Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/139548


Title: 機器學習結合波動聚集分析之投資策略實證研究
An Empirical Study of Investment Strategies Based on Machine Learning and Volatility Cluster Analysis
Authors: 李強
Li, Qiang
Contributors: 廖四郎
Liao, Szu-Lang
李強
Li, Qiang
Keywords: 機器學習
因子合成
波動聚集
量化投資
Machine Learning
Factor Composition
Volatility Clustering
Quantitative Investment
Date: 2022
Issue Date: 2022-04-01 15:02:02 (UTC+8)
Abstract: 量化投資和機器學習在大數據時代充分展現了其獨特的優勢和魅力,兩者結合更是如虎添翼。機器學習不僅可以彌補量化投資的短板,還可以為量化投資的發展提供新的思路和方向。本文主要研究是否可以通過機器學習算法進行因子合成,以及該方法是否比傳統方法更有效。選取波動率、年化收益率、最大回撤、信息比率、夏普比率等評價指標進行因子分層回測結果的對比分析。
本文認為機器學習算法對量化投資和股票預測具有一定的重要性影響,為投資者的決策提供可行的解決方案。另外,本文詳細介紹了波動聚集現象和金融時間序列模型。然後本文將波動聚集性作為因子加入到策略的機器學習部分進行應用,以原策略為對照,並對回測結果進行詳細對比分析。
綜上可見,波動聚集現象的實際應用是值得研究的,這樣可以提高量化策略的性能和穩健性,同時對波動聚集現象的應用和發展提供了新想法。本文對 XGBoost 算法和量化投資中的波動聚集現象進行了研究和改進,為滬深 300 股票市場的量化投資者提供了一個新觀點。
Quantitative investment and machine learning have adequately demonstrated their unique advantages and charms in this big data era and they become even more powerful by combination. Machine learning not only help to overcome the disadvantages of quantitative investment, but also provides new ideas and directions for its development. This paper mainly studies whether machine learning algorithm can be applied in factor composition, and whether its effect is better than the traditional method. Evaluation indexes such as volatility, annualized rate of return, maximum drawdown, information ratio and Sharpe ratio are selected to compare and analyze the results of factors' layered back test.
This paper points out that machine learning algorithm has guiding significance for quantitative investment and stock prediction, providing investors a feasible plan for decision-making. In addition, this paper introduces the phenomenon of volatility clustering and the financial time series model in detail. Then, this paper adds the volatility clustering as a factor to the machine learning part of the strategy, and compares and analyzes the back test results in detail by taking the initial strategy as a comparison.
We concluded that the practical application of volatility clustering is worth studying, which can improve the performance and robustness of quantitative strategy, and also provide a new idea for the application and development of volatility clustering. Our research and improvement on XGBoost algorithm and volatility clustering provide a new perspectives for quantitative investors in CSI 300.
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Description: 碩士
國立政治大學
金融學系
107352040
Source URI: http://thesis.lib.nccu.edu.tw/record/#G0107352040
Data Type: thesis
Appears in Collections:[金融學系] 學位論文

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