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Value and Growth Investing: Review and Update

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  • Louis K.C. Chan
  • Josef Lakonishok

Abstract

A great deal of academic empirical research has been published on value and growth investing. We review and update this literature, discuss the various explanations for the performance of value versus growth stocks, review the empirical research on the alternative explanations, and provide some new results based on an updated and expanded sample. The evidence suggests that, even after taking into account the experience of the late 1990s, value investing generates superior returns. Common measures of risk do not support the argument that the return differential is a result of the higher riskiness of value stocks. Instead, behavioral considerations and the agency costs of delegated investment management lie at the root of the value–growth spread. Value and growth investing are widely recognized specializations adopted by money managers. These specializations draw on, and stimulate, a large body of academic empirical research. Building on earlier studies of stock market “anomalies,” the research on value versus growth generally agrees that value investment strategies, on average, outperform growth investment strategies. The reward to value investing is more pronounced for small-capitalization stocks and is present in equity markets outside the United States as well. The reasons for the superior performance of value investing, however, are controversial. Some researchers attribute the higher returns to the higher risk of value stocks; other researchers contend that the rewards to value investing stem from cognitive biases underlying investor behavior and the agency costs of delegated investment management.We summarize and bring up to date the academic empirical research on value and growth investing. The bulk of current research stopped short of the late 1990s, a period that was not kind to value stocks. When the data through 2001 are considered, however, value investing still outperforms growth investing. During the 1990s, the performance of growth stocks rocketed, prompting speculation that value investors were an endangered species. The careful examination of the data that we present suggests that the differences among equity asset classes with respect to their performance in the late 1990s were not grounded in fundamental patterns of profitability growth. A more plausible interpretation is that investor sentiment reached exaggerated levels of optimism about the prospects for such “growth” opportunities as telecommunications, media, and technology stocks. This interpretation is consistent with evidence that investors tend to focus on past growth and extrapolate too far into the future. Furthermore, the sharp rise and decline in recent years of these growth-oriented stocks calls into question the argument that growth stocks are the less risky investments. In fact, the superior returns on value stocks for the overall period were not accompanied by higher risk, as calculated by a variety of indicators. We also suggest methods of enhancing the returns to value strategies by defining value and growth by more than the familiar ratio of book value to market value. A value portfolio based on book value to market value, cash flow to price, earnings to price, and sales to price dominated the Russell 1000 Value Index large-cap value benchmark by 5 percentage points a year on average for the 1979–2001 period. Favorable results were also obtained when this enhanced value strategy was applied to U.S. small-cap stocks and to the largest stocks in the MSCI Europe/Australasia/Far East universe.

Suggested Citation

  • Louis K.C. Chan & Josef Lakonishok, 2004. "Value and Growth Investing: Review and Update," Financial Analysts Journal, Taylor & Francis Journals, vol. 60(1), pages 71-86, January.
  • Handle: RePEc:taf:ufajxx:v:60:y:2004:i:1:p:71-86
    DOI: 10.2469/faj.v60.n1.2593
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    Cited by:

    1. Moitra, Agnij, 2024. "Directional Stock Price Forecasting Based on Quantitative Value Investing Principles for Loss Averted Bogle-Head Investing using Various Machine Learning Algorithms," OSF Preprints y3mr6, Center for Open Science.

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