在古典理性預期模型中，理性的投資者會蒐集並利用各種相關訊息來幫助其做最適的投資行為。然而市場價格也是訊息來源之一。在效率市場中，價格完全透露出訊息，則無人會有蒐集訊息的動機；若市場有干擾，價格無法顯露出所有相關訊息，私人訊息便有價值。因此，價格資訊性的高低，會影響到私人訊息對投資的重要性。其次，訊息若要能幫助投資者獲得套利利潤，仍須假設投資者必須能夠持有資產一直至資產價值實現為止。顯然的，短期投資者並不符合此一假設。我們相信在通常情況下，長期投資者會比短期投資者更有動機去成為消息靈通者，而且短期投資者對訊息處理的態度有許多特性迴異於古典模型的投資者，他們的存在對市場價格資訊性也會有相當程度的衝擊。本文基於上述想法，利用干擾不對稱訊息下之多期理性預期模型，假設市場中有長期與短期投資者，來探討影響價格資訊性之各項因素及短期投資者之行為特性。

FDA decisions and late-stage clinical trial results regarding new pharmaceutical approvals can cause extreme moves in the share price of small biopharmaceutical companies. Throughout the clinical trial process, many potential investors are exposed to market-moving information before such information is made available to the investing public. An investor who wished to profit from advance knowledge about clinical trial results may use the publicly traded options markets in order to increase leverage and maximize profits. This research examined options data surrounding the public release of information pertaining to the efficacy of clinical trials and approval decisions made by the FDA. Events were identified for small pharmaceutical companies with fewer than three currently approved drugs in an attempt to isolate the effect of individual clinical trial and FDA-related events on the share price of the underlying company. Option data were analyzed using logistic regression models in an attempt to predict phase II and III clinical trial outcome results and FDA new drug approval decisions. Implied volatility, open interest, and option contract delta values were the primary independent variables used to predict positive or negative event outcomes. The dichotomized version of a predictor variable designed to estimate total investment exposure incorporating open interest, option contract delta values, and the underlying stock price was a significant predictor of negative pharmaceutical related events. However, none of ii the variables examined in this research were significant predictors of positive drug research related events. The estimated total investment exposure variable used in this research can be applied to the prediction of future clinical trial and FDA decision related events when this predictor variable shows a negative signal. Additional research would help confirm this finding by increasing the sample size of events that potentially follow the same pattern as those examined in this research. / text