博碩士論文 108428007 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:29 、訪客IP:3.129.253.65
姓名 潘立佳(Li-Chia Pan)  查詢紙本館藏   畢業系所 財務金融學系
論文名稱 盈餘慣性之影響因素:資訊不對稱、流動性以及行為偏誤
(The Reasons of PEAD: Information Asymmetry, Liquidity, and Behavioral Bias)
相關論文
★ 買賣價差之拆解-以台灣加權指數期貨與新加坡摩根台股指數期貨為例★ 在不同模型、分組方式以及貝他估計情況下之Fama和French三因子模型表現
★ 延長交易時間對台灣股市之日內影響★ 法人日內投資行為及跟隨法人投資策略
★ 結算制度與到期日效應★ 台灣股市系統性風險之檢定
★ 提前平倉與轉倉策略對股價指數期貨到期日效應之實證:以台灣股票市場為例★ 指數期貨操縱模型--以台灣股價指數為例
★ 台灣股市日內效果之研究★ 從日、美看台灣股市的資產定價實證研究
★ 選擇權交易獲利性之分析:以交易資料為例★ 資訊不對稱與公司特徵:市場微結構觀點
★ 企業商業模式及品牌價值創造—以宏達電為例★ 企業商業模式及品牌價值創造—以宏碁集團為例
★ 法人期貨與選擇權交易量對市場報酬的影響★ 台灣市場的關係公司與內部人交易
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 響盈餘宣告後股價飄移(post-earnings-announcement drift, PEAD)的因素有許多,本
文從中整理出三大原因分別為資訊不對稱、流動性以及投資人的行為偏誤, 並進一步探
討三大因素對飄移的影響,同時驗證三大因素都考慮的情況下對股價漂移的影響,檢驗
哪項因素是造成漂移最具解釋能力的原因。
整理出三項主要因素下, 在每項因素中各選取兩項變數來對飄移做驗證。 股價飄移
以宣告後 60 天的累積超額報酬(CAR)為觀察, 並將其依正負號分為 CAR+ 及 CAR- 以
利於觀察股價飄移方向。資訊不對稱部分選擇PIN和AIV兩變數作為代替, Realize spread
(Realsp) 和 Illiquidity 則為流動性因素的代理,而行為偏誤為過度自信及注意力不集中
兩類,又分別以 Turnover 和 Friday (公司盈餘宣告日是否為星期五) 兩變數代理。本文
主要研究 2014 年全年公司盈餘宣告後的股價飄移,並依各項變數所需不同之數據抓取
2013 至 2015 年的資料做計算。 大多數公司為季宣告,因此每三個月為一次研究事件,
實證研究顯示在 CAR+ 或 CAR- 的結果中,只有流動性這項因素是兩個變數
(Realsp 及 Illiquidity) 都顯著的,因此可以說在這三項因素中流動性是對 PEAD 最具
解釋能力的, 其中 Realsp 這項變數的顯著水準更高,顯示本文結果表示其比起
Illiquidity 能夠更好的衡量流動性。
摘要(英) Many factors cause post-earnings-announcement drift (PEAD). This thesis points out
three major causes as information asymmetry, liquidity, and investor behavioral bias, and
further explores the impact of these factors on the drift of stock price. This impact is then
verified and tests are conducted to find out which factor is the most crucial cause of PEAD.
After summarizing three main factors, I select two variables in each factor to examine
their effect on drift. The drift of stock price is observed based on cumulative abnormal return
(CAR), which is divided into CAR+ and CAR- to facilitate the observation of the direction of
stock price drift according to the sign. For the information asymmetry part, the probability of
informed trading (PIN) and abnormal idiosyncratic volatility (AIV) are selected. Realize
spread (Realsp) and Illiquidity are proxies for liquidity factors. Overconfidence and
inattention represent behavioral biases and I use Turnover and if firms announce on Friday or
not as proxies. This thesis mainly studies the drift after the companies’ earnings
announcement for the whole year of 2014, and as most companies announce quarterly, every
three months is a research event.
Empirical research shows that in the regression results of CAR+ and CAR-, only the
liquidity factor (Realsp and Illiquidity) is significant for both. Therefore, it can be said that
liquidity is the most important reason for PEAD among these three factors. Furthermore, the
significant level of the variable Realsp is higher, showing that the results of this thesis
indicate that it can better measure liquidity than Illiquidity
關鍵字(中) ★ 盈餘宣告後股價飄移
★ 資訊不對稱
★ 流動性
★ 行為偏誤
關鍵字(英) ★ PEAD
★ information asymmetric
★ liquidity
★ behavioral bias
論文目次 摘要......................................................................................................................................i
Abstract ...............................................................................................................................ii
Contents..............................................................................................................................iii
1. Introduction ................................................................................................................1
2. Literature review and hypothesis................................................................................3
2.1. PEAD..........................................................................................................3
2.2. Proxies variables.........................................................................................5
2.3. Hypothesis ..................................................................................................7
3. Methodology and data ................................................................................................8
3.1. Information asymmetry variables.............................................................10
3.1.1. PIN....................................................................................................10
3.1.2. AIV ...................................................................................................12
3.2. Liquidity variables....................................................................................14
3.2.1. Illiquidity ..........................................................................................14
3.2.2. Realized spread.................................................................................14
3.3. Behavioral bias variables..........................................................................15
3.3.1. Friday................................................................................................15
3.3.2. Turnover ...........................................................................................15
3.4. Other variables..........................................................................................16
3.5. Methodology.............................................................................................18
3.6. Data...........................................................................................................20
4. Empirical results and discussion ..............................................................................25
4.1. Empirical results.......................................................................................25
4.2. Discussion.................................................................................................29
5. Conclusion................................................................................................................31
References .........................................................................................................................32
參考文獻 Abarbanell, J. S., and Bernard, V. L. (1992). Tests of analysts′ overreaction/underreaction to
earnings information as an explanation for anomalous stock price behavior. The Journal
of Finance, 47(3), 1181-1207.
Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal
of Financial Markets, 5(1), 31–56
Amihud, Y., & Noh, J. (2021). Illiquidity and Stock Returns II: Cross-section and Time-series
Effects. The Review of Financial Studies, 34(4), 2101-2123.
Balakrishnan, K., Bartov, E. and Faurel, L. (2010). Post loss/profit announcement drift.
Journal of Accounting and Economics, 50(1), 20-41
Barber, B. M., and Odean, T. (2000). Trading is hazardous to your wealth: the common stock
investment performance of individual investors. The Journal of Finance, 55(2), 773-806.
Bartov, E., Radhakrishnan, S., and Krinsky, I. (2000). Investor sophistication and patterns in
stock returns after earnings announcements. The Accounting Review, 75(1), 43-63.
Beaver, W. H. (1968). The information content of annual earnings announcements. Journal of
Accounting Research, 67-92.33
Bernard, V. L., and Thomas, J. K. (1989). Post-earnings-announcement drift: delayed price
response or risk premium? Journal of Accounting Research, 27, 1-36.
Bhushan, R. (1994). An informational efficiency perspective on the post-earnings
announcement drift. Journal of Accounting and Economics, 18(1), 45-65.
Brennan, M. J., Huh, S. W., and Subrahmanyam, A. (2016). Asymmetric effects of informed
trading on the cost of equity capital. Management Science, 62(9), 2460-2480.
Brown, S., Hillegeist, S. A., and Lo, K. (2009). The effect of earnings surprises on
information asymmetry. Journal of Accounting and Economics, 47(3), 208-225.
Chen, J. Z., Lobo, G. J., & Zhang, J. H. (2017). Accounting quality, liquidity risk, and post‐
earnings‐announcement drift. Contemporary Accounting Research, 34(3), 1649-1680.
Cherono, I., Olweny, T., and Nasieku, T. (2019). Investor behavior biases and stock market
reaction in Kenya. Journal of Applied Finance and Banking, 9(1), 147-180.
Chordia, T., Goyal, A., Sadka, G., Sadka, R., and Shivakumar, L. (2009). Liquidity and the
post-earnings-announcement drift. Financial Analysts Journal, 65(4), 18-32.
Daniel, K., Hirshleifer, D., and Subrahmanyam, A. (2005). Investor psychology and security
market under-and overreaction. Advances in Behavioral Finance, 2, 460-501.
DellaVigna, S., and Pollet, J. M. (2009). Investor inattention and Friday earnings
announcements. The Journal of Finance, 64(2), 709-749.
Dey, M. K., and Radhakrishna, B. (2007). Who trades around earnings announcements?
evidence from TORQ data. Journal of Business Finance and Accounting, 34(1‐2), 269-
291.
Easley, D., and O’Hara, M. (1992). Time and the process of security price adjustment. The
Journal of Finance, 47(2), 577-605.
Easley, D., Hvidkjaer, S., and O’Hara, M. (2002). Is information risk a determinant of asset
returns? The Journal of Finance, 57(5), 2185-2221.
Easley, D., Kiefer, N. M., O’Hara, M., and Paperman, J. B. (1996). Liquidity, information,
and infrequently traded stocks. The Journal of Finance, 51(4), 1405-1436.
Fama, E. F., (1970). Efficient capital markets: A review of theory and empirical work. Journal34
of Finance, 25(2), 383-417.
Fama, E. F. (1998). Market efficiency, long-term returns, and behavioral finance (pp. 174-
200). University of Chicago Press.
Foster, G., Olsen, C. and Shevlin, T. (1984). Earnings releases, anomalies, and the behavior of
security returns. The Accounting Review, 59 (4), 574–603.
Foucault, T., Pagano, M., Roell, A., and Röell, A. (2013). Market Liquidity: Theory, Evidence,
and Policy. Oxford University Press.
Garfinkel, J. A., and Sokobin, J. (2006). Volume, opinion divergence, and returns: A study of
post–earnings announcement drift. Journal of Accounting Research, 44(1), 85-112.
Goyenko, R. Y., Holden, C. W., and Trzcinka, C. A. (2009). Do liquidity measures measure
liquidity? Journal of Financial Economics, 92(2), 153-181.
Hasbrouck, J. (2009). Trading costs and returns for US equities: Estimating effective costs
from daily data. The Journal of Finance 64(3), 1445-1477.
Huang, R. D. and Stoll, H. R. (1996). Dealer versus auction markets: a paired comparison of
execution costs on NASDAQ and the NYSE. Journal of Financial Economics 41(3), 313-
357.
Hung, M., Li, X., and Wang, S. (2015). Post-earnings-announcement drift in global markets:
evidence from an information shock. The Review of Financial Studies, 28(4), 1242-
1283.
Kandel, E., and Pearson, N. D. (1995). Differential interpretation of public signals and trade
in speculative markets. Journal of Political Economy, 103(4), 831-872.
Jhang, Y., L. (2018) Financial Management. Gao Dian limited company.
Lerman, A., Livnat, J., and Mendenhall, R. R. (2007). The high-volume return premium and
post-earnings announcement drift. Working paper. Available at SSRN: 1122463
Lou, X., & Shu, T. (2017). Price impact or trading volume: Why is the Amihud (2002)
measure priced?. The Review of Financial Studies, 30(12), 4481-4520.
Taylor, D. J. (2010). Individual investors and corporate earnings, Stanford University.
Vega, C. (2006). Stock price reaction to public and private information. Journal of Financial35
Economics, 82(1), 103-133.
Yang, Y. C., Zhang, B., and Zhang, C. (2020). Is information risk priced? Evidence from
abnormal idiosyncratic volatility. Journal of Financial Economics, 135(2), 528-554.
指導教授 賴弘能(Hung-Neng Lai) 審核日期 2021-7-1
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明