教學大綱 Syllabus

科目名稱：財務計量研討

Course Name: Seminar on Financial Econometrics
  • 學年學期：113-2 Spring Semester, 2025
  • 科目代碼：357040001 Course No.357040001

修別：必

Type of Credit: Required

3.0

學分數

Credit(s)

5

預收人數

Number of Students

課程資料Course Details
  • 開課單位：財管博一 Course Department:Finance/D/1
  • 授課老師：陳鴻毅 Instructor: CHEN HONG-YI
  • 先修科目：無Prerequisite(N/A)
  • 上課時間：二234 Session: tue09-12

課程簡介Course Description

This course is to introduce empirical econometrics issues for finance-related topics including corporate finance and asset pricing. Students will be exposed to a broad overview of modern finance and econometrics.

核心能力分析圖 Core Competence Analysis Chart

能力項目說明

    課程目標與學習成效Course Objectives & Learning Outcomes

    The goal will be accomplished by carefully reading published research papers. This course will be conducted primarily as a seminar with lectures given by me and the students. When a student is asked to present a paper, the student will be required to present as if he/she was the author. The discussion leader is not only to summarize the papers but also to help facilitate an in-depth understanding of the methodological innovations and problems. In addition, practical issues, such as data collecting, data manipulation, and data analysis, are also introduced. Students are expected to apply the empirical techniques learned in class to actual stock price data. By the end of this course, every student should replicate a research paper in one of the leading journals.

    每周課程進度與作業要求 Course Schedule & Requirements

    教學週次Course Week 彈性補充教學週次Flexible Supplemental Instruction Week 彈性補充教學類別Flexible Supplemental Instruction Type

    Term Project

    1. Please select a research paper published in one of the leading finance journals (Journal of Finance, Journal of Financial Economics, Review of Financial Studies, Journal of Financial and Quantitative Analysis).
    2. Please collect data from the database and replicate all tables in the selected paper.
    3. Please compare the results shown in the paper and your results and discuss potential problems if they are different.

    * Students can choose replicated papers on their own. I will verify the papers in Meeting 9.

     Schedule of the Course:

    Note: I will adjust this schedule to meet the needs of the class. 

    1. Meeting 1 (preperation & review: 6 hrs)
    • Course Overview
    • Lecture: Ordinary Least Square, Maxima Likelihood Estimation, and Generalized Least Square
    1. Meeting 2 (preperation & review: 6 hrs)
    • Lecture: Capital Asset Pricing Model - How to test CAPM?
    • Paper 1: Fama, E. F and J. D. MacBeth, 1973. Risk, return, and equilibrium: Empirical tests. Journal of Political Economy 81, 607-636.
    1. Meeting 3 (preperation & review: 6 hrs)
    • Lecture: Capital Asset Pricing Model - Problems in CAPM tests (Errors-in-variable)
    • Paper 2: Kim, D., 1995. The errors in the variables problem in the cross-section of expected stock returns. The Journal of Finance 50, 1605-1634.
    • Paper 3: Kim, D., 1997. A reexamine of firm size, book-to-market, and earnings price in the cross-section of expected stock returns. Journal of Financial and Quantitative Analysis 32, 463-489.
    • Lecture: Factor Models - Pricing factors (Arbitrage pricing theory)
    • Paper 4: Fama, E. F. and K. R. French, 1992. The cross-section of expected stock returns. The Journal of Finance 47, 427-465.
    • SAS program 1: Basic commends
    1. Meeting 4 (preperation & review: 6 hrs)
    • Paper 5: Fama, E. F. and K. R. French, 1993. Common risk factors in the returns on stocks and bonds. The Journal of Finance 33, 3-56.
    • Paper 6: Fama, E. F. and K. R. French, 2015. A five-factor asset pricing model. Journal of Financial Economics 116, 1-22.
    • Paper 7: Daniel, K., D. Hirshleifer, and L. Sun, 2020. Short- and long-horizon behavioral factors. The Review of Financial Studies 33, 1673-1736.
    • Lecture: Fama & French 3-factor model and Fama & French 5-factor model
    1. Meeting 5 (preperation & review: 6 hrs)
    • Lecture: Market Efficiency - Efficient market hypothesis
    • Lecture: Market Efficiency - How to test efficient market hypothesis? – Event study
    • SAS program 2: Data manipulation (combining CRSP and Compustat)
    1. Meeting 6 (preperation & review: 6 hrs)
    • Paper 8: Hsu, H. C., A. V. Reed, and J. Rocholl, 2010. The New Game in Town: Competitive Effect of IPOs. Journal of Finance 65, 495-528.
    • Lecture: Market Efficiency - Financial anomalies & zero-investment strategies
    • Paper 9: Jegadeesh, N. and S. Titman, 1993. Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance 48, 65-91.
    • SAS program 3: Event Study
    1. Meeting 7 (preperation & review: 6 hrs)
    • Lecture: Return decomposition
    • Paper 10: George, T. J., and C. Y. Hwang, 2004. The 52-week high and momentum investing. The Journal of Finance 59, 2145-2176.
    • Paper 11: Da, Z., U. G. Gurun, and M. Warachka, 2014. Frog in the pan: Continuous information and momentum. The Review of Financial Studies 27, 2171-2218.
    • SAS program 4: Data manipulation (momentum strategies)
    1. Meeting 8 (preperation & review: 6 hrs)
    • Project discussion
    1. Meeting 9 (preperation & review: 6 hrs)
    • Paper 13: Barinov, A., 2018. Stocks with extreme past returns: Lotteries or insurance? Journal of Financial Economics 129, 458-478.
    • Replicated paper decided
    • Lecture: Mutual Funds - Fund flows and fund performance
    • Paper 14: Sirri E. R. and P. Tufano, 1998. Costly search and mutual fund flows. The Journal of Finance 53, 1589-1622.
    • Meeting 10 (preperation & review: 6 hrs)
    • Lecture: Mutual Funds – Survivorship
    • Paper 16: Jayaraman, N., A. Khorana, and E. Nelling, 2002. An analysis of the determinants and shareholder wealth effects of mutual fund managers. The Journal of Finance 57, 1521-1551.
    • Paper 17: Carhart, M, M., J. N. Carpenter, A. W. Lynch, and D. K. Musto, 2002. Mutual fund survivorship. The Review of Financial Studies 15, 1439-1463.
    • SAS application 2: Peicewise linear model, quantile regression
    1. Meeting 11 (preperation & review: 6 hrs)
    • Lecture: Mutual Funds: Manager and investor skills
    • Paper 18: Ma, L., Y. Tang, and J. P. Gomez, 2019. Portfolio manager compensation in the US mutual fund industry. The Journal of Finance 74, 587-638.
    • Paper 19: Massa, M., and R. Ratgiri, 2009. Incentives and mutual fund performance: Higher performance or just higher risk taking? Review of Financial Studies 22, 1777-1815.
    • SAS application 3: Censored life regression, survivorship model, multi-logit model
    1. Meeting 12 (preperation & review: 6 hrs)
    • Econometric Issues: Heterogeneity, fixed effects, clustering effects
    • Paper 20: Petersen M. A., 2009. Estimating standard errors in finance panel data sets: Comparing approaches. The Review of Financial Studies 22, 435-480.
    • Paper 21: Choi, J., S. Hoseinzade, S. S. Shin, and H. Tehranian, 2020. Corporate bond mutual funds and asset fire sales. Journal of Financial Economics 138, 432-457.
    • SAS application 4: Fixed effects model, clustering effects model
    1. Meeting 13 (preperation & review: 6 hrs)
    • Econometric Issues: Endogeneity (2sls, exogenous shock, difference in difference)
    • Paper 22: Shi, Z., 2017. The impact of portfolio disclosure on hedge fund performance. Journal of Financial Economics 126, 36-53.
    • Paper 23: Eisele, A., T. Nefedova, G. Parise, and K. Peijenburg, 2020. Trading out of sight: An analysis of cross-trading in mutual fund families. Journal of Financial Economics 135, 359-378.
    • SAS application 5: Two stage least square (2SLS)
    1. Meeting 14
    • No Class
    1. Meeting 15 (preperation & review: 6 hrs)
    • Econometric Issues: Sample selection bias
    • Paper 24: Moreno, D., R. Rodriguez, and R. Zambrana, 2018. Management sub-advising in the mutual fund industry. Journal of Financial Economics 127, 567-587.
    • Paper 25: Bonaime, A., and M. Ion, 2018. Does policy uncertainty affect mergers and acquisitions? Journal of Financial Economics 129, 531-558.
    • SAS application 6: Heckman model
    1. Meeting 16 (preperation & review: 6 hrs)
    • Paper 12: Bali, T. G., N. Cakici, and R. F. Whitelaw, 2011. Maxing out: Stocks as lotteries and the cross-section of expected returns. Journal of Financial Economics 99, 427-446.
    • Paper 15: Harris, L. E., and S. M. Hartzmark, and D. H. Solomon, 2015. Juicing the dividend yield: Mutual funds and the demand for dividends. Journal of Financial Economics 116, 433-451.
    • Econometric Issues: Economic significance
    • SAS application 1: Standard regression coefficient, marginal effects in logit model
    1. Meeting 17 (preperation & review: 6 hrs)
    • Project discussion
    1. Meeting 18 (preperation & review: 6 hrs)
    • Project presentation

     

     

    授課方式Teaching Approach

    30%

    講述 Lecture

    50%

    討論 Discussion

    20%

    小組活動 Group activity

    0%

    數位學習 E-learning

    0%

    其他： Others:

    評量工具與策略、評分標準成效Evaluation Criteria

    Class Participation             50%

    Term Project                      50%

    指定/參考書目Textbook & References

    1. Journal papers in Journal of Finance, Journal of Financial Economics, Review of Financial Studies, and Journal of Financial & Quantitative Analysis.
    2. Cochrane, J., 2005, Asset Pricing 2nd edition, Princeton University Press.
    3. Campbell, Lo, and MacKinley, 1997, The Econometrics of Financial Markets, Princeton University Press.
    4. Lee, C. F., H. Y. Chen, and John Lee, 2019. Financial Econometrics, Mathematics, and Statistics: Theory, Method, and Application, Springer.
    5. Copeland, Thomas E. and J. Weston, 2005, Financial Theory and Corporate Policy, 4th edition, Addison-Wesley Publishing Company
    6. Bodie Z., A. Kane, and A. J. Marcus, 2014, Investments 10th edition, McGrow- Hill Irwin.

    已申請之圖書館指定參考書目 圖書館指定參考書查詢 |相關處理要點

    書名 Book Title 作者 Author 出版年 Publish Year 出版者 Publisher ISBN 館藏來源* 備註 Note

    維護智慧財產權，務必使用正版書籍。 Respect Copyright.

    本課程可否使用生成式AI工具Course Policies on the Use of Generative AI Tools

    完全開放使用 Completely Permitted to Use

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