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台灣證券交易所投資人交易行為與股票報酬關係之研究 / Investor Trading Behavior and Stock Returns in Taiwan Stock Exchange夏清田, Hsia, Ching-Tian Unknown Date (has links)
This paper investigates the investor trading behavior and the relationship between investor sentiment and stock returns. First we explore whether individual investors behave as the Disposition Effect stated — hold their losers too long while realize their winners too soon. Second, we apply four sentiment indicators — number of recommended stocks, margin purchase value, net fund redemption and odd-lot trade value — to examine relationship between investor sentiment and stock returns. We would like to see if past returns have anything to do with current sentiment, and if sentiment provides predictive power to future returns.
First of all, from our analysis to over eight hundreds cash accounts trading records in two research periods, January to March and September to December in 2000, we found the Disposition Effect holds in average but not statistically.
Second, the number of recommended stocks, weighted number of recommended stocks, margin purchase value, change in margin purchase value, net fund redemption and odd-lot trade value as proxies of investor sentiment are good at measuring the effect of past 4-week and 26-week returns on sentiment.
Third, the margin purchase value, net fund redemption and odd-lot trade value provide predictive power to future 26-week returns in our study, which also implies there is likely underlying mean-reversion within half year during the research period.
Finally, exploiting the change in margin purchase value as proxy of investor sentiment, we found the past 4-week returns volatility is inversely related with the indicator. That is, investors are scared on facing with high returns volatility.
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現金流量與相關會計變數對於股價報酬率關連性之研究 / The Relationship Between Free Cash Flow, Related Accounting Variable and Stock Returns許欣欣, Shue, Sing-Sing Unknown Date (has links)
本研究試圖瞭解投資人所關心的財務資訊,是否真能帶來超額報酬,亦即探討上市公司股票報酬率與各財務資訊之間的關係,包括每股盈餘成長率及益本比、市價淨值比、自由現金流量相關變數對長期股票報酬之影響,並嘗試對各變數給予經濟上的解釋。
其中並試圖找出財務資訊究竟是在何時反應於股票報酬與超額報酬上,因此將股票報酬與超額報酬以領先財務資訊一季、與財務資訊同季及落後財務資訊一季等三個時點加以衡量,並以表面無關迴歸模式(Seemingly Unrelated Regression model ; SUR)與混合橫斷面及縱斷面之迴歸模式進行統計分析。
結果發現益本比及每股盈餘成長率與股票報酬及超額報酬的關係並不顯著;市價淨值比與落後一季的股價報酬具有顯著的負相關,而與超額報酬不論衡量時點為何均存在有顯著正相關;自由現金流量與股價報酬及額報酬均無顯著關係;毛現金流量與經濟利益率與股價報酬在部分產業中具有顯著關係,而與當期超額股價報酬則存在顯著的關係,惟影響符號不一致。此外各自變數對於股票超額報酬的關連性較自變數與股票報酬間具有較顯著的關係。
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Cross-Section of Stock Returns: : Conditional vs. Unconditional and Single Factor vs. Multifactor ModelsVosilov, Rustam, Bergström, Nicklas January 2010 (has links)
<p>The cross-sectional variation of stock returns used to be described by the Capital Asset Pricing Model until the early 90‟s. Anomalies, such as, book-to-market effect and small firm effect undermined CAPM‟s ability to explain stock returns and Fama & French (1992) have shown that simple firm attributes, like, firm size and book-to-market value can explain the returns far better than Beta. Following Fama & French many other researchers examine the explanatory powers of CAPM and other asset pricing models. However, most of those studies use US data. There are some researches done in different countries than US, however more out-of-sample studies need to be conducted.</p><p>To our knowledge there are very few studies using the Swedish data and this thesis contributes to that small pool of studies. Moreover, the studies testing the CAPM use the unconditional version of the model. There are some papers suggesting the use of a conditional CAPM that would exhibit better explanatory powers than the unconditional CAPM. Different ways of conditioning the CAPM have been proposed, but one that we think is the least complex and possible to make use of in the business world is the dual-beta model. This conditional CAPM assumes a different relationship between beta and stock returns during the up markets and down markets. Furthermore, the model has not thoroughly been tested outside the US. Our study is the first to use the dual-beta model in Sweden. In addition, the momentum effect has lately been given some attention and Fama & French‟s (1993) three factor model has not been able to explain the abnormal returns related to that anomaly. We test the Fama & French three factor model, CAPM and Carhart‟s four factor model‟s explanatory abilities of the momentum effect using Swedish stock returns. Ultimately, our aim is to find the best model that describes stock return cross-section on the Stockholm Stock Exchange.</p><p>We use returns of all the non-financial firms listed on Stockholm Stock Exchange between September, 1997 and April, 2010. The number of companies included in our time sample is 366. The results of our tests indicate that the small firm effect, book-to-market effect and the momentum effect are not present on the Stockholm Stock Exchange. Consequently, the CAPM emerges as the one model that explains stock return cross-section better than the other models suggesting that Beta is still a proper measure of risk. Furthermore, the conditional version of CAPM describes the stock return variation far better than the unconditional CAPM. This implies using different Betas to estimate risk during up market conditions and down market conditions.</p>
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Cross-Section of Stock Returns: : Conditional vs. Unconditional and Single Factor vs. Multifactor ModelsVosilov, Rustam, Bergström, Nicklas January 2010 (has links)
The cross-sectional variation of stock returns used to be described by the Capital Asset Pricing Model until the early 90‟s. Anomalies, such as, book-to-market effect and small firm effect undermined CAPM‟s ability to explain stock returns and Fama & French (1992) have shown that simple firm attributes, like, firm size and book-to-market value can explain the returns far better than Beta. Following Fama & French many other researchers examine the explanatory powers of CAPM and other asset pricing models. However, most of those studies use US data. There are some researches done in different countries than US, however more out-of-sample studies need to be conducted. To our knowledge there are very few studies using the Swedish data and this thesis contributes to that small pool of studies. Moreover, the studies testing the CAPM use the unconditional version of the model. There are some papers suggesting the use of a conditional CAPM that would exhibit better explanatory powers than the unconditional CAPM. Different ways of conditioning the CAPM have been proposed, but one that we think is the least complex and possible to make use of in the business world is the dual-beta model. This conditional CAPM assumes a different relationship between beta and stock returns during the up markets and down markets. Furthermore, the model has not thoroughly been tested outside the US. Our study is the first to use the dual-beta model in Sweden. In addition, the momentum effect has lately been given some attention and Fama & French‟s (1993) three factor model has not been able to explain the abnormal returns related to that anomaly. We test the Fama & French three factor model, CAPM and Carhart‟s four factor model‟s explanatory abilities of the momentum effect using Swedish stock returns. Ultimately, our aim is to find the best model that describes stock return cross-section on the Stockholm Stock Exchange. We use returns of all the non-financial firms listed on Stockholm Stock Exchange between September, 1997 and April, 2010. The number of companies included in our time sample is 366. The results of our tests indicate that the small firm effect, book-to-market effect and the momentum effect are not present on the Stockholm Stock Exchange. Consequently, the CAPM emerges as the one model that explains stock return cross-section better than the other models suggesting that Beta is still a proper measure of risk. Furthermore, the conditional version of CAPM describes the stock return variation far better than the unconditional CAPM. This implies using different Betas to estimate risk during up market conditions and down market conditions.
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股票報酬決定因素及股票報酬與盈餘間關係之研究 / The Determinants of Stock Returns and the Relationship between Stock Returns and Earnings彭火樹, Peng, Huo-Shu Unknown Date (has links)
台灣早期有關系統風險(β)的研究皆指出β不能解釋台灣股票報酬的變異,故控尋更能解釋股票報酬的風險因素為本文的主要目的之一。
本研究分析民國71年7月至85年5月股票上市公司資料(排除金融、保險、及變更交易方式的公司)。因民國79年股價指數從2月的最高點12,495急遽下滑至10月的2,560,故分析上將79年度予以排除。在71年7月至78年12月的時段中,整體市場因素(RM-RF)不能解釋股票報酬的變異。此點發現與台灣早期研究的結論一致。其他變數顯著者僅有與規模有關的因素(SZSMB),或與負債比率有關的因素(DEHML),其中以 SZSMB的解釋能力最強。在民國80年1月至85年5月的時段中,所有模式中整體市場因素( RM-RF)的係數皆顯著,並且是所有因素中最顯著者。這點發現與前時段(71年7月至78年12月)的結果有很大的不同。其他的變數顯著者,有代表成長機會的BMHML(與淨值市價比有關的因素)、EPHML(與益本比有關的因素)、或CPHML(與營運現金市價比有關的因素),及代表利率結構有關的風險因素TERM(與利率期間結構有關的風險溢酬)、或DFT(與利率違約風險有關的風險溢酬)。其中以(RM-RF)、EPHML、CPHML及TERM的風險組合最能解釋股票報酬的變異。
應用更完整的股票報酬解釋變數,探討股票報酬與盈餘間的關係,亦為本文主要目的之一。經分析以(1)各時段最能解釋股票報酬的因素組合為基礎,計算異常報酬;(2)單獨的以整體市場因素(RM-RF)為基礎計算異常報酬,然後再分別估出盈餘反應比較係數(ERC)比較之。結果顯示,以各時段最能顯著解釋股票報酬的因素組合為基礎的ERC為正的顯著,且其ERC大於只以整體市場因素(RM-RF)為基礎所算出的ERC。
另外,關於盈餘品質假說之測試,經以公司規模大小為虛擬變數放入迴歸式中,結果顯示,代表大公司的虛擬變數之係數時而為正,時而為負,且都不顯著,故盈餘品質假說未獲得支持。
再者,關於成長機會與ERC關係之測試,經以公司成長機會大小為虛擬變數放迴歸式中,結果顯示,代表成長機會的虛擬變數之系數時而為正,時而為負,且大都不顯著,故成長機會大的公司之ERC大於成長機會小的公司之ERC的假說,未獲得實證的支持。 / Earlier studies (Chen 1990; Chiu 1990; and Wang 1992) found that systematic risk (β) could not explain the variance of stock returns in Taiwan. The findings were inconsistent with the Capital Asset Pricing Model (CAPM). One of the major purposes of this paper is to examine the factors that have higher explanatory power of stock returns.
To test the hypotheses, this study uses the data of Taiwanese listed companies covering the period from July 1982 to may 1996. The 1990 data are excluded because the stock market index climbed to a record high of 12,495 in February 1990 and then fell sharply to allow level of 2,560 in October 1990. The "crash" might cause structural changes in stock market, so the analyses are conducted separately for the periods before and after the crash, namely the prior-crash period (from July 1982 to December 1989) and the post-crash period (from January 1991 to May 1996).
The empirical results show that for the prior-crash period the overall market factor (market returns minus risk free rate, RM-RF) can not explain the variance of stock returns. The findings are consistent with those of previous studies. However, we find that the factor-related to size (SZSMB) and the factor related to debt/equity ratio (DEHML) have significant association with stock returns. Furthermore, SZSMB has higher explanatory power. In contrast, the overall market factor is the most significant factor for the post-crash period. Other factors that are significant consisted of (1) proxies for growth opportunities, including book-to-market equity (BMHML), earnings/price ratio (EPHML), and cash flow/price ratio (CPHML), and (2) the factors related to interest structure, including term structure (TERM) and default risk (DFT). Among these factors, the set of RM-RF, EPHML, CPHML, and TERM explains the variance of stock returns most.
Another purpose of this paper is to use the aforementioned findings to study the relationship between stock returns and earnings. The results show that the earnings response coefficients based on the most explanatory factor portfolio of each period are positive and significant, and are greater than those based on the traditional systematic risk (β).
The tests for earnings quality hypothesis indicate that the coefficients of the dummy variable proxies for big companies are insignificant. The earnings quality hypothesis is not supported.
The tests regarding the relationship between growth opportunities and earnings response coefficients show that the coefficients of the dummy variable proxies for high growth companies are unstable. The hypothesis that the earnings response coefficients of high growth companies are greater than those of low growth companies is not supported by empirical evidence.
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