Title

以情境吃角子老虎機演算法推薦股票投資行為的研究

Translated Titles

Using Contextual Multi-Armed Bandit Algorithms for Recommending Investment in Stock Market

Authors

簡志樺

Key Words

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 ; LinUCB ; Contextual Bandit Problem ; Stock Recommendation ; Contextual Multi-Armed Bandit ; Personalized Recommendation System

PublicationName

中山大學資訊管理學系研究所學位論文

Volume or Term/Year and Month of Publication

2016年

Academic Degree Category

碩士

Advisor

楊淯程;黃三益

Content Language

英文

Chinese Abstract

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。

English Abstract

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.

Topic Category 管理學院 > 資訊管理學系研究所
社會科學 > 管理學
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