THEORETICAL FRAMEWORK

The Theory of Bounded Rationality (Simon, 2000) indicates that individuals are confined or ‘bounded’ in their ability to absorb and process information. While consumers attempt to make rational decisions, they are constrained in their ability to do so due to time or resource constraints. Specifically, an individuals’ decision-making process may be limited by their knowledge, access to information, ability to process information, and having the necessary time or resources available to process information (Simon, 2000). To compensate for these constraints, biases are formed, and decisions are made based on restricted beliefs or information. There is significant evidence that bounded rationality can play a role in financial decision making and attitudes (Altman, 2014; Robb et al., 2015).

The use of a financial professional provides individuals the opportunity to rent expertise. This expertise should mitigate the effects of bounded rationality, leading to more consistent and rational behavior and expectations. Rational expectations would be tied to understanding historical trends, as well as a realization that an individual’s ability to obtain an above average return is limited. Consequently, a rational individual would tend to expect long-term returns to align with historical averages and for their portfolio to perform similarly to the market (Altman, 2014).

Based on the bounded rationality framework and relevant literature, the following hypothesis is proposed.

Hypothesis 1: Financial advice (full investment advisor help or some investment advisor help rather than self-directed) from a broker will be associated with a higher likelihood of investors being realistic about the performance of the stock market.

Data

Methodology

The 2015 National Financial Capability Study (NFCS), provided by the FINRA Investor Education Foundation, surveyed roughly 27,000 individuals from across the United States for its state-bystate survey. The 2015 version saw the addition of the investor survey, which captures additional data from 2,000 respondents who hold investments outside of a retirement account. For the current study, the data in the state-to-state survey and the investor survey were combined. The key independent variable used in this study is the use of financial professionals

(e.g., investment advisor/broker use). Due to changes in this question after 2015, these data were used to best investigate the question of interest.

Dependent Variables

Investor stock market outlook. Investor stock market outlook was measured using a question which asked, “What do you expect the approximate average annual return of the S&P 500 stock index to be over the next 10 years (without adjusting for inflation)? Respondents were given eight choices: (a) Less than 0%; (b) 0% to 4.9%; (c) 5% to 9.9%; (d) 10% to 14.9%; (e) 15% to 19.9%; (f) 20% or more; (g) don’t know; or (h) prefer not to say. This was consolidated to create one variable with four categories: pessimistic (0 – 4.9%), cautious-realistic 5% to 9.9%, realistic-optimistic (10%-14.9%), and highly optimistic (over 14.9%). These categories were created based on expected rates of return based on long term historical averages (Bogle, 2016). If respondents did not respond, did not know, or preferred not to say, the observation was dropped.

Independent Variables

Variable of Interest

The key independent variable in this study is related to the use of a professional to make investment decisions. The question used from the investor survey was as follows, “Which of the following best describes your current investment style?” Responses included, (a) “I make all my investment decisions on my own without the help of a broker or professional adviser” (self-directed); (b) “I make some decisions on my own and some with the help of a broker or professional adviser” (some professional help) and (c) “I let my broker or professional adviser make all my decisions for me (full professional help).” Responses that included missing, prefer not to say, or don’t know were excluded from the analysis.

Key Control Variables

The Theory of Bounded Rationality indicates an individual’s ability to form rational expectations is informed by their knowledge, ability, and resource constraints. Consequently, a number of key variables may be controlled for. These variables include (a) objective investment knowledge, (b) subjective investment knowledge, (c) formal financial education, (d) comfort using investment products, and (e) risk tolerance.

The investor survey provides ten knowledge questions tied specifically to investments. Three of these questions were found to be highly correlated with seven of the other questions and therefore were not used in the study. The remaining seven questions were summed to create an objective investment knowledge scale, with the highest possible score being seven and the lowest possible score being zero (Cronbach alpha = 0.67). These questions measured respondents’ knowledge of stocks and bonds, stock market risk, average stock market returns, municipal bonds, margin, and short sells. Respondents with missing data or non-responses were not included in the analysis. It is expected that objective investment knowledge is positively related to rational market and portfolio expectations. Similarly, the investor survey contains a question specifically related to subjective investment knowledge. Respondents were asked “On a scale from 1 to 7, where 1 means very low and 7 means very high, how would you assess your overall knowledge about investing?”. It is expected that subjective investment knowledge is positively related to rational market and portfolio expectations.

Financial education was measured using the state-by-state survey question, “Was financial education offered by a school or college you attended, or a workplace where you were employed?” Answer choices included “yes and participated”, “yes and did not participate”, or “no”. If respondents chose not to say or did not know, their responses were removed. It is expected that having received financial education is positively related to rational market and portfolio expectations.

Investment comfort, a proxy for investment experience, was measured using the investor survey question, “How comfortable are you when it comes to making investment decisions?”

Responses were offered on a Likert-type scale ranging from one to 10.

Investment risk tolerance was measured using the investor survey question, “Which of the following statements comes closest to describing the amount of financial risk that you are willing to take when you save or make investments?”

Respondents were given six choices: (a) take substantial financial risks expecting to earn substantial returns; (b) take above average financial risks expecting to earn above average returns; (c) take average financial risks expecting to earn average returns; (d) not willing to take any financial risks, (e) don’t know; and (f) prefer not to say. For this study, the first two choices were combined to create the “high risk category”. The third and fourth options were operationalized as the medium and low risk categories, respectively. Answer choices “don’t know” and “prefer not to say” were excluded from the analysis.

Other Control Variables

Control variables included age, income, employment status, education, gender, and race. In the 2015 NFCS, age is composed of five categories including, ages 18 to 34, 35 to 44, 45 to 54, 55 to 64 and over 64. Income is composed of five groups with less than $35,000 being the lowest income group and over $100,000 being the highest income group. Education has four categories including less than high school, some college, college degree, and graduate degree or higher. Gender is described as female and male and race is categorized into two groups in the dataset, White and non-White.

Analytic Methods

The purpose of this paper is to understand the relationship between the use of a financial professional and market expectations. A multinomial logistic regression was performed to identify the factors that influenced being pessimistic, cautious-realistic, realistic-optimistic, and highly optimistic about future returns.

Results

Table 1. Sample Characteristics

Descriptive Statistics

Table 1 provides an overview on the sample, which included 1,545 observations. Half of the respondents were cautiousrealistic regarding their expectations of returns over the next 10 years. A similar number of respondents presented pessimistic and realistic-optimistic beliefs about market returns, with each category representing about 19% of the sample. The remaining 11% of respondents were in the highly optimistic category.

Regarding the use of an investment professional, most of the sample was self-directed (44%) or used some broker help (42%) with the remainder using full broker help (14%). The sample had an average subjective investment knowledge score of 5.10 (out of 7) and objective investment knowledge score of 3.77 (out of 10). These may indicate a general misalignment of objective knowledge and investors perception of knowledge. About 31% of the sample had received financial education in an educational setting or in the workplace. Investors had a relatively high comfort level in making investment decisions (7.37) and were mostly split between having medium risk tolerance (48%) and high-risk tolerance (45%).

Most of the sample was White (80%), over 55 years old (~51%), male (57%), had incomes over $75,000 per year (58%), and had a college degree or higher (72%), In addition, about 45% of respondents were full-time workers and about 30% of them were retired.

Multinomial Results: Investor Stock Market Outlook

To predict a respondent’s stock market optimism, a multinomial logistic regression model was used. Results are shown in Table 2.

Cautious-Realistic (5 - 9.9%) versus Pessimistic (0 - 4.9%)

Compared to being self-directed, investors’ odds of being cautious-realistic versus pessimistic increased when using investment advice. Investors had 1.904 times the odds of being cautious-realistic when using a full broker and 1.611 times the odds when using some broker help.

There were significant additional results related to key variables informed by the theory of bounded rationality. As an investor’s objective investment knowledge increased, they were more likely to be cautious-realistic than pessimistic (OR 1.083).

Compared to those who were 64 and older, respondents between ages 35 and 44 were more likely to be cautiousrealistic than pessimistic. When compared to respondents with low risk tolerance, respondents with high risk tolerance were more likely to be cautious-realistic rather than pessimistic. Compared to those who were unemployed, respondents working full-time were more likely to be cautious-realistic versus pessimistic. Subjective investment knowledge, financial education, comfort, income, educational level, gender and race were not significant.

Realistic-Optimistic (10-14.9%) versus Pessimistic (0 - 4.9%).

If an investor received investment advice rather than being self-directed, their odds of being realistic-optimistic versus pessimistic increased. When using full or some professional investment advice, respectively, investors had 2.125 and 1.561 times the odds of being realistic-optimistic versus pessimistic about future stock market returns.

As an investor’s objective investment knowledge increased, they were less likely to be realistic-optimistic versus pessimistic (OR .838). However, they were more likely to be realisticoptimistic as their subjective investment knowledge (OR 1.191) and comfort with making investment decisions increased (OR 1.193). Compared to those who were 64 and older, respondents between ages 35 and 44 were more likely to be realisticoptimistic than pessimistic. When compared to respondents with low risk tolerance, respondents with high risk tolerance had 2.20 times the odds of being realistic-optimistic rather than pessimistic. Compared to those who were unemployed, respondents working part-time were more likely to be realisticoptimistic. Financial education, educational level, income, gender, and race were not significant.

Highly Optimistic (>15%) versus Pessimistic (0 - 4.9%)

In this comparison, professional investment advice was not significant. However, objective investment knowledge, subjective investment knowledge, comfort, high risk tolerance, and income were significant. As objective investment knowledge and comfort with investments increased, the odds of being highly optimistic versus pessimistic decreased. However, there was an opposite effect with subjective investment knowledge; as it increased, the odds of being highly optimistic versus pessimistic increased. When compared to respondents with low risk tolerance, respondents with high risk tolerance had 2.404 times the odds of being highly optimistic rather than pessimistic. Compared to those earning over $100,000 per year, respondents earning between $75,000 - $100,000 were more likely to be highly optimistic than pessimistic. Financial education, educational level, age, employment status, gender, and race were not significant.

Cautious-Realistic (5 - 9.9%) versus Highly Optimistic (>15%)

Financial advice was not found to be related to whether a respondent was cautious-realistic versus highly optimistic.

When comparing respondents who were cautious-realistic to those who were highly optimistic, objective and subjective investment knowledge, comfort in investing, income, employment status and educational level were significant. Specifically, as objective knowledge increased, the odds of being cautious-realistic versus highly optimistic increased (OR 1.432). On the other hand, as subjective investment knowledge (OR .752) and comfort with investing (0.761) increased, the odds of being cautious-realistic decreased. Compared to those earning over $100,000 per year, respondents earning between $75,000 - $100,000 were less likely to be cautious-realistic versus highly optimistic (OR 0.611). Retirees had 2.402 times higher odds of being cautious-realistic than the unemployed. Those with college degrees were more likely to be cautiousrealistic than highly optimistic (OR 1.669) when compared to respondents with only some college. Financial education, age, risk tolerance, gender, and race were not significant.

Realistic-Optimistic (10-14.9%) versus Highly Optimistic (>15%)

When comparing realistic-optimistic investors to highly optimistic investors, only comfort with investing, income, and employment status were significant. None of the other variables, including the use of a professional, were significant. As comfort with investing increased, the odds of being realistic-optimistic versus highly optimistic decreased (OR 0.829). Compared to those earning over $100,000 per year, respondents earning between $75,000 - $100,000 were less likely to be realistic-optimistic versus highly optimistic (OR 0.528). When comparing retirees to those who were unemployed, retirees had higher odds of being realisticoptimistic versus highly optimistic (OR 2.213).

Realistic-Optimistic (10-14.9%) versus CautiousRealistic (5 - 9.9%)

Financial advice was not related to differences in individuals that were realistic-optimistic and cautious-realistic. Objective and subjective investment knowledge and educational level were significant factors when comparing the odds of being realistic-optimistic to the odds of being cautious-realistic. As objective investment knowledge increased, the log odds of being realistic-optimistic versus cautious-realistic decreased by 0.256. As subjective investment knowledge increased, the odds of being realistic-optimistic versus cautious-realistic increased by 1.190 times. Respondents with bachelor’s (OR 0.649) and graduate degrees (OR 0.616) had lower odds of being realisticoptimistic versus cautious-realistic. Financial education, comfort in making investment decisions, age, risk tolerance, income level, income, gender, and race were not significant.

Table 2. Multinomial Logit Results - Investor Stock Market Outlook

Note. OR = odds ratio

Investor Stock Market Outlook Ranges: Pessimistic = 0 - 4.9%; Cautious Realistic = 5 - 9.9%; Realistic-Optimistic = 10 - 14.9% ; Highly Optimistic = 15% or greater

*** is significant at the 1 percent level; ** is significant at the 5 percent level; and * is significant at the 10 percent level.