Investments
Zvi Bodie, Alex Kane, Alan J. Marcus · 1989 (this text: 5th edition, 2003) · 923 pages
This is the MBA textbook that built modern portfolio theory into finance, not a trading book. It hands you the CAPM, the efficient market hypothesis, and the math for building an optimal portfolio, then spends a full chapter explaining why that math will not make you rich.
The one idea
Bodie, Kane, and Marcus organize the entire book around one claim: security markets are close to efficient, so there are few free lunches, and any strategy for beating the market has to argue against that default. Chapter 6 opens with a formal risk premium (a $100,000 stake with a 60% chance of $150,000 and a 40% chance of $80,000 has an expected profit of $22,000, against a $5,000 sure thing from a 5% T-bill, for a $17,000 risk premium) and turns it into a utility score, U = E(r) minus 0.005 times A times variance, where A is the investor's risk aversion coefficient.
Chapters 7 through 9 build from there: combine a risky portfolio with a risk-free asset (the capital allocation line), find the one risky portfolio that maximizes reward per unit of risk (the tangency portfolio), and show that in equilibrium every asset's expected return is pinned to its beta, not its own volatility. The book's worked example uses a bond fund (8% expected return, 12% standard deviation) and a stock fund (13% return, 20% standard deviation, 0.30 correlation) to build an optimal mix of 40% bonds and 60% stocks, yielding an 11% expected return with a 14.2% standard deviation.
The back half turns the lens on the reader. Chapter 12 walks through decades of mutual fund data showing that active managers as a group do not beat a passive index after fees, and Chapter 26 replaces stock picking with an investment policy statement built from objectives (return target, risk tolerance) and five constraints (liquidity, time horizon, regulation, taxes, unique needs). The book's implicit answer to 'how do I beat the market' is that you probably should stop trying and instead get your asset allocation and risk tolerance right.
What it actually teaches
The book does not offer trading rules; it offers a sequence for building and holding a portfolio, laid out across Chapters 6 through 9 and 26.
SCORE YOUR RISK AVERSION
Assign a risk aversion coefficient A and score any portfolio with U = E(r) minus 0.005 x A x variance (in percent terms). Higher A means the investor penalizes volatility more heavily; a risk-free asset always scores its own return since there is no variance to penalize.
BUILD THE OPPORTUNITY SET
For any two risky assets, portfolio variance is not a weighted average of the individual variances, it depends on their covariance: variance-p = wD^2 x sigma-D^2 + wE^2 x sigma-E^2 + 2wDwE x Cov(rD,rE). Lower or negative correlation between the two assets pulls portfolio risk below the weighted average of the components, which is the entire mechanical case for diversification.
FIND THE TANGENCY PORTFOLIO
Maximize the reward-to-variability ratio (expected return minus the risk-free rate, divided by standard deviation) across every combination of the risky assets. In the book's own two-fund example, this lands on 40% bonds and 60% stocks: an 11% expected return with a 14.2% standard deviation, a Sharpe ratio of about 0.42 against a 5% T-bill rate.
SPLIT BETWEEN THE TANGENCY PORTFOLIO AND CASH
Once the optimal risky portfolio P is found, the investor's personal allocation to it is y = [E(rP) - rf] / (0.01 x A x variance-P). For A = 4 in the book's example, y comes out to 74.39% in Portfolio P and 25.61% in T-bills, which nets out to roughly 30% bonds and 45% stocks overall.
PRICE INDIVIDUAL ASSETS AGAINST BETA, NOT VOLATILITY
The CAPM states E(r) = rf + beta x [E(rM) - rf]. An asset's own standard deviation does not matter once it sits in a diversified portfolio, only its beta (covariance with the market divided by market variance) does. A stock with beta 1.2, when the market is expected to return 14% and T-bills yield 6%, has a fair expected return of 6 + 1.2(14-6) = 15.6%; anything you expect above that is a positive alpha, the thing active management is actually trying to find.
WRITE AN INVESTMENT POLICY STATEMENT BEFORE PICKING ANYTHING
Chapter 26 has the investor set objectives (target return, risk tolerance) and identify five constraints: liquidity needs, investment horizon, legal or regulatory limits, tax situation, and any unique needs. The asset allocation, diversification, and risk positioning decisions all flow from that statement, not from a market view.
What it looks like Monday morning
A reader who takes this seriously stops looking for a stock-picking edge and instead writes down an actual risk tolerance number and time horizon, then checks whether their current portfolio's mix of stocks, bonds, and cash matches it. The book's own example (40% bonds, 60% stocks inside the risky sleeve, then split against T-bills by risk aversion) is a template for that exercise, not a recommendation to copy.
The other concrete habit is to stop evaluating a position by its own volatility and start asking what it does to the portfolio as a whole, since Chapter 8's covariance math is the entire argument for holding assets that do not move together. Anyone drawn to this book for a system to beat the market on Monday morning is reading the wrong 900 pages; the book's own Chapter 12 argues that expectation is largely misplaced.
| Fund | Expected return | Std. deviation | Weight in optimal portfolio P |
|---|---|---|---|
| Bond fund (D) | 8% | 12% | 40% |
| Stock fund (E) | 13% | 20% | 60% |
| Portfolio P (correlation 0.30) | 11% | 14.2% | 100% |
With a 5% risk-free rate, Portfolio P's reward-to-variability (Sharpe) ratio is about 0.42; an investor with risk aversion A=4 then puts 74.39% of total wealth in P and 25.61% in T-bills.
There are few free lunches found in markets as competitive as the financial market.Bodie, Kane, and Marcus, Investments
Where it fails
- It is a textbook, not a trading manual, and reads like one. Every chapter ends in concept-check questions and CFA exam problems, the prose is built for a semester-long course with an instructor, and there is no chapter that says 'here is what you do Monday morning.' A reader looking for an actionable system will be fighting the book's own structure the entire way through.
- The core numbers came from a market this edition already calls unusual. Chapter 13's discussion of the equity premium puzzle and Chapter 12's mutual fund data both lean on the 1972-1999 stretch, a long bull run with survivorship bias in the fund samples the authors themselves flag. A reader pulling threshold numbers like fund alpha distributions or the 4%-per-year small-stock underperformance in 1945-1964 needs to check they still hold in the data since 2003, this edition does not.
- The efficient market case is argued more forcefully than the book's own anomalies chapter can support. Chapter 12 spends 40-plus pages cataloging the size effect, momentum, book-to-market effect, and P/E effect as documented deviations from efficiency, then falls back on behavioral explanations (overconfidence, regret avoidance, framing) without resolving whether they are exploitable risk premiums or genuine free money. A reader who only reads the CAPM chapters and skips the anomalies chapter gets a falsely tidy picture.
- It assumes a level of math comfort most individual investors do not have. The optimal risky portfolio formula, the bordered covariance matrix, and the CAPM derivation in Chapter 9 all require comfort with calculus-based optimization and matrix algebra; the authors say they 'strip away unnecessary mathematical detail' but the two-asset variance formula alone stops most casual readers cold.
- The specific numbers are illustrative, not live market data, and the 2003 printing predates two decades of index fund growth. The book's own worked example (8%/12% bonds, 13%/20% stocks, a 5% T-bill rate) reflects rate and return assumptions from that era; a reader plugging in 2026 numbers gets a materially different optimal portfolio, and the passive-versus-active debate in Chapter 12 predates the scale of Vanguard and index investing that exists today.
- This is Volume 1 of a McGraw-Hill Primis custom course printing, assembled for a specific university course. The copyright page names an instructor (David Whitehurst, UMIST) and this printing is a McGraw-Hill Primis custom compilation of the Fifth Edition, not the standard retail edition; a reader buying 'Investments' expecting this exact page layout, problem sets, or chapter selection may get a different assembly of the same underlying textbook.
Who it is for
Buy it if
You want the academic foundation behind terms like beta, alpha, Sharpe ratio, and efficient markets explained rigorously with real formulas and worked numbers, or you are studying for the CFA and want a textbook that maps to that curriculum. It also works as a reference to look up a specific model when you already know which chapter you need.
Skip it if
You want a trading system, a stock-picking method, or anything you can apply this week. Skip it too if formulas and matrix algebra make your eyes glaze over, since the book leans on both from Chapter 6 onward and does not apologize for it.
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