本研究結合價量分析，旨在探討強制性財務預測是否具備資訊內涵，依其性質分為原始財測、正向更新和負向更新三種，循序驗證各種財務預測宣佈時，股市是否有異常價量反應。並進一步依序檢驗未預期盈餘（或更新幅度）、公司規模及行業別與股市累積異常價量之關係。最後比較『公開發行公司財務預測資訊公開體系實施要點』修改前後對研究結果之影響。本論文以民國八十二年初至民國八十七年底之上市公司為研究對象，實證結果如下： 一、股價反應原始強制性財務預測資訊較交易量快，但持續期間較短。 二、正向更新強制性財務預測發佈時，股價有顯著異常反應，但交易量卻無明顯變動。 三、負向更新強制性財務預測發佈時，價量皆有異常反應。 四、『實施要點』修改後，對平均異常報酬率的影響較大，對平均異常交易量的影響較小。 五、原始強制性財務預測發佈時，相較於訊息內容所含之未預期盈餘大小，投資者更側重公司規模及所屬行業別。 六、正向更新強制性財務預測發佈時，相較於目前公司規模之大小，投資者更重視正向更新幅度及所屬行業別。 七、負向更新強制性財務預測發佈時，投資者不只重視負向更新幅度，亦重視目前之公司規模及所屬行業別。 八、『實施要點』修改後，因放寬更新標準規定，管理當局更會利用機會高估財務預測。

過去研究指出，公司管理當局可藉由即時揭露更多攸關資訊以降低市場之資訊不對稱，而管理當局所發布之財務預測亦為揭露資訊之一種。我國證管會於民國八十年五月起正式實施強制性財務預測制度，影響資本市場甚鉅。本研究旨在探討管理當局所發布之財務預測對資本市場資訊不對稱之影響，進而推論我國強制性財務預測制度對於降低資本市場之資訊不對稱是否有其功效。 本研究採用股票交易量、股價變異性及市場深度作為資訊不對稱之代理變數，實證結果顯示： 1.在強制性財務預測制度實施前，自願發布財務預測之公司於預測發布後，其資訊不對稱顯著較預測發布前降低；然與未發布財務預測公司相較之結果卻顯示，以股票交易量為資訊不對稱之代理變數時，發布財測公司於預測發布後之資訊不對稱反而顯著較未發布財測者為高。 2.在強制性財務預測制度實施後，強制或自願發布財務預測之公司於預測發布後，其資訊不對稱程度仍顯著較其發布前降低；而以股價變異性為資訊不對稱代理變數之結果亦顯示，發布財測公司於預測發布後之資訊不對稱顯著較未發布公司為低。 3.以市場深度為資訊不對稱代理變數之結果顯示，在強制性財務預測制度實施後，發布強制性財務預測之公司，其資訊不對稱於預測發布後顯著降低；此外，與發布自願性財務預測公司相較之結果顯示，發布強制性財測公司於預測發布後，其資訊不對稱程度不顯著高於發布自願性財務預測者。 / A firm can increase levels of disclosure to lower the information asymmetry. Financial forecast released by managers is also one of information about corporation. Our country began to implement the mandatory financial forecast regulations since May, 1991. This study mainly investigates the association between financial forecast released by companies and the mandatory financial forecast regulations. Furthermore , it also investigates that if the regulations effectively mitigate information asymmetry. This study uses trading volume, price volatility, and market depth as proxies for the information asymmetry. The empirical results show that: 1.Before May, 1991, corporations with voluntary forecast significantly mitigated the information asymmetry after the forecast released. But the information asymmetry (use trading volume as a proxy) of corporations after forecast released was not significantly lower than corporations without forecast. 2.After May, 1991, corporations with mandatory or voluntary forecast also significantly mitigated the information asymmetry after the forecast released. And the information asymmetry (use price volatility as a proxy) of corporations after forecast released was significantly lower than corporations without forecast. 3.After May, 1991, corporations with mandatory forecast significantly mitigated the information asymmetry (use market depth as a proxy) after the forecast released. And the information asymmetry of corporations after mandatory forecast released was not significantly higher than corporations with voluntary forecast.

傳統探討分析師推薦資訊價值的研究多採用累積超額報酬的方式，近年來研究顯示個別投資人的績效顯著低於機構投資人，因此是否分析師推薦能夠幫助提升個別投資人的福利。本論文的第一個貢獻在檢定是否個別投資人能夠獲取分析師推薦的資訊價值，為區分推薦資訊分別對於個別與機構投資人的價值為何，本研究採用的每種投資人實際的交易利潤作為衡量指標。 研究結果顯示所有投資人都可以透過買入推薦獲取顯著的正報酬，但在賣出推薦上，僅外資與共同基金仍能維持獲取正的報酬。同時發現在推 薦事件期間，專業機構投資人的利潤顯著高於一般散戶的獲利。 進一步，本論文的第二的主題在探討此推薦的資訊價值對於不同投資人的差異，是否肇因於推薦券商所提供的私有資訊，因此進一步將各類投資人分成推薦券商的客戶與非客戶。結果顯示國內機構投資人的利潤在客戶的身上顯著高於非客戶的獲利，顯示推薦券商在對外公佈推薦資訊前的確提供私有資訊給其國內機構客戶，但此現象在賣出推薦並不存在。 第三，本論文進一步分析是否拿到推薦券商所提供私有資訊的客戶也是推薦券商的經紀業務收益的主要貢獻者。在比較推薦券商與非推薦券商在被推薦股票上的相對交易量(金額)中，發現推薦券商的確因為買入推薦股票而增加經紀業務量，但很驚訝的發現貢獻最多交易量的是個別投資人，而非拿到最多好處的機構投資人。 最後，本研究透過迴歸分析探討不同投資人的交易利潤與推薦券商所獲得的經紀業務量的關係。在控制推薦類型、推薦評等與被推薦股票之股票特性後，發現投資人的交易利潤與推薦券商的經紀業務收益成正相關，再次顯示券商推薦與其各項業務收益間的關係。 / Traditionally, information value of analysts’ recommendations has been well-recognized by cumulative abnormal returns. Recent studies show that individuals are underperformed, and therefore, it is a critical issue on if analysts’ recommendations are helpful to individuals’ welfares. The first contribution of this dissertation to the literature is to examine whether individual investors are capable of capturing the information value. To classify the information value of recommendations for individuals and institutions, respectively, I, thus, use a direct measure to calculate the actual trading profits of types of traders. To our best knowledge, this is the first paper that demonstrates the information value for types of investors. Our results indicate that, all investors get positive and significant profits in brokerages’ buy recommendations, no matter what types of investors are measured. As to sell recommendations, only foreign investors and mutual funds have positive returns. We also find that professional institutions earn more profits than retail investors during the recommendation event periods. Further, the second objective of this dissertation is to test whether the information values are caused by private information from brokerages’ houses, we separate the profits of types of investors into customers and non-customers based. The findings are that only domestic institutional customers of recommending brokerages are more beneficial than those of non-recommending brokerages in buy recommendations, which implies that brokerage houses may reveal private information to their own institutional customers before buy recommendations make public. This does not hold for sell recommendations. Third, we are interested in analyzing whether the private information that recommending brokerages provide to their own customers may, indeed, contribute to brokerages’ commission revenues. By comparing the trading volume of recommending brokerages and non-recommending brokerage for the covered stocks, we find that the volumes of covered stocks issued in the recommending brokerages are increased for buy recommendations. Particularly, we find that the main contribution of trading volume is from individuals. Furthermore, we run regressions to study the relationship between trading profits of types of investors and trading volume of recommending brokerages. After controlling recommendation types, consensus rating of recommendations, and stock characteristics, our results indicate that trading profits of all types of investors are positively related to commission revenues of brokerages. This may justify the importance of brokerage recommendations on their business relationships.

傳統財務理論中均假設市場為效率市場，然而不動產市場並非效率市場，投資者對於市場資訊之反應並非完全理性。若投資者忽視自身擁有之資訊，選擇追隨其他人的投資決策，將使投資人間存在相互牽制之行為，因而產生行為財務學中之「從眾行為」，其決策結果將無法完全反應市場資訊，並造成投資人集體買進、賣出之行為，使市場價格與交易量存在不正常之波動。由於台灣不動產市場長期以來存在有價格漲幅波動超越合理範圍之現象，因此本研究探討台灣不動產市場是否存有從眾行為，使得投資人具有非理性的投資傾向。 有鑑於過去關於從眾行為之研究仍以股票市場中報酬率或交易量驗證為主，對於台灣運用交易量進行不動產市場之從眾行為驗證則付之闕如，而從眾行為對於不動產市場之影響，首先將反映於交易量之波動，因此本研究運用自我迴歸分配落遲模型對於台灣不動產市場是否存在從眾行為進行驗證，並比較不動產報酬率波動不同之交易市場，其從眾行為存在情形之異同。 模型結果顯示台灣三大都會區（臺北市、臺中市與高雄市）與臺北市分區（分為市中心、郊區與郊外）中，僅臺北市整體與臺北市分區之住宅市場明顯存在從眾行為現象。結果顯示當該住宅市場存在從眾行為時，當期交易量將受到當期持有成本與前期市場報酬率之影響。此外，交易量除受從眾行為之影響外，尚受到經濟成長率、營建類股股價指數、物價指數租金年增率、營造工程物價指數等之正向影響，而購屋貸款利率與通貨膨脹則和交易量呈反向變動現象。 本研究以探討從眾行為、交易量與總體經濟因素之關連性，進一步釐清影響住宅市場交易量波動之因素，使購屋者於決策時參考前期市場交易情形能更加理性，避免盲目跟隨下的從眾行為產生。 / Base on the Efficient Market Hypothesis, the traditional financial theory assumes the market is efficient. However, the real estate market is not. For this reason, investors could not react to market information entirely. If investors ignore their own information, they may choose to follow other peoples’ investment decisions. Therefore, this situation will lead to herding behavior of behavioral finance that may cause price volatility and unusual transactions. On account of the real estate market exists unreasonable price fluctuations for a long time in Taiwan, this thesis examines whether the herding behavior exists in Taiwan real estate market or not. Although many researchers study the herding behavior in the stock market by using the transactions and the returns on investment, few attempts have been made to discuss the herding behavior in Taiwan housing market by using the housing transactions. Hence, this study examines the herding behavior in Taiwan housing market by establishing the Auto-regressive Distributed Lag (ARDL) model with housing transaction data. Results found the herding behavior of real estate market do exist in the whole Taipei city and the three region of Taipei city (downtown, suburb and outskirt). And it shows the transactions in the housing market with herding behavior may be affected by user cost of housing and pre-market returns. Furthermore, the study finds some macroeconomic factors affecting the housing transactions positivity, such as economic growth rate, construction stocks index, consumer price index of house renting and consumer price index of construction engineering. On the contrary, loan interest rate of housing and consumer price index has negative influence. To conclude, this study aims to examine the influential factors on the volatility of housing transactions though clarifying the relationship between the herding behavior, the transactions in housing market and the macroeconomic factors. It may help investors follow other peoples’ investment decisions more reasonable, and avoid blind herding behavior in real estate markets.

本研究探討選擇權交易量之大小對於波動度預測之效率性所造成之對偶效果(dual effect)，驗證〝正常的高交易量〞與〝異常的高交易量〞對於波動度預測能力是否有不同的影響。本研究採用panel data之資料型態，以LIFFE上市的個股買權為對象，資料長度為三年左右。主要欲探討之假說為： 1.一般而言，交易量大的選擇權，其波動度估計誤差較交易量小的選擇權來得小。 2.相對於平日水準而言，某日交易量異常高的選擇權將有較大的波動度估計誤差。 本研究所使用的波動度預測模型為隱含波動度(ISD)，採用的是最接近到期月份及最接近價平的合約。實證以組合迴歸、固定效果模型、隨機效果模型分別估計之，加以比較。結果發現固定效果模型為較佳之解釋模型，然而結果顯示交易量的對偶效果並不明確影響波動度預測誤差，故推測有某種影響公司間差異的因素，即公司間之異質性，比相對交易量更容易影響波動度預測之誤差。另外，透過組間與組內效果之分析，發現不論是長期還是短期，由於公司間的異質性存在，使得相對交易量對於波動度預測誤差均無明顯影響。 / The purpose of this research is to study the dual effect on the efficiency of volatility forecasting which is caused by the volume of option market, with the intent to test whether〝normal high volume〞and〝abcdrmal high volume〞cause different results on the ability of volatility forecasting. The data used is in the form of panel data. It is drawn from LIFFE, and has a length of about three years. The hypotheses to be examined in this study are：1. High-average-volume options have smaller volatility forecasting errors than low-average-volume options; 2. Options have larger volatility forecasting errors on abcdrmally-high-volume days than on normal-volume days. In this research, volatility is forecasted by implied standard deviation (ISD) which is implied in the at-the-money and the nearest expiry month options. Pooled regression、fixed effect model、and random effect model methods were applied. The results show that the fixed effect model made the best analysis amongst the three models. However, the result does not support the hypotheses made above, which means that volume does not have much influence on volatility forecasting error. It is inferred that there exists some other factors which could cause the difference between firms, namely heterogeneity, and these factors have much more powerful influence over volatility forecasting error than volume. Finally, it was found that no matter for long run or short run, because of the existence of heterogeneity, relative volume doesn’t have obvious influence on volatility forecasting errors when analyzing the difference between the between-individual effect and the within-individual effect.