There Is No Unique Rational Decision Strategy in Financial Markets

2021 ◽

Author(s):

Nijolė MAKNICKIENĖ ◽

Jovita MASĖNAITĖ ◽

Viktorija STASYTYTĖ ◽

Raimonda MARTINKUTĖ-KAULIENĖ

Keyword(s):

Deep Learning ◽

Financial Markets ◽

Negative Impact ◽

Investment Decision ◽

Investor Sentiment ◽

Technical Analysis ◽

Financial Instruments ◽

Decision Strategy ◽

Financial Decisions ◽

Investor Behaviour

Purpose – The paper analyses two different paradigms of investor behaviour that exist in the financial mar-ket – the herding and contrarian behaviour. The main objective of the paper is to determine which pattern of investor behaviour better reflects the real changes in the prices of financial instruments in the financial markets. Research methodology – Algorithms of technical analysis, deep learning and classification of sentiments were used for the research; data of positions held by investors were analysed. Data mining was performed using “Tweet Sentiment Visualization” tool. Findings – The performed analysis of investor behaviour has revealed that it is more useful to ground financial decisions on the opinion of the investors contradicting the majority. The analysis of the data on the positions held by investors helped to make sure that the herding behaviour could have a negative impact on investment results, as the opinion of the majority of investors is less in line with changes in the prices of financial instruments in the market. Research limitations – The study was conducted using a limited number of investment instruments. In the future, more investment instruments can be analysed and additional forecasting methods, as well as more records in social networks can be used. Practical implications – Identifying which paradigm of investor behaviour is more beneficial to rely on can offer ap-propriate practical guidance for investors in order to invest more effectively in financial markets. Investors could use investor sentiment data to make practical investment decisions. All the methods used complement each other and can be combined into one investment decision strategy. Originality/Value – The study compared the ratio of open positions not only with real price changes but also with data obtained from the known technical analysis, deep learning and sentiment classification algorithms, which has not been done in previous studies. The applied methods allowed to achieve reliable and original results.

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Online Decision-Making in Receiving Spam Emails Among College Students

International Journal of Cyber Behavior Psychology and Learning ◽

10.4018/ijcbpl.2012010101 ◽

2012 ◽

Vol 2 (1) ◽

pp. 1-12 ◽

Cited By ~ 5

Author(s):

Zheng Yan ◽

Hamide Y. Gozu

Keyword(s):

College Students ◽

Decision Making ◽

Rational Decision ◽

Limited Information ◽

Decision Strategy ◽

Information Availability ◽

The Everyday ◽

Online Decision Making ◽

Practical Implications ◽

Experimental Survey

Decision-making in the real world has been extensively studied, whereas decision-making in the cyber world is relatively unknown. The present study investigated how email users made their decisions to read or delete spam emails, unsolicited junk emails sent indiscriminately, when they received various kinds of emails in the everyday life. An experimental survey was designed to manipulate two variables, decision-making strategy (intuitive or rational decision) and information availability (limited or detailed information), and administrated via a professional survey website PsycData to 171 college students. It was found that (a) rational decision strategy outperformed intuitive decision strategy, (b) better decision was made with detailed information than with limited information, (c) the effect size of information availability was approximately ten times as large as that of decision-making strategy, and (d) no interaction effects was present between decision-making strategy and information availability. Practical implications are discussed.

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Psychology and Economics: Evidence from the Field. Part II: Social Preferences and Nonstandard Beliefs

Voprosy Ekonomiki ◽

10.32609/0042-8736-2011-5-56-74 ◽

2011 ◽

pp. 56-74 ◽

Cited By ~ 1

Author(s):

S. DellaVigna

Keyword(s):

Decision Making ◽

Behavioral Economics ◽

Financial Markets ◽

Charitable Giving ◽

Social Preferences ◽

Previous Experience ◽

Altruistic Behavior ◽

Rational Decision ◽

Rational Decision Making ◽

Psychology And Economics

The second part of a larger work devoted to the modern behavioral economics considers nonstandard preferences that are manifest in altruistic behavior and charitable giving. The author also deals with nonstandard beliefs and shows how overconfidence, incorrect estimation of probabilities and extrapolation of previous experience produce biases in the rational decision-making, including the behavior on financial markets.

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Apparent sunk cost effect in rational agents

10.1101/2021.03.26.437119 ◽

2021 ◽

Author(s):

Torben Ott ◽

Paul Masset ◽

Thiago Santos Gouvea ◽

Adam Kepecs

Keyword(s):

Generative Models ◽

Behavioral Pattern ◽

Sunk Costs ◽

Rational Decision ◽

Decision Strategy ◽

Sunk Cost ◽

Attrition Bias ◽

Cognitive Behaviors ◽

Economic Decisions ◽

Cost Effect

Rational decision makers aim to maximize their gains, but humans and other animals often fail to do so, exhibiting biases and distortions in their choice behavior. In a recent study of economic decisions, humans, mice, and rats have been reported to succumb to the sunk cost fallacy, making decisions based on irrecoverable past investments in detriment of expected future returns. We challenge this interpretation because it is subject to a statistical fallacy, a form of attrition bias, and the observed behavior can be explained without invoking a sunk cost-dependent mechanism. Using a computational model, we illustrate how a rational decision maker with a reward-maximizing decision strategy reproduces the reported behavioral pattern and propose an improved task design to dissociate sunk costs from fluctuations in decision valuation. Similar statistical confounds may be common in analyses of cognitive behaviors, highlighting the need to use causal statistical inference and generative models for interpretation.

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Team Decision Making: The Dynamic Effects of Team Decision Style Composition and Performance via Decision Strategy

Journal of Management ◽

10.1177/0149206320916232 ◽

2020 ◽

pp. 014920632091623

Author(s):

X. Susan Zhu ◽

Mikhail A. Wolfson ◽

Dev K. Dalal ◽

John E. Mathieu

Keyword(s):

Decision Making ◽

Team Performance ◽

Team Development ◽

Team Composition ◽

Rational Decision ◽

Decision Strategy ◽

Decision Style ◽

Team Decision Making ◽

Prior Performance ◽

Team Decision

As teams continue to become more prevalent in modern-day organizations, researchers and organizations alike can benefit from a more nuanced understanding of teams’ decision-making process, which can ultimately impact organizational effectiveness. Although team processes are conceptualized as dynamic phenomena, they have largely been treated as static in research. In this study, we draw on the input-mediator-output-input and episodic team performance frameworks to advance a theoretical model of the dynamic, reciprocal effects of team rational decision strategy and team performance as well as the role of team composition of individual rational decision style. We sampled 320 participants in 85 teams competing in a 10-week business strategy simulation where teams made weekly strategic decisions that contributed to team performance. Teams composed of individuals with rational decision styles were more likely to adopt rational decision strategies, which led to better team performance. Additionally, results revealed a positive reciprocal effect between rational decision strategy and team performance such that teams with positive prior performance were more likely to engage in subsequent rational decision strategy. As hypothesized, team composition of members’ rational decision style was the primary determinant of team rational strategy during initial stages of team development, but the valence of outcome feedback (i.e., prior performance) took over as the stronger predictor of team rational strategy during later stages of team development. We contribute to the team and decision-making literatures by examining the dynamic process of team decision making and team performance.

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