Roger G. Ibbotson

Price performance of common stock new issues

Abstract The paper studies both the initial and aftermarket performance (measured by risk-adjusted returns) on newly issued common stocks which were offered to the public during the 1960s. The results confirm that average initial performance is positive (11.4 percent), while the distribution of returns is skewed so that the subscriber of a single random new issue offering has about an equal chance for gain or loss. The results are generally consistent with aftermarket efficiency. Positive initial performance along with aftermarket efficiency indicate that new issue offerings are underpriced. The paper provides insights into this underpricing mystery, but does not solve it.

Does Asset Allocation Policy Explain 40, 90, or 100 Percent of Performance?

Disagreement over the importance of asset allocation policy stems from asking different questions. We used balanced mutual fund and pension fund data to answer the three relevant questions. We found that about 90 percent of the variability in returns of a typical fund across time is explained by policy, about 40 percent of the variation of returns among funds is explained by policy, and on average about 100 percent of the return level is explained by the policy return level. Does asset allocation policy explain 40 percent, 90 percent, or 100 percent of performance? The answer depends on what the analyst is trying to explain. According to some well-known studies, more than 90 percent of the variability of a typical plan sponsors performance over time is attributable to asset allocation. So, if an analyst is trying to explain the variability of returns over time, asset allocation is very important. The results of previous studies are often applied, however, to questions the studies never intended to address. Rather than the importance of asset allocation over time, an analyst might want to know how important it is in explaining the differences in return from one fund to another or what percentage of the level of a typical funds return is the result of asset allocation. To address these aspects of the role of asset allocation policy, we investigated these three questions: How much of the variability of returns across time is explained by asset allocation policy? How much of the variation of returns among funds is explained by differences in asset allocation policy? What portion of the return level is explained by returns to asset allocation policy? We examined 10 years of monthly returns to 94 balanced mutual funds and 5 years of quarterly returns to 58 pension funds. For the mutual funds, we used return-based style analysis for the entire 120-month period to estimate policy weights for each fund. We carried out the same type of analysis on quarterly returns of 58 pension funds for the five-year 1993–97 period. For the pension funds, rather than estimated policy weights, we used the actual policy weights and asset-class benchmarks of the pension funds. We answered the three questions as follows: Question #1: We regressed each funds total returns against its policy returns and recorded the R2 value for each fund in the study. We found that, on average, about 90 percent of the variability of returns of a typical fund across time is explained by asset allocation policy. Most of a funds ups and downs are explained by the ups and downs of the overall market. Question #2: We ran a cross-sectional regression of compound annual fund returns for the entire period on compound annual policy returns for the entire period. The R2 statistics from this regression showed that for the mutual funds, 40 percent of the return difference from one fund to another (and for the pension fund sample, 35 percent) is explained by policy differences. For example, among mutual funds, if one funds return is 13 percent and another funds return is 8 percent, then on average, about 2 percent of the difference is explained by the difference in asset mix policy; the remaining 3 percent difference is explained by other factors, such as timing, security selection, and fee differences between the funds. Question #3: For each fund, we divided the compound annual policy return for the entire period by the compound annual fund return. We found that, on average, about 100 percent of the return level is explained by the return to asset allocation policy. Thus, the average funds return to asset-mix policy is about the same as the return to the benchmarks for the asset classes. In summary, our analysis shows that asset allocation explains about 90 percent of the variability of a funds returns over time but explains only about 40 percent of the variation of returns among funds. Furthermore, on average across funds, asset allocation policy explains slightly more than 100 percent of the level of returns. Thus, the answer to the question of whether asset allocation policy explains 40 percent, 90 percent, or 100 percent of performance depends on how the question is interpreted.

Real Estate Returns: A Comparison with Other Investments

Real estate returns, measured unleveraged, have been between those of stocks and bonds over 1960-1982. Due to appraisal smoothing and imperfect marketability, one must be careful about directly comparing measured real estate returns with those on other assets. It is likely, however, that low correlations with stocks and bonds make real estate a diversification opportunity for traditional portfolio managers. In addition, the issue of how various assets are priced is addressed. While stocks are priced primarily on market or beta risk, and bonds are priced primarily on interest rate and default risk, the real estate pricing mechanism includes residual risk and non-risk factors such as taxes, marketability costs and information costs. Copyright American Real Estate and Urban Economics Association.

Long-Run Stock Returns: Participating in the Real Economy

In the study reported here, we estimated the forward-looking long-term equity risk premium by extrapolating the way it has participated in the real economy. We decomposed the 1926–2000 historical equity returns into supply factors—inflation, earnings, dividends, the P/E, the dividend-payout ratio, book value, return on equity, and GDP per capita. Key findings are the following. First, the growth in corporate productivity measured by earnings is in line with the growth of overall economic productivity. Second, P/E increases account for only a small portion of the total return of equity. The bulk of the return is attributable to dividend payments and nominal earnings growth (including inflation and real earnings growth). Third, the increase in the equity market relative to economic productivity can be more than fully attributed to the increase in the P/E. Fourth, a secular decline has occurred in the dividend yield and payout ratio, rendering dividend growth alone a poor measure of corporate profitability and future growth. Our forecast of the equity risk premium is only slightly lower than the pure historical return estimate. We estimate the expected long-term equity risk premium (relative to the long-term government bond yield) to be about 6 percentage points arithmetically and 4 percentage points geometrically. Until 2001, investors had not seen consecutive negative annual stock market returns since the 1970s. To the contrary, during the 1980s and 1990s, the market produced its best 20-year performance ever. But neither the past two years nor the past two decades are good predictors of the long run. We establish a new method for forecasting the future return of stocks over bonds by building on the relationship between the stock market, earnings, and the overall economy. We analyze historical equity returns by decomposing the 1926–2000 returns into supply factors commonly used to describe the aggregate equity market and overall economic productivity—inflation, earnings, dividends, the P/E, the dividend-payout ratio, book value, return on equity, and GDP per capita. We examine each factor and its relationship to the long-term supply-side framework. We discuss several key findings. First, the growth in corporate productivity, as measured by earnings, is in line with the growth of overall economic productivity. Second, P/E increases account for only a small portion of the total return of equity (1.25 percentage points of the total 10.70 percent). The bulk of the return is attributable to dividend payments and growth in nominal earnings (including inflation and real earnings growth). Third, the increase in share of equity relative to the overall economy can be more than fully attributed to the increase in the P/E. Fourth, a secular decline has occurred in the dividend yield and payout ratio, rendering dividend growth alone a poor measure of corporate profitability and future growth. We used historical information in the supply-side models to forecast the equity risk premium. Contrary to several recent studies on the premium that declared the forward-looking equity risk premium to be close to zero or negative, we found the long-term supply of the equity risk premium to be only slightly lower than the straight historical estimate. We estimated the expected premium to be 3.97 percentage points in geometric terms and 5.90 percentage points on an arithmetic basis. This estimate is about 1.25 percentage points lower than the straight historical estimate. The differences between our estimates and those provided in several other recent studies arise principally from the inappropriate assumptions those authors used, assumptions that violate the Miller and Modigliani theorem. Also, our models interpret the current high market P/E as the market forecasting high future growth, rather than a low discount rate or overvaluation. Our estimate is in line with both the historical supply measures of public corporations (i.e., earnings) and overall economic productivity (GDP per capita). Our estimate of the equity risk premium is far closer to the historical premium than it is to zero or a negative number. The implication is that stocks are expected to outperform bonds over the long run. For long-term investors, such as pension funds and individuals saving for retirement, stocks should continue to be a favored asset class in a diversified portfolio. Because our estimate of the equity risk premium is lower than historical performance of the premium, however, some investors should lower their equity allocations and/or increase their savings rate to meet future liabilities.

A New Historical Database For The NYSE 1815 To 1925: Performance And Predictability

In this paper, we collect individual stock prices for NYSE stocks over the period 1815 to 1925 and individual dividend data over the period 1825 to 1870. We use monthly price and dividend information on more than 600 individual securities over the period to estimate a stock price index and total return series that extends virtually to the beginning of the New York Stock Exchange. We use this data to estimate the power of past returns and dividend yields to forecast future long-horizon returns. We find some evidence of predictabiity in sub-periods but little predictability over the long term. We estimate the time-varying volatility of the U.S. market over the period 1815 to 1925 and find evidence of a leverage effect on risk. This new database will allow future researchers to test a broad range of hypotheses about the U.S. capital markets in a rich, untouched sample.

The A,B,Cs of Hedge Funds: Alphas, Betas, and Costs

In this paper, we focus on two issues. First, we analyze the potential biases in reported hedge fund returns, in particular survivorship bias and backfill bias, and attempt to create an unbiased return sample. Second, we decompose these returns into their three A,B,C components: the value added by hedge funds (alphas), the systematic market exposures (betas), and the hedge fund fees (costs). We analyze the performance of a universe of about 3,500 hedge funds from the TASS database from January 1995 through April 2006. Our results indicate that both survivorship and backfill biases are potentially serious problems. The equally weighted performance of the funds that existed at the end of the sample period had a compound annual return of 16.45% net of fees. Including dead funds reduced this return to 13.62%. Excluding backfill further reduced the return to 8.98%, net of fees. In this last sample, we estimate a pre-fee return of 12.72%, which we split into a fee (3.74%), an alpha (3.04%), and a beta return (5.94%). Overall, even after correcting for data biases, we find that the alphas are significantly positive and are approximately equal to the fees, meaning that excess returns were shared roughly equally between hedge fund managers and their investors.

History and the Equity Risk Premium

We summarize some of our own past findings and place them in the context of the historical development of the idea of the equity risk premium and its empirical measurement by financial economists. In particular, we focus on how the theory of compensation for investment risk developed in the 20th century in tandem with the empirical analysis of historical investment performance. Finally, we update our study of the historical performance of the New York Stock Exchange over the period 1792 to the present, and include a measure of the U.S. equity risk premium over more than two centuries. This last section is based upon indices constructed from individual stock and dividend data collected over a decade of research at the Yale School of Management, and contributions by other scholars.

The Equal Importance of Asset Allocation and Active Management

What is the relative importance of asset allocation policy versus active portfolio management in explaining variability in performance? Considerable confusion surrounds both time-series and cross-sectional regressions and the importance of asset allocation. Cross-sectional regressions naturally remove market movements; therefore, the cross-sectional results in the literature are equivalent to analyses of excess market returns even though the regressions were performed on total returns. In contrast, time-series analyses of total returns do not naturally remove market movements. Time-series analyses of excess market returns and cross-sectional analyses of either total or excess market returns, however, are consistent with each other. With market movements removed, asset allocation and active management are equally important in determining portfolio return differences within a peer group. Finally, an examination of period-by-period cross-sectional results reveals why researchers using the same regression technique can get widely different results. Our study helped identify and alleviate a significant amount of the long-running confusion surrounding the importance of asset allocation. First, by decomposing a portfolio’s total return into its three components—(1) the market return, (2) the asset allocation policy return in excess of the market return, and (3) the return from active portfolio management—we found that market return dominates the other two return components. Taken together, market return and asset allocation policy return in excess of market return dominate active portfolio management. This finding confirms the widely held belief that market return and asset allocation policy return in excess of market return are collectively the dominant determinant of total return variations, but it clarifies the contribution of each. More importantly, after removing the dominant market return component of total return, we answered the question, Why do portfolio returns differ from one another within a peer group? Our results show that within a peer group, asset allocation policy return in excess of market return and active portfolio management are equally important. Critically, this finding is not the result of a mathematical truth. In contrast to the mathematical identity that in aggregate, active management is a zero-sum game (and thus, asset allocation policy explains 100 percent of aggregate pre-fee returns), the relative importance of both asset allocation policy return in excess of market return and active portfolio management is an empirical result that is highly dependent on the fund, the peer group, and the period being analyzed. The key insight that ultimately enabled us to conclude that asset allocation policy return in excess of market return and active portfolio management are equally important is the realization that cross-sectional regression on total returns is equivalent to cross-sectional regression on excess market returns because cross-sectional regression naturally removes market movement from each portfolio. We believe that this critical and subtle fact has not been clearly articulated in the past and has been overlooked by many researchers, especially when interpreting cross-sectional results vis-à-vis the overall importance of asset allocation. The insight that cross-sectional regression naturally removes market movement leads to the notion that removing market movement from traditional total return time-series regression is necessary should one want to put the time-series and cross-sectional approaches on an equal footing. After putting the two approaches on an equal footing, we found that the values of R2 for the excess market time-series regressions and the cross-sectional regressions (on either type of return) are consistent. Finally, by examining period-by-period cross-sectional results and highlighting the sample period sensitivity of cross-sectional results, we explained why different researchers using the same regression technique can get widely different results. More specifically, cross-sectional fund dispersion variability is the primary cause of the period-by-period cross-sectional R2 variability.

Chapter 30 Initial public offerings

Publisher Summary Because initial public offerings (IPO) involve the sale of securities in closely-held firms in which some of the existing shareholders may possessnonpublic information, some of the classic problems caused by asymmetric information may be present. This chapter describes some of the mechanisms that are used in practice to overcome the problems created by information asymmetries. Evidence is presented in the chapter on three anomalies associated with IPOs: (i) new issue underpricing, (ii) cycles in the extent of underpricing, and (iii) long-run underperformance. These patterns have generated a large empirical literature.Various theories that have been advanced to explain these patterns are also discussed in the chapter. In some respects, the poor performance of IPOs in the long run makes the new issues underpricing phenomenon even more of a puzzle. Whether or not IPOs underperform in the long run, the question of why issuers set their IPO price at a level that is lower on average than the market price at the end of the first day has generated a large literature. While no one model can provide a definitive explanation of these anomalies, collectively the theories can account for many of the patterns that are observed.

The World Market Wealth Portfolio

T hree years ago in this Journal, we presented returns and market values for the United States Market Wealth Portfolio.’ That hypothetical portfolio included the principal investable asset classes in the United States: equities, fixed income securities, cash, and real estate. The wealth of the United States makes up onlya fraction of the world’s wealth, however. Indeed, this phenomenon is not purely a recent occurrence. Great Britain, for example, had the world’s largest capital markets in 1850. In the twentieth century, United States capital markets have been larger than those of any other single country, but they account for appreciably less than half of the world supply of equities, bonds, and real property. The sheer size of the foreign capital markets motivates us to reexamine the concept of a market wealth portfolio from a world perspective. In addition, returns in foreign markets have proved attractive to United States investors. Therefore, we have constructed a World Market Wealth Portfolio consisting of equities, bonds, cash, and monetary metals plus real estate in the United States (the returns on foreign real estate being difficult to measure). The analysis covers the capital markets of the United States, Northern and Western Europe, Japan, Hong Kong, Singapore, Canada, and Australia. Our study runs from the beginning of 1960 through the end of 1980. Our World Market Wealth Portfolio consists of a value-weighted combination of five major categories of assets: (1) equities (stocks), (2) bonds, (3) cash, (4) real estate, and (5) metals. Each category of assets includes several components. United States equities are a value-weighted aggregate of the stocks listed on the New York and American stock exchanges and NAS-