We develop a strategic trading model in which an insider exploits noise traders' overreaction. A feedback effect arises from the insider's trading on fundamental information (the expected growth rate of dividends) and nonfundamental information (insider's inventory or noise supply). We find that the stock price is not fully revealing; a faster mean-reverting noise supply leads to a more volatile price; the price impact can increase with insider's risk-aversion; and a risk-averse insider can trade more aggressively on fundamental information than a risk-neutral one does. Insider's current trade and his previous inventory exhibit simultaneously positive forecasting powers for future stock returns.

We develop a strategic trading model in which an insider exploits noise traders' overreaction. A feedback effect arises from the insider's trading on fundamental information (the expected growth rate of dividends) and nonfundamental information (insider's inventory or noise supply). We find that the stock price is not fully revealing; a faster mean-reverting noise supply leads to a more volatile price; the price impact can increase with insider's risk-aversion; and a risk-averse insider can trade more aggressively on fundamental information than a risk-neutral one does. Insider's current trade and his previous inventory exhibit simultaneously positive forecasting powers for future stock returns.

Unique to the world, China adopts a “T+1 trading rule”, which prevents investors from selling stocks bought on the same day. We develop a dynamic price manipulation model to study the effects of the “T+1 trading rule”. Compared to the “T+0 trading rule”, which allows investors to buy and sell the same stocks during the same day, we show that the “T+1 trading rule” reduces the total trading volume and price volatility, and improves the trend chasers’ welfare when trend-chasing is strong. An empirical test using data on China’s B-share stock market supports the model’s theoretical predictions.