or acquisitions). However, because the traditional approach relies heavily on the judgment of analysts, it is subject to potentially severe subjective biases such as selective perception, hindsight bias, stereotyping, and overconfidence that can reduce forecast quality.2 Moreover, the traditional approach is costly to apply, which makes it impracticable for a large investment universe comprising many small stocks. The high cost and subjective nature also make it difficult to evaluate, because it is hard to create the history necessary for testing. Testing an investment process is important because it helps to distinguish factors that are reflected in stock prices from those that are not. Only factors that are not yet impounded in stock prices can be used to identify profitable trading opportunities. Failure to distinguish between these two types of factors can lead to the familiar "good company, bad stock" problem in which even a great company can be a bad investment if the price paid for the stock is too high.3 Quantitative managers use statistical models to map a parsimonious set of measurable factors into objective forecasts of each stock's return, risk, and cost of trading. The quantitative approach formalizes the relation between the key factors and forecasts, which makes the approach transparent and largely free of subjective biases. Quantitative analysis can also be highly cost-effective. Although the fixed costs of building a robust quantitative model are high, the marginal costs of applying the model, or extending it to a broader investment universe, are low. Consequently, quantitative portfolio managers can choose from a large universe of stocks, including many small and otherwise neglected stocks that have attractive fundamentals. Finally, because the quantitative approach is model-based, it can be tested historically on a wide cross section of stocks over diverse economic environments. While quantitative analysis can suffer from specification errors and overfit-ting, analysts can mitigate these errors by following a well-structured and disciplined research process. On the negative side, quantitative models can be misleading when there are bad data or significant structural changes at a company (i.e., "garbage in, garbage out"). For this reason, most quantitative managers like to spread their bets across many names so that the success of any one position will not make or break the strategy. Traditional managers, conversely, prefer to take fewer, larger bets given their detailed hands-on knowledge of the companies and the high cost of analysis. A summary of the major advantages of each approach to equity portfolio management is presented in Table 23.1.4 Given that our expertise is quantitative equity 2For a discussion of the systematic errors in judgment and probability assessment that people frequently make, please see Kahneman, Slovic, and Tversky (1982). 3For a good discussion of the traditional approach, please see White, Sondhi, and Fried (1998). 4For an excellent comparison of clinical (traditional) and actuarial (quantitative) decision analysis, please see Dawes, Faust, and Meehl (1989). They find clinical analysts do a good job identifying a set of relevant factors, but actuarial analysts do a better job assigning weights to each of several factors. For a comparison of traditional and quantitative portfolio managers, please see Jones (1998).