博碩士論文 etd-0703116-130605 詳細資訊


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姓名 簡志樺(Zhi-hua Chien) 電子郵件信箱 E-mail 資料不公開
畢業系所 資訊管理學系研究所(Information Management)
畢業學位 碩士(Master) 畢業時期 104學年第2學期
論文名稱(中) 以情境吃角子老虎機演算法推薦股票投資行為的研究
論文名稱(英) Using Contextual Multi-Armed Bandit Algorithms for Recommending Investment in Stock Market
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    紙本論文:2 年後公開 (2018-08-03 公開)

    電子論文:使用者自訂權限:校內 2 年後、校外 2 年後公開

    論文語文/頁數 英文/59
    統計 本論文已被瀏覽 5104 次,被下載 0 次
    摘要(中) 情境式拉霸問題 (Contextual Bandit Problem) 經常被使用來模擬線上推薦的應用,像是文章、音樂、影片等推薦系統。線性上信賴界(LinUCB)是目前解決情境式拉霸問題的演算法之一,它主要使用線性回歸並且從環境當中所得到的回饋(feedback)進行不斷的學習並更新其內部的模型。然而我們觀察到在股票投資市場當中,使用情境式拉霸問題來解決股票推薦問題的應用少之又少,大部分研究推薦目的為投資營利,並非根據投資者本身的投資風險屬性、投資標的的特性推薦他們符合投資屬性的股票。
    我們提出一個情境式拉霸問題模型來模擬推薦股票給使用者的個人化推薦系統。情境式多拉桿拉霸問題模型從投資者過往的投資紀錄找出他的投資屬性,再根據這些屬性來推薦股票的組合。而股票組合是從公司財務分析的基本面及股票變化的技術面二者分類出來的結果,決定推薦組合後,再根據推薦組合和所有股票的相似性去做排名,然後推薦股票。
    我們實證資料來源是網路上的模擬投資股市的資料集,實驗的結果顯示我們提出的方法在推薦股票的領域比現有的方法好。
    摘要(英) The Contextual Bandit Problem (CMAB) is usually used to recommend for online applications on article, music, movie, etc. One leading algorithm for contextual bandit is the LinUCB algorithm, which updates internal linear regression models by the partial feedback from the environment. However, we observe that CMAB is rarely used in the stock recommendation, while most of the recommendations are for the purpose of profit, and ignore investor’s features (risk tolerance, investment features, and the others).
    We propose a personalized recommendation system for stock by using contextual multi-armed bandit algorithm. We take investor’s investment records as user features, and recommend the “arm”, which is a type of stock, based on two kinds of analysis, the technical and fundamental analysis. To the chosen arm, we rank the stocks according to the similarity of the stock and the arm. Our experiment is base on an online investment dataset, and the result demonstrates that our method outperforms other algorithms.
    Our experiment dataset collects simulation investment on the online website, and the result demonstrates that our method outperforms other algorithms.
    關鍵字(中)
  • 情境式多拉桿拉霸問題
  • 個人化推薦系統
  • 股票推薦系統
  • 情境式拉霸問題
  • 線性上信賴界
  • 關鍵字(英)
  • LinUCB
  • Contextual Bandit Problem
  • Stock Recommendation
  • Contextual Multi-Armed Bandit
  • Personalized Recommendation System
  • 論文目次 TABLE OF CONTENTS
    CHAPTER 1 – Introduction 1
    1.1 Background 1
    1.2 Motivation 3
    CHAPTER 2 – Literature Review 6
    2.1 Definition of Multi-armed bandit Problem 6
    2.2 Bandit Algorithms Overview 7
    2.2.1 ϵ - greedy 7
    2.2.2 Upper Confidence Bounds (UCB) 8
    2.3 Contextual Multi-armed bandit 9
    2.3.1 LinUCB 10
    2.3.2 Application in CMAB 13
    2.4 Investment Measure 14
    2.4.1 Sharpe Ratio 14
    2.4.2 Prospect Theory 15
    2.4.3 Stock Measurement 16
    CHAPTER 3 – Our Approach 19
    3.1 Architecture of our Approach 19
    3.2 Definition of Arms 20
    3.3 The LinUCB Algorithm 22
    3.4 The Stock Recommendation 25
    CHAPTER 4 – Data Description 31
    4.1 Data Collection 31
    4.2 Data Description 33
    CHAPTER 5 – Evaluation 35
    5.1 Experiment Design 35
    5.1.1 Compared Algorithms 35
    5.2 Performance Measure 37
    5.2.1 Cumulative Reward (Rank Ratio) 37
    5.2.2 Cumulative Reward (Sliding Window) 37
    5.3 Experiment Results 38
    5.3.1 Parameter Tuning 38
    5.3.2 Comparison of Features 41
    5.3.3 Size of the Data 42
    CHAPTER 6 – Conclusion 46
    Reference 48
    參考文獻 Reference
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    口試委員
  • 楊婉秀 - 召集委員
  • 蔡維哲 - 委員
  • 楊淯程 - 指導教授
  • 黃三益 - 指導教授
  • 口試日期 2016-07-07 繳交日期 2016-08-03

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