Investment Information Sources and Investment Grip: Evidence from Japanese Retail Investors

Abstract

Understanding how investors maintain positions during adverse market conditions, investment grip, is increasingly important as retail participation rises and information environments diversify. While prior research identifies demographic, psychological, and economic determinants of investment grip, little is known about how information sources influence investors’ tolerance for losses. This study examines the relationship between investment information channels and investment grip among Japanese retail investors using a large-scale dataset of 161,677 respondents from the 2025 Survey on Life and Money. Investment grip is measured through a hypothetical loss scenario, and ordered probit and probit models are used to analyze associations between loss tolerance, information sources, and investor characteristics. Results show that reliance on professional information sources such as outsourced independent financial advisors, one’s own securities company, other securities firms, and external financial experts is negatively associated with investment grip. Free information sources, including mass media and personal networks, are also linked to lower loss tolerance. In contrast, reliance on social media is consistently associated with higher investment grip. Financial literacy, wealth, and age increase investment grip, whereas risk aversion, short-term outlooks, and family responsibilities reduce it. These results have implications for policy design, advisory practices, and digital and AI-enhanced investment platforms.

1. Introduction

Understanding how investors continue to hold their investments during adverse market conditions, often known as investment grip, has become a central concern in behavioral finance, particularly as global markets experience increased volatility and retail participation expands. Prior studies have examined determinants of loss tolerance, highlighting demographic, psychological, and economic factors that shape individuals’ willingness to sustain losses without liquidating their portfolios (Nabeshima et al. 2025; Yamaguchi et al. 2025; Kuramoto et al. 2025). However, this literature has largely overlooked the crucial role of investment information sources, despite strong evidence that information environments fundamentally shape investor emotions, expectations, and behaviors (Fujiki 2019; Murashima 2024; Safitri and Wendy 2025).

Recent papers using Japanese data illustrate the importance of information sources in shaping investment behavior. Lal et al. (2023) examine how information channels affect general investment decisions, while Takemura et al. (2018) investigate how reliance on information sources is associated with holdings of risky assets. These studies demonstrate that investors’ preferred information channels significantly influence both performance and crisis-driven liquidation decisions. Nevertheless, no study has explicitly examined investment grip from the perspective of information sources. This omission is particularly relevant in the context of Japan’s New NISA system introduced in 2024, which has led to a rapid increase in retail participation, including a substantial number of first-time investors seeking to utilize the expanded, permanent tax-exempt investment framework (JSDA 2024; Katauke et al. 2025). Because NISA is designed to promote long-term asset formation, its success depends critically on whether investors can maintain their positions through short-term losses, making investment grip a key behavioral outcome of interest. Investment grip refers to an investor’s capacity and willingness to maintain positions through temporary losses and routine market fluctuations. It represents a foundational yet understudied dimension of real-world portfolio behavior. From the perspective of rational finance, sufficient investment grip follows naturally from optimal intertemporal choice: investors with stable preferences and coherent expectations should continue holding assets as long as expected future returns outweigh short-term volatility (Markowitz 1952; Siegel 1998). In this framework, investment grip is a byproduct of disciplined decision-making under uncertainty. However, behavioral finance emphasizes that investment grip is not mechanically determined by fundamentals alone. Psychological dispositions, affective reactions to losses, heuristics, and subjective beliefs play critical roles in shaping investors’ willingness to endure drawdowns (Kahneman and Tversky 1979; Shefrin and Statman 1985). Behavioral biases such as loss aversion, regret sensitivity, time inconsistency, and attentional biases can either strengthen or erode persistence in the face of adverse price movements (Nabeshima et al. 2025; Kuramoto et al. 2025; Yamaguchi et al. 2025).

Understanding investment grip is therefore essential for explaining variation in long-term investment outcomes. Sustained asset accumulation requires investors to tolerate routine volatility and avoid premature liquidation, making investment grip a critical behavioral input to compounding and wealth formation (Siegel 1998; Mehra and Prescott 1985). From this perspective, information environments—including professional advice, financial media, social networks, and algorithmically curated content—play an important role in shaping perceived risk, expectations of recovery, and tolerance for losses. When information is noisy, biased, or framed in ways that amplify perceived downside risk, investors may interpret market downturns as more threatening than warranted, thereby weakening investment grip. Investment grip, in sum, reflects an interaction between rational evaluation and behavioral tendencies, operating continuously rather than episodically, and thereby offering a more comprehensive lens for understanding how investors navigate uncertainty over time.

The investment information landscape has become increasingly diverse and technologically mediated, fundamentally reshaping how investors form expectations and make financial decisions (Eysenbach 2008; Kramer 2012; Metzger and Flanagin 2013; Yang et al. 2017). Professional sources such as independent financial advisors, securities company analysts, and certified experts traditionally offer structured guidance, yet may also be subject to conflicts of interest, institutional herding, or conservative bias, potentially narrowing investors’ perspectives during periods of uncertainty (Hackethal et al. 2012; Higgins 2024). In contrast, free information sources—including mass media, online financial portals, and social media—provide rapid and low-cost access to information, but often with uneven quality, fragmented narratives, and exposure to sentiment-driven or misleading content (Bartov et al. 2018; Gu and Kurov 2020). As AI-driven advisory tools, algorithmic news feeds, and social investing platforms proliferate, information sources increasingly function not merely as channels of data transmission but as behavioral contexts that frame how investors interpret risk and evaluate losses.

The theoretical foundations linking information environments and investment grip draw on prospect theory, which highlights loss aversion, reference dependence, and framing effects as central drivers of decision-making under risk (Kahneman and Tversky 1979, 1984). Japanese empirical studies consistently indicate that investors’ willingness to tolerate losses is shaped by behavioral tendencies such as the disposition effect, where individuals hold losing positions too long and sell winning ones too early (Kohsaka et al. 2013; Komai et al. 2018). Loss tolerance, typically measured as the loss threshold at which an investor chooses to liquidate, differs conceptually from ex-ante risk tolerance and is particularly sensitive to psychological traits such as overconfidence, hyperbolic discounting, and framing (Takemura et al. 2011; Nabeshima et al. 2025; Yamaguchi et al. 2025; Kuramoto et al. 2025). Information sources influence these mechanisms by shaping expectations, altering reference points, and affecting perceived loss severity. Both professional and non-professional information sources can therefore strengthen or weaken investment grip, depending on whether they emphasize long-term fundamentals or amplify short-term emotional responses. Against this backdrop, the present study examines how different investment information sources relate to investment grip among Japanese retail investors, drawing on a large-scale dataset from the 2025 “Survey on Life and Money” conducted by Rakuten Securities and Hiroshima University. The study addresses two research questions: (1) How are different information channels—particularly professional paid sources and non-professional free sources—associated with investors’ tolerance for capital losses? and (2) How do demographic, socioeconomic, and psychological characteristics interact with these information environments in predicting investment grip? Rather than presuming uniform directional effects, the analysis empirically evaluates how each information source is associated with loss tolerance in practice. By providing large-scale evidence on the relationship between information environments and investment grip, this study contributes to the behavioral finance literature and offers policy-relevant insights for financial education, advisory practices, and investor protection in Japan’s evolving retail investment landscape. Importantly, investment grip captures a stable behavioral disposition toward loss endurance under normal market conditions, which differs conceptually from episodic panic selling triggered by crisis events.

2. Literature Review

Investment grip has emerged as an important behavioral construct for understanding how investors respond to deteriorating portfolio performance and navigate periods of market volatility. Drawing on prospect theory, loss tolerance reflects an investor’s willingness to bear paper losses before liquidating positions and is closely linked to reference dependence, loss aversion, and subjective risk perceptions (Nabeshima et al. 2025; Yamaguchi et al. 2025; Kuramoto et al. 2025). From the perspective of rational finance, sufficient investment grip follows naturally from optimal intertemporal choice: investors with stable preferences and coherent expectations should continue holding assets as long as expected future returns outweigh short-term volatility (Markowitz 1952; Siegel 1998). In this framework, investment grip is a byproduct of disciplined decision-making under uncertainty. Behavioral finance, however, emphasizes that investment grip is not determined by fundamentals alone. Psychological dispositions, affective reactions to losses, heuristics, and subjective beliefs play critical roles in shaping investors’ willingness to endure drawdowns (Kahneman and Tversky 1979; Shefrin and Statman 1985). Behavioral biases such as loss aversion, regret sensitivity, time inconsistency, and attentional biases can either strengthen or weaken persistence in the face of adverse price movements (Nabeshima et al. 2025; Kuramoto et al. 2025; Yamaguchi et al. 2025).

Previous studies conceptualized loss tolerance within the broader framework of the disposition effect; the tendency of investors to sell winning assets too early while holding losing assets too long. Kohsaka et al. (2013) provided foundational evidence using a simulated stock market, showing that prospect-theoretic loss-aversion parameters significantly explain the disposition effect among Japanese investors. Their experimental approach validated core assumptions of prospect theory in a Japanese setting and clarified how reference points shape selling decisions. Complementing this laboratory evidence, transaction-level analyses using brokerage data further document behavioral biases in real trading environments. Komai et al. (2018), using second-by-second trading data from online brokerage accounts, identified strong contrarian trading patterns and disposition effects among retail investors, reinforcing the external validity of experimental findings.

Beyond experimental and transaction-based approaches, large-scale survey studies offer broader insights into the determinants of loss tolerance. Nabeshima et al. (2025), analyzing data from over 160,000 Japanese online investors, found that overconfidence, often associated with greater risk-taking, is linked to lower loss tolerance in hypothetical loss scenarios, suggesting that unrealistic expectations about avoiding losses lead to faster selling once losses materialize. Kuramoto et al. (2025) show that individuals exhibiting hyperbolic discounting display distinct patterns of loss tolerance, reflecting time-inconsistent preferences. Studies conducted during the COVID-19 market crisis further demonstrate that information framing significantly affects panic selling behavior: negative framing reduced liquidation, while positive framing increased it, and higher education and financial literacy mitigated panic-driven responses (Kuramoto et al. 2024). While these studies illuminate crisis-driven behavior, they do not fully explain stable, day-to-day variation in investors’ willingness to endure losses under normal market conditions.

Loss tolerance differs conceptually from panic selling in that it captures an individual’s ex-ante willingness to bear losses rather than episodic, fear-driven reactions triggered by extreme market events. Understanding investment grip therefore requires attention not only to psychological traits but also to the informational context in which investors form expectations and evaluate risk. In this regard, the investment information environment plays a central role.

A substantial literature shows that information channels and media exposure materially influence investor behavior and market outcomes. Classic studies demonstrate that social interaction and information diffusion shape investment participation and trading activity. Hong et al. (2004) show that individuals who interact more with neighbors and peers are significantly more likely to participate in stock markets, highlighting the importance of informal information networks. Peress (2014) provides causal evidence that when newspapers go on strike and information dissemination declines, trading volume and volatility fall, underscoring the role of media coverage in directing investor attention. Together, these studies frame information environments as active drivers of market behavior rather than passive conduits of data.

The investment information landscape available to Japanese investors has undergone a profound transformation over the past decade, becoming increasingly diverse and technologically mediated (Eysenbach 2008; Kramer 2012; Metzger and Flanagin 2013; Yang et al. 2017). Whereas investment decisions were once dominated by professional and institution-based channels, investors now increasingly rely on mass media, social media, robo-advisors, and AI-driven information tools. Using data from 65,000 active brokerage users, Lal et al. (2023) document a growing reliance on social and mass media relative to traditional advisory services, with information-source choice strongly correlated with demographic and socioeconomic characteristics. This shift reflects broader changes in access costs, information speed, and user-generated content. Empirical studies further indicate that attention allocation, sentiment transmission, and imitation amplify trading activity and volatility through coordinated attention and herding (Kadous et al. 2017; Safitri and Wendy 2025; Awad et al. 2025).

Professional advisory services exert important but heterogeneous effects on investor behavior. While advice can improve diversification and portfolio structure when adopted, uptake and adherence vary widely, and advice does not uniformly improve outcomes (Junaidi and Nurhidayah 2023). Evidence shows that reliance on advisors can increase trading activity and shift portfolios toward advisor-favored products, potentially reflecting institutional incentives (Björklund and Erlingsson 2001). Mugerman et al. (2020) further demonstrate that the framing and payment structure of advice itself influences investor behavior, underscoring that the context of advice provision matters. These findings align with evidence that professional information sources may simultaneously reduce uncertainty while reinforcing conservative narratives or risk salience (Hackethal et al. 2012; Higgins 2024; Yeh 2020).

In contrast, free information sources including mass media, online financial portals, and social media provide rapid and low-cost access to information but often with uneven quality, fragmented narratives, and exposure to sentiment-driven or misleading content (Bartov et al. 2018; Gu and Kurov 2020). Social media platforms magnify sentiment and facilitate coordination, with message volume and valence predicting trading surges and short-horizon price movements (Kadous et al. 2017; Safitri and Wendy 2025). Evidence further shows that sentiment-only cues can influence investment decisions even in the absence of new fundamentals, highlighting susceptibility to affective signals (Awad et al. 2025; Choudhary and Yamuna 2025). As AI-driven advisory tools, algorithmic news feeds, and social investing platforms proliferate, information sources increasingly function not merely as transmission channels but as behavioral contexts shaping how investors interpret risk and evaluate losses.

Several mechanisms link information environments to investment grip. Retail investors are particularly sensitive to highly accessible media such as newspapers and television, while institutional investors respond differently, emphasizing the roles of accessibility, framing, and salience in shaping risk perception (Ben-Rephael et al. 2017). Survey evidence further indicates that reliance on informal information sources is associated with lower holdings of risky assets, suggesting heightened perceived risk rather than informed loss endurance (Takemura et al. 2018). These mechanisms align with prospect theory, which highlights loss aversion, reference dependence, and framing effects as central drivers of behavior under risk (Kahneman and Tversky 1979, 1984). Information sources shape expectations, alter reference points, and affect perceived loss severity; accordingly, both professional and non-professional environments can strengthen or weaken investment grip depending on whether they emphasize long-term fundamentals or amplify short-term emotional responses.

The Japanese cultural and institutional context further conditions these relationships. Japanese households often rely on family members, peers, and financial institutions for guidance, reflecting distinctive trust structures (Yeh 2020). Cross-country studies show that responses to information vary across cultural and historical contexts, including between Japan and other Asian economies (Marciano et al. 2025). Informational gaps in emerging investment domains, such as sustainable finance, may further increase perceived uncertainty and reduce loss tolerance (Gutsche et al. 2021). Methodologically, the Japanese literature draws on diverse empirical approaches, including brokerage microdata (Nabeshima et al. 2025; Lal et al. 2023), high-frequency trading data (Komai et al. 2018), laboratory experiments (Kohsaka et al. 2013), and national household surveys (Takemura et al. 2011; Takemura et al. 2018), providing a strong foundation while leaving conceptual gaps.

Despite substantial progress, the literature has yet to directly examine how investment information sources relate to investment grip itself. Existing studies primarily focus on asset allocation, trading frequency, panic selling, or general risk tolerance, rather than on investors’ willingness to endure losses under normal market conditions. Panic-selling research captures crisis-driven liquidation but does not explain everyday variation in loss endurance, while studies of risk-taking do not capture behavioral resilience once losses occur. Content-level features such as sentiment, credibility, and algorithmic curation also remain underexplored. These gaps are particularly salient in the context of Japan’s New NISA system introduced in 2024, which has rapidly expanded retail participation and brought a large number of first-time investors into long-horizon, tax-exempt investing (JSDA 2024; Katauke et al. 2025). Because NISA is designed to promote long-term asset formation, its success depends critically on investors’ ability to maintain positions through temporary losses. Accordingly, the objective of this study is to examine how different investment information sources are associated with investment grip among Japanese retail investors.

3. Data and Methods

3.1. Data

This study utilized large-scale data from the 2025 wave of the “Survey on Life and Money,” an online survey jointly administered by Rakuten Securities, one of Japan’s largest online securities companies, and the Kadoya Lab of Hiroshima University. Data collection took place between January and February of 2025 and targeted Rakuten Securities account holders who (i) were 18 years or older, (ii) resided in Japan, and (iii) had logged into their account at least once during the preceding year. As survey participants who enrolled in the 2022 or 2023 waves were tracked as a part of ongoing panel, several variables were merged from the 2022 and 2023 waves. The survey collected detailed information on investment behaviors, demographic and socioeconomic characteristics, and psychological attributes. After excluding observations with missing values, the final analytical sample consists of 161,677 respondents, representing 69.9% of the initial 231,307 participants.

3.2. Variables

The primary dependent variable, investment grip, measures the degree of investment loss an investor can tolerate while maintaining their position in a mutual fund. For example, following the expansion of Japan’s New NISA program in 2024, investors exhibiting strong investment grip can be understood as those who maintain their mutual fund holdings within tax-exempt accounts despite experiencing routine market drawdowns, whereas investors with weak investment grip are more likely to liquidate positions in response to moderate losses, thereby undermining the long-term asset accumulation objectives of the program. Following Nabeshima et al. (2025), Yamaguchi et al. (2025), and Kuramoto et al. (2025), respondents evaluated the following hypothetical scenario: Q1. Suppose you invest 1 million JPY in an investment trust and make a loss. How much will you keep the investment until (choose one)?:

• 990,000 JPY (10,000 JPY loss or 1% loss)
• 900,000 JPY (100,000 JPY loss or 10% loss)
• 800,000 JPY (200,000 JPY loss or 20% loss)
• 700,000 JPY (300,000 JPY loss or 30% loss)
• 600,000 JPY or less (400,000 JPY loss or more, or 40% loss or more)

Based on the response, we constructed a discrete ordinal variable investment grip, capturing tolerance levels of 1%, 10%, 20%, 30%, 40% or more. Thus, this measure reflects investors’ willingness to sustain capital losses in mutual fund investments, which is critical for long-term asset formation. To assess robustness, we also created a binary variable, following Yamaguchi et al. (2025), and Kuramoto et al. (2025), coded as: 1 if the respondent can tolerate losses ≥ 30% and 0 otherwise.

It is important to clarify the interpretation of investment grip as measured in this study. The investment grip measure captures an investor’s stated willingness to endure paper losses under a hypothetical but concrete investment scenario, and thus reflects an attitudinal, ex-ante behavioral disposition rather than realized trading behavior under actual market stress. Investment grip differs conceptually from standard risk tolerance measures, which typically assess preferences over return variability at the portfolio selection stage, and from the disposition effect (Shefrin and Statman 1985), which is inferred from observed selling behavior. Instead, investment grip captures the degree of loss endurance investors believe they can sustain before liquidating an existing position during normal market fluctuations. While prior studies using similar measures suggest that stated loss tolerance is informative for understanding investor behavior, we do not directly observe respondents’ realized trading decisions during recent market volatility. Accordingly, investment grip should be interpreted as a proxy for behavioral resilience to losses rather than as a direct measure of actual liquidation behavior, a distinction we emphasize when interpreting the results.

Our main independent variables capture the primary information source respondents rely on when making investment decisions. Respondents selected one option in the following question:

Q2. What are the most helpful sources of information for you to make investment decisions (choose one)?

• Outsourced independent financial advisors (IFAs) of the account-holding securities company
• Free information from the account-holding securities company
• Information from other securities companies
• Information from financial experts outside securities companies
• Mass media
• Social media
• Personal networks such as colleagues, family members, and friends
• Self-decision

We created eight corresponding binary variables. Information sources from IFAs, other securities companies, and external experts (options 1, 3, and 4) are classified as professional and fee-based sources, whereas information from mass media, social media, personal networks, and free information provided by the account-holding firm (options 2, 5–7) represent free or informal information sources.

We include a standard set of demographic, socioeconomic, and psychological controls that prior research links to loss tolerance and sell/hold decisions. Demographic characteristics (gender, age, age squared, marital status, and number of children) capture lifecycle effects, household responsibilities, and differential exposure to financial risk. Socioeconomic characteristics (years of education, employment status, household income, and household financial assets) proxy for resources and constraints that affect an investor’s ability to absorb temporary losses, and for heterogeneity in financial experience and decision-making capacity. Financial literacy is included because more financially literate investors are expected to better contextualize volatility and evaluate long-run return prospects, which may strengthen investment grip. Financial literacy was constructed from the three questions in Appendix A.

To capture individual risk preferences, we use a widely adopted survey-based proxy elicited through an “umbrella question”: respondents report the probability of rain at which they would typically bring an umbrella when going out. This measure reflects the respondent’s general tendency to avoid unfavorable outcomes under uncertainty; higher stated probabilities indicate a stronger preference for precaution and are interpreted as higher risk aversion. In the context of investing, greater risk aversion is theoretically expected to reduce investment grip because losses are perceived as more psychologically and financially costly, increasing the inclination to exit losing positions.

We control for time horizon using a survey item measuring agreement with the statement: “The future is uncertain, so there is no point in thinking about it.” Responses are recorded on a 5-point Likert scale (1 = completely opposite to 5 = completely agree). Higher values indicate a more myopic (short-horizon) view. From an intertemporal and behavioral perspective, myopic investors are less likely to tolerate short-term losses because they place greater weight on immediate outcomes and may discount long-run recovery, which should weaken investment grip.

For ease of interpretation, all binary variables are coded as 1 if the condition holds and 0 otherwise. Household income and household financial assets enter the regressions in logarithmic form to reduce skewness and reflect diminishing marginal effects of resources on loss tolerance.

Table 1. Variable definitions.

Variable	Definition
Dependent Variable
Investment grip	Discrete variable: How much loss respondents can withstand if they invest JPY 1 million in an investment trust (1% loss/10% loss/20% loss/30% loss/40% or more loss)
Investment grip dummy	Binary variable: 1 = respondents can withstand a loss of 30% or more if they invest JPY 1 million in an investment trust, 0 = otherwise
Independent Variable
Outsourced independent financial advisors (IFAs) of the account-holding securities company	Binary variable: 1 = the investor prefers financial information/advice from the outsourced IFAs of the securities company where they maintain investment accounts, 0 = otherwise
Free information from the account-holding securities company	Binary variable: 1 = the investor prefers free financial information/advice from the securities company where they maintain investment accounts, 0 = otherwise
Information from other securities companies	Binary variable: 1 = the investor prefers financial information/advice from other securities companies, 0 = otherwise
Information from financial experts outside securities companies	Binary variable: 1 = the investor prefers financial information/advice from financial experts apart from securities companies, 0 = otherwise
Mass media	Binary variable: 1 = the investor prefers financial information/advice from mass media (newspaper, magazines and television), 0 = otherwise
Social media	Binary variable: 1 = the investor prefers financial information/advice from blogs, YouTube, Instagram, etc., 0 = otherwise
Personal networks (family, friends, colleagues)	Binary variable: 1 = the investor prefers financial information/advice from close associates such as family, friends, and colleagues, 0 = otherwise
Self-decision (base)	Binary variable: 1 = the investor prefers self-decision, 0 = otherwise
Male	Binary variable: 1 = male, 0 = female
Age	Continuous variable: respondents’ age
Age squared	Continuous variable: age squared
Marital status	Binary variable: 1 = having a spouse, 0 = otherwise
Number of children	Continuous variable: the number of children
Education year	Continuous variable: years of education
Having a job	Binary variable: 1 = having a job, 0 = otherwise
Financial literacy	Discrete variable: average score of three financial literacy questions
Household income	Continuous variable: the total annual income including tax for the household in 2024 (unit: JPY)
Log of household income	Continuous variable: logarithm of household income
Household assets	Continuous variable: the total household financial assets
Log of household assets	Continuous variable: logarithm of household assets
Risk aversion	Continuous variable: respondents’ risk aversion (the answer to the following question: when you usually go out with an umbrella, what is the probability of rain?)
Myopic view of the future	Discrete variable: 1 = completely opposite, 2 = somewhat opposite, 3 = cannot say, 4 = somewhat agree, 5 = completely agree with the idea that “the future is uncertain, so there is no point in thinking about it.”

provides complete variable definitions, coding, and response categories for replication. Finally, for panel respondents, financial literacy, education, and myopic view were obtained from the 2022 and 2023 waves.

3.3. Descriptive Statistics

Descriptive statistics for this study are presented in

Table 2. Descriptive Statistics.

Variable	Mean	Std. Dev.	Min	Max
Dependent variable
Investment grip	0.245	0.128	0.01000	0.400
Investment grip dummy	0.471	0.499	0	1
Independent variable
Outsourced independent financial advisors (IFAs) of the account-holding securities company	0.020	0.139	0	1
Free information from the account-holding securities company	0.080	0.271	0	1
Information from other securities companies	0.024	0.154	0	1
Information from financial experts outside securities companies	0.031	0.174	0	1
Mass media	0.127	0.333	0	1
Social media	0.354	0.478	0	1
Personal networks (family, friends, colleagues)	0.094	0.292	0	1
Self-decision	0.269	0.443	0	1
Control variables
Male	0.670	0.470	0	1
Age	46.4	12.28	18	90
Age squared	2303	1181	324	8100
Marital status	0.670	0.470	0	1
Number of children	1.134	1.116	0	12
Education year	15.1	2.088	9	21
Having a job	0.896	0.305	0	1
Financial literacy	0.791	0.301	0	1
Household income	7,689,000	4,288,000	1,000,000	20,000,000
Log of household income	15.69	0.622	13.82	16.81
Household assets	21,610,000	25,560,000	2,500,000	100,000,000
Log of household assets	16.28	1.111	14.73	18.42
Risk aversion	0.535	0.238	0	1
Myopic view of the future	2.428	0.966	1	5
Observations	161,677

. On average, respondents were willing to tolerate a 24.5% loss when investing 1 million yen in mutual funds.

Regarding investment information sources:

-

2.0% relied on outsourced IFAs

-

8.0% on free information from the account-holding securities company

-

2.4% on other securities companies

-

3.1% on external financial experts

-

12.8% on mass media

-

35.4% on social media

-

9.4% on personal networks

-

26.9% relied on their own judgment

The sample consisted predominantly of males (67%) with an average age of 46.4 years. About 67.0% were married, and respondents had slightly more than one child on average. Mean education was 15.1 years, and 89.6% were employed. Average household income and financial assets were JPY 7.7 million and JPY 21.6 million, respectively. The mean financial literacy score was 0.791. Average risk aversion was 0.53, and the average myopic view score was 2.4.

3.4. Methods

To examine how investment information sources are related to investment grip, we estimated the following general equations:

$$Y_{1i}=f\left(I{S}_i, X_i, {\epsilon }_i\right)$$

(1)

$$Y_{2i}=f\left(I{S}_i, X_i, {\epsilon }_i\right)$$

(2)

here $Y_{1i}$ and $Y_{2i}$ are the indicators of the dependent variables, “Investment grip” and “Investment grip dummy”, respectively. The variable $I{S}_i$ represents individual information source. $X_i$ indicates vectors of respondent’s sociodemographic, economic, and psychological characteristics. ${\epsilon }_i$ is the error term. Since investment grip is an ordered categorical variable, Equation (1) was estimated using ordered probit models. Equation (2), based on the binary outcome, was estimated using probit models. We examined multicollinearity through correlation matrices and variance inflation factors (VIFs). All pairwise correlations were below 0.5 and all VIFs were under 2, indicating no multicollinearity concerns.

$$\begin{array}{l}Investment {grip}_i\\ ={\beta }_0+{\beta }_{1}Outsourced IFAs of the account-holding securities {company}_i\\ +{\beta }_{2}Free information from the account-holding securities {company}_i\\ +{\beta }_{3}Information from other securities {companies}_i\\ +{\beta }_{4}Information from financial experts outside securities {companies}_i\\ +{\beta }_{5}Mass {media}_i+{\beta }_{6}Social {media}_i+{\beta }_{7}Personal {networks}_i+{\beta }_8{Male}_i\\ +{\beta }_9{Age}_i+{\beta }_{10}Age {squared}_i+{\beta }_{11}Martial {status}_i\\ +{\beta }_{12}Number of {children}_i+{\beta }_{13}Education {year}_i+{\beta }_{14}Having a {job}_i\\ +{\beta }_{15}Financial {literacy}_i+{\beta }_{16}Log of household {income}_i\\ +{\beta }_{17}Log of household {assets}_i+{\beta }_{18}Risk {aversion}_i\\ +{\beta }_{19}Myopic view of the {future}_i+{\epsilon }_i\end{array}$$

(3)

$$\begin{array}{l}Investment grip {dummy}_i\\ ={\beta }_0+{\beta }_{1}Outsourced IFAs of the account-holding securities {company}_i\\ +{\beta }_{2}Free information from the account-holding securities {company}_i\\ +{\beta }_{3}Information from other securities {companies}_i\\ +{\beta }_{4}Information from financial experts outside securities {companies}_i\\ +{\beta }_5{Massmedia}_i+{\beta }_{6}Social {media}_i+{\beta }_{7}Personal {networks}_i+{\beta }_8{Male}_i\\ +{\beta }_9{Age}_i+{\beta }_{10}Age {squared}_i+{\beta }_{11}Martial {status}_i\\ +{\beta }_{12}Number of {children}_i+{\beta }_{13}Education {year}_i+{\beta }_{14}Having a {job}_i\\ +{\beta }_{15}Financial {literacy}_i+{\beta }_{16}Log of household {income}_i\\ +{\beta }_{17}Log of household {assets}_i+{\beta }_{18}Risk {aversion}_i\\ +{\beta }_{19}Myopic view of {the future}_i+{\epsilon }_i\end{array}$$

(4)

4. Results

Table 3. Regression results using the ordered probit model.

Variable	Dependent Variable: Investment Loss Tolerance
	Model 1	Model 2	Model 3	Model 4
(Self-decision = base outcome)
Outsourced independent financial advisors (IFAs) of the account-holding securities company	−0.5169 ***	−0.5065 ***	−0.3707 ***	−0.3677 ***
	(0.0199)	(0.0202)	(0.0204)	(0.0204)
Free information from the account-holding securities company	−0.2230 ***	−0.1825 ***	−0.1723 ***	−0.1716 ***
	(0.0101)	(0.0102)	(0.0103)	(0.0103)
Information from other securities companies	−0.1595 ***	−0.1223 ***	−0.1504 ***	−0.1501 ***
	(0.0166)	(0.0169)	(0.0172)	(0.0172)
Information from financial experts outside securities companies	−0.1122 ***	−0.0730 ***	−0.0705 ***	−0.0699 ***
	(0.0155)	(0.0157)	(0.0158)	(0.0158)
Mass media	−0.1030 ***	−0.0821 ***	−0.1107 ***	−0.1109 ***
	(0.0087)	(0.0087)	(0.0088)	(0.0088)
Social media	0.2450 ***	0.2735 ***	0.2483 ***	0.2473 ***
	(0.0070)	(0.0071)	(0.0071)	(0.0071)
Personal networks (family, friends, colleagues)	−0.4107 ***	−0.2819 ***	−0.2107 ***	−0.2104 ***
	(0.0101)	(0.0104)	(0.0106)	(0.0106)
Male		0.3555 ***	0.3068 ***	0.3064 ***
		(0.0061)	(0.0062)	(0.0062)
Age		0.0386 ***	0.0189 ***	0.0190 ***
		(0.0015)	(0.0016)	(0.0016)
Age squared		−0.0005 ***	−0.0003 ***	−0.0003 ***
		(0.0000)	(0.0000)	(0.0000)
Marital status		−0.0288 ***	−0.0867 ***	−0.0877 ***
		(0.0068)	(0.0073)	(0.0073)
Number of children		−0.0268 ***	−0.0083 ***	−0.0097 ***
		(0.0029)	(0.0030)	(0.0030)
Education year		0.0254 ***	−0.0173 ***	−0.0168 ***
		(0.0013)	(0.0014)	(0.0014)
Having a job		−0.0674 ***	−0.0136	−0.0130
		(0.0098)	(0.0103)	(0.0103)
Financial literacy			0.6013 ***	0.5943 ***
			(0.0099)	(0.0099)
Log of household income			0.0140 **	0.0132 **
			(0.0057)	(0.0057)
Log of household assets			0.2178 ***	0.2180 ***
			(0.0030)	(0.0030)
Risk aversion				−0.1059 ***
				(0.0119)
Myopic view of the future				−0.0222 ***
				(0.0029)
/cut1	−1.6916 ***	−0.4282 ***	2.4476 ***	2.3327 ***
	(0.0071)	(0.0413)	(0.0820)	(0.0830)
/cut2	−0.5686 ***	0.7160 ***	3.6477 ***	3.5333 ***
	(0.0056)	(0.0412)	(0.0822)	(0.0831)
/cut3	0.0744 ***	1.3705 ***	4.3334 ***	4.2193 ***
	(0.0055)	(0.0412)	(0.0823)	(0.0832)
/cut4	0.4983 ***	1.8022 ***	4.7839 ***	4.6700 ***
	(0.0056)	(0.0413)	(0.0824)	(0.0833)
Observations	161,677	161,677	161,677	161,677
Pseudo R-squared	0.0142	0.0249	0.0508	0.0511
Log likelihood	−237,143	−234,561	−228,325	−228,255

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05.

presents the ordered probit regression estimates. Model 1 consists solely of information sources. Model 2 adds demographic characteristics; Model 3 incorporates financial literacy and economic status; Model 4 introduces psychological factors and represents the full specification.

Across all models, professional information sources- outsourced IFAs, other securities companies, and external financial experts are negatively associated with investment grip at the 1% significance level. Similarly, free information sources, from the account-holding securities company, mass media, and personal networks, also show negative associations. These results indicate that investors who rely on these information sources tend to have lower tolerance for investment losses than those who make decisions independently.

In contrast, social media exhibits a positive and highly significant association (1% level) with investment grip across all models, suggesting that investors who depend on social media are more tolerant of losses relative to self-directed investors.

To facilitate interpretation of the economic magnitude of these effects,

Table 4. Average Marginal Effects from the Ordered Probit Model of Investment Loss Tolerance.

Variable	Dependent Variable: Investment Loss Tolerance
	Coefficient	Marginal Effect
(Self-decision = base outcome)
Outsourced independent financial advisors (IFAs) of the account-holding securities company	−0.3677 ***	0.0342 ***
	(0.0204)	(0.0019)
Free information from the account-holding securities company	−0.1716 ***	0.0160 ***
	(0.0103)	(0.0010)
Information from other securities companies	−0.1501 ***	0.0140 ***
	(0.0172)	(0.0016)
Information from financial experts outside securities companies	−0.0699 ***	0.0065 ***
	(0.0158)	(0.0015)
Mass media	−0.1109 ***	0.0103 ***
	(0.0088)	(0.0008)
Social media	0.2473 ***	−0.0230 ***
	(0.0071)	(0.0007)
Personal networks (family, friends, colleagues)	−0.2104 ***	0.0196 ***
	(0.0106)	(0.0010)
Male	0.3064 ***	−0.0285 ***
	(0.0062)	(0.0006)
Age	0.0190 ***	−0.0018 ***
	(0.0016)	(0.0001)
Age squared	−0.0003 ***	0.0000 ***
	(0.0000)	(0.0000)
Marital status	−0.0877 ***	0.0082 ***
	(0.0073)	(0.0007)
Number of children	−0.0097 ***	0.0009 ***
	(0.0030)	(0.0003)
Education year	−0.0168 ***	0.0016 ***
	(0.0014)	(0.0001)
Having a job	−0.0130	0.0012
	(0.0103)	(0.0010)
Financial literacy	0.5943 ***	−0.0553 ***
	(0.0099)	(0.0010)
Log of household income	0.0132 **	−0.0012 **
	(0.0057)	(0.0005)
Log of household assets	0.2180 ***	−0.0203 ***
	(0.0030)	(0.0003)
Risk aversion	−0.1059 ***	0.0099 ***
	(0.0119)	(0.0011)
Myopic view of the future	−0.0222 ***	0.0021 ***
	(0.0029)	(0.0003)

*** p < 0.01, ** p < 0.05.

reports average marginal effects from the ordered probit model. Relative to self-directed investors, reliance on professional information sources is associated with a higher probability of exhibiting lower investment grip. For example, investors who primarily rely on outsourced independent financial advisors are, on average, 3.4 percentage points more likely to fall into lower loss-tolerance categories, while those relying on free information from their account-holding securities company and information from other securities companies are 1.6 and 1.4 percentage points more likely, respectively. Free information sources such as mass media and personal networks are also associated with reduced loss tolerance, with marginal effects of 1.0–2.0 percentage points. In contrast, reliance on social media is associated with a 2.3 percentage point lower probability of low loss tolerance, indicating a higher willingness to endure investment losses. Although these effects are modest in magnitude, they are precisely estimated and economically meaningful given the large sample size and the cumulative nature of long-term investment decisions.

Among control variables:

• Positive associations: male, age, financial literacy, log household income, log household assets
• Negative associations: age squared, marital status, number of children, risk aversion, myopic view
• Education: shifts from positive to negative after including economic and psychological controls
• Employment: becomes insignificant in the full model

4.1. Robustness Check

4.1.1. Alternative Measure of Investment Grip

To validate main findings, we re-estimated the models using binary outcome investment grip dummy (≥30% loss tolerance), employing probit estimation.

Table 5. Regression results using probit model.

Variable	Dependent Variable: Investment Loss Tolerance Dummy
	Model 1	Model 2	Model 3	Model 4
(Self-decision = base outcome)
Outsourced independent financial advisors (IFAs) of the account-holding securities company	−0.5120 ***	−0.5018 ***	−0.3659 ***	−0.3632 ***
	(0.0243)	(0.0246)	(0.0253)	(0.0253)
Free information from the account-holding securities company	−0.2598 ***	−0.2192 ***	−0.2063 ***	−0.2060 ***
	(0.0128)	(0.0129)	(0.0131)	(0.0132)
Information from other securities companies	−0.2175 ***	−0.1787 ***	−0.2066 ***	−0.2071 ***
	(0.0212)	(0.0215)	(0.0220)	(0.0220)
Information from financial experts outside securities companies	−0.1230 ***	−0.0856 ***	−0.0856 ***	−0.0857 ***
	(0.0187)	(0.0189)	(0.0193)	(0.0193)
Mass media	−0.1402 ***	−0.1136 ***	−0.1412 ***	−0.1420 ***
	(0.0107)	(0.0108)	(0.0110)	(0.0110)
Social media	0.2630 ***	0.2849 ***	0.2604 ***	0.2592 ***
	(0.0080)	(0.0081)	(0.0083)	(0.0083)
Personal networks (family, friends, colleagues)	−0.4042 ***	−0.2849 ***	−0.2117 ***	−0.2113 ***
	(0.0122)	(0.0126)	(0.0129)	(0.0129)
Male		0.3259 ***	0.2779 ***	0.2768 ***
		(0.0072)	(0.0074)	(0.0074)
Age		0.0447 ***	0.0250 ***	0.0252 ***
		(0.0019)	(0.0019)	(0.0019)
Age squared		−0.0005 ***	−0.0004 ***	−0.0004 ***
		(0.0000)	(0.0000)	(0.0000)
Marital status		−0.0402 ***	−0.0895 ***	−0.0906 ***
		(0.0080)	(0.0087)	(0.0087)
Number of children		−0.0316 ***	−0.0115 ***	−0.0129 ***
		(0.0035)	(0.0036)	(0.0036)
Education year		0.0212 ***	−0.0213 ***	−0.0209 ***
		(0.0016)	(0.0017)	(0.0017)
Having a job		−0.0699 ***	−0.0007	−0.0003
		(0.0117)	(0.0124)	(0.0125)
Financial literacy			0.5783 ***	0.5694 ***
			(0.0119)	(0.0120)
Log of household income			−0.0108	−0.0118 *
			(0.0068)	(0.0068)
Log of household assets			0.2335 ***	0.2334 ***
			(0.0036)	(0.0036)
Risk aversion				−0.0999 ***
				(0.0138)
Myopic view of the future				−0.0272 ***
				(0.0034)
Constant	−0.0738 ***	−1.3926 ***	−4.2167 ***	−4.0845 ***
	(0.0060)	(0.0496)	(0.0968)	(0.0978)
Observations	161,677	161,677	161,677	161,677
Pseudo R-squared	0.0244	0.0396	0.0796	0.0801
Log likelihood	−109,058	−107,356	−102,888	−102,832

Robust standard errors in parentheses. *** p < 0.01, * p < 0.10.

summarizes the results.

The robustness check confirms the main conclusions:

• Reliance on professional information sources, free information from the securities company, mass media, and personal networks remains negatively associated with loss tolerance.
• Social media continues to show a significant positive association.

Control variable patterns are also consistent:

• Male, age, financial literacy, and log household assets remain positively associated with the binary indicator.
• Age squared, marital status, number of children, risk aversion, and myopic view show significantly negative associations.
• The coefficient for years of education again switches sign after including economic and psychological characteristics.
• The log of household income becomes significant only in the full model.

4.1.2. Average Marginal Effects

As an additional robustness check, we computed average marginal effects from the ordered probit model to assess the economic magnitude of the estimated associations. The marginal effects, reported in Table 4, confirm the patterns observed in the coefficient estimates. Reliance on professional and traditional information sources is associated with a higher probability of remaining in lower loss-tolerance categories and a lower probability of exhibiting strong investment grip. In particular, relying on outsourced independent financial advisors, free information from the account-holding securities company, other securities companies, financial experts, mass media, and personal networks is associated with increases of approximately 0.6 to 3.4 percentage points in the probability of exhibiting lower loss tolerance, relative to self-directed investors. In contrast, reliance on social media is associated with a decrease of about 2.3 percentage points in the probability of low loss tolerance, indicating greater willingness to endure larger losses.

The marginal effects for control variables are also consistent with the main results. Male respondents, older investors (at a decreasing rate), individuals with higher financial literacy, higher household income, and greater household assets exhibit lower probabilities of low loss tolerance, reflecting stronger investment grip. Conversely, higher risk aversion, a more myopic view of the future, being married, having more children, and lower education levels are associated with higher probabilities of lower loss tolerance. Overall, the marginal effects analysis reinforces the robustness of the main findings and demonstrates that the estimated relationships are not only statistically significant but also economically meaningful in terms of changes in predicted loss-tolerance probabilities.

4.1.3. Subgroup Analysis

As a robustness check, we re-estimated the ordered probit model separately by gender and age group, as reported in

Table 6. Ordered Probit Estimates of Investment Loss Tolerance by Gender and Age Group.

Variables	Female	Male	Age < 40	Age 40–65	Age ≥ 65
(Self-decision = base outcome)
Outsourced independent financial advisors (IFAs) of the account-holding securities company	−0.257 *** (0.037)	−0.425 *** (0.025)	−0.494 *** (0.032)	−0.282 *** (0.027)	−0.231** (0.101)
Free information from the account-holding securities company	−0.107 *** (0.019)	−0.203 *** (0.012)	−0.232 *** (0.021)	−0.158 *** (0.013)	−0.084 *** (0.032)
Information from other securities companies	−0.022 (0.031)	−0.214 *** (0.021)	−0.233 *** (0.032)	−0.127 *** (0.022)	−0.004 (0.053)
Information from financial experts outside securities companies	0.038 (0.027)	−0.133 *** (0.020)	−0.121 *** (0.028)	−0.050 ** (0.021)	−0.029 (0.057)
Mass media	−0.028 (0.018)	−0.138 *** (0.010)	−0.139 *** (0.019)	−0.101 *** (0.011)	−0.035 (0.025)
Social media	0.232 *** (0.013)	0.264 *** (0.009)	0.166 *** (0.013)	0.284 *** (0.009)	0.279 *** (0.028)
Personal networks	−0.169 *** (0.016)	−0.244 *** (0.015)	−0.235 *** (0.017)	−0.205 *** (0.014)	−0.168 *** (0.044)
Male	—	—	0.329 *** (0.010)	0.298 *** (0.008)	0.192 *** (0.029)
Age	0.017 *** (0.003)	0.015 *** (0.002)	0.081 *** (0.014)	0.033 *** (0.008)	−0.114 * (0.066)
Age squared	−0.00027 *** (0.00003)	−0.00030 *** (0.00002)	−0.00130 *** (0.00022)	−0.00048 *** (0.00008)	0.00065 (0.00046)
Marital status	−0.079 *** (0.012)	−0.087 *** (0.009)	−0.047 *** (0.013)	−0.104 *** (0.009)	−0.142 *** (0.028)
Children	−0.023 *** (0.005)	−0.003 (0.004)	−0.011 * (0.006)	−0.012 *** (0.004)	0.007 (0.010)
Education	−0.012 *** (0.003)	−0.017 *** (0.002)	−0.014 *** (0.003)	−0.019 *** (0.002)	−0.002 (0.005)
Working	−0.025 (0.015)	0.005 (0.014)	0.030 (0.025)	−0.005 (0.014)	−0.040 * (0.021)
Financial literacy	0.613 *** (0.015)	0.558 *** (0.013)	0.684 *** (0.016)	0.557 *** (0.013)	0.287 *** (0.039)
Log household income	0.023 ** (0.010)	0.004 (0.007)	−0.005 (0.011)	0.010 (0.007)	0.051 *** (0.018)
Log household assets	0.221 *** (0.005)	0.218 *** (0.004)	0.243 *** (0.006)	0.219 *** (0.004)	0.168 *** (0.010)
Risk aversion	−0.043 * (0.022)	−0.134 *** (0.014)	−0.086 *** (0.021)	−0.110 *** (0.015)	−0.121 *** (0.045)
Myopic	−0.021 *** (0.005)	−0.025 *** (0.004)	−0.021 *** (0.005)	−0.028 *** (0.004)	−0.011 (0.011)
Observations	53,305	108,372	51,930	96,984	12,763
Pseudo R2	0.0419	0.0456	0.0593	0.0495	0.0261

*** p < 0.01, ** p < 0.05, * p < 0.10.

. The results are highly consistent with the main findings from the full sample. Across all subsamples, reliance on professional and traditional information sources—such as outsourced independent financial advisors, free advice from the account-holding securities company, mass media, and personal networks—is generally associated with lower investment loss tolerance relative to self-directed decision-making. Conversely, social media use is robustly and positively associated with investment grip across gender and all age groups, reinforcing the main conclusion that digitally mediated peer-based information environments are linked to greater willingness to endure losses.

Some heterogeneity in magnitudes emerges across subsamples, but not in direction. The negative association between professional advice and loss tolerance is strongest among male and younger investors (below age 40), while the coefficients are smaller and less precisely estimated among investors aged 65 and above, likely reflecting both lower statistical power and different portfolio objectives in later life stages. Similarly, mass media and personal networks exhibit stronger negative associations among younger and middle-aged investors, whereas these effects are weaker or statistically insignificant among older respondents. Control variables largely behave as expected and mirror the main results: higher financial literacy and household assets are positively associated with loss tolerance across all groups, while greater risk aversion and myopic preferences are consistently linked to lower investment grip.

The subgroup analysis reveals that the main findings are broadly robust across gender and age groups, while also uncovering several notable differences. The negative association between professional information sources, particularly outsourced IFAs and advice from other securities companies, and investment grip is markedly stronger for men and for younger investors (below age 40) than for women and older cohorts, suggesting greater sensitivity to professional advice among these groups. In contrast, the positive association between social media use and investment grip is observed across all subgroups but is especially pronounced among investors aged 40–65 and those aged 65 and above. These patterns indicate that while information environments matter for all investors, their behavioral implications vary systematically across demographic groups.

5. Discussion

The empirical findings provide clear evidence that the information environments in which Japanese investors operate are systematically associated with their investment grip, defined as their willingness to tolerate portfolio losses and maintain long-term investment positions. These results are consistent with prior work showing that information channels structure investor expectations, perceived risk, and decision-making processes (Fujiki 2019; Murashima 2024; Lal et al. 2023). The present study extends this literature by demonstrating that information environments are also closely related to loss tolerance itself, a behavioral dimension that has received limited attention in previous work. Across all specifications, investors who rely primarily on internal or external professional information such as outsourced IFAs, professional information from own company, analysts from other securities companies, or independent financial experts display significantly lower investment grip than those who make decisions independently. This pattern suggests that professional information, despite offering structured guidance, may increase the salience of downside risks or reinforce cautionary narratives that reduce willingness to endure losses. From a behavioral perspective, such information can shift investors’ reference points, elevate perceived loss probabilities, or trigger conservative heuristics, thereby lowering loss tolerance (Arkes et al. 2008; Qiu and Weitzel 2016; Kahneman and Tversky 1979).

At the same time, these negative associations should be interpreted with caution, as reverse causality is a plausible explanation. Following the introduction of the expanded New NISA system in 2024, a large number of novice investors entered the market for the first time. Professional advisers—particularly those affiliated with securities companies—are more likely to engage with these less-experienced investors, who may inherently possess lower tolerance for volatility and losses (Hackethal et al. 2012). Under this interpretation, the observed relationship may reflect selection into professional information channels by investors with lower baseline investment grip, rather than a direct causal effect of professional advice on loss tolerance. Accordingly, information environments may both shape and reflect investors’ underlying behavioral characteristics. Free information sources such as mass media and personal networks, are negatively associated with investment grip. These channels often deliver information that is fast, fragmented, or framed around short-term market movements (Li et al. 2018). Behavioral finance predicts that such environments amplify loss aversion, availability bias, and narrative-driven risk perceptions, all of which can increase the psychological burden of losses and reduce willingness to persist through volatility.

More broadly, the observed relationships between information sources and investment grip may be affected by endogeneity arising from self-selection and unobserved investor characteristics. Investors’ choices of information environments are unlikely to be random and may reflect latent traits such as confidence in one’s own judgment, trust in external expertise, prior investment experience, or general attitudes toward uncertainty, which are difficult to fully observe in survey data. These unmeasured factors may simultaneously influence both information source selection and loss tolerance, potentially biasing the estimated associations in either direction. For example, inherently cautious or ambiguity-averse investors may be more inclined to seek professional advice and also exhibit lower tolerance for losses, independent of any direct effect of the information received. Conversely, investors with greater confidence or higher engagement may both rely on social media and display higher loss tolerance. Given the cross-sectional nature of the data and the absence of credible instruments, panel structure, or exogenous variation in information access, the analysis cannot disentangle these channels. Accordingly, the results should be interpreted as descriptive associations between information environments and investment grip rather than as evidence of causal effects.

In contrast, social media use is robustly and positively associated with investment grip. One interpretation is that social media communities normalize market fluctuations by providing retail investors with peer reinforcement, long-term narratives, or alternative framings that recast losses as temporary or strategic rather than threatening (Kou et al. 2024). Exposure to diverse user-generated viewpoints may also broaden expectation sets, reducing the emotional intensity of temporary drawdowns. However, the same environments may also encourage speculative risk tolerance in certain contexts, indicating that social media can strengthen or weaken grip depending on the content individuals encounter. This does not necessarily imply welfare-improving behavior, as higher grip may reflect either improved behavioral resilience or excessive risk tolerance depending on content exposure.

The positive association between social media use and investment grip may also reflect the recent diffusion of AI-based services integrated into digital investment environments, including robo-advisors, algorithmic content curation, and automated recommendation systems (Takayanagi et al. 2023). These technologies increasingly shape the information investors encounter on social platforms by emphasizing long-term narratives, filtering out noise, and framing market volatility as a routine component of investing. By reducing ambiguity, simplifying decision processes, and exposing users to peer discussions that normalize temporary losses, AI-mediated information environments may help stabilize expectations and mitigate emotional reactions to downturns (Bhardwaj 2025). Consequently, the resilience associated with social media use may be partially driven by the technological and behavioral effects of AI-enhanced investment guidance rather than by social interaction alone.

Japan’s institutional and behavioral environment provides a distinctive setting for interpreting these results. Japanese households have historically exhibited strong preferences for capital preservation, high sensitivity to losses, and relatively low participation in risky assets, shaped by decades of economic stagnation and deflation (Khan et al. 2021). These characteristics may heighten the behavioral impact of information framing and amplify responses to downside risk. At the same time, relatively high levels of trust in financial institutions and professional advice may increase the salience of cautious narratives when investors rely on formal information sources, potentially contributing to lower observed loss tolerance. The introduction of the expanded New NISA system in 2024 further distinguishes the Japanese context by rapidly bringing first-time and long-horizon retail investors into the market, making investment grip a particularly relevant behavioral outcome. Moreover, the growing reliance on digital platforms and social media has altered how Japanese investors access and interpret financial information, reducing entry barriers while reshaping expectations about volatility. While the magnitude of the estimated effects may differ across countries, the behavioral mechanisms highlighted in this study such as reference dependence, framing, and the role of dominant information environments are likely to be relevant in other markets experiencing expanding retail participation, and increasingly, digital information ecosystems.

The estimated effects of the control variables align closely with established findings in the behavioral finance literature and further illuminate heterogeneity in investment grip. Higher financial literacy and greater household wealth are positively associated with loss tolerance, suggesting that more knowledgeable and financially secure investors are better equipped to contextualize market fluctuations and maintain confidence in long-term recovery prospects. These characteristics likely reduce ambiguity and dampen emotional responses to losses, reinforcing behavioral resilience. This interpretation is consistent with evidence that financial literacy mitigates behavioral biases and improves investment decision-making (Khan 2020; Agarwal et al. 2025). By contrast, higher risk aversion, a more myopic view of the future, and greater family-related responsibilities, such as marriage or having more children, are linked to lower grip. These traits reflect psychological and socioeconomic pressures that heighten the perceived severity of losses and increase the subjective costs of maintaining risky positions. Highly risk-averse individuals may encode short-term losses as disproportionately threatening, while those with short planning horizons may prioritize immediate capital preservation over long-term gains. Family obligations may similarly constrain tolerance for volatility, as individuals facing financial commitments may lack the flexibility to endure drawdowns. These findings are consistent with prior evidence emphasizing the roles of time inconsistency, risk attitudes, and household circumstances in shaping loss tolerance (Yamaguchi et al. 2025; Nabeshima et al. 2025; Kuramoto et al. 2024). Taken together, these findings support a broader behavioral interpretation in which loss tolerance is shaped not only by intrinsic traits but also by the informational and socioeconomic contexts that condition how losses are perceived. Investment grip emerges as a context-dependent outcome formed through the interaction of beliefs, emotions, and constraints, rather than as a fixed individual trait. This perspective underscores the importance of understanding how modern information environments interact with investor characteristics to influence behavioral resilience in long-term investing. Several limitations should be noted. First, the analysis relies on self-reported survey data, including respondents’ primary information sources and hypothetical loss tolerance, which may not fully capture actual behavior under real market conditions. Second, although a comprehensive set of controls is included, unobserved heterogeneity remains a concern, particularly with respect to selection into information sources. Third, the cross-sectional design precludes causal inference. While the observed associations are robust, they should not be interpreted as causal effects of information sources on loss tolerance. Future research employing longitudinal data, experimental designs, or content-level analyses of information environments would help clarify the mechanisms through which information ecosystems influence investment grip.

6. Conclusions

This study examines associative rather than causal relationships; therefore, the results should be interpreted as descriptive evidence on how different investment information environments are related to investment grip. This study provides large-scale empirical evidence on how investment information sources are associated with investment grip, defined as investors’ willingness to tolerate losses and maintain investment positions over time. Using a dataset of over 160,000 Japanese retail investors during the early implementation period of the expanded New NISA system, the analysis demonstrates that information environments are systematically related to loss tolerance in ways that differ markedly across source types. Professional and free information sources, including IFAs, mass media, and personal networks, are consistently associated with lower investment grip, whereas reliance on social media is robustly associated with higher loss tolerance, even after controlling for demographic, economic, and psychological characteristics.

These findings contribute to the behavioral finance literature by highlighting that investment grip is shaped not only by individual risk preferences and financial literacy but also by the informational contexts in which investors form expectations and interpret losses. By framing investment grip as a behavioral outcome influenced by information environments, this study extends the existing research on risk tolerance and panic selling and emphasizes the role of cognitive framing and narrative exposure in shaping investors’ resilience to market fluctuations. Methodologically, the results underscore the importance of accounting for selection into information sources, particularly given that professional advisory services tend to cater to less-experienced investors who may exhibit lower baseline tolerance for losses.

The results carry several immediate implications for policymakers, financial institutions, and investor-education initiatives concerned with promoting long-term household wealth formation. First, regulators and securities companies should recognize that information framing, not only its accuracy, substantially shapes loss tolerance. Providing investors with materials that contextualize short-term losses within long-term historical return patterns may strengthen investment grip and improve adherence to long-horizon strategies. Second, the negative association between professional information sources and loss tolerance should not be viewed as a shortcoming of advisory services but rather as evidence that professional advisers tend to work closely with less-experienced investors who naturally exhibit lower grip. This creates an opportunity for securities companies and advisers to strengthen investors’ resilience by emphasizing long-term principles, clarifying expected volatility, and providing stable guidance that helps novice investors develop greater confidence in managing temporary losses. Third, given the positive association between social media use and loss tolerance, policymakers should not reflexively view digital platforms as destabilizing; instead, they may leverage these environments by promoting credible, long-term-oriented content or collaborating with platforms to amplify investor education messages. At the same time, careful monitoring is needed to prevent the spread of overly speculative narratives. Finally, the emergence of AI-driven advisory tools presents an opportunity for designing automated systems that nudge investors toward resilient, fundamentals-based decision-making. Incorporating behavioral insights into robo-advisors such as volatility normalization features, recovery-probability dashboards, or explanations that reduce ambiguity, could meaningfully enhance investor stability. Collectively, these strategies would strengthen the behavioral foundations required to achieve the policy goal of long-term asset formation in Japan’s increasingly digital and retail-driven investment landscape.

Several limitations of this study warrant consideration. First, the analysis relies on self-reported survey data and a hypothetical loss-tolerance scenario, which captures investors’ stated, ex ante willingness to endure paper losses but may not fully reflect realized trading behavior under actual market stress or crisis conditions. Second, although the models incorporate a rich set of demographic, socioeconomic, and psychological controls, unobserved heterogeneity and reverse causality remain concerns, particularly regarding selection into information sources based on investors’ underlying loss tolerance and related behavioral traits. Third, the cross-sectional nature of the data precludes causal inference concerning the effects of information sources on investment grip. Finally, the survey identifies respondents’ dominant information source and does not capture the simultaneous or sequential use of multiple information channels. Future research employing longitudinal data, experimental approaches, or content-level analyses of information environments as well as survey designs that allow investors to report multiple information sources would help clarify how combined and interacting information environments shape investment grip and the underlying behavioral mechanisms.

Overall, this study underscores the importance of information environments in shaping investors’ behavioral responses to risk and loss. As retail participation continues to expand and information channels become increasingly digital and algorithmically mediated, understanding how different sources influence loss tolerance will be essential for promoting stable, long-term investment behavior and achieving policy objectives related to household asset formation.