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论文编号：	16013
作者编号：	1120201083
上传时间：	2026/6/2 19:52:19
中文题目：	AI投资建议的明确性、参考来源与投资者投资判断
英文题目：	The Explicitness of AI Investment Recommendations, the Reference Sources of Recommendations and Investors’ Judgments
指导老师：	张继勋
中文关键字：	人工智能投资建议；投资建议明确性；投资建议参考来源；投资者投资判断
英文关键字：	AI investment recommendations; Explicitness of AI Investment Recommendations; Reference Sources of AI Investment Recommendations; Investors'' Investment Judgment
中文摘要：	随着人工智能在资本市场的广泛应用，AI智能投顾已成为投资决策的重要决策辅助工具，像同花顺“问财”、东方财富“妙想”、蚂蚁财富“蚂小财”等AI投顾的兴起与普及，显著降低了投资者的投资门槛，极大地提升了投资者的决策效率。然而，在实践中，不同AI投顾在提供投资建议时，存在建议明确性不一致的问题：有些AI会给出明确的股票推荐意见，但有些AI则采取谨慎模糊的表达方式，仅仅提供股票优势以及风险的分析内容而避免给出明确的建议。此外，AI在提供投资建议时，是否提供参考来源也存在显著差异：部分平台在提供投资建议的最后会明确列出参考信息来源，而有些平台则完全不显示任何信息来源。上述不一致性不仅显著影响了投资建议的实用性，更引发了关于AI决策可信性的怀疑，以及如何影响投资者判断的深层思考。本文基于心理学中的信息来源可信性理论、模糊厌恶理论、加工流畅性理论以及算法厌恶等，采用实验研究方法，分析并检验了AI投资建议的明确性（明确/模糊）以及AI投资建议的参考来源（有/无）对投资者投资判断的影响。 本文共分为六章，具体内容如下：第一章为引言，在研究背景的基础上提出研究问题及研究意义，同时对研究框架、研究方法和研究创新进行了说明；第二章为文献综述，对人工智能建议采用的影响因素以及经济后果相关联的文献进行了系统的梳理，并进行文献述评；第三章以心理学中的信息来源可信性理论、模糊厌恶理论、加工流畅性理论以及算法厌恶为基础，分析AI投资建议的明确性和建议的参考来源对投资者投资判断的影响，并提出本文的研究假设；第四章为实验设计，具体介绍本文的实验设计、实验被试、实验过程、自变量的操控以及因变量和中介变量的测量方法；第五章为实验结果分析，首先进行了随机化检验和操控检验，随后对本文的假设进行验证，并进行附加分析；第六章为本文的研究结论，并指出本文的研究局限性以及未来可能的研究方向。 本文主要的研究结论包括： 1.基于心理学中的信息来源可信性理论、模糊厌恶理论、加工流畅性理论以及算法厌恶，本文认为AI投资建议的明确性和建议的参考来源会单独以及共同影响投资者的投资判断。 2.实验结果表明，AI投资建议的明确性和AI投资建议的参考来源分别影响投资者的投资判断。具体来说，相比于AI提供模糊的投资建议，当AI提供明确的投资建议时，投资者判断的投资吸引力更高。另外，相比于AI提供的投资建议无参考来源，当AI提供的投资建议有参考来源时，投资者判断的投资吸引力更高。 3.实验结果表明，AI投资建议的明确性和建议的参考来源会共同影响投资者的投资判断，即AI投资建议的明确性对投资者投资判断的影响取决于参考来源的有无。具体来说，在AI提供的投资建议有参考来源的情况下，相比于AI提供模糊的投资建议，当AI提供明确的投资建议时，投资者判断的投资吸引力更高；但在AI提供的投资建议无参考来源的情况下，AI提供明确建议和模糊建议之间，投资者判断的投资吸引力没有明显差异。 4.中介效应分析的结果表明，在AI提供的投资建议有参考来源的情况下，AI投资建议的明确性对投资者判断的投资吸引力的影响，被投资者感知的AI投资建议的可信性所中介。
英文摘要：	With the widespread application of Artificial Intelligence (AI) in the capital market, AI robo-advisors have become crucial decision-making aids for investment decisions. The rise and proliferation of AI investment advisors such as Tonghuashun "Wen Cai," East Money "Miao Xiang," and Ant Fortune "Ma Xiaocai" have significantly lowered the investment threshold for investors and greatly enhanced their decision-making efficiency. However, in practice, there exists inconsistency in the explicitness of recommendations provided by different AI robo-advisors: some AIs offer clear stock recommendations, while others adopt cautious and vague expressions, merely providing analyses of stock advantages and risks without giving explicit advice. Additionally, there is significant variation in whether AIs provide reference sources when offering investment recommendations: some platforms clearly list reference information sources at the end of their recommendations, whereas others display no information sources at all. Such inconsistencies not only significantly affect the practicality of investment recommendations but also raise doubts about the credibility of AI decision-making and prompt deeper reflections on how they influence investors' judgments. Based on psychological theories including source credibility, ambiguity aversion, processing fluency, and algorithm aversion, this paper employs experimental research methods to analyze and examine the impact of the explicitness (explicit/vague) and reference sources (with/without) of AI investment recommendations on investors' investment judgments. This paper consists of six chapters, which are structured as follows: Chapter 1 introduces the research background, presents the research questions and significance, and explains the research framework, methods, and innovations. Chapter 2 reviews the literature, systematically summarizing studies related to factors influencing the adoption of AI recommendations and their economic consequences, followed by a critical review. Chapter 3, grounded in source credibility theory, ambiguity aversion theory, processing fluency theory, and algorithm aversion, analyzes the impact of the explicitness of AI investment recommendations and the presence of reference sources on investors' investment judgments, and proposes the research hypotheses. Chapter 4 details the experimental design, including the experimental design, participants, procedures, manipulation of independent variables, and measurement methods for dependent and mediating variables. Chapter 5 presents the analysis of experimental results, starting with randomization and manipulation checks, followed by hypothesis testing and additional analyses. Chapter 6 concludes with the research findings, highlights the study's limitations, and suggests directions for future research. The main research conclusions of the dissertation are as follows: 1.Drawing on source credibility theory, ambiguity aversion theory, processing fluency theory, and algorithm aversion, this paper argues that both the explicitness of AI investment recommendations and the presence of reference sources will independently and jointly influence investors' investment judgments. 2.Experimental results indicate that the explicitness of AI investment recommendations and the presence of reference sources each affect investors' investment judgments. Specifically, compared to vague AI investment recommendations, explicit recommendations lead to higher perceived investment attractiveness. Additionally, compared to recommendations without reference sources, those with reference sources result in higher perceived investment attractiveness. 3.Experimental results show that the explicitness of AI investment recommendations and the presence of reference sources jointly influence investors' investment judgments—that is, the impact of recommendation explicitness depends on the presence of reference sources. Specifically, when AI recommendations include reference sources, explicit recommendations lead to higher perceived investment attractiveness than vague ones. However, when no reference sources are provided, there is no significant difference in perceived investment attractiveness between explicit and vague recommendations. 4.Mediation analysis reveals that when reference sources are present, the effect of AI recommendation explicitness on perceived investment attractiveness is mediated by investors' perceived credibility of the AI recommendations.
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