基金投资决策中老手与新手的信息加工差异及干预

doi:10.3724/SP.J.1041.2024.00799

摘要/Abstract

摘要：

以往的基金研究多从专业的角度构建复杂的决策模型, 难以直接被普通基金投资者所理解和应用。为了引导众多的基金投资新手正确理财, 我们引入“老手−新手”比较范式探寻二者投资决策时的信息加工过程差异, 并探讨如何对新手予以干预以提升其决策质量。研究1通过Mouselab技术收集信息加工过程数据, 发现在基金信息搜索和加工过程中, 老手比新手更倾向于基于属性的信息搜索模式, 信息加工的补偿性更低; 基于属性的信息搜索模式有利于决策质量, 但这一结果仅在新手中存在, 这可能在于老手的信息搜索模式较为稳定, 其变异性难以为决策质量提供解释。由于现有的理财客户端的页面呈现方式使得投资新手难以使用基于属性的信息搜索模式, 研究2向被试提供表格纸来帮助他们进行结构化加工, 以降低基于属性进行搜索的难度, 结果发现结构化加工组被试的决策质量高于控制组。上述结果反映了老手和新手基金投资决策的信息加工特征, 为改进新手理财决策提供了干预思路。

关键词: 投资决策, 老手, 新手, 信息加工, 干预

Abstract:

Many individuals now participate in online fund investment, but novice investors often struggle with the complex information they encounter due to the lack of professional guidance in traditional offline financing. Previous research on decision-making has primarily focused on outcomes and utilized statistical methods to construct decision models, which fail to provide direct evidence of information processing. To assist novices in developing the necessary skills for making investment decisions, this study employs process tracking technology in the field of fund investment for the first time. The aim is to explore the differences in information processing between experienced investors and novices, thereby identifying the advantages experienced investors possess in information processing. Additionally, this research investigates the relationship between the decision-making process and outcomes, proposing interventions based on information processing to aid novices in making accurate investment decisions.

To achieve the research objectives, two studies were conducted. Study 1 involved a comprehensive exploration that traced the fund investment decision-making process using Mouselab. It compared various information processing indicators between experienced investors and novices, including decision-making time, depth of search, variability of search, compensatory index, and SM (strategy measure) value of the search pattern. The study also examined the impact of the search pattern on decision quality for experienced investors and novices through grouping logistic regression. Study 2 involved an intervention experiment utilizing a single- factor (structured intervention group vs. control group) between-subject design. Participants in the structured intervention group were provided a piece of form paper to guide them to structure information of funds, while participants in the control group were provided blank paper. Then all participants completed a simulated fund investment task and their decision quality was recorded.

Study 1 revealed that: (1) Experienced investors, compared to novices, preferred to utilize attribute-based search pattern during fund investment decision-making, and displayed a more non-compensatory approach to information processing. (2) Only the decision quality of novices in fund investment was affected by the information search pattern, indicating that their decision quality improved when they searched for information based on attributes. In contrast, the decision quality of experienced investors was unaffected by the information search pattern but positively influenced by working memory. Study 2 demonstrated that participants who used form paper for intervention had higher decision-making quality than those who used blank paper, indicating the effectiveness of the structured intervention.

This study makes theoretical and practical contributions to the literature. First, it explores the characteristics of the information processing process during fund investment decision-making and its relationship with decision outcomes, filling the research gap regarding the “process” of information processing and deepening the understanding of the essence of decision-making ability in fund investment. Second, it extends the “expert-novice” paradigm to the field of fund investment, summarizing the differences in the search pattern and compensatory behavior between experts and novices, further supporting the heuristic decision model. Third, it proposes effective interventions to assist novice investors in improving their online fund investment and inspires the interface design of fund applications.

Key words: fund investment decision, experienced investors, novices, information processing, intervention

中图分类号:

B849

辛自强, 王鲁晓, 李越. (2024). 基金投资决策中老手与新手的信息加工差异及干预. 心理学报, 56(6), 799-813.

XIN Ziqiang, WANG Luxiao, LI Yue. (2024). Differences in information processing between experienced investors and novices, and intervention in fund investment decision-making. Acta Psychologica Sinica, 56(6), 799-813.

图/表 5

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变量	老手组		新手组		t
变量	M	SD	M	SD	t
决策时长	50.18	28.58	50.60	20.15	−0.07
搜索深度DS	0.90	0.15	0.96	0.07	−2.20^*
搜索变异性VS	0.09	0.09	0.05	0.08	1.95
补偿性指数CI	0.77	0.25	0.88	0.19	−2.15^*
搜索模式SM	−5.48	4.34	−2.88	5.67	−2.20^*
风险承受能力	3.11	0.82	2.67	0.70	2.51^*
工作记忆	54.50	11.33	61.84	9.12	−2.94^**

变量	老手组		新手组		t
变量	M	SD	M	SD	t
决策时长	50.18	28.58	50.60	20.15	−0.07
搜索深度DS	0.90	0.15	0.96	0.07	−2.20^*
搜索变异性VS	0.09	0.09	0.05	0.08	1.95
补偿性指数CI	0.77	0.25	0.88	0.19	−2.15^*
搜索模式SM	−5.48	4.34	−2.88	5.67	−2.20^*
风险承受能力	3.11	0.82	2.67	0.70	2.51^*
工作记忆	54.50	11.33	61.84	9.12	−2.94^**

变量	整体上(N = 67)				老手组(n = 30)				新手组(n = 37)
变量	B	SE	Wald χ²	OR	B	SE	Wald χ²	OR	B	SE	Wald χ²	OR
控制变量
性别	0.35	0.70	0.26	1.43	1.50	1.23	1.48	4.46	0.02	1.08	<0.01	1.02
年龄	0.02	0.05	0.25	1.02	0.18	0.10	3.22	1.19	-0.03	0.37	0.01	0.97
受教育程度	0.76	0.71	1.15	2.14	3.97^*	2.01	3.90	53.11	0.76	1.69	0.21	2.15
专业	0.95	0.68	1.96	2.58	1.24	1.20	1.05	3.44	1.22	0.94	1.68	3.38
风险承受能力	−0.62	0.43	2.06	0.54	−1.61	0.97	2.75	0.20	−0.21	0.60	0.13	0.81
工作记忆	0.07	0.03	3.67	1.07	0.16^*	0.08	4.15	1.18	0.02	0.05	0.11	1.02
搜索深度	3.55	2.84	1.56	34.63	5.18	5.65	0.84	176.93	−2.43	5.81	0.18	0.09
预测变量
SM	−0.18^*	0.08	4.72	0.84	−0.03	0.13	0.07	0.97	−0.17^*	0.08	4.12	0.84
经验水平	1.30	1.04	1.57	3.67
经验水平×SM	0.15	0.13	1.23	1.16

变量	整体上(N = 67)				老手组(n = 30)				新手组(n = 37)
变量	B	SE	Wald χ²	OR	B	SE	Wald χ²	OR	B	SE	Wald χ²	OR
控制变量
性别	0.35	0.70	0.26	1.43	1.50	1.23	1.48	4.46	0.02	1.08	<0.01	1.02
年龄	0.02	0.05	0.25	1.02	0.18	0.10	3.22	1.19	-0.03	0.37	0.01	0.97
受教育程度	0.76	0.71	1.15	2.14	3.97^*	2.01	3.90	53.11	0.76	1.69	0.21	2.15
专业	0.95	0.68	1.96	2.58	1.24	1.20	1.05	3.44	1.22	0.94	1.68	3.38
风险承受能力	−0.62	0.43	2.06	0.54	−1.61	0.97	2.75	0.20	−0.21	0.60	0.13	0.81
工作记忆	0.07	0.03	3.67	1.07	0.16^*	0.08	4.15	1.18	0.02	0.05	0.11	1.02
搜索深度	3.55	2.84	1.56	34.63	5.18	5.65	0.84	176.93	−2.43	5.81	0.18	0.09
预测变量
SM	−0.18^*	0.08	4.72	0.84	−0.03	0.13	0.07	0.97	−0.17^*	0.08	4.12	0.84
经验水平	1.30	1.04	1.57	3.67
经验水平×SM	0.15	0.13	1.23	1.16