公募基金投資者結構及行為分析 —以M基金為例

Abstract

本研究旨在深入探討公募基金投資者的結構及其行為特徵，並分析這些行為對投資表現的影響。通過採用文獻研究法、資料分析法和案例分析法，對M基金公司的客戶行為進行了全面而細緻的研究。在文獻研究方面，廣泛查閱學術期刊、專業書籍、行業報告及權威資料庫，梳理投資者行為相關理論和研究成果；資料分析法上，運用M基金公司內部客戶資料，經清洗預處理後，採用描述性統計、相關性分析等方法挖掘行為特徵；案例分析法選取不同年齡段、風險偏好和投資經驗的客戶群體深入剖析。 研究發現，客戶的人口統計特徵(如性別、地域、年齡等)、資產狀況(包括資產規模和投資經驗)以及行為特徵(購買行為、持有行為和回饋行為)有不同的交易行為。具體而言，女性客戶平均持倉金額略高於男性，且持有的基金總規模更大；經濟發達地區客戶數量和平均持倉金額相對較高；隨著投資經驗增加，客戶投資信心增強，願意持有更多資產。在購買行為上，債券型、混合型、貨幣型產品購買金額之間存在一定正相關關係，其中混合型與貨幣型產品相關性稍強。新客在貨幣基金購買中占比較高，債券型基金則依賴老客，混合型產品新老客占比波動大。在持有行為方面，債券型和混合型基金人均持有時長呈逐步增長趨勢，反映出投資者投資理念的成熟，而貨幣型基金人均持有時長最短且增長幅度較小。 在投資回報方面，不同性別、年齡和新老客戶存在差異。女性投資者在固收和權益產品投資回報上均低於男性，其中權益產品差異顯著。年齡較小的客戶在固收產品上人均回報率和總體回報較低，權益產品方面年輕客戶資產少虧損金額小，65歲以上客戶人均持有金額大但單客虧損金額大。新進入的權益產品客戶在2024年展現出良好盈利態勢，而固收產品客戶持有時間越長回報率越高。 基於這些發現，本文提出了一系列個性化服務策略，包括定制化投資產品的設計與開發、加強客戶教育與培訓、建立完善的投資者關係管理系統等，以期提升客戶滿意度和投資回報。定制化服務策略通過大資料分析等技術滿足客戶個性化需求；多元化投資者教育採用直播、發紅包等方式引導客戶理性投資；精細化客戶關係管理借助建立完善系統增強投資者信任。未來研究可以進一步細化客戶行為分析，結合大資料和人工智慧技術提高客戶行為預測的準確性，為基金公司提供更為精准的市場洞察和決策支援。

This study aims to delve deeply into the structure and behavioral characteristics of public fund investors, and to analyze the impact of these behaviors on investment performance. By employing literature review, data analysis, and case study methods, a comprehensive and detailed investigation was conducted on the client behavior of M Fund Company. In terms of literature review, extensive research was conducted on academic journals, professional books, industry reports, and authoritative databases to synthesize theories and research findings related to investor behavior. For data analysis, internal client data from M Fund Company was utilized, which, after cleaning and preprocessing, was subjected to descriptive statistics and correlation analysis to uncover behavioral patterns. The case study method involved an in-depth analysis of client groups segmented by age, risk preference, and investment experience. The research findings reveal that clients' demographic characteristics (such as gender, region, and age), asset status (including asset size and investment experience), and behavioral traits (purchasing behavior, holding behavior, and feedback behavior) influence their trading behaviors differently. Specifically, female clients, on average, hold slightly higher positions than male clients and possess a larger total fund size. Clients from economically developed regions exhibit higher numbers and average holding amounts. As investment experience increases, clients' investment confidence grows, leading them to hold more assets. In terms of purchasing behavior, there is a positive correlation between the amounts invested in bond, hybrid, and money market products, with a slightly stronger correlation between hybrid and money market products. New clients constitute a higher proportion in money market fund purchases, while bond funds rely more on existing clients, and the proportion of new and existing clients in hybrid products fluctuates significantly. Regarding holding behavior, the average holding duration for bond and hybrid funds shows a gradual increasing trend, reflecting the maturity of investors' investment philosophies, whereas money market funds have the shortest and least increasing average holding duration. In terms of investment returns, differences exist across genders, ages, and between new and existing clients. Female investors exhibit lower returns in both fixed-income and equity products compared to male investors, with significant differences in equity products. Younger clients have lower per capita and overall returns in fixed-income products, and while younger clients in equity products have smaller asset bases and losses, clients over 65 years old hold larger amounts per capita but experience larger individual losses. New clients in equity products showed promising profitability in 2024, whereas the longer the holding period for fixed-income product clients, the higher the return rate. Based on these findings, this paper proposes a series of personalized service strategies, including the design and development of customized investment products, enhanced client education and training, and the establishment of a comprehensive investor relationship management system, aiming to improve client satisfaction and investment returns. Customized service strategies leverage big data analysis to meet individual client needs; diversified investor education employs methods like live streaming and red packet distribution to guide clients towards rational investment; and refined client relationship management utilizes a well-established system to enhance investor trust. Future research could further refine client behavior analysis, integrating big data and artificial intelligence technologies to improve the accuracy of client behavior predictions, providing fund companies with more precise market insights and decision-making support.