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國立中山大學 111學年度第1學期 課程教學大綱
National Sun Yat-sen University 111Academic year1st Semester Course syllabus
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中文名稱
Course name(Chinese) |
證券市場微結構實務 |
課號
Course Code |
FM527 |
英文名稱
Course name(English) |
SECURITIES MARKETS MICROSTRUCTURE PRACTICE |
課程類別
Type of the course |
講授類 | 必選修
Required/Selected | 選修 |
系所
Dept./faculty |
財務管理學系碩士班 |
授課教師
Instructor |
馬黛 邱敬貿
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學分
Credit |
3 |
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因應嚴重特殊傳染性肺炎(武漢肺炎)，倘若後續需實施遠距授課，授課方式調整如下： |
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因應嚴重特殊傳染性肺炎(武漢肺炎)，倘若後續需實施遠距授課，評分方式調整如下： |
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尚未建立傳染性肺炎(武漢肺炎)課程評分方式﹝評分標準及比例﹞
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課程大綱 Course syllabus |
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課程目標 Objectives |
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本課程旨在介紹證券市場之結構、行為與績效 , 以使學生對於證券市場究竟如何運作、價格是受到那些因素所影響問題有較深入的了解。內容包括各交易制度及其對價格影響、行情締造 ( Market Making)、資訊與價格、市場績效之衡量、價格行為、投資人結構與行為、技術分析之有效性、融資與融券、高頻交易、證券網絡分析、機器學習預測市場、泡沫以及虛擬貨幣交易市場等議題。以授課及研討兩種方式同時進行。學生須執行日內資料的實證以及計量交易模型的建構，以從中學習處理日內資料，瞭解影響價格行為之各項重要因素。
This course is designed to introduce the structure, behavior and performance of the securities market so that students have a better understanding of how the securities market operates and how the price is formed. The topics include trading systems and their impact on prices, market making, information and prices, measurement of market performance, price and volume patterns, investor structure and behavior, effectiveness of technical analysis, high frequency trading, securities network analysis, machine learning forecasting, bubbles, and cryptocurrency trading markets. Classes are taught in lectures and seminars. Students are required to implement the empirical analyses based on intraday data and to construct trading strategy models.
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授課方式 Teaching methods |
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*本課程採行的遠距平台: Google Meet
9/29 google meet連結:https://meet.google.com/vwa-yabx-kkc
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以授課及研討兩種方式同時進行，並邀學者專家專題演講。
※每次上課有 2~3 篇指定文章，2~3 篇新知報告，所有作業請按時上傳網大。學生須：
1. 於課堂上就指定 reading 為口頭報告及自訂新知報告，必須做扼要投影片。
2. 指定文章的口頭報告除 PPT 之外，必須要有補充資料及心得。
3. 參與網大討論。
Both lectures and student participations in each class, occasional guest speakers will be invited.
※ Students should expect to do 2~3 designated articles and 2~3 reports.
Students must complete the following:
(1) Do oral reports on assigned readings. A brief ppt is required. Comments are necessary.
(2) Students are required to participate in cyber university discussion.
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評分方式﹝評分標準及比例﹞Evaluation (Criteria and ratio)等第制單科成績對照表 letter grading reference
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1.上課參與/新知報告Discussion/ Report：15%
2.作業Written Assignment：40%
3.口頭報告Oral Presentation：30%
4.期末報告Final project：15%
5.會依上課情況調整：%
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參考書/教科書/閱讀文獻 Reference book/ textbook/ documents
〔請遵守智慧財產權觀念，不可非法影印。教師所提供之教材供學生本人自修學習使用,不得散播及做為商業用途〕
No copies for intellectual property rights. Textbooks provided by the instructor used only for self-study, can not broadcast or commercial use
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• Ernie P. chan, “Quantitative Trading: How to Build Your Own Algorithmic Trading Business”, John Wiley & Sons, Inc., 2008(ch2、3、6、7)
• Ernie P. chan, “Algorithmic Trading: Winning Strategies and Their Rationale”, John Wiley & Sons, Inc., 2013
• Michael Dlirbin, “All about high-frequency trading” , The McGraw-Hill Companies, 2010. Nikolaus Hautsch, “Econometrics of Financial High- Frequency Data”, Springer, 2012.
• Irene Aldridge, “High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems” , John Wiley & Sons, Inc., 2010.
• Paul Ubulake, Sang Lee, “The High Frequency Game Changer: How Automated Trading Strategies Have Revolutionized the Markets”, John Wiley & Sons, Inc., 2011.
• Barry Johnson, “Algorithmic Trading & DMA: An introduction to direct access trading strate”, Myeloma Press, 2010.
• Larry Harris, Trading and Exchange- Market Microstructure for Practioners, Oxford, 2003.
• Maureen O’Hara, Market Microstructure Theory, Basil Blackwell, 1997.
• Robert A. Schwartz, Reshaping the Equity Market, Harper Business, 1991.
• Miller, Merton H., Financial Innovations and Market Volatility, Basil Black- Well, 1991. Shiller, R. J., Market Volatility, MIT, 1989.
• 酆士昌, R 語言:金融演算法與台指期貨程式交易實務,2017.
• 酆士昌, R 語言:金融演算法與台指期貨程式交易實務,2017.
• 林大貴, Python+Spark 2.0+Hadoop 機器學習與大數據分析實戰,2016
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彈性暨自主學習規劃 Alternative learning periods
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每週課程內容及預計進度 Weekly scheduled progress |
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| 週次 | 日期 | 授課內容及主題 | | Week | Date | Content and topic | | 1 | 2021/09/19~2021/09/25 | Ch1. Introduction to Trading System | | 2 | 2021/09/26~2021/10/02 | Ch2.Call vs. Continuous Trading | | 3 | 2021/10/03~2021/10/09 | Ch3.Price Limits, Trading Halts, short selling and Transaction Taxes | | 4 | 2021/10/10~2021/10/16 | Ch4.Limit Order Book | | 5 | 2021/10/17~2021/10/23 | Ch5.Market Fragmentation | | 6 | 2021/10/24~2021/10/30 | Ch6.Market Making | | 7 | 2021/10/31~2021/11/06 | Ch7.Efficiency | | 8 | 2021/11/07~2021/11/13 | Ch8.Liquidity | | 9 | 2021/11/14~2021/11/20 | Ch9.Volatility | | 10 | 2021/11/21~2021/11/27 | Ch10.Cryptocurrency | | 11 | 2021/11/28~2021/12/04 | Ch11.Bid-Ask Spread | | 12 | 2021/12/05~2021/12/11 | Ch12.Transparency | | 13 | 2021/12/12~2021/12/18 | Ch13.Information | | 14 | 2021/12/19~2021/12/25 | Ch14.Algo Trading & High Frequency Trading | | 15 | 2021/12/26~2022/01/01 | Ch15.Intraday Patterns | | 16 | 2022/01/02~2022/01/08 | Ch16.Trading Volume and Return | | 17 | 2022/01/09~2022/01/15 | Ch17.Intermarket Relation | | 18 | 2022/01/16~2022/01/22 | Ch18.Institutional Trading / Individual Trading |
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課業討論時間 Office hours |
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時段1 Time period 1:
時間 Time：星期三13:10-16:00
地點 Office/Laboratory：管CM2020
時段2 Time period 2：
時間 Time：
地點 Office/Laboratory：
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系所學生專業能力/全校學生基本素養與核心能力 basic disciplines and core capabilitics of the dcpartment and the university |
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本課程與SDGs相關項目：The course relates to SDGs items: |
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本課程校外實習資訊: This course is relevant to internship: |
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