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| Title: | 使用機器學習選股之投資績效研究—台灣股票市場之實證
A Study on the Investment Performance of Machine Learning-Based Stock Selection — Empirical Evidence from the Taiwan Stock Market |
| Authors: | 許雅筑
Hsu, Ya-Chu |
| Contributors: | 吳啟銘
許雅筑
Hsu, Ya-Chu |
| Keywords: | 機器學習
選股能力
股價預測
隨機森林模型
五分位數投資組合策略
投資組合績效
Machine Learning
Stock Selection Ability
Stock Price Prediction
Random Forest Model
Quintile Investment Portfolio Strategy
Portfolio Performance |
| Date: | 2024 |
| Issue Date: | 2024-09-04 15:25:43 (UTC+8) |
| Abstract: | 近年來機器學習已廣泛應用於金融領域，如何使用機器學習來幫助分析師捕捉隱含的訊息，已是許多學術及實務上的發展方向。因此，本文主要研究以人工智慧的機器學習法，應用於台灣股票市場的交易。本文運用個股過去的股票報酬率、交易周轉率與月營收年增率，應用隨機森林演算法預測未來可能上漲的股票作為投資標的，再搭配模型的特徵重要性來建構五分位數投資組合，並分為等權重與市值加權來進行分析。
實證顯示，依照排名的升高，投資組合的報酬跟風險也越大，風險調整後均有較佳的報酬，且可建構的年平均報酬率均高於市場投資組合的報酬率。透過三因子模型分析顯示，模型傾向於選擇投資小型股及成長股，而在排名最高的兩組等權重投資組合，均能獲得顯著正的超額報酬。
本研究實證顯示，機器學習對於投資策略與投資組合建構具有正面的影響，能夠幫助識別影響股價的重要因子，尋找隱含的訊息。這項研究能幫助投資人探索如何利用機器學習建構投資策略與投資組合。
In recent years, machine learning has been widely applied in the financial sector. How to use machine learning to help analysts capture implicit information has become a major direction for both academic and practical development. This study primarily investigates the application of machine learning, specifically artificial intelligence methods, in trading on the Taiwan stock market. By utilizing historical stock returns, turnover rates, and monthly revenue growth rates, this paper employs the random forest algorithm to predict stocks that are likely to rise in the future as investment targets. It further constructs quintile investment portfolios based on feature importance from the model, and analyzes them under both equal-weighted and market-cap-weighted schemes.
Empirical results show that as the portfolio ranking increases, both returns and risks also increase. Adjusted for risk, the portfolios exhibit superior returns, with annual average returns outperforming the market portfolio. Analysis using the three-factor model indicates that the model tends to select small-cap and growth stocks. The top two quintile equal-weighted portfolios achieve significant positive excess returns.
This study empirically demonstrates that machine learning has a positive impact on investment strategies and portfolio construction. It effectively aids in identifying key factors influencing stock prices and uncovering hidden information. This research can assist investors in exploring how to utilize machine learning to develop investment strategies and portfolios. |
| Reference: | 一、中文部分
田鈜元 (2022)。以隨機森林法建構投資組合績效—以台灣股票市場為例。清華大學財務金融在職專班碩士論文。
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孫嘉蔚 (2021) 。運用機器學習模型分析影響公司風險的 ESG 因子：以台灣市場為例。政治大學金融學系碩士論文。
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| Description: | 碩士
國立政治大學
財務管理學系
107357012 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107357012 |
| Data Type: | thesis |
| Appears in Collections: | [Department of Finance] Theses
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