Intuition and mood of investors in financial markets

2017 ◽  
Vol 30 (2) ◽  
pp. 231-248
Author(s):  
Robson Braga ◽  
Luiz Paulo Lopes Fávero ◽  
Renata Turola Takamatsu
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Real Data ◽  
Financial Assets ◽  
Risky Assets ◽  
Content Type ◽  
Binomial Regression ◽  
Investor Behaviour ◽  
Investment Portfolios

Purpose The purpose of this paper is to evaluate investor behaviour related to the timing of selling financial assets based on an intuitive evaluation of the current market trend and growth expectation. Design/methodology/approach The experiment involved 1,052 volunteer participants who made decisions about stock sales in an environment that simulated a home broker platform to negotiate stocks. Zero-inflated regression models were used. Findings The results show that investors’ attitudes, or beliefs, determine whether they will buy or keep risky assets in their investment portfolios; they may decide to sell such assets, even though market shows an upward trend. Such results make a new contribution to behavioural finance within the context of prospect theory and the disposition effect. Originality/value The originality of this paper lies in the use of new and innovative techniques (zero-inflated Poisson and negative binomial regression models) applied to real data obtained experimentally.

Armed burglary: a marker for extreme instrumental violence

2017 ◽  
Vol 7 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Matt DeLisi ◽  
Eric Beauregard ◽  
Hayden Mosley
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Criminal Careers ◽  
Content Type ◽  
Violent Offending ◽  
Binomial Regression ◽  
Material Gain ◽  
Sexually Abuse ◽  
Severe Type

Purpose Most burglaries are property offenses yet some offenders perpetrate burglary for the purpose of violent instrumental crimes. Sexual burglars are distinct from non-sexual burglars because the former seek to rape or sexually abuse victims within the homes they burgle whereas the latter seek theft and material gain. It is unclear to what degree burglars who are armed with firearms or knives represent a type of sexual burglar, or perhaps a more severe type of offender who enters homes not merely to rape a victim, but to perhaps murder them as well. The paper aims to discuss these issues. Design/methodology/approach Drawing on data from 790 felons in Florida, t-test and negative binomial regression models were used to compare armed burglars to offenders who were not convicted of armed burglary. Findings Compared to offenders not convicted of armed burglary, armed burglars were involved in significantly more instrumental crimes of violence including first-degree murder, kidnapping, armed rape, armed robbery and assault with intent to murder. Armed burglary may be a marker of extreme instrumental violent offending and warrants further study. Originality/value To the authors’ knowledge, this is among the first studies of armed burglary offenders and adds understanding to the heterogeneity of burglary offenders and their criminal careers.

Adverse childhood experiences, paraphilias, and serious criminal violence among federal sex offenders

2017 ◽  
Vol 7 (2) ◽  
pp. 105-119 ◽  
Author(s):  
Alan Drury ◽  
Tim Heinrichs ◽  
Michael Elbert ◽  
Katherine Tahja ◽  
Matt DeLisi ◽  
...  
Keyword(s):  
Sex Offenders ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Childhood Experiences ◽  
Content Type ◽  
Criminal Violence ◽  
Adverse Childhood ◽  
Binomial Regression ◽  
Pornography Addiction

Purpose Adverse childhood experiences (ACEs) are a broad conceptual framework in the social sciences that have only recently been studied within criminology. The purpose of this paper is to utilize this framework by applying it to one of the most potentially dangerous forensic populations. Design/methodology/approach Archival data from 225 federal sex offenders was used to perform descriptive, correlational, and negative binomial regression models. Findings There was substantial evidence of ACEs including father abandonment/neglect (36 percent), physical abuse (nearly 28 percent), verbal/emotional abuse (more than 24 percent), and sexual abuse (approximately 27 percent). The mean age of sexual victimization was 7.6 years with the youngest age of victimization occurring at the age of 3. Offenders averaged nearly five paraphilias, the most common were pedophilia (57 percent), pornography addiction (43 percent), paraphilia not otherwise specified (35 percent), exhibitionism (26 percent), and voyeurism (21 percent). The offenders averaged 4.7 paraphilias and the range was substantial (0 to 19). Negative binomial regression models indicated that sexual sadism was positively and pornography addiction was negatively associated with serious criminal violence. Offenders with early age of arrest onset and more total arrest charges were more likely to perpetrate kidnaping, rape, and murder. Originality/value ACEs are common in the life history of federal sex offenders, but have differential associations with the most serious forms of crime.

How marketer-generated content characteristics affect consumer engagement? Empirical evidence from China's WeChat food marketing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Si Tan ◽  
Weiping Chen
Keyword(s):  
Body Text ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Food Marketing ◽  
Consumer Engagement ◽  
Content Type ◽  
Positive Effects ◽  
Binomial Regression ◽  
Media Research ◽  
Text Content

Purpose Leveraging marketer-generated content (MGC) can increase firms' success. However, few studies uncover the effects of MGC-related attributes on consumer engagement in the context of food marketing. This paper aims to explore the influence of MGC characteristics (valence, content types, vividness and interactivity) on consumer engagement.Design/methodology/approachThis study uses WeChat official account data of seven food companies from China and conducts negative binomial regression models.FindingsThe findings indicate that different MGC-related characteristics have separate impacts on consumer WeChat engagement. Title valence, transactional title content and title with punctuation vividness negatively affect consumers' consuming engagement. Knowledgeable or entertaining title content and title with interactivity both positively affect consumers' consuming engagement. Moreover, transactional body text content negatively influences consumers' contributing engagement, whereas entertaining body text content shows positive effects. Vivid and interactive MGC body text attributes enhance consumers' contributing engagement behavior.Originality/value This study contributes to social media research in food marketplaces and sheds light on the effect of different WeChat MGC characteristics on separate consumer engagement.

Managing Inflation

2016 ◽  
Vol 63 (1) ◽  
pp. 77-87 ◽  
Author(s):  
William H. Fisher ◽  
Stephanie W. Hartwell ◽  
Xiaogang Deng
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Excess Zeros ◽  
Binomial Regression ◽  
Negative Binomial Models ◽  
Using Data ◽  
Over Dispersion ◽  
Binomial Models

Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany

2018 ◽  
Vol 37 (20) ◽  
pp. 3012-3026 ◽  
Author(s):  
Saptarshi Chatterjee ◽  
Shrabanti Chowdhury ◽  
Himel Mallick ◽  
Prithish Banerjee ◽  
Broti Garai
Keyword(s):  
Health Care ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Health Care Demand ◽  
Binomial Regression

What Do We Know About Changing Economic Activity of Firms?

2019 ◽  
pp. 232102221886979
Author(s):  
Radhika Pandey ◽  
Amey Sapre ◽  
Pramod Sinha
Keyword(s):  
Economic Activity ◽  
Regression Models ◽  
Statistical Approach ◽  
Negative Binomial ◽  
Large Data ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Binomial Regression ◽  
History Of

Identification of primary economic activity of firms is a prerequisite for compiling several macro aggregates. In this paper, we take a statistical approach to understand the extent of changes in primary economic activity of firms over time and across different industries. We use the history of economic activity of over 46,000 firms spread over 25 years from CMIE Prowess to identify the number of times firms change the nature of their business. Using the count of changes, we estimate Poisson and Negative Binomial regression models to gain predictability over changing economic activity across industry groups. We show that a Poisson model accurately characterizes the distribution of count of changes across industries and that firms with a long history are more likely to have changed their primary economic activity over the years. Findings show that classification can be a crucial problem in a large data set like the MCA21 and can even lead to distortions in value addition estimates at the industry level. JEL Classifications: D22, E00, E01

Traffic accident modeling: some statistical issues

2006 ◽  
Vol 33 (9) ◽  
pp. 1115-1124 ◽  
Author(s):  
Z Sawalha ◽  
T Sayed
Keyword(s):  
Road Safety ◽  
Regression Models ◽  
Model Building ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Outlier Analysis ◽  
Accident Prediction ◽  
Binomial Regression ◽  
Statistical Issues

Accident prediction models are invaluable tools that have many applications in road safety analysis. However, there are certain statistical issues related to accident modeling that either deserve further attention or have not been dealt with adequately in the road safety literature. This paper discusses and illustrates how to deal with two statistical issues related to modeling accidents using Poisson and negative binomial regression. The first issue is that of model building or deciding which explanatory variables to include in an accident prediction model. The study differentiates between applications for which it is advisable to avoid model over-fitting and other applications for which it is desirable to fit the model to the data as closely as possible. It then suggests procedures for developing parsimonious models, i.e., models that are not over-fitted, and best-fit models. The second issue discussed in the paper is that of outlier analysis. The study suggests a procedure for the identification and exclusion of extremely influential outliers from the development of Poisson and negative binomial regression models. The procedures suggested for model building and conducting outlier analysis are more straightforward to apply in the case of Poisson regression models because of an added complexity presented by the shape parameter of the negative binomial distribution. The paper, therefore, presents flowcharts detailing the application of the procedures when modeling is carried out using negative binomial regression. The described procedures are then applied in the development of negative binomial accident prediction models for the urban arterials of the cities of Vancouver and Richmond located in the province of British Columbia, Canada. Key words: accident prediction models, overfitting, parsimony, outlier analysis, Poisson regression, negative binomial regression.

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