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PSYCHOLOGICAL COGNITION AND PREFERENCE SELECTION IN THE DECISION-MAKING PROCESS OF FINANCIAL INVESTMENT

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Revista Argentina de Clínica Psicológica 2020, Vol. XXIX, N°1, 1222-1227

DOI: 10.24205/03276716.2020.175 1222

P

SYCHOLOGICAL

C

OGNITION AND

P

REFERENCE

S

ELECTION IN THE

D

ECISION

-M

AKING

P

ROCESS OF

F

INANCIAL

I

NVESTMENT

Feng Yuan

Abstract

Since its introduction to economics, cognitive psychology has often been adopted to analyze risk-based judgement and decision-making. This paper attempts to identify the relationship between cognitive bias and decision-making behavior in financial investment. Therefore, several variables were selected to investigate the changes of risk preferences among investors in various scenarios, and a structural equation model was established to study how the bias of investment behavior is affected by the cognitive bias of investors in decision-making. The results show that the bias of investment behavior is influenced by psychological cognition, preference selection and personal background of the decision-maker; among them, personal background has the weakest impact on behavior bias, and maintains a moderate correlation with decision-making psychology; in stock investment, the behavior bias mainly comes from price anchoring, misjudgment, and attributional bias. This research helps to control the investment risk, and promote the regulation of China’s financial market.

Key words: Decision-Making Psychology, Structural Equation Model, Cognitive Bias, Behavior Bias.

Received: 23-01-19 | Accepted: 08-07-19

INTRODUCTION

Traditional economics usually assumes that market behaviors are driven by the power of the materials, people's economic decisions are rational, and these are the inevitable results of their pursuit of the interests (Park, Ramesh, & Cao, 2016). The rationality here means that when decision makers are facing many choices, they have to analyze all available information systematically to make the optimal decision (Todd Maddox, & Markman, 2010). Decision-making is also forward-looking, that is, it weights all possible consequences in the future (Meder, Le Lec, & Osman, 2013). In other words, the traditional western economics believes that economic behaviors are determined by external incentives.

Psychology, especially cognitive psychology,

School of Trade and Economics, Jilin Engineering Normal University, ChangChun 130052, China

E-Mail: 405503424@qq.com

considers the decision makers as a complex system that can reasonably identify and interpret some of the available information (Wray, 2017). At the same time, however, there are also factors that are difficult to be perceived by the consciousness, and these factors systematically influence the behavior of human (Sniezek, Wilkins, Wadlington et al., 2002). In short, human behavior is determined by intrinsic motivation (Kambam & Thompson, 2009; Costa, de Melo Carvalho, de Melo Moreira et al., 2017; Okuyama, & Francis, 2006).

In sharp contrast to the economic concept, cognitive psychology also believes that several important factors in the decision-making process can have a major impact on decision-making (Schwab, 2008). These factors include perception, intrinsic motivation, and attitude. Perception is similar to belief, the interpretation of things varies from person to person (Van Der Maas, Molenaar, Maris et al., 2011). Intrinsic motivation is mainly derived from the inner feelings of decision makers, while attitude is the

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FENG YUAN

1223

stable psychology associated with the

environment around them (Yechiam,

Busemeyer, Stout et al., 2005). In addition, the memories of decision makers on past decisions and their consequences have a crucial influence on their current decisions (Van Duijvenvoorde, & Crone, 2013). Ultimately, human behavior is the result of the interaction of these complex factors that change with the external environment (Sahi, Arora, & Dhameja, 2013).

Therefore, in this context, this paper attempts to discover and study the phenomenon of cognitive bias in financial investment activities from the perspective of decision-making theory of cognitive psychology, explores the influence of human psychological cognition on their investment decision-making activities in real life, and finds out the deep-level relationship between cognitive bias and

decision-making behavior bias through

statistical approach. The conclusions of this study can better apply the cognitive psychology to the field of securities investment.

MODEL SETTING

Research hypothesis

This study aims to explore the influence of

investors' decision-making psychological preferences on their investment behavior preferences through an empirical model of path analysis. At the same time, it also introduces some important personal characteristics, and compares the age, investment life and so on. This chapter establishes a path analysis model to test the influence of cognitive bias of investment behavior on the investors' irrational investment behavior. In the model, decision-making psychology is taken as a potential variable which is mainly measured by observation variables.

Establishment of structural equation model

The purpose of this study is to examine the influence of exogenous latent variable on endogenous latent variable and to explore the relationship between the two. Therefore, the measure unit of each latent variable has been defined, that is, the load of any indicator variable for each latent variable is 1, and the path relationship between exogenous latent variables is set to be a free parameter. The theoretical model is shown in Figure 1.

This study used AMOS 5.0 software to calculate the sample data in the questionnaires based on the established model.

The structural equation model is constructed

Figure 1

.

Theoretical model structure

Gambler's fallacy

Hand heat phenomenon

Anchoring and Adjustment

Frame deviation

Psychological Account

Conservatism

Attributional bias

Representational intuition

Availability Intuition

Psychological bias in

decision-making

Personal background

Investment Behavior Deviation

Ambiguity aversion

Loss aversion

Disposal effect

Over-confidence

Herd Effect

Over-trading

Security Selection Gender

Age

Educational background

Investment life

Investment income ratio

Types of investment

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PSYCHOLOGICAL COGNITION AND PREFERENCE SELECTION IN THE DECISION-MAKING PROCESS OF FINANCIAL INVESTMENT 1224

by the following basic structural equations:

x

x=   + (1)

y

y

=  +

 

(2) B

=

+  +

 

(3)

EMPIRICAL RESEARCH

Statistical tool

SPSS11.0 is used as the main statistical tool for frequency analysis under cross-grouping. The frequency significance analysis under cross-grouping adopts the chi-square test, which is mathematically defined as:

(

)

2

2

1 1

o e r c

ij ij e i j ij

f

f

f

= =

=



(4)

*

e

RT

CT

RT CT

f

n

n

n

n

=

=

(5)

Evaluation of the structural equation model

Normal distribution morphological statistics of the initial model (Table 1)

The fitting statistics of the initial model

According to above fitting statistic results, the theoretical model needs to be evaluated and corrected continuously to obtain a better

and corrected continuously to obtain a better model with both theoretical significance and statistical significance. The evaluation and modification of the model include the following steps:

(1) Check the appropriateness of the estimation parameters. It includes whether the size of the parameters and symbols conforms to the theoretical concept and the practical significance. If it is not appropriate, the model should be modified.

(2) Test the fitting indices. The fitting indices can be used to measure the overall fitting degree of the model.

By running the initial model described above, preliminary results were obtained. Table 3 provides some statistics that reflect the absolute goodness of fit of the initial model.

Modification of structural equation model

Considering that the “psychological accounting” had a lower path influencing

coefficient on the decision makers' psychological cognition and preference selection, the path

influencing coefficients of “hot hand fallacy” and “investment type” were even negative numbers,

and the path influencing coefficient of “age” was

inconsistent significantly, therefore, the structural equation model was modified by deleting these variables one by one.

Table 1.

Normal distribution morphological statistics of the initial model

Variables Minimum Maximum skewness Critical Ratio Kurtosis Critical Ratio

Types of investment 2 5 0.379 1.524 -1.335 -2.766 Investment income ratio 1.05 5 5.505 22.659 41.009 84.528 Investment life 2 21 1.109 4.53 0.298 0.601 Educational background 2 5 0.476 1.921 -0.016 -0.047 Age 21 77 0.494 1.998 -0.57 -1.189 Gender 2 3 -0.105 -0.473 -1.973 -4.081 Fuzzy Delay 2 6 0.159 0.617 0.612 1.248 Loss Delay 3 6 -0.588 -2.466 -1.114 -2.311 Disposal effect 2 6 -0.509 -2.138 -0.932 -1.934 Overconfidence 2 6 -0.055 -0.269 -0.436 -0.922 Herd Effect 2 6 1.171 4.787 0.448 0.909 Overtrading 2 6 0.28 1.114 -1.027 -2.131 Security Selection 2 6 0.604 2.448 0.33 0.668 Gambler's fallacy 2 6 0.115 0.434 -1.169 -2.424 Hand heat phenomenon 2 6 0.411 1.653 -0.809 -1.682 Anchoring and Adjustment 2 6 -0.379 -1.604 -1.273 -2.638 Frame deviation 2 6 1.342 5.492 0.802 1.64 Psychological Account 2 6 1.64 6.723 2.195 4.51 Conservatism 2 6 0.02 0.041 -0.928 -1.927 Attributional bias 2 6 -0.387 -1.636 -0.171 -0.367 Representational Perception 2 6 -0.3 -1.277 -1.155 -2.395 Availability Perception 2 6 -0.407 -1.72 -0.796 -1.654

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FENG YUAN

1225

Table 2.

Estimated values of path coefficients of the initial model

Path relationship Coefficient

Estimate P Value

Standard Coefficient Estimate

Investment Behavior < Psychological Deviation in Decision Making 1.384 0.207 0.345 Investment Behavior < Personal Background 22.508 0.255 0.208 Availability Perception < Psychological Deviation in Decision Making 1.123 0.123 0.447 Representational Perception < Psychological Deviation in Decision Making 0.833 0.151 0.702 Attribution Bias < Decision-making Cognitive bias 0.318 0.145 0.754 Conservatism <Psychological Deviation in Decision-making 2.16 0.164 0.39 Psychological Account < Psychological Deviation in Decision Making 0.643 0.707 0.132

Frame Bias < Decision-making Cognitive bias 0.546 0.155 0.486 Anchoring and Adjustment <Psychological Deviation in Decision-making 7.104 0.264 0.851 Hand Fever Phenomenon < Psychological Deviation in Decision Making -2.045 0.57 -0.154 Gambler's Fallacy <Psychological Deviation in Decision-making 0.876 0.168 0.619 Stock Selection Strategy <Psychological Deviation in Decision-making 1.123 0.123 0.257 Overtrading < Psychological Deviation in Decision Making 0.125 0.296 0.437 Herd Effect < Psychological Deviation in Decision Making 0.123 0.142 0.554 Overconfidence < Psychological Deviation in Decision Making 0.666 0.199 0.442 Disposal Effect < Psychological Deviation in Decision Making 0.557 0.195 0.35

Loss aversion <Cognitive bias in decision-making 0.796 0.222 0.466 Fuzzy Delay <Psychological Deviation in Decision Making 0.625 0.826 0.432 Gender < Personal Background 1.123 0.123 0.151 Age < Personal Background -108.466 0.458 0.112 Education Background < Personal Background -24.004 0.148 -0.044 Investment Years < Personal Background -109.983 0.506 0.107 Investment Income Ratio < Personal Background 3.225 0.651 0.521 Investment Type < Personal Background -4.365 0.183 -0.243

Table 3.

Statistics of the absolute goodness of fit of the initial model

Fitting index χ2 df χ2/df P RMR GFI AGFI RMSEA

Model indicators 335.220 205 1.621 0.000 0.309 0.762 0.715 0.065

Each time a new path is established, or each time an item is deleted or altered, it needs to be recalculated, and the change of the correction index needs to be observed, and then further corrections are conducted until the main discriminant index of the applicability of the model reaches a satisfactory level. The statistical indicators of the final modified model are as follows.

It can be seen from the fitting indicators of Table 4 and Table 5 that all model indicators meet the requirements.

Table 6 shows the values of the

determination coefficients of the modified

model, which is mathematically defined as:

µ

(

)

(

)

$

(

)

(

)

2 2

2 1 1

2 2

1 1

1

n n

i i

i i

n n

i i

i i

y

y

y

y

R

y

y

y

y

= =

= =

=

= −

(6)

Based on the above analysis, the complete model of the influencing factors of the investor's psychological cognition and preference selection on the financial investment process is shown in Figure 2.

Table 4.

Statistics of the absolute goodness of fit of the modified model

Fitting index χ2 df χ2/df P RMR GFI AGFI RMSEA

Model indicators 616.02 207 2.687 0.000 0.008 0.892 0.924 0.069

Table 5.

Statistics of the relative goodness of fit of the initial model

Fitting index NFI RFI IFI TLI CFI

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PSYCHOLOGICAL COGNITION AND PREFERENCE SELECTION IN THE DECISION-MAKING PROCESS OF FINANCIAL INVESTMENT 1226

Table 6.

Determination coefficients of the modified model

Variable name

Investment

Behavior Overconfidence

Investment income ratio

Investment life

Educational background

Representational Perception Coefficient

value 0.679 0.865 0.761 0.749 0.619 0.752 Variable

name Herd Effect Overtrading

Stock Selection Measurement

gambler's

fallacy Disposal effect

Anchoring and Adjustment Coefficient

value 0.914 0.703 0.517 0.849 0.753 0.821 Variable

name

Availability

Perception Fuzzy Delay Loss Delay

Frame

deviation Conservatism Attributional bias Coefficient

value 0.792 0.574 0.705 0.392 0.937 0.768

Figure 2

.

The complete model of the factors influencing the financial investment process of

psychological cognition and preference selection

Gambler's fallacy

Anchoring and Adjustment

Frame deviation

Conservatism

Attributional bias

Representatio nal intuition

Availability Intuition

Psychological bias in

decision-making

Personal background

Investment Behavior Deviation

Ambiguity aversion

Loss aversion

Disposal effect

Over-confidence

Herd Effect

Over-trading Security Selection

Educational background

Investment life

Investment income ratio

0.70

0.79

0.39

0.57

0.63

0.48

0.41 0.34

0.23

0.56

0.59

0.76

0.44

0.29

0.82

0.49

0.55 0.38

0.78

0.31 0.38 0.43 0.64 0.83 0.51

0.76 0.35 0.64 0.29 0.69 0.48 0.54

-0.41

-0.35

0.40

0.89

0.12

0.56

0.66

CONCLUSIONS

This paper drew the following conclusions by exploring the influence of human psychological cognitive bias on their financial investment decision-making activities in real life:

(1) The psychological cognition, preference selection and personal background of the decision makers have an influence on their irrational financial investment behavior;

(2) In the cognitive bias of the investors’

irrational behavior in stock investment, the

previous empirical price anchoring, event probability misjudgment and wrong attribution pattern are most likely to result in investment behavioral bias;

(3) The influence of personal background on investment behavioral bias is slightly less than that of decision makers' psychological cognition and preference selection, and there is a

moderate correlation between personal

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FENG YUAN

1227

REFERENCES

Costa, D. F., de Melo Carvalho, F., de Melo Moreira, B. C., & do Prado, J. W. (2017). Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias. Scientometrics, 111(3), 1775-1799.

Kambam, P., & Thompson, C. (2009). The development of decision-making capacities in children and adolescents: Psychological and neurological perspectives and their implications for juvenile defendants. Behavioral Sciences & the Law, 27(2), 173-190.

Meder, B., Le Lec, F., & Osman, M. (2013). Decision making in uncertain times: what can cognitive and decision sciences say about or learn from economic crises? Trends in Cognitive Sciences, 17(6), 257-260.

Okuyama, N., & Francis, G. (2006). Disentangling cognitive bias in the assessment of investment decisions: derivation of generalized conditional risk attribution. Journal of Behavioral Finance, 7(2), 75-87.

Park, E. H., Ramesh, B., & Cao, L. (2016). Emotion in it investment decision making with a real options perspective: The intertwining of cognition and regret. Journal of Management Information Systems, 33(3), 652-683.

Sahi, S. K., Arora, A. P., & Dhameja, N. (2013). An exploratory inquiry into the cognitive biases in financial investment behavior. Journal of Behavioral Finance, 14(2), 94-103.

Schwab, A. P. (2008). Putting cognitive psychology to work: improving decision-making in the medical encounter. Social Science and Medicine, 67(11),

1861-1869.

Sniezek, J. A., Wilkins, D. C., Wadlington, P. L., & Baumann, M. R. (2002). Training for crisis decision-making: psychological issues and computer-based solutions. Journal of Management Information Systems, 18(4), 147-168.

Todd Maddox, W., & Markman, A. B. (2010). The motivation-cognition interface in learning and decision making. Current Directions in Psychological Science, 19(2), 106-110.

Van Der Maas, H. L., Molenaar, D., Maris, G., Kievit, R. A., & Borsboom, D. (2011). Cognitive psychology meets psychometric theory: on the relation between process models for decision making and latent variable models for individual differences. Psychological Review, 118(2), 339-356.

Van Duijvenvoorde, A. C. K., & Crone, E. A. (2013). The teenage brain: a neuroeconomic approach to adolescent decision making. Current Directions in Psychological Science, 22(2), 108-113.

Wray, C. (2017). A proposed new psychological model for judgement and decision-making.

Leadership & Organization Development Journal, 38(4), 549-563.

Yechiam, E., Busemeyer, J. R., Stout, J. C., & Bechara, A. (2005). Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits.

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