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Return Predictability for presentation at UESTC, June 2011PowerPoint Presentation

Return Predictability for presentation at UESTC, June 2011

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Return Predictability for presentation at UESTC, June 2011

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Return Predictability for presentation at UESTC, June 2011

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Return Predictabilityfor presentation at UESTC, June 2011

- 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.

- 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.

- 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!!

- 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)

- (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)

- 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)

- survival bias, data snooping, look-ahead bias, bid-ask bounce
- Second group:
- The return differences are related to relative risk.
- Fama and French (1992).

- The return differences are related to relative risk.
- Third group:
- attributes return predictability to bias in the market’s pricing.
- Lakonishok, Shleifer, and Vishny (1994).

- attributes return predictability to bias in the market’s pricing.

- 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.

- 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

- risk,

- five classes of factors:

- 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.

- form the stocks into ten equally weighted deciles,
- 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.

- a regression of realized annual return on decile ranking.

- 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.

- the stocks of decile 10 might be better named GAIP stocks
- in light of their relatively high earnings, cash flow, and dividend yields.

- 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.

- Who are more likely to own stocks with high expected stock returns?
- Who are more likely to own stocks with low expected stock returns?

- 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.

- 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.

- 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.

- whereby decision makers (insiders) repurchase or sell shares

- 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.

- by low returns after share creation

- 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???

- 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

- 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.

- 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.

- because financial ratios are extremely persistent;
- 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.

- in rolling 30-year regressions of annual log CRSP value-weighted returns on lagged log dividend-price ratios,

- 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?

- 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.

- changes in the steady-state growth rate of economic fundamentals resulting from

- 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.

- First, in the presence of steady-state shifts,

- “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.

- are more stable and significant over time.

- such as the earnings-price ratio and the book-to-market ratio.

- 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).

- 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.

- 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.

- 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.

- 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.

- The equity premium puzzle is that
- given the observed low volatility of consumption,
- the average excess return of stocks over bonds is too high
- Mehra and Prescott (1985)

- the average excess return of stocks over bonds is too high

- given the observed low volatility of consumption,
- A simple restatement of the puzzle:
- given the observed high equity premium,
- measured consumption is too smooth.

- given the observed high equity premium,
- Is the problem with the model or with the data?

- This paper uses municipal solid waste, or simply garbage,
- as a new measure of consumption.

- Virtually all forms of consumption produce waste, and
- they do so at the time of consumption.

- Rates of garbage generation should be informative
- about rates of consumption.

- The main sample consists of 47 years of annual data
- from the U.S. Environmental Protection Agency (EPA).

- Per capita garbage growth,
- the ratio of next year's garbage (in tons) to this year's garbage,

- is two and a half times more volatile and
- one and a half times more highly correlated with stock returns than

- personal expenditure on nondurable goods and services
- from the National Income and Product Accounts (NIPA).

- The CCAPM of Lucas (1978) and Breeden (1979)
- using garbage as a measure of consumption

- matches the observed equity premium
- with an estimated relative risk aversion coefficient of 17 versus 81
- for NIPA expenditure.

- with an estimated relative risk aversion coefficient of 17 versus 81

- whereas all expenditure-based measures are strongly rejected.

- the joint equity premium-risk-free rate puzzle of Weil (1989).

- test the garbage-based model on 10 years of data from 19 European countries.
- As in the U.S.,
- garbage growth leads to much lower risk aversion estimates and implied interest rates than expenditure.