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Using Multivariate Statistics

This study analyzes the role that two psychological attributes%u2014sensation seeking and overconfidence%u2014play in the tendency of investors to trade stocks Equity trading data are combined with data from an investor%u2019s tax filings, driving record, and psychological profile We use the data to construct measures of overconfidence and sensation seeking tendencies Controlling for a host of variables, including wealth, income, age, number of stocks owned, marital status, and occupation, we find that overconfident investors and those investors most prone to sensation seeking trade more frequently

Sensation Seeking, Overconfidence, and Trading Activity

This study analyzes the role that two psychological attributes—sensation seeking and overconfidence—play in the tendency of investors to trade stocks. Equity trading data from Finland are combined with data from investor tax filings, driving records, and mandatory psychological profiles. We use these data, obtained from a large population, to construct measures of overconfidence and sensation seeking tendencies. Controlling for a host of variables, including wealth, income, age, number of stocks owned, marital status, and occupation, we find that overconfident investors and those investors most prone to sensation seeking trade more frequently. RECENTLY, EMPIRICISTS HAVE BEGUN to study and document that behavioral attributes influence trading volume. 1 This evidence is compelling but it is difficult to conclusively argue that particular traits influence trading without better data. Much of the data used in the past to establish a connection between behavioral attributes and trading are experimental or aggregated across individuals. When actual trades are studied at the individual level, the results come from self-reported surveys, sometimes combined with brokerage trading

/pdf/sensation-seeking-overconfidence-and-trading-activity-4pc59xpalj.pdf

The understanding of decision-making systems has come together in recent years to form a unified theory of decision-making in the mammalian brain as arising from multiple, interacting systems (a planning system, a habit system, and a situation-recognition system). This unified decision-making system has multiple potential access points through which it can be driven to make maladaptive choices, particularly choices that entail seeking of certain drugs or behaviors. We identify 10 key vulnerabilities in the system: (1) moving away from homeostasis, (2) changing allostatic set points, (3) euphorigenic "reward-like" signals, (4) overvaluation in the planning system, (5) incorrect search of situation-action-outcome relationships, (6) misclassification of situations, (7) overvaluation in the habit system, (8) a mismatch in the balance of the two decision systems, (9) over-fast discounting processes, and (10) changed learning rates. These vulnerabilities provide a taxonomy of potential problems with decision-making systems. Although each vulnerability can drive an agent to return to the addictive choice, each vulnerability also implies a characteristic symptomology. Different drugs, different behaviors, and different individuals are likely to access different vulnerabilities. This has implications for an individual's susceptibility to addiction and the transition to addiction, for the potential for relapse, and for the potential for treatment.

/pdf/a-unified-framework-for-addiction-vulnerabilities-in-the-5gi26h0h1s.pdf

A unified framework for addiction: Vulnerabilities in the decision process

This article is a critical review of risk factors for pathological gambling categorized by demographics, physiological and biological factors, cognitive distortions, comorbidity and concurrent symptoms, and personality symptoms and characteristics There is also a varia section (availability, parents playing, sensory characteristics, schedules of reinforcement, age of onset, and playing duration) The review found very few well established risk factors for pathological gambling (ie more than two studies to support the conclusions) Well established risk factors included demographic variables (age, gender), cognitive distortions (erroneous perceptions, illusion of control), sensory characteristics, schedules of reinforcement, comorbid disorders (OCD, drug abuse), and delinquency/illegal acts An understanding of risk factors for pathological gambling should enhance prevention and treatment approaches

/pdf/risk-factors-for-problematic-gambling-a-critical-literature-4cb56j75yn.pdf

Risk factors for problematic gambling: a critical literature review.

https://espace.library.uq.edu.au/view/UQ:11000/cpr2272002.pdf

Pathological gambling. A comprehensive review.

Aims The aims of this study are to develop and validate a measure to screen for a range of gambling-related cognitions (GRC) in gamblers. Design and participants A total of 968 volunteers were recruited from a community-based population. They were divided randomly into two groups. Principal axis factoring with varimax rotation was performed on group one and confirmatory factor analysis (CFA) was used on group two to confirm the best-fitted solution. Measurements The Gambling Related Cognition Scale (GRCS) was developed for this study and the South Oaks Gambling Screen (SOGS), the Motivation Towards Gambling Scale (MTGS) and the Depression Anxiety Stress Scale (DASS-2 1) were used for validation. Findings Exploratory factor analysis performed using half the sample indicated five factors, which included interpretative control/bias (GRCS-IB), illusion of control (GRCS-IC), predictive control (GRCS-PC), gambling-related expectancies (GRCS-GE) and a perceived inability to stop gambling (GRCS-IS). These accounted for 70% of the total variance. Using the other half of the sample, CFA confirmed that the five-factor solution fitted the data most effectively. Cronbach's alpha coefficients for the factors ranged from 0.77 to 0.91, and 0.93 for the overall scale. Conclusions This paper demonstrated that the 23-item GRCS has good psychometric properties and thus is a useful instrument for identifying GRC among non-clinical gamblers. It provides the first step towards devising/adapting similar tools for problem gamblers as well as developing more specialized instruments to assess particular domains of GRC.

The Gambling Related Cognitions Scale (GRCS): development, confirmatory factor validation and psychometric properties.

http://www.jogoremoto.pt/docs/extra/HFJJNp.pdf

Role of culture in gambling and problem gambling

Gambling among the Chinese: a comprehensive review.

The urge to gamble is a physiological, psychological, or emotional motivational state, often associated with continued gambling. The authors developed and validated the 6-item Gambling Urge Questionnaire (GUS), which was based on the 8-item Alcohol Urge Questionnaire (M. J. Bohn, D. D. Krahn, & B. A. Staehler, 1995), using 968 community-based participants. Exploratory factor analysis using half of the sample indicated a 1-factor solution that accounted for 55.18% of the total variance. This was confirmed using confirmatory factor analysis with the other half of the sample. The GUS had a Cronbach's alpha coefficient of.81. Concurrent, predictive, and criterion-related validity of the GUS were good, suggesting that the GUS is a valid and reliable instrument for assessing gambling urges among nonclinical gamblers.

https://psycnet.apa.org/journals/adb/18/2/100.pdf

Namrata Raylu

Papers

Pathological gambling. A comprehensive review.

The Gambling Related Cognitions Scale (GRCS): development, confirmatory factor validation and psychometric properties.

Role of culture in gambling and problem gambling

Gambling among the Chinese: a comprehensive review.

The gambling urge scale: development, confirmatory factor validation, and psychometric properties.