25%)

Latest Current Ratio (30% - higher is better) TTM Interest Coverage (45% - higher is better) Latest Total Debt to Capital (25% - higher is better)

The Portfolio123 Basic: Value Ranking System

Value Based on Income Stream (65%)

Based on Earnings (50%)

PE based on Trailing 12 months EPS (33.33%) PE based on consensus estimate of Current Yr. EPS (33.33%) PEG ratio with G based on estimate of long-term growth rate (33.33%)

Other (50%)

Price/Trailing 12 month Sales (50%) Price/Trailing 12 month Free Cash Flow (50%)

Value Based on Assets (35%)

Price/Book (100%)

The Portfolio123 Basic: Growth Ranking System

EPS Growth (75%)

Basic Growth (50%)

YTY growth in latest quarter (higher is better – 33%) Trailing 12 Month (TTM) growth (higher is better – 33%) 5 year growth rate (higher is better – 33%)

Acceleration in Growth (50%)

Latest qtr. growth compared to TTM (higher is better – 50%) TTM growth compared to 5-year growth TTM (higher is better – 50%)

Sales Growth (25%)

Basic Growth (75%)

YTY growth in latest quarter (higher is better – 33%) Trailing 12 Month (TTM) growth (higher is better – 33%) 5 year growth rate (higher is better – 33%)

Acceleration in Growth (25%)

Latest qtr. growth compared to TTM (higher is better – 50%) TTM growth compared to 5-year growth TTM (higher is better – 50%)

The Portfolio123 Basic: Momentum Ranking System

Share Price Change (65%)

Last 120 days (20% - higher is better) Last 140 days (20% - higher is better) Last 160 days (30% - higher is better) Last 180 days (30% - higher is better)

Technical Indicator (35%)

20 Day Up-Down Ratio* (50% - higher is better) 60 Day Up-Down Ratio* (30% - higher is better) 120 Day Up-Down Ratio* (20% - higher is better)

* Volume in up days in defined period divided by volume in down days

The Portfolio123 Basic: Sentiment Ranking System

Estimates (50%)

% 4 Week Change in Consensus Current Yr. EPS Est. (higher is better - 33.33%) % 4 Week Change in Consensus Current Qtr. EPS Est. (higher is better - 33.33%) Standard Deviation of Current Qtr. EPS estimates/absolute value of consensus estimate (lower is better - 33.33%)

EPS Surprise (30%)

Surprise % most recent quarter (higher is better - 65%) Surprise % second most recent quarter (lower is better - 35%)

Analyst Recommendations (20%)

4-week change in consensus recommendation (more bullish is better - 75%) Current consensus recommendation (more bullish is better - 25%)

What Works . . . Gerstein Style

• Goal: Similar to what James O’Shaughnessy did in What Works on Wall

Street

• Difference is that here, we are not looking to articulate universal truths

but are instead creating strategies that can be narrowed to the point of selecting manageable (approx. 15 stocks) portfolios

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Testing Value Factors

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Assessing Value

• Value seems very promising on a multifactor basis

Consistent with what we saw above regarding the importance of value

• But chart is erratic, suggesting we need more in order to have a truly

complete strategy

It’s about not just value per se; it’s about value thoughtfully applied

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Testing Growth Factors

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Assessing Growth

• It’s not great enough to stand on its own, but there’s enough here to merit

consideration

• Pictorial presentation of multifactor growth ranking system (unimpressive

suggesting a definite need for more work):

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Testing Quality Factors

Surprisingly lackluster considering the theoretical strength of these metrics

ROE is the penultimate measure of a “good company”

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Testing Sentiment Factors

There is something to estimate revision, as theoretically weak as it seems

Think about this later, when we discuss “noise” as distinct from “value”

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Testing Multifactor Approaches

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Top 15 per QVG Ranking System

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Top 15 per QVGM Ranking System

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QVG vs. QVGM

Based purely on overall 10-year backtest results, QVG looks slightly superior

Not clear the difference is especially significant

Consideration of the charts, however, suggests a nod to QVGM

More consistent performance over longer periods of time

These multifactor sorting/ranking approaches, although not perfect, do show promise

The key is to do what successful sales people do; prospect from “pre-qualified” lists (as opposed to, say, the white pages) Screens are a great way to create pre-qualified lists

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A Very Simple Screen

Choose stocks from within Russell 3000 universe PE (using estimate of next year’s EPS) < 15 ROE trailing 12 months > 25% EPS gain vs. year-ago quarter < 15%

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Testing Performance of All Passing Stocks (usually 40-70 of them)

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Choose 15 Stocks at Random

• Decent in spots but erratic; “good” periods likely due to chance

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Top 15 per Value Ranking System

Well that’s disappointing! The value ranking system seemed promising on its own.

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Let’s Change The Screen

There’s a dramatic increase in the number of factors being considered (note the addition of two rules using multi-factor ranking systems), and the carryover rules now are much less demanding in terms of excellence

Choose stocks from within Russell 3000 universe Forward PE is in the bottom 65% of the universe Trailing 12 month ROE is in the top 25% EPS growth rate vs. year ago quarter in top 25% Score under Sentiment ranking system is above 80 Score under QVGM ranking system is above 80

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Top 15 per Value Ranking System

• Now we’re starting to make progress

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Let’s Relax the Screen Even More

ROE Rule

Old

Company in top 25%

New

Company in top 50%

EPS Growth Rule

Old

Company in top 25%

New

Company in top 50%

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Top 15 per Value Ranking System

• Yes! It looks like we’ve got something.
• But I notice way too many Finance stocks in the result set

Fine for some, but not my cup of tea

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Tweaking the Screen Again

Add a rule eliminating companies in the Financial Sector Tweak the PE rule

I still want bottom 65% But instead of bottom 65% relative to all stocks, I now want bottom 65% relative to industry peers

The original rule caused the portfolio to tilt heavily toward Financial; stocks, which normally carry relatively low PEs

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Top 15 per Value Ranking System

• This is usable!

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Easy-to-Use Excel Valuation Tools

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Tool 1: Stock Analysis Excel Template

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The Formulas

• Formula for G14

=((D14*$F$3)+(E14*$F$4)+(F14*$F$5))*C14

• Copy to all other cells in the same column
• Formula for H14

=IF(F14+E14+D14<>1,"***ERROR - Place 1 in just one, column)","")

• Copy to all other cells in the same column
• Formula for G9

=SUM(G14:G29)

• Its’ the sum of all columns for which we can have a score
• Formulas for:

M14 =D14*C14*$F$3 N14 =E14*C14*$F$4 O14 =F14*C14*$F$5

• Copy Row M14, N14 and O14 down through Row 29

M30 =SUM(M14:M29)

• Copy M30 to N30 and O30

D30 =M30/$G$9

• Copy D30 to E30 and F30

G30 =SUM(D30:F30) D31 =IF(ABS(E30)>0.65,"Company is a generalist","Company is a specialsit")

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Tool 1: Noise-Value Calibrator

• 1. The Idea
• 2. The Calculations
• 3. The Excel Spreadsheet

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Noise-Value Calibrator: The Idea

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Starting Point: “Beat the Gun”

Traditional notion

Investors buy stocks they believe will deliver good returns

John Maynard Keynes

[T]he professional investor is forced to concern himself with the anticipation of impending changes, in the news or in the atmosphere, of the kind by which experience shows that the mass psychology of the market is most influenced . . . . The social object of skilled investment should be to defeat the dark forces of time and ignorance which envelop our future. The actual, private object of the most skilled investment to-day is “to beat the gun”, as the Americans so well express it, to outwit the crowd, and to pass the bad, or depreciating, half-crown to the other fellow.

John Maynard Keynes, The General Theory of Employment, Interest and Money (Signalman Publishing, 2010 Kindle Ed.), p. 101, Emphasis supplied.

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Implications of “Beat The Gun”

We recognize that some will trade on the basis

• f reasons that aren’t “right” and . . .

That many who do so are knowledgeable investors who really ought to know what’s right and what isn’t, and . . . That what they’re doing is perfectly rational

It’s perfectly rational to pay $50 for a stock you know is worth $40 if you expect it to go to $60

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Fisher Black

He not only acknowledges the existence of such “noise” trading but goes further to state that it’s essential to the functioning of the market

If all traders did only what’s “right,” no trading could ever take place since one party would have to be willing to make a mistake. But . . . “Noise trading provides the essential missing ingredient . . . . With a lot of noise traders in the market, it now pays for those with information to trade. It even pays for people to seek out costly information which they will then trade on.”

• Fisher Black, Noise Papers and Proceedings of the Forty-Fourth Annual Meeting of the America

Finance Association, New York, New York, December 20-30, 1985, Journal of Finance, Vol. 41, Issue 3 (July 1986), p. 529-543 at 531 )

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Implications of Black

Noise trading is not only understandable, but the market can’t function without it We can’t hope improved investor education or an outbreak of mass rationality will eliminate noise and cause everybody to do the right thing It simply won’t happen, not now, not ever

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The Next Step: Robert Shiller

The market consists of two types of traders

Smart-money investors

Make decisions based on information relevant to valuation subject only to wealth constraints

Ordinary Investors

Everyone else; i.e., those who do “not respond to expected returns as optimally forecasted”

They often over-react to news and follow fads since they “have no model or at best a very incomplete model of the behavior of stock prices . . . .”

Robert J. Shiller, Stock Prices and Social Dynamics Brookings Papers on Economic Activity, Vol. 2 (1984) p. 477

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A new theory courtesy of Shiller

Note: This is a bona fide theory. We’re only seeing how concepts interact. We’re not supposed to try to plug in any numbers. Pt = Qt + Yt

Where

Pt = Demand for all shares in the market at time t Qt = Demand for shares by Smart Money at time t Yt = Demand for shares by Ordinary Investors at time t

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To refine it, we’ll add two kinds of discount rates ρ (rho) = the expected real return φ (phi) = the risk premium that compensates for “arbitrage costs,” which are influenced by

Trading costs (brokerage costs, slippage, etc.) Holding costs (the cost of maintaining a position) Information costs (the cost of getting the information needed to acquire and monitor a position)

This is the main item!

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The Refined Theory

Pt = Σ (Et (Dt+k) + φEt(Yt+k)) / (1 + ρ + φ)

Gag, gag, retch . . . RELAX

This is just a theoretical framework. We’re not going to try to plug in any numbers.

Generally, this can be translated to English as stock prices are equal to . . . .

The present value of all expected future dividends

Nothing new here; we know all about this

Plus the sum of all future ordinary-trader demand multiplied by arbitrage costs

This is new

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A Change in Nomenclature

Courtesy of Prof. Charles M.C. Lee, Stanford

Smart Money Demand = Value Trading

Value Trading need not specifically be the Dividend Discount Model

It simply refers to a truly rational approach to valuation, which includes sensible fundamental analysis that helps knowledgeable investors derive inputs to valuation models The key is that these investors trade with their head, not their hearts or guts

Ordinary Investor Demand = Noise Trading

These investor can’t or don’t apply rational valuation concepts They trade with their emotions or guts, not their heads

So now, stocks price = value + noise

Or, P = V + N

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Deconstructing the Model

Suppose φ, arbitrage costs, approach zero φEt(Yt+k), the “noise” component, likewise approaches zero

So as arbitrage costs diminish, the role of noise-

• riented investors also diminishes and the role of

value investors grows and approaches 100%

In this case, we approach a truly efficient market, where the price of a stock is equal to what it’s really worth

Deconstruction - Continued

Suppose φ, arbitrage costs, rise and approach infinity φEt(Yt+k), the “noise” component, likewise approaches rises and would eventually approach 100%

So as arbitrage costs rise, value investors become increasingly drowned out by noise

Focusing on the Information-Cost Component of φ (Arbitrage Costs) . . . .

• As information becomes harder and more expensive to get, the role
• f value diminishes

We see this all the time

In some global markets, where information is largely unavailable and/or unreliable, the markets are very much or, in some cases entirely noise based Smaller segments of the U.S. market, where analytic coverage tends to be skimpier, we tend to see less efficient pricing

• As information becomes more accessible, i.e. as information costs

decline), the role of valuation increases

Domestically, the S&P 500 stocks, for which information is most readily available, prices tend to be more efficient than in other parts of the market as value plays a bigger role The nature of the business is another aspect

As it becomes more feasible to value stocks, value traders become more prominent and noise trading diminishes As conventional valuation becomes more difficult, noise investors find happier hunting ground

See, e.g., Tesla, Amazon, many start-ups, many boiotechs

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Practical Implication

Since arbitrage costs are not likely to reach zero, we cannot expect stock prices to truly match “value” Noise is a normal part of the market’s equilibrium condition

It cannot be dismissed as an unfortunate circumstance We cannot sit back and sneer or hope it will be conquered by investor education We CAN factor it into our approaches to stock finding and analysis P = V + N, or Price = Value + Noise

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Noise-Value Calibrator: The Calculations

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Quantifying the Impacts of Noise and Value

Deconstruct a stock’s market cap (MC) into two components

Value based strictly on present business operation (VPO)

This is a very conservative bond-like valuation which assumes the income component never rises We might call it a “stand-still valuation”

Value based on future growth prospects (FGV)

Therefore . . .

MC = VPO + FGV

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VPO is easy to calculate

VPO = NOPAT / CC

Where

NOPAT = Net Operating Profit After Tax CC = Cost of Capital

NOPAT = Operating profit * (1 – tax rate)

For this purpose, Operating Profit = EBIT We see many abnormal tax rates in the real world

You can simply eliminate such stocks, or just assume all tax rates are normal; i.e. NOPAT = Oper Prof * .65

CC can be incredibly difficult to calculate

For our purposes, it’s OK to just pick a simple heuristic assumption, like .07

Therefore, VPO = (OpProf *.65) / .07

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After getting VPO, FGV falls into place

FGV = Market Cap – VPO, or FGV = Market Cap – ((OperProf *.65) / .07)

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Let’s jump to Value and Noise

V% = VPO / MktCap N% = FGV / MktCap

Where

V% is the percent of a stock’s market cap attributable to Value N% is the percent of a stock’s market cap attributable to Noise

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A Simple Back-of-the-Envelope Formula to Calculate the Impact of Noise in a Stock Price

N = (Mkt Cap – ((OperProf *0.65) / 0.07))/MktCap

Recall that

OperProf, for this purpose, is the same as EBIT 0.65 is a simplistic across-the-board assumption for 1-TaxRate 0.07 is a shorthand heuristic assumption for cost of capital

A Bit of Cheating

• We assumed that FGV = Noise, based on an assumed “standstill

valuation”

• In truth, we know that FGV = RG + NG + PN

Where

RG = realistic growth expectations NG = noise-based growth expectations PN = pure noise

• To go forward, we’ll have to assume that RG is always zero

We’ll live with that

Value gurus often talk about a margin of safety; here it is

Unlike with DDM, DCF, etc. etc. etc., we’re not going to pretend to be more precise than we can actually be Our estimate of Noise is likely to be a bit overstated, but as long as we understand that and refrain from getting carried away with specifics, we can live with this, and even use it for screening and analysis

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Value as a % or Market Cap

• Quantified using the previously-described technique – sector medians
• Value is important, but it doesn’t stand alone
• Value tends to be more stable

Many near- and intermediate-term stock price are likely to be noise driven

• Can you see a link between value % and ability to calculate value?

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Some Well Known Stocks

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What it Means

Unless we expect arbitrage costs to be zero (which is not the case), we cannot expect stock prices to strictly be equal to value Noise is a normal part of the market’s equilibrium condition

It cannot be dismissed as an unfortunate circumstance We cannot sit back and sneer or hope it will be conquered by investor education We CAN factor it into our approaches to stock finding and analysis

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Noise-Value Calibrator: The Excel Spreadsheet

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The Spreadsheet

Your inputs are highlighted in yellow

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Tool 2: Residual Income Model (RIM) Calculator

• 1. The Approach
• 2. The Items
• 3. The Excel Spreadsheet

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The RIM Approach

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Starting Point: the Dividend Discount Model (DDM)

Stock price is equal to present value of all future dividends P = D / (k – g)

Where

P = Stock Price D = Dividend k = required rate of return g = expected dividend growth rate

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Discounted Cash Flow (DCF)

Stock price is equal to present value of all cash flows

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Problems with DCF

It’s hard enough to predict one quarter ahead Do we seriously think it’s possible to predict cash flows five, 10 or haven forbid 15 years ahead!

I know I talked about being vaguely right or being less wrong than other, but there have to be limits to everything

There’s a difference between being imprecise or less wrong, versus being just-plain whacky

Although we can never eliminate the need to predict, we should, at least, try our best to cope by:

Making as smaller number of predictions as possible, and Trying to have as many of those predictions be of items that are less unpredictable

DCF doe not mitigate the predictability problem. It maximizes it!

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RIM as an Alternative to DCF

Its Much Simpler

Only 8 inputs into an Excel spreadsheet (one of which is the current stock price)

Its More effective

The main driver of this model is return on equity, a metric that’s extremely relevant to determining company quality and which tends to be stable

• ver time

It minimizes the unpredictability problem

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RIM Calculator: The Items

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Introducing RIM – The Residual Income Model

Value = Capital + Present Value of Future Enhancements to Wealth Capital = Book Value Present Value of Future Enhancements to Wealth = Present Value of “Residual Income” Residual Income = Book Value * (Return on Equity – Cost of Equity)

Residual income formula is analogous to basic net income, which is equity multiplied by return on equity What we’re doing here is considering only that portion of equity that is above cost of equity; bona fide enhancements to shareholder wealth

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RIM Calculator: The Spreadsheet

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The Spreadsheet- The Whole Thing!

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The Formulas

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Optional: An Expanded Version

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Expanded Version Formulas (1)

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Expanded Version Formulas (2)

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Thank You!

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