Noise traders, firm-specific uncertainty and technical trading effectiveness

ABSTRACT

This is the first study to document evidence of technical trading effectiveness at firm level in the Chinese A-share market by investigating the relationship between excess profits of technical trading rules and firm-specific characteristics. Our results reveal that firms with higher excess profits from technical trading have more noise traders and higher institutional ownership and that those firms tend to be growth firms with lower liquidity and higher firm-specific uncertainty. Further analysis shows that the profitability of technical trading rules is unsustainable and the excess profits of the highest technical trading profit quintile portfolio disappear in the following year.

KEYWORDS:

• Noise traders
• firm-specific uncertainty
• technical trading effectiveness
• Chinese A-share market

JEL CLASSIFICATION:

• G02
• G11
• G12
• G14

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ Corredor, Ferrer and Santamaria (2013) found that investor sentiment plays a important role in determining returns of stocks that are hard to value and more costly to arbitrage.

² IVO is the ratio of idiosyncratic volatility to total volatility, where idiosyncratic volatility is measured as the SD of the Fama and Macbeth (1973) three factor model residuals by regressing the firm’s daily returns on the Fama and Macbeth (1973) three factors. NIBVOL is introduced by Dontoh, Radhakrishnan and Ronen (2000) and it is estimated by using the distribution of individual analysts’ earnings forecast revisions, and then obtain NIBVOL by subtracting this estimated information-based trading volume from the actual trading volume.

³ SUE is introduced by Foster, Olsen and Shevlin (1984), and calculated as difference between current quarter earnings and earnings 4 quarter ago, scaled by the SD of unexpected earnings.

⁴ The short period and long period are 1 day and 120 days, respectively, for TRB120 and VMA120.

Additional information

Funding

Bin Yu gratefully acknowledges financial support from the National Natural Science Foundation of China (No. 71463018). All errors are ours.