This is a 940-page university finance textbook, not a trading book. It builds the capital asset-pricing model (CAPM) step by step with real formulas and data, then spends an equal number of pages showing you the empirical evidence that breaks it.
The one idea
The book's real subject is mean-variance portfolio theory: given the expected returns, variances and covariances of a set of assets, how do you build the portfolio that gets the most return for a given amount of risk. Levy and Post walk from a single risky asset mixed with a risk-free asset, up through the full capital market line (CML), and finally to the capital asset-pricing model (CAPM), where an asset's required return is pinned entirely to its beta, its covariance with the market divided by the market's variance.
The CAPM's promise is that you do not need to analyze a company at all to know what return it should earn. You need one number: beta. A stock with a beta of 2 is expected to move twice as hard as the market in both directions; a beta of 0.5 moves half as hard. The equation is E(Ri) = r + [E(Rm) - r]beta_i, the risk-free rate plus the market's risk premium scaled by beta. Everything else in the risk of an individual stock, the part not explained by the market, is treated as diversifiable and unpriced.
The second half of the CAPM chapter, and the whole of the market-efficiency chapter that follows it, is the book quietly taking the theory apart. Real portfolios sorted by size, book-to-market and momentum earn returns the CAPM cannot explain. The authors do not resolve this. They lay out both sides and let you watch a canonical academic finance model fail its own empirical tests in the same textbook that just taught it to you.
What it actually teaches
The book builds the CAPM in a strict sequence, each step depending on the one before it.
ADD A RISK-FREE ASSET
Start with one risky asset A and a risk-free rate r. Mixing them creates the 'opportunity line': E(Rp) = r + [(E(RA) - r)/sigma_A] * sigma_p. The slope of this line, (E(RA) - r)/sigma_A, is the Sharpe ratio. The book uses this exact term for it.
FIND THE TANGENCY PORTFOLIO
With many risky assets available, the investor mixes the risk-free asset with whichever risky portfolio maximizes that slope. That portfolio, called the tangency portfolio (labeled m), is the one all rational investors hold in some proportion. Mixing m with the risk-free asset produces the capital market line (CML): E(Rp) = r + [(E(Rm) - r)/sigma_m] * sigma_p.
COMPUTE BETA
Beta standardizes an asset's covariance with the market: beta_i = sigma_i,m / sigma_m^2. It is estimated in practice as the slope of a regression of the asset's excess return on the market's excess return (the 'characteristic line'), Ri - r = alpha_i + beta_i(Rm - r) + e_i. The book's worked example: Microsoft, monthly excess returns January 1991 to December 2000 (120 observations), estimated beta 1.535, but with a standard error of 0.216, giving a 95% confidence interval of 1.103 to 1.967. R-squared was 0.299, meaning only about 30% of Microsoft's return variance in that window was explained by the market.
PRICE THE ASSET OFF THE SECURITY MARKET LINE
The equilibrium relationship is E(Ri) = r + [E(Rm) - r] * beta_i, the security market line (SML). An asset above the line has positive alpha and is underpriced; below it, negative alpha and overpriced. The book's Exhibit 10.9 gives real 30-industry beta estimates over January 1993 to December 2002: beer and liquor had the lowest beta at 0.17, business equipment the highest at 1.73, with the market portfolio's beta fixed at 1.00 by construction.
What it looks like Monday morning
Nobody trades off this book, and it isn't written to be traded off. What a reader takes into Monday morning is the discipline of separating a portfolio decision into two stages: first pick the risky portfolio that maximizes the reward-to-risk slope, then dial risk up or down by mixing in cash or leverage, rather than hand-picking individual stocks on conviction. If you already build model portfolios or benchmark manager performance with alpha and beta, this is where those terms actually come from, formula and all, not just the industry shorthand for them.
The more useful habit the book leaves you with is distrust of beta itself. The Microsoft example runs a real 10-year regression and gets a beta anywhere from 1.10 to 1.97 with 95% confidence, and an R-squared of just 0.30. That is not a rounding error, that is a coin flip on the number risk models are built on. Anyone using beta to size a position or set a hedge ratio should read this section before trusting a single point estimate.
| Industry | Alpha | Beta | R-squared (%) |
|---|---|---|---|
| Beer and liquor | 0.81 | 0.17 | 3 |
| Utilities | 0.08 | 0.27 | 7 |
| Healthcare | 0.50 | 0.61 | 34 |
| Financial sector | 0.47 | 0.82 | 56 |
| Services | -0.01 | 1.62 | 75 |
| Business equipment | 0.24 | 1.73 | 70 |
| Market portfolio | 0.00 | 1.00 | 100 |
R-squared is the share of an industry's return variance explained by the market; the rest is non-market risk the CAPM does not price.
Good luck if you try to make money on this, you'll need it.Levy and Post, Investments
Where it fails
- It is a textbook, not a trading manual, and it says so. The preface states outright that the book 'won't enable you to make predictions about security prices that are any better than simply flipping a coin' and that good investing 'generally does not require forecasting skills.' Anyone buying this looking for entries, exits, or a system will be reading 940 pages against the grain of the authors' own stated purpose.
- The chapter spends more pages killing the CAPM than building it. Section 10.3, titled 'Conclusion: dead or alive?', lays out three decades of empirical challenges: size, value and momentum anomalies that let investors beat the CAPM's predicted risk-return tradeoff, plus data-mining, data-snooping and sample-selection bias in the studies that supposedly support it. The authors conclude 'we cannot draw an unambiguous conclusion about the empirical validity of the CAPM.' The model taught in the first half of the chapter is explicitly unresolved by the second half.
- The market anomalies chapter documents exactly why beta-based pricing keeps losing to simpler screens. Chapter 12's anomaly table lists low P/E, low market-to-book, small size, and positive earnings momentum as return patterns that persisted for decades and that the CAPM's beta framework does not explain. Fama and French's 1992 decile sort found the lowest market-to-book decile returning 1.65% a month against 0.72% for the highest, a gap CAPM has no room for.
- The math bar is higher than the preface admits. The preface claims 'no more than high-school-level algebra is assumed,' but the CAPM chapter runs regression analysis, standard errors, R-squared, and covariance algebra with footnoted proofs, and points readers to a full appendix on regression to follow along. A reader without at least an introductory statistics course will stall well before Section 10.2.
- It is a 2005 UK-published edition with UK institutional detail baked in. The book was first published by South-Western College Publishing and this Pearson/Financial Times edition, first published 2005, layers in UK security regulation and FT-sourced case studies (Chapter 5's regulation content, the FT 'Investment in the News' boxes). US-market readers get real value from the CAPM, APT and efficient-markets chapters but will skim past regulatory sections written for a different market.
- Every historical number in the book predates 2003 and some of the anomalies it reports have since been arbitraged away. The industry-beta table (Exhibit 10.9) runs January 1993 to December 2002, and the January-effect data (Exhibit 10.10) stops at 1996. The book itself notes that the January effect 'was reduced dramatically' once investors traded on it in the 1987-96 window shown; anyone treating the anomaly tables in Chapter 12 as live, tradeable edges rather than historical case studies is reading a 20-year-old dataset as current.
Who it is for
Buy it if
You want the actual derivation of CAPM, the CML, the SML and beta, with real regression output and confidence intervals, as background for reading academic finance research or building risk models with your eyes open about their limits. It also works as a structured refresher if you already have a finance or business degree and want the formulas back in one place.
Skip it if
You are looking for a trading system, a screen, or a rule you can apply on Monday. Skip it too if algebra and regression already make you tune out; this is closer to a graduate finance course than a book you read once on a plane.
As an Amazon Associate I earn from qualifying purchases. It costs you nothing extra, and it does not change what gets recommended or what the verdict says.