1. Forecast Revision and Information Uncertainty in Australia Stocks
Hou, Lee and Chen
Presenter: Tony Cheih-Tse Hou
National Dong Hwa University, Taiwan
NTU ICF
December 2010

2. Purpose of the Study:
Positive (negative) returns following the positive (negative) earnings news
Investigate whether the stock return continuation patterns outside U.S. market
Use Australia market
Sample period: July 1992 – June 2009.
Investigate the relation between analysts’ forecast revision and information uncertainty
If there are high uncertainty should generate higher returns follow by good news, vice verse.

3. Unceratinty Variables
Firm size (1/MV)
Book-to-market ratio (1/BM)
Analyst coverage (1/Coverage)
Dispersion in analysts earnings forecasts (DISP)
Forecast revisions:
average monthly earnings forecast change in EPS as percentage of the absolute mean value of the prior consensus forecasts.

4. Table 1 Descriptive statistics
Table 1 reports descriptive statistics for the period July 1992 through June 2009 for all stocks in Australia. For each year, we report the number of I/B/E/S firms, their mean and median size (in A$ millions), mean and median book-to-market (B/M) ratios, the number of analysts per firm at coverage percentiles ranging from 10% to 90%.

5. Table 2 Forecast revisions and return continuation
Table 2 reports average monthly portfolio returns for the period July 1992 through June 2009 for all stocks in Australia.
The forecast revision ratio is defined as the average monthly earnings forecast change in expected earnings per share as a percentage of the absolute mean value of the prior consensus forecasts. Each month we sort stocks into three news categories based on analyst forecast revision ratio in month t, which stocks in portfolio P1 (P3) are those that contain negative (positive) revision, and stocks in P2 contains no revision (zero). The forecast revision is the average of individual revisions by analysts who cover the firm in both months t-1 and t. The portfolio are equally weighted at formation month and held for 1, 3, 6, 9, 12, 18, 24, 36 months. Mean forecast revision ratio, analyst coverage, and firms’ average market capitalization for each news formed portfolio are reported in the last column. t-statistics are reported in parenthesis. RETt is the portfolio return during t-period.

6. Table 3 Portfolio returns sorts by information uncertainty proxy and analyst forecast revision
Table 3 reports average monthly portfolio returns for the period July 1992 through June 2009 for all stocks in Australia. Each month we sort stocks into three categories based on whether the forecast revision ratio is negative, zero, or positive. The forecast revision ratio is defined as the average monthly earnings forecast change in expected earnings per share as a percentage of the absolute mean value of the prior consensus forecasts. For each news category, we further sort stocks into three groups based on information uncertainty proxy, U1 contains lower 30% uncertainty stocks and U3 contains higher 30% uncertainty stocks. Firm size (MV) is the market capitalization (AU$ in millions) at the end of month t. The book-to-market ratio (BM) is computed by matching the yearly book equity value (BE) figure for all fiscal years ending in calendar year t to returns starting in July of year t; this figure is then divided by market capitalization at month t to form the book-to-market ratio, so that the book-to-market ratio is updated each month. Analyst coverage is the total number of estimates covering the firms for the fiscal period. Dispersion is defined as the ratio of the standard deviation of analysts’ current-fiscal-year annual earnings per share forecasts to the absolute value of the mean forecast, as reported in the I/B/E/S Summary History file. 1/MV, 1/BM and 1/COV are the reciprocals of MV, BM, and analyst coverage. The portfolio are equally weighted at formation and held for 1 month. Mean size is in A$ million. t-statistics are reported in parenthesis.

7. Table 4 Portfolio returns sorts by forecast revision and interaction of information uncertainty proxies
Table 4 reports average monthly forecast revisions portfolio returns for the period July 1992 through June 2009 for all stocks in Australia. Each month all stocks are sorted into portfolios based on news and then sorted by information uncertainty proxies. Each month we sort stocks into three categories based on whether the forecast revision ratio is negative, zero, or positive. The forecast revision ratio is defined as the average monthly earnings forecast change in expected earnings per share as a percentage of the absolute mean value of the prior consensus forecasts. For each news group, we further conduct triple sort of stocks into three divisions by firm size and other uncertainty proxies. Firm size (MV) is the market capitalization (AU$ in millions) at the end of month t. The book-to-market ratio (BM) is computed by matching the yearly BE figure for all fiscal years ending in calendar year t to returns starting in July of year t; this figure is then divided by market capitalization at month t to form the book-to-market ratio, so that the book-to-market ratio is updated each month. Analyst coverage is the total number of estimates covering the firms for the fiscal period. Dispersion is defined as the ratio of the standard deviation of analysts’ current-fiscal-year annual earnings per share forecasts to the absolute value of the mean forecast, as reported in the I/B/E/S Summary History file. 1/MV, 1/BM and 1/COV are the reciprocals of MV, BM, and analyst coverage. The portfolio are equally weighted at formation and held for 1 month. Mean size is in million A$. t-statistics are reported in parenthesis.

9. Table 5 Robustness check: four-factor model results
This table reports the intercepts of the four-factor regression model for monthly excess returns of the four information uncertainty portfolios for three analyst forecast revisions portfolios. The return on each portfolio is then taken in excess of the risk-free rate and regressed against a number of factors. We investigate persistence using Carhart's (1997) four-factor model as follows:
where rpt is the excess return on portfolio p in month t. RMt is the excess return on the market, SMBt is the return on the mimicking size portfolio, HMLt is the return on the mimicking book-to-market portfolio and construct in the same way as in Fama and French (1996). UMDt is the return on the mimicking momentum factor and construct as Carhart (1997). A significantly positive α indicates performance persistence and vice versa for a significantly negative α. t-statistics are reported in parenthesis. pttptptptppptUMDuHMLhSMBsRMbrεα+++++=

10. Conclusions
Stocks with upward revised forecasts receive higher positive future returns
Higher information uncertainty will decrease stock returns following by bad news, but will further increase returns following good news.
Firm size tends to be an important factor to measure the information uncertainty.

11. Thank you for listening!