scholarly journals Adaptive Heuristics That (Could) Fit: Information Search and Communication Patterns in an Online Forum of Investors Under Market Uncertainty

2018 ◽  
Vol 37 (6) ◽  
pp. 734-749
Author(s):  
Niccolò Casnici ◽  
Marco Castellani ◽  
Flaminio Squazzoni ◽  
Manuela Testa ◽  
Pierpaolo Dondio
Keyword(s):  
Information Search ◽  
Large Scale ◽  
Daily Basis ◽  
Communication Patterns ◽  
Market Uncertainty ◽  
Online Forum ◽  
Data Set ◽  
Communication Processes ◽  
Adaptive Heuristics ◽  
Context Specific

This article examines information-search heuristics and communication patterns in an online forum of investors during a period of market uncertainty. Global connections, real-time communication, and technological sophistication have created an unpredictable market environment. As such, investors try to deal with semantic, strategic, and operational uncertainty by following heuristics that reduce information redundancy. In this study, we have tried to find traces of cognitive communication heuristics in a large-scale data set including 8 years of online posts (2004–2012) for a forum of Italian investors. We identified various market volatility conditions on a daily basis to understand the influence of market uncertainty on cognitive and communication processes. We found that investors communicated more dynamically when the market was unstable, while they were more prone to anchor heuristic when market uncertainty was invariant. Furthermore, abnormal market trends triggered more availability-based communication patterns. We also found that expertise matters. This would suggest that online communities need intelligent, context-specific tools to support partner selection and stimulate nonredundant communication.

Information search in times of market uncertainty: an examination of aggregate and disaggregate uncertainty

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marshall A. Geiger ◽  
Rajib Hasan ◽  
Abdullah Kumas ◽  
Joyce van der Laan Smith
Keyword(s):  
Information Search ◽  
Institutional Investors ◽  
Volume Index ◽  
Individual Investor ◽  
Market Uncertainty ◽  
Data Set ◽  
Current Time ◽  
Content Type ◽  
Information Demand ◽  
Google Search

PurposeThis study explores the association between individual investor information demand and two measures of market uncertainty – aggregate market uncertainty and disaggregate industry-specific market uncertainty. It extends the literature by being the first to empirically examine investor information demand and disaggregate market uncertainty.Design/methodology/approachThis paper constructs a measure of information search by using the Google Search Volume Index and computes measures of aggregate and disaggregate market uncertainty using institutional investors' trading data from Ancerno Ltd. The relation between market uncertainty, as measured by trading disagreements among institutional investors, and information search is analyzed using an OLS (Ordinary Least Squares) regression model.FindingsThis paper finds that individual investor information demand is significantly and positively correlated with aggregate market uncertainty but not associated with disaggregated industry uncertainty. The findings suggest that individual investors may not fully incorporate all relevant uncertainty information and that ambiguity-related market pricing anomalies may be more associated with disaggregate market uncertainty.Research limitations/implicationsThis study presents an examination of aggregate and disaggregate measures of market uncertainty and individual investor demand for information, shedding light on the efficiency of the market in incorporating information. A limitation of our study is that our data for market uncertainty is based on investor trading disagreement from Ancerno, Ltd. which is only available till 2011. However, we believe the implications are generalizable to the current time period.Practical implicationsThis study provides the first concurrent empirical assessment of investor information search and aggregate and disaggregate market uncertainty. Prior research has separately examined information demand in these two types of market uncertainty. Thus, this study provides information to investors regarding the importance of assessing disaggregate component measures of the market.Originality/valueThis paper is the first to empirically examine investor information search and disaggregate market uncertainty. It also employs a unique data set and method to determine disaggregate, and aggregate, market uncertainty.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

Team Communication Patterns in Critical Situations

2021 ◽  
pp. 155534342098665
Author(s):  
Floris van den Oever ◽  
Jan Maarten Schraagen
Keyword(s):  
Information Seeking ◽  
Communication Patterns ◽  
Team Communication ◽  
Communication Structures ◽  
Communication Processes ◽  
Event Model ◽  
Or Team ◽  
Short Time ◽  
Time Frames ◽  
Critical Situations

Teams operating in time-pressured, dynamic environments frequently need to cope with critical situations varying in complexity and hazard. To cope with critical situations, teams may have to adapt their communication processes. Adaptation of team communication processes has been studied mostly at short time frames (minutes). Literature on adapting communication at longer time frames is limited (hours, relative to minutes). We used the relational event model to compare team communication in critical and noncritical situations of pediatric cardiac surgeries and Apollo 13 flight director’s voice loops. Teams showed some flattening of communication structures in critical situations. Both teams maintained institutional roles and displayed closed-loop and information-seeking communication. Communication patterns may change further with increasing criticality. The exact way teams adapt to critical situations may differ depending on team, team size and situation. Findings may inform team training procedures or team structure development.

scholarly journals Relationship between large-scale ionospheric field-aligned currents and electron/ion precipitations: DMSP observations

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Chao Xiong ◽  
Claudia Stolle ◽  
Patrick Alken ◽  
Jan Rauberg
Keyword(s):  
Time Distribution ◽  
Large Scale ◽  
Particle Flux ◽  
Particle Energy ◽  
Lower Latitude ◽  
Particle Precipitation ◽  
Data Set ◽  
The Mean ◽  
Two Parameters ◽  
Meteorological Satellite

Abstract In this study, we have derived field-aligned currents (FACs) from magnetometers onboard the Defense Meteorological Satellite Project (DMSP) satellites. The magnetic latitude versus local time distribution of FACs from DMSP shows comparable dependences with previous findings on the intensity and orientation of interplanetary magnetic field (IMF) By and Bz components, which confirms the reliability of DMSP FAC data set. With simultaneous measurements of precipitating particles from DMSP, we further investigate the relation between large-scale FACs and precipitating particles. Our result shows that precipitation electron and ion fluxes both increase in magnitude and extend to lower latitude for enhanced southward IMF Bz, which is similar to the behavior of FACs. Under weak northward and southward Bz conditions, the locations of the R2 current maxima, at both dusk and dawn sides and in both hemispheres, are found to be close to the maxima of the particle energy fluxes; while for the same IMF conditions, R1 currents are displaced further to the respective particle flux peaks. Largest displacement (about 3.5°) is found between the downward R1 current and ion flux peak at the dawn side. Our results suggest that there exists systematic differences in locations of electron/ion precipitation and large-scale upward/downward FACs. As outlined by the statistical mean of these two parameters, the FAC peaks enclose the particle energy flux peaks in an auroral band at both dusk and dawn sides. Our comparisons also found that particle precipitation at dawn and dusk and in both hemispheres maximizes near the mean R2 current peaks. The particle precipitation flux maxima closer to the R1 current peaks are lower in magnitude. This is opposite to the known feature that R1 currents are on average stronger than R2 currents.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

scholarly journals COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

2021 ◽  
pp. 1-13
Author(s):  
Usman Naseem ◽  
Imran Razzak ◽  
Matloob Khushi ◽  
Peter W. Eklund ◽  
Jinman Kim
Keyword(s):  
Sentiment Analysis ◽  
Large Scale ◽  
Data Set ◽  
Twitter Data

scholarly journals Scaling Up Delivery of Biofortified Staple Food Crops Globally: Paths to Nourishing Millions

2021 ◽  
pp. 037957212098250
Author(s):  
Jennifer K. Foley ◽  
Kristina D. Michaux ◽  
Bho Mudyahoto ◽  
Laira Kyazike ◽  
Binu Cherian ◽  
...  
Keyword(s):  
Pearl Millet ◽  
Large Scale ◽  
Scale Up ◽  
Lessons Learned ◽  
Raw Material ◽  
Micronutrient Deficiencies ◽  
Seed Systems ◽  
Public And Private ◽  
Context Specific ◽  
Public And Private Sector

Background: Micronutrient deficiencies affect over one quarter of the world’s population. Biofortification is an evidence-based nutrition strategy that addresses some of the most common and preventable global micronutrient gaps and can help improve the health of millions of people. Since 2013, HarvestPlus and a consortium of collaborators have made impressive progress in the enrichment of staple crops with essential micronutrients through conventional plant breeding. Objective: To review and highlight lessons learned from multiple large-scale delivery strategies used by HarvestPlus to scale up biofortification across different country and crop contexts. Results: India has strong public and private sector pearl millet breeding programs and a robust commercial seed sector. To scale-up pearl millet, HarvestPlus established partnerships with public and private seed companies, which facilitated the rapid commercialization of products and engagement of farmers in delivery activities. In Nigeria, HarvestPlus stimulated the initial acceptance and popularization of vitamin A cassava using a host of creative approaches, including “crowding in” delivery partners, innovative promotional programs, and development of intermediate raw material for industry and novel food products. In Uganda, orange sweet potato (OSP) is a traditional subsistence crop. Due to this, and the lack of formal seed systems and markets, HarvestPlus established a network of partnerships with community-based nongovernmental organizations and vine multipliers to popularize and scale-up delivery of OSP. Conclusions: Impact of biofortification ultimately depends on the development of sustainable markets for biofortified seeds and products. Results illustrate the need for context-specific, innovative solutions to promote widespread adoption.

Distributed learning with indefinite kernels

2019 ◽  
Vol 17 (06) ◽  
pp. 947-975 ◽  
Author(s):  
Lei Shi
Keyword(s):  
Large Scale ◽  
Substantial Reduction ◽  
Computation Time ◽  
Distributed Learning ◽  
Rates Of Convergence ◽  
Regression Problem ◽  
Data Set ◽  
Regularization Scheme ◽  
Original Algorithm ◽  
Indefinite Kernel

We investigate the distributed learning with coefficient-based regularization scheme under the framework of kernel regression methods. Compared with the classical kernel ridge regression (KRR), the algorithm under consideration does not require the kernel function to be positive semi-definite and hence provides a simple paradigm for designing indefinite kernel methods. The distributed learning approach partitions a massive data set into several disjoint data subsets, and then produces a global estimator by taking an average of the local estimator on each data subset. Easy exercisable partitions and performing algorithm on each subset in parallel lead to a substantial reduction in computation time versus the standard approach of performing the original algorithm on the entire samples. We establish the first mini-max optimal rates of convergence for distributed coefficient-based regularization scheme with indefinite kernels. We thus demonstrate that compared with distributed KRR, the concerned algorithm is more flexible and effective in regression problem for large-scale data sets.

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