Return Predictability for presentation at UESTC, June 2011

1. Return Predictabilityfor presentation at UESTC, June 2011

2. Commonality in the determinants ofexpected stock returnsHaugen and Baker (1996, JFE) • Two findings distinguish this paper from others in the contemporary literature: • First, stocks with higher expected and realized rates of return are • unambiguously lower in risk than stocks with lower returns. • Second, the important determinants of expected stock returns are • strikingly common to the major equity markets of the world. • Overall, the results seem to reveal a major failure in the Efficient Markets Hypothesis.

3. Robert Haugen • the leading proponent of the case that stock markets are inherently inefficient. • in 1996, he published his breakthrough research on the predictive power of an expected return factor model in JFE. • Shortly thereafter, having enhanced and expanded his prototype, he opened Haugen Custom Financial Systems. • HCFS is one of Investars top ranked research firms. • Analyzing more than70 factors, its model predicts expected returns for about 7,000 US and international stocks.

4. My take • To a large extent, I don’t agree with his view that the markets are inherently inefficient. • However, I like his scientific way of forecasting stock returns. • It isn’t easy, but it can be done! • His approach nicely captures time-varying risk premium!!

5. Return predictability from earlier studies • The return history of a stock is useful in predicting relative returns. • DeBondt and Thaler (1985), Jegadeesh and Titman (1993), Chopra, Lakonishok, and Ritter (1992), and Jegadeesh (1990) • Future returns can be predicted by the relative sizes of • (a) the current market price of a stock and • (b) the current values of accounting numbers, such as book value or earnings per share • Fama and French (1992), Lakonishok, Shleifer, and Vishny (1994), and Davis (1994)

6. Three possible interpretations of return predictability • Some believe it is flawed and results, at least in part, from bias. • survival bias, data snooping, look-ahead bias, bid-ask bounce • Brown, Goetzmann, and Ross (1995), Lo and MacKinlay (1990) • Second group: • The return differences are related to relative risk. • Fama and French (1992). • Third group: • attributes return predictability to bias in the market’s pricing. • Lakonishok, Shleifer, and Vishny (1994).

7. Position of this paper • This paper shows that • it seems unlikely that these return differentials are merely artifacts of bias in methodology. • Also, since the differences in realized returns are • too large to be credibly called risk premiums and • since the high return deciles are not relatively risky, • their results strongly favor the pricing bias hypothesis.

8. Factors • Their predictions of expected stock returns are based on • five classes of factors: • risk, • market-related beta and betas related to macroeconomic variables. • liquidity, • price per share, trading volume, volume trend, firm size • price level, • the level of current market price relative to various accounting numbers. • profitability, • the ratios of net earnings to book equity, operating income to total assets, operating income to total sales, total sales to total assets. • price history. • previous one to two months, six to 12 months, three to five years

9. Estimating factor payoffs

10. Results of the monthly regressions:adjusted R-squareds

11. Fama-MacBeth means and t-statistics for the factor payoffs

12. projecting factor payoffs

13. the predictions of expected returns • Given the exposures of each stock and the projected factor payoffs for the next month, • we can calculate each stock’s relative expected rate of return. • We then rank by the relative expected returns, and • form the stocks into ten equally weighted deciles, • with decile 1 containing the stocks with the lowest expected rates of return. • Over the entire period, the spread between decile 10 and decile 1 • is approximately 35%. • The slopes reported in Table 2 are derived from • a regression of realized annual return on decile ranking. • They can be interpreted as the expected increase in realized return when moving from one decile to the next.

15. Robustness checks • Run the tests excluding all but selected factors: • using only excess returns over the previous three, six, and 12 months, a significant deterioration in predictive power occurs, and the overall spread drops from 35% to 15%. • rerun the tests using book to price and earnings to price as lone factors. The spread drops to 12% for book to price; and the spread drops to 14% for earnings to price. • It is the collective power of many of the factors in the group that accounts for the high level of accuracy in the predictions of return.

17. GAIP stocks (growth at an inexpensive price) • the stocks of decile 10 might be better named GAIP stocks • in light of their relatively high earnings, cash flow, and dividend yields.

18. Risk-adjusting the realized returns

23. Commonality in the primary factors for the five countries

24. Conclusion • The determinants of differential stock returns are surprisingly stable over time, and • the forecasting power of our expected return factor model is also surprisingly high. • We also find high power in other countries: • There seems to be a great deal of commonality across markets in firm characteristics that explain differences in expected returns. • Thus, the determinants of expected stock returns appear to be • common across different time periods and different markets.

25. A research question • Who are more likely to own stocks with high expected stock returns? • Who are more likely to own stocks with low expected stock returns?

26. Share issuance and cross-sectional returnsPontiff and Woodgate (2008, JF) • Share issuance occurs as a firm purchases or sells its own stock. • Some participants in the long-run return debate argue that • post-SEO and post-stock merger long-run returns are abnormally low, and that • post-share repurchase long-run returns are abnormally high. • This debate motivates them to examine • whether share issuance can be used to forecast stock returns in the cross-section.

27. Main findings • Post-1970, share issuance exhibits a strong cross-sectional ability to predict stock returns. • This predictive ability is more statistically significant than the individual predictive ability of size, book-to-market, or momentum. • They estimate the issuance relation pre-1970 and find no statistically significant predictive ability for most holding periods.

28. Implication • The post-1970 results are consistent with an opportunistic view of capital structure • whereby decision makers (insiders) repurchase or sell shares • in order to take advantage of variability in expected returns.

29. Share issuance and cross-sectional returns: International evidenceMcLean, Pontiff, and Watanabe (2009, JFE) • Share issuance predicts cross-sectional returns • in a non-U.S. sample of stocks from 41 different countries. • Issuance predictability has greater statistical significance than either size or momentum, and is similar to book-to-market. • As in the U.S., the international issuance effect is robust across both small and large firms. • Unlike the U.S., the effect is driven more • by low returns after share creation • rather than positive returns following share repurchases.

30. Cross-country differences • Issuance return predictability is stronger in countries • with greater issuance activity, • greater stock market development, and • stronger investor protection. • The results suggest that the share issuance effect is related to • the ease with which firms can issue and repurchase their shares. • Market-based economies???

31. Reconciling the Return Predictability EvidenceLettau and Nieuwerburgh (2008, RFS) • The question of whether stock returns are predictable • has received an enormous amount of attention. • This is not surprising because the existence of return predictability • is not only of interest to practitioners but also • has important implications for financial models of risk and return. • Classic predictive variables are financial ratios, such as • the dividend-price ratio, • the earnings price ratio, and • the book-to-market ratio

32. variations in expected returns • Growth rates of fundamentals, such as dividends or earnings, • are much less forecastable than returns, • suggesting that most of the variation of financial ratios is • due to variations in expected returns.

33. Present value model Campbell and Shiller (1988)

34. problems • Correct inference is problematic • because financial ratios are extremely persistent; • in fact, standard tests leave the possibility of unit roots open. • the statistical evidence of forecastability is weaker • once tests are adjusted for high persistence. • Financial ratios have poor out-of-sample forecasting power. • The forecasting relationship of returns and financial ratios exhibits significant instability over time. • in rolling 30-year regressions of annual log CRSP value-weighted returns on lagged log dividend-price ratios, • the OLS regression coefficient varies between zero and 0.5 and • the associated R2 ranges from close to zero to 30%, depending on the subsample.

35. problems • In summary, the return predictability literature has yet to provide convincing answers to the following four questions: • What is the source of parameter instability? • Why is the out-of-sample evidence so much weaker than the in-sample evidence? • Why has even the in-sample evidence disappeared in the late 1990s? • Why are price ratios extremely persistent?

36. A simple solution • The puzzling empirical patterns can be explained • if the steady-state mean of financial ratios has changed over the course of the sample period. • Such changes could be due to • changes in the steady-state growth rate of economic fundamentals resulting from • permanent technological innovations and/or • changes in the expected return of equity caused by, for example, improved risk sharing, • changes in stock market participation, • changes in the tax code, or • lower macroeconomic volatility.

38. Implications • The implications for forecasting regressions with the dividend-price ratio are immediate. • First, in the presence of steady-state shifts, • a nonstationary dividend-price ratio is not a well-defined predictor and • this nonstationarity could cause problems. • Second, the dividend-price ratio must be adjusted to remove the • nonstationary component to render a stationary process.

39. adjusted dividend-price series

43. in-sample results • “adjusted” price ratios have favorable properties • compared to unadjusted price ratios. • In the full sample, the slope coefficient in regressions of • annual log returns on the lagged log dividend-price ratio • increases from 0.094 for the unadjusted ratio • to 0.235 and 0.455 for the adjusted ratio with one and two steady-state shifts, respectively. • the regression coefficients using adjusted price ratios as regressors • are more stable and significant over time. • similar differences exist for other price ratios, • such as the earnings-price ratio and the book-to-market ratio.

44. Inferences • Taking changes in the long-run mean of the dividend-price ratio into account is crucial for forecasts of stock returns. • Forecasting with the unadjusted dividend-price ratio series • results in coefficient instability in the forecasting regression and • unreliable inference • (insignificance in small samples, and results depending on the subsample). • These disconcerting properties are due to a nonstationary component that shifts the mean of the dividend-price ratio.

47. other financial ratios • the other financial ratios indicate a predictability pattern • similar to that of the dividend-price ratio. • Without an adjustment for the change in their long-run mean, • the relationship between 1-year ahead returns and financial ratios is unstable over time. • However, once we filter out the nonstationary component, • we find a stable forecasting relationship and a large predictability coefficient.

48. in real time (out-of-sample tests) • In real time, however, the changes in the steady state are • not only difficult to detect • but also estimated with significant uncertainty, • making the in-sample return forecastability hard to exploit. • In real time an investor faces two challenges. • First, she has to estimate the timing of a break. • Second, if she detects a new break, • she has to estimate the new mean after the break occurs.

49. The reason for the lack of out-of-sample predictability • tests to evaluate the relative difficulty of • estimating the break dates versus • estimating the means relative to the pure out-of-sample forecasts and the ex post adjusted dividend-price ratio. • Findings: • (i) the estimation of the break dates in real time is not crucial, and • (ii) the eof the magnitude of the break in the mean dividend-price ratistimation o • entails substantial uncertainty, and • is ultimately responsible for the failure of the real-time out-of-sample predictions.

50. Conclusion • Reconciling the Return Predictability Evidence • Enhance our understanding of the relationships between stock returns and price ratios • Illustrate the differences between in-sample results and out-of-sample forecasts.