• No results found

4. Empirical Design and Analysis

4.5 Sorts

35

6 0.08378 13.230 86.770

7 0.08379 13.241 86.760

8 0.08380 13.263 86.737

9 0.08381 13.276 86.724

10 0.08382 13.288 86.712

36 Table 4-17. Sorts: Stock Turnover Rate and Firm Characteristics. For each month, I place the observations

into deciles according to different characteristics, which is firm size (ME), age, volatility, earnings-to-book

ratio (E/BE), book-to-market ratio (BE/ME). The table reports average portfolio returns for each decile over

months where the stock turnover rate is positive, negative, and the differences between them.

37 Table 4-18. Sorts: Investor Sentiment and Firm Characteristics. For each month, I place the observations

into deciles according to different characteristics, which is firm size (ME), age, volatility, earnings-to-book

ratio (E/BE), book-to-market ratio (BE/ME). The table reports average portfolio returns for each decile over

months where investor sentiment is positive, negative, and the differences between them.

38

In this model I collect 144 monthly returns of all 3121 A-share stocks from January 2009 to December 20 from CSMAR database and place the observations into decile sorts according to a characteristic which takes at the end of the month.

Months with the highest values are placed in top decile. The composite rank of a stock is the first principal component of the decile ranks of its age, book-to-market ratio (BE/ME), earnings-to-book ratio (E/BE), firm size (ME) and total risk (RISK), and is intended to capture the stock’s sensitivity to investor sentiment.

I compute average monthly return and report decile sorts on TURN in Table 4-17 and ISI in Table 4-18. I condition the turnover rate in year t on the average value from 250 trading days prior to year t. For brevity I only use TURN as the

comparison variable. Though the other variables could give similar results, the variable TURN is able to reveal the result more intuitively. A high turnover rate usually means high liquidity and trading willingness, it is also a good indicator to predict stock prices. By comparing the effects TURN brings to the effects investor sentiment index brings, the empirical test would be more general and convincing.

Start from effects of size conditional on turnover rate and sentiment. Note that when the turnover rate is above average or investor sentiment is positive, the returns are sorted into ‘positive’ category. When the turnover rate is below average or investor sentiment is negative, the returns are sorted into ‘negative’

category. Table 4-18 shows that when investor sentiment is negative, the average return per month ranges from 1.29 percent to 2.28 percent, with the percentage of 2.28 for the top decile and 1.29 for the bottom decile. When investor sentiment is positive, the average return ranges from -0.32 percent to 1.19 percent, with the percentage of 1.09 for the top decile and -0.32 for the bottom decile. When it turns to the turnover rate, the difference is not as apparent as investor sentiment shows.

The return averages from 1.91 percent to 2.74 percent for positive groups and from -0.36 percent to 0.47 percent for negative groups. It is widely known that size effect is largely a January effect (Keim (1983)), and the January effect was also known to be stronger after a period of low returns (Reinganum (1983)),

39

which is when sentiment is likely to be low. The result of Table 4-18 is consistent with prior papers and also the result of this paper.

Then it comes to the conditional effect of Age. As is shown in the last column of the table, there is a gap of -1.63 percent between top-decile Age firms and bottom-decile firms when sentiment is negative, but 0.05 percent when sentiment is positive. Table 4-18 shows the same result with Baker’s paper that most investors prefer young stocks when sentiment is positive and old stocks when sentiment is negative.

The next rows examine the cross-sectional effect of Volatility. As is obviously shown in the table, when sentiment is high, portfolios of low volatility tend to be more attractive, which earn returns of 1.43 percent per month, while high sigma portfolios earn -0.12 percent. The cross-sectional effect of Volatility reverses in low sentiment situations. It is consistent with the result of Baker and Wurgler (2004) that when sentiment is high, subsequent returns are higher on low-return volatility stocks than high-return volatility stocks.

The next rows examine the cross-sectional effect of profitability, which is earnings-to-book ratio. E/BE ratio is a simple and intuitive way for investors to distinguish profitable (E>0) firms from unprofitable (E<0) firms. The

characteristic displays a conditional fluctuating pattern. The table shows that when sentiment is positive, monthly returns are 0.13 percent higher on high-profitable firms than low-profitable firms. It’s probably because low-profitable or

unprofitable firms tend to be harder to arbitrage and more sensitive to investor sentiment. The remaining variable, which is book-to-market ratio, displays similar pattern with above characteristic. BE/ME ratio may identify distress situation of the firm and is regarded as a common valuation indicator. Simply speaking, expected returns are generally higher for high BE/ME stocks. No matter how sentiment or turnover rate changes, high BE/ME stocks tend to have higher

40

expected returns than low BE/ME stocks. It shows a kind of unconditional explanatory power.

Overall, the above patterns suggest that when sentiment is high, stocks are probably more attractive to optimists and unattractive to arbitrage. Small-cap stocks, young stocks, high-volatility stocks, unprofitable stocks and distressed stocks are likely to experience lower subsequent returns.