Introduction
Behavioral finance challenges traditional assumptions of rational investor decision-making as behavioral biases systematically influence investment decisions in the financial world. This article explores how these biases influence investment decisions and examines how financial product design can exploit or mitigate such biases.
First, we explain the fundamental concept of behavioral biases, by highlighting some key examples. We then underline the importance of being aware of such biases and demonstrate through an example how investors often tend to place greater value on certain product structures, not due to sound economic reasoning but rather driven by behavioral factors. To conclude, our analysis summarizes the critical importance of behavioral considerations in financial markets.
Common Biases Affecting Fixed Income
While bond investing is sometimes viewed as a logical, numbers-driven activity, investor psychology is far more important than most would believe. Practically, many fixed income decisions are influenced not only by facts but also by strongly rooted cognitive biases.
- Mental Accounting: Mental accounting is the theory explaining why individuals tend to think of money differently based on its categorization. Investors frequently treat each investment alone, rather than considering all assets in a unified portfolio. This way of thinking can cause people to make poor decisions, like choosing one bond over another just because of how its cash flows are set up, even if both offer the same returns.
- Present Bias and Time-Inconsistent Preferences: Present bias is the tendency to value instant gratification more than a future one, which frequently results in decisions favoring short-term above long-term satisfaction. Investors may make a decision that supports their long-term objectives today but subsequently change their minds to prioritize short-term gratification or convenience, all because of this bias. In the context of investment, this may show as choosing a product with quicker rewards even if waiting would produce superior returns.
- Framing Effect: The framing effect is the reaction people have to the same information depending on its presentation. For instance, a bond that is said to “offer a steadily increasing income stream” might seem more appealing than one that is said to “offer fixed annual payments”, even if the returns are the same. When using framing, one’s intuitive vision takes precedence over rational thought.
- Loss Aversion: The idea behind loss aversion is that people feel the pain of losses more strongly than the pleasure of wins of the same kind. Investors may so hang onto poor assets in order to prevent realizing a loss, even if selling would be more logical. This kind of behavior makes people hesitant to move money from bad assets to good ones or to get out of bad investments quickly enough.
- Status Quo Bias (Inertia): People with status quo bias tend to stick with the decisions or things they already have instead of making changes, even if those changes would be better. Reasons for this include a lack of motivation, an overabundance of information, or the fear of making a mistake. In the financial markets, this could mean keeping a bond until maturity—even if early exercise would produce greater returns.
- Conservatism Bias: Conservatism bias is the reluctance to use fresh data, particularly in cases when it challenges established wisdom. Investors might cling too tightly to out-of-date assumptions instead of modifying their expectations or models in view of changing data—such as new inflation figures or signals of monetary policy. Delayed reactions to market changes and ongoing mispricing can follow from this.
- Hindsight Bias and Illusion of Validity: Hindsight bias is the inclination to view past events as being more predictable than they really were. Once a result is known, investors sometimes feel they “knew it all along,” which fuels the illusion of validity—that overconfidence in the accuracy of their assessments and projections. This distorted view of the past can lead to overconfidence in the future and poor decision-making.
Many behavioral anomalies seen in financial markets are based on these biases. Realizing why investors sometimes act in ways that challenge conventional financial wisdom depends on an awareness of them. In the following sections, we will look at how these cognitive patterns manifest in the fixed-income market, as well as how they influence bond design, investment, and redemption.
Getting Deeper: How the Coupon Structure of Bonds Creates Bias
How the Coupon Structure Affects Investment and Exercise Decisions
Conventional wisdom in finance holds that investors base decisions on logical economic ideas including net present value (NPV) and optimal reinvestment opportunities. But there is evidence that psychological biases play a big part in how people make financial decisions. Although behavioral biases are well-documented in equity markets, fixed-income investing also heavily relies on them.
We examine how these prejudices show up in real-world investing using Eickholt’s research, which provides convincing empirical data showing biases—not only economic principles—have a major influence on investment and redemption decisions.
Eickholt’s study on the influence of different coupon structures of putable bonds on the financial behavior of individual investors considers German Federal Saving Bonds (GFSN), which are standard putable step-up bonds issued by the German government. Eickholt analyzed two types of GFSN bonds: Type A, a 6-year bond with rising annual coupons, and Type B, a 7-year zero-coupon bond. Both of these bonds contain an early exercise right that gives investors the right to redeem the investment early. For his study, he defines the variables UPSTEEP, DURATION, and PVEV-ratio. UPSTEEP is the steepness of the coupon structure, while DURATION is the calculated Fisher-Weil duration (see Fisher and Weil, 1971) of the bonds. The PVEV-ratio is the ratio of the present value and the exercise value of the bond, which, from an evaluation standpoint, is only economically rational to exercise the embedded option for an investor if it equals 1.
Table 1 presents statistics on coupon offerings for Type A and Type B GFSN across all 204 issuances (102 of each type) within the sample period from July 1996 to February 2009, along with corresponding spot rates in Germany. For years 1 to 6, both Type A and Type B GFSN offer identical coupons at each issuance date, whereas in year 7, coupons apply exclusively to Type B. ∆SR represents the difference between the coupon rate and the respective spot rate for each year, with spot rates reflecting the term structure of interest rates on listed Federal securities, as determined by the Svensson method and provided by Deutsche Bundesbank. The absolute steepness of the coupon structure refers to the difference between the final and initial coupon, while relative steepness is the ratio of the last to the first coupon payment.
Investment Decisions
Eickholt’s study reveals among other important facts that investors’ preferences for bonds are much influenced by the steepness of the coupon structure (UPSTEEP). This inclination is based on behavioral patterns including mental accounting and framing effects rather than logical economic calculations. For his regression, Eickholt defined two variables: LAST6INV, which includes the number of investors in the last six issuances of the respective GFSN type, and LAST6VOL, which includes the cumulated investment volume. He uses both of these variables as measures of attractiveness for the issue. The results of the regression on an issuance’s attractiveness, based on the coupon structure and the coupon variables, are presented in Table 2.
Table 2 presents the results of standard regressions analyzing Individual Investors’ investment decisions in Type A and Type B GFSN. In the left section, the dependent variable is the total number of investments, while in the right section, it is the cumulative volume per issuance. Only investments and decisions made after the initial one-year blocking period are included. The lifetime variable is excluded for Type B GFSN, as it corresponds to maturity. The variables LAST6INV and LAST6VOL capture the cumulative number of investors and the cumulative GFSN volume over the last six issuances, reflecting a mid-term trend. Elasticities are calculated at mean values and are not reported for dummy variables. Robust standard errors are applied, and * indicates statistical significance at the 5% level.
The regressions in all four models explain around 50% of the response variable variation (R²). Particularly interesting for our purposes is that the regressions reveal that the coupon structure has a statistically significant influence on the attractiveness of an issuance. Across all models, the variable UPSTEEP—which shows the coupon structure’s steepness—gets rather strong positive loadings. This implies that in their choice of investments, investors value increasing coupon structures and a large final coupon payment.
Additionally, the coefficients for our second proxy, the duration (DURATION), are consistently negative. Hence, individual investors apparently look for a short weighted average time until the investment and coupons are paid back. This means that investors are more likely to purchase bonds with steeply increasing coupon payments and tend to avoid those with a longer duration, even when the actual economic value of different structures is equivalent. Investors tend to focus disproportionately on high final-year coupons, overvaluing these future payments instead of assessing the bond rationally.
This fits the idea of mental accounting, in which investors split future cash flows into several psychological “buckets” instead of assessing the present value of overall returns. It also emphasizes the framing effect; even if a flatter-coupon bond provides the same economic benefit, investors view a bond with a steep coupon increase as more appealing.
Exercise Decisions
Traditional financial theory suggests that investors will redeem bonds early if reinvestment opportunities offer better returns. However, Eickholt’s findings reveal that investors irrationally delay early exercise, particularly for bonds with a steep coupon structure. His pooled logit regression (Table 3) confirms that while the PVEV-ratio is a strong predictor of exercise probability, behavioral biases distort rational decision-making. Additionally, the model accounts for external economic conditions, though psychological factors appear to have a stronger impact on investor choices. Accordingly, Table 3 shows the results of a pooled logit regression of investors’ holding and exercise decisions on the defined variables for Type A and Type B GFSN.
As discussed before, according to theory, an investor’s exercise decision should be linked only to the current valuation of the GFSN. Besides the defined PVEV-ratio, which indicates if an exercise is economically reasonable, and the proxies for the coupon structure, he also controls this analysis for three environmental factors, which are beyond the scope of this article but important to consider with this particular instrument.
In addition, Eickholt estimated the respective elasticities at means, indicating the proportional change in the exercise probability for a proportional change in the respective dependent variable.
Table 3 presents the results of a pooled logit regression analyzing Individual Investors’ early exercise decisions for Type A and Type B GFSN. The analysis includes only decisions to hold or exercise made after the initial one-year blocking period and before the final year of maturity. The lifetime variable is excluded for Type B GFSN, as it is perfectly correlated with duration. Elasticities are calculated at mean values and are not reported for dummy variables. Robust standard errors are applied. Pseudo-R² represents the percentage improvement in log-likelihood achieved by the model compared to a constant-only model. * denotes statistical significance at the 5% level.
The data Eickholt uses for the regression is based on 15.924 million decisions for Type A and 9.991 million decisions for Type B GFSN. The pseudo-R² lies at 5.34% and 3.90%, and we can highlight two regression results.
First, the coefficient for the valuation variable (PVEV) is strongly negative, implying that the exercise probability decreases with a higher valuation of the respective product. As noted before, this is economically rational since only at the lowest possible PVEV-ratio of 1 is an exercise profitable from a theoretical point of view. Moreover, the high elasticity for this variable indicates that individual investors are indeed very sensitive to valuation changes in their investments, which is in line with our previous observations on investment decisions.
Second, the regression shows that the coupon structure of a GFSN again has a significant influence on the probability of exercise. Both regressions for Type A and Type B GFSN in Table 3 exhibit negative coefficients for the upcoming steepness of the coupon structure (UPSTEEP) and positive loadings for the duration variable (DURATION). This negative correlation with the steepness of the coupon structure and positive correlation with the duration suggests that individual investors hesitate to exercise early investments that promise strongly rising coupons in the future, whereas GFSN with flatter or only slightly sloping coupon structures are redeemed more quickly.
Further, Table 3 demonstrates that investors apparently value products with a comparatively short weighted average time until coupons and investments are paid back more highly than financially fully equivalent products with a longer duration. Separate analyses show that these relations are robust even when we focus only on either UPSTEEP or DURATION to describe a product’s coupon structure.
Overall, the empirical patterns in individual investors’ exercise decisions are consistent with our above-described analysis on investment decisions, where it becomes clear that issuances with increasing coupon payments until maturity attract more investors and higher investment volumes. In summary, we can interpret the economically irrational—and contrary to standard theory—penchant for high future coupons in both decisions as a “behavioral bias” of individual investors, which may be regarded as based on psychological reasons.
What Does This Mean for Investors and Issuers
Eickholt’s research reveals a strong insight: investors do not always act in line with logical models; this has real implications on the institutions constructing bonds as well as those who are purchasing them.
The results should serve as a wake-up call to individual investors. Many people are unintentionally affected by cognitive biases that cause them to overvalue steep coupon structures or hesitate to exercise early—even if doing so would make financial sense. Investors can start to make more deliberate, logical decisions by knowing how mental accounting, framing, loss aversion, and status quo bias might skew judgment.
Conversely, fixed-income product issuers can learn to create bonds in line with investor psychology, by improving product appeal. Eickholt presents the idea of “behavioral financial engineering,” in which issuers design product structures—like convex coupon step-ups—that leverage investor inclination to hang onto investments with high future payouts. This behavior reduces early redemption rates, so affecting the issuer’s need for liquidity and simplifying cash flow planning.
Conclusion
To conclude, this primer reinforces the idea that behavioral finance disputes the notion of purely rational investors and reveals the significant role of psychological biases in financial markets. Over the course of this article, we mentioned some of the key behavioral biases and additionally illustrated their impact through the analysis of a real-world example.
Understanding these biases is crucial for both individual investors and institutions, as they significantly influence decisions in financial markets for both investors and institutions. A fundamental awareness of these biases gives participants in financial markets the opportunity to profit from the irrational tendencies that influence financial decisions. If analyzed correctly, this leads to more efficient financial choices and improved investment decisions.
Finally, behavioral finance complements traditional frameworks for understanding financial markets. It acknowledges the human elements of investing, as integrating behavioral insights provides a deeper and more accurate understanding of market behavior, which helps investors navigate financial decisions more effectively in an environment where psychology and finance are deeply interconnected.
References
[1] Eickholt, Mathias, “Behavioural Financial Engineering in the Fixed-Income Market: The Influence of the Coupon Structure”, 2014
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