Reference point formation: Does the market whisper in the background?

• Published date: 01 June 2022
• Author: Tianyang Wang,Robert G. Schwebach,Sriram V. Villupuram
• Date: 01 June 2022
• DOI: http://doi.org/10.1111/jfir.12278

Received: 26 June 2020

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Accepted: 15 March 2022

DOI: 10.1111/jfir.12278

ORIGINAL ARTICLE

Reference point formation: Does the market

whisper in the background?

Tianyang Wang

1

|Robert G. Schwebach

1

|

Sriram V. Villupuram

2

1

Department of Finance and Real Estate,

Colorado State University, Fort Collins,

Colorado, USA

2

Department of Finance and Real Estate,

University of Texas at Arlington, Arlington,

Texas, USA

Correspondence

Tianyang Wang, Department of Finance and

Real Estate, Colorado State University, Fort

Collins, CO 80523‐1272, USA.

Email: Tianyang.Wang@colostate.edu

Abstract

Well‐established studies in behavioral finance confirm that

investors are highly influenced by subjective reference

points when making economic decisions. Although the

literature examines reference point formation for individual

stocks based on observed price sequences, the stocks are

often treated without considering market context. We

extend this literature by incorporating market price

information as background to the behavior experiment,

and we consider both prospect theory and anticipation/

disappointment‐based utility functions in a combined

framework to investigate the impact of market context

on reference point formation. Our article provides new

evidence and insights for investment practitioners and

behavioral researchers.

KEYWORDS

Behavioral Finance, Market Contextual, Reference‐point

Formation

JEL CLASSIFICATION

C09, G01, G41

J Financ Res. 2022;45:384–421.384

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wileyonlinelibrary.com/journal/JFIR

This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which

permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no

modifications or adaptations are made.

© 2022 The Authors. Journal of Financial Research published by Wiley Periodicals LLC on behalf of The Southern Finance

Association and the Southwestern Finance Association.

1|INTRODUCTION

A common assumption in behavioral finance is that individuals measure gains and losses relative to a subjective

reference point. This assumption underlies Kahneman and Tversky's (1979) prospect theory, which posits that

perceived value derives not from absolute wealth but from changes in wealth, in contrast to standard expected

utility (EU) theory (Savage, 1972; Von Neumann & Morgenstern, 1947), which assumes that utility is based solely on

final wealth.

Because reference points play such a critical role in behavioral finance, researchers are naturally interested in

understanding the processes by which reference points form and evolve. For instance, Kőszegi and Rabin (2006)

propose a model in which reference points form and adapt by a process of anticipation. Baucells and Hwang (2016)

suggest that reference points are determined by a psychological process and link it to a variety of behavioral biases,

including sunk‐cost effects, payment depreciation, reluctance to trade, preference for prepayment, and

flat‐rate bias.

In reality, reference points may shift over time in response to price changes and other factors, reflecting the

phenomenon that decision makers habituate to new wealth levels just as individuals adapt their sensory

perceptions to physical changes in brightness or temperature. In investment contexts, a shift in the reference point

toward a newly realized price is called adaptation. Adaptation is naturally a dynamic process and therefore

reference points should be viewed as a function of both current and past prices such as a weighted average of

values in an observed price sequence (Feng & Seasholes, 2005; Grinblatt & Keloharju, 2001; Odean, 1998; Shefrin

& Statman, 1985). In an experimental setting, Baucells et al. (2011; hereafter BWW) analyze reference point

dynamics in which subjects formulate reference points for hypothetical stocks based on contrived price patterns.

BWW show that although the reference point initially anchors to the purchase price, it subsequently adapts as the

price path evolves to include intermediate prices and the current price, and may also have a built‐in profit

expectation. Recent work by Riley et al. (2020) shows that the traditional capital gains overhang model can be

significantly improved by incorporating reference point adjustments based on the BWW framework.

Despite the success of the BWW framework in explaining reference point formation for individual stocks, its

simplified setting only considers each stock price movement as isolated from market contextual information and

hence overlooks the market backdrop as one key feature of investments in reality. Toward that end, we incorporate

market information as background to the BWW experiment to develop a new platform for analyzing the impact of

market context on reference price formation.

There are several reasons why market context might affect reference point formation for individual stocks.

Because covariation between stocks and the market is well known, investor expectations about the stock price may

depend on the observed market state in combination with comovement measures such as beta or correlation. The

current market trend combined with observed comovement may induce feelings of optimism or pessimism about

the stock. Investor beliefs about mean reversion (Odean, 1998) or momentum (Grosshans & Zeisberger, 2018) may

transfer to individual stocks, biasing expectations. Comparisons between own stock performance and the overall

market may influence investor satisfaction (Merkle et al., 2015), affecting expectations and the propensity to sell or

hold the stock (Grosshans & Zeisberger, 2018). Moreover, the limited attention theory (Kahneman, 1973) suggests

that when market information is available, investors may pay less attention to some stock‐related variables,

focusing instead on market factors such as current market trend. Investor attentiveness or nonattentiveness to the

stock may be selective based on the observed market state due to the psychological utility of positive or negative

information, as suggested by the asymmetric ostrich effect (Karlsson et al., 2009). In addition, investors may worry

more about losses for stocks that are highly correlated with the market, as suggested by the downside risk

hypothesis (Ang et al., 2006). Last but not least, because of heuristic biases such as anchoring (Tversky &

Kahneman, 1974), in which reference points gravitate toward irrelevant or noninformative values, investors may

adapt their reference points to the market backdrop as an unconscious anchor even when they have no investment

experience and consider the market as irrelevant information.

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Recent studies document that context‐specific effects vary by market condition. Lee et al. (2013)report

that investors with extreme capital losses are more active in redeeming mutual fund shares in a bear market

than in a bull market, whereas investors with moderate capital gains are less active in redeeming fund shares

in a bull market. In neutral markets, they find that investors redeem both winner and loser funds except when

they have extreme capital losses. Karlsson et al. (2009) conclude that adaptation is faster in rising markets

than in falling markets because investors pay more attention in rising markets. However, Arkes et al. (2008)

find that adaption to gains is greater than adaptation to losses whether the market is up or down, suggesting

that attention is not the only factor. Kliger and Kudryavtsev (2008) find that reference point adaptation is

more reactive when stocks are more sensitive to market fluctuations, that is, for high‐beta stocks. All of these

findings suggest that reference point effects are context dependent. This limits the generalizability of BWW's

findings and complicates the task of sorting the interplay between stock price factors and context‐driven

effects, highlighting the need for more work in this area.

In addition to incorporating market context in the experimental methodology, we contribute to the literature by

broadening the analysis to consider both prospect theory and emotion‐based explanations of reference point

behavior in a unified framework, whereas BWW focus only on prospect theory. Although prospect theory has

largely been supported empirically, research has shown that some of its key results are unstable across different

contexts or framing domains (Fischhoff, 1983; Hershey & Schoemaker, 1980; Schneider & Lopes, 1986), thus

motivating the need for an expanded framework.

One of the well‐known results associated with prospect theory is the disposition effect, which is based

on Kahneman and Tversky's (1979) reflection effect or the tendency for decision makers to reverse

preferences in loss versus gain domains relative to some neutral reference point, in a manner that exhibits

risk seeking in the negative domain and risk aversion in the positive domain. The reflection effect and the

related concept of mental accounting (Thaler, 1985) form a theoretical basis in prospect theory for what

Shefrin and Statman (1985)callthe“disposition effect,”which states that investors tend to dispose of gains

quickly but resist selling at a loss.

Empirical support for the disposition effect has been documented in studies using aggregate market data (Kliger

& Kudryavtsev, 2008; Lakonishok & Smidt, 1986), individual investor trading accounts (Barber & Odean, 1999;

Karlsson et al., 2009; Odean, 1998), and experimental settings (Arkes et al., 2008; Weber & Camerer, 1998).

However, empirical validation is not uniform, and recent studies show that cognitive biases related to the visual

processing of graphical information can affect investor behavior and reference point formation in ways not

necessarily consistent with disposition effect behavior. In addition, investor perceptions of risk are frequently

shaped by intuitive or heuristic processing of graphical features of price series, rather than orthodox optimization

rules based on statistical moments. This is relevant in real‐world settings where investment performance is

frequently displayed graphically to investors, and in experimental settings such as in our article where stock and

market price sequences are presented in graphical format to subjects.

Duclos (2015) documents an end‐anchoring bias or the unconscious tendency to attach dis-

proportionately high importance to the last trading day in a visual price sequence when forming expectations

such that when controlling for other factors, an upward (downward) sloping last trade direction results in a

higher (lower) willingness to purchase the stock. This is consistent with momentum investing or a belief in

short‐term trend continuation.

Grosshans and Zeisberger (2018) document order preferences for gains and losses in visual price patterns

and demonstrate that investor satisfaction is highest for down‐up patterns and lowest for up‐down patterns,

when controlling for other factors. They conclude that higher levels of satisfaction associated with down‐up

patterns correlate with more optimistic forecasts and lead to greater holding propensity for the stock. Their

results are consistent with end‐anchoring and momentum beliefs. They find that reference point adaptation

for stocks is more complete in the gain domain than in the loss domain, and they suggest that in up‐market

environments, overall momentum beliefs may transfer to winner stocks and amplify the increase in reference

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