The Prediction of Venture Capitalists' Investment Propensity Using Machine Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 18-31
Author(s):  
Youngkeun Choi ◽  
Jae W. Choi
Keyword(s):  
Risk Taking ◽  
Educational Level ◽  
Data Science ◽  
Individual Characteristics ◽  
Organizational Characteristics ◽  
Science Methods ◽  
Venture Capitalists ◽  
Full Model ◽  
Accuracy Rate ◽  
Fund Size

This paper describes the most visible data science methods suitable for entrepreneurial research and provides links to literature and big data resources for venture capitalists. In the results, first, all organizational characteristics such as the characteristic of parent company of VC, the fund size of VC, and the reputation of VC, have significant influences on the risk-taking investment of venture capitalists, while functional background, school prestige, and VC experience except educational level among individual characteristics have significant influences on the risk-taking investment of venture capitalists. Second, for the full model, the accuracy rate is 0.855, which implies that the error rate is 0.145. Among the venture capitalists who are predicted not to do risk-taking investment, the accuracy that would not do risk-taking investment is 85.75%, and the accuracy that do risk-taking investment is 79.59% among the venture capitalists who are predicted to do risk-taking investment.

Predicting Stock Market Performance

2020 ◽  
pp. 1-10
Author(s):  
Thomas Plieger ◽  
Thomas Grünhage ◽  
Éilish Duke ◽  
Martin Reuter
Keyword(s):  
Stock Market ◽  
Risk Taking ◽  
Life Stress ◽  
Price Volatility ◽  
Individual Characteristics ◽  
Self Report ◽  
Financial Behavior ◽  
Market Simulation ◽  
Stock Market Performance ◽  
Risk Taking Behavior

Abstract. Gender and personality traits influence risk proneness in the context of financial decisions. However, most studies on this topic have relied on either self-report data or on artificial measures of financial risk-taking behavior. Our study aimed to identify relevant trading behaviors and personal characteristics related to trading success. N = 108 Caucasians took part in a three-week stock market simulation paradigm, in which they traded shares of eight fictional companies that differed in issue price, volatility, and outcome. Participants also completed questionnaires measuring personality, risk-taking behavior, and life stress. Our model showed that being male and scoring high on self-directedness led to more risky financial behavior, which in turn positively predicted success in the stock market simulation. The total model explained 39% of the variance in trading success, indicating a role for other factors in influencing trading behavior. Future studies should try to enrich our model to get a more accurate impression of the associations between individual characteristics and financially successful behavior in context of stock trading.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Effect of Visual Feedback on the Static and Kinematic Characteristics of Handwriting

2017 ◽  
Vol 27 ◽  
pp. 5-21 ◽  
Author(s):  
Michael Pertsinakis
Keyword(s):  
Visual Feedback ◽  
Educational Level ◽  
Movement Analysis ◽  
Individual Characteristics ◽  
Software Module ◽  
Medium Education ◽  
Education Levels ◽  
Pen Tablet ◽  
The Individual ◽  
Document Examiners

Research on visual feedback has not produced consistent results to show how visual feedback or the lack, thereof, influences individual handwriting characteristics. A two-pronged approach was designed to investigate the degree of this influence. For this purpose, samples of signatures as well as cursive and block text, written with and without visual feedback, were collected from 40 volunteers and imported into a PC via a pen tablet, using an electronic inking pen. The data was analyzed in a handwriting movement analysis software module specially designed for this research that was added to the software MovAlyzeR by Neuroscript LLC. Two forensic document examiners (FDEs) independently analyzed samples from the two groups (samples executed with normal visual feedback versus the group of samples executed without visual feedback). They found no fundamental differences between these two groups. Their analyses also demonstrated that a large number of similarities existed in the general design of the allographs (alternative forms of a letter or other grapheme) and in the pictorial aspects, regardless of the complexity of the samples. In the cursive and block handwriting, four main qualitative characteristics were linked to the absence of visual feedback: change of overall size, non-uniformity of left margins, change of baseline alignment, and inclusion of extra trajectories. The statistical analysis verified the above findings. The comparative analysis also suggests that gender, educational level (above high school) and handedness create an insignificant influence on the individual characteristics of writing produced with and without visual feedback. The only notable exception is the relationship between signature duration and educational level. The volunteers with a medium education level showed a significant increase in duration while signing their names without visual feedback in comparison to those with higher education levels. The combination of the above findings suggests that handwriting is not fundamentally influenced by visual feedback.  Purchase Article - $10

Using of data science in healthcare

2021 ◽  
pp. 149-156
Author(s):  
Ihor Ponomarenko ◽  
Oleksandra Lubkovska
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Business Intelligence ◽  
Data Science ◽  
Large Data ◽  
Science Methods ◽  
Medical Field ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

The subject of the research is the approach to the possibility of using data science methods in the field of health care for integrated data processing and analysis in order to optimize economic and specialized processes The purpose of writing this article is to address issues related to the specifics of the use of Data Science methods in the field of health care on the basis of comprehensive information obtained from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the possibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty the main sources of data on key processes in the medical field. Examples of innovative methods of collecting information in the field of health care, which are becoming widespread in the context of digitalization, are presented. The main sources of data in the field of health care used in Data Science are revealed. The specifics of the application of machine learning methods in the field of health care in the conditions of increasing competition between market participants and increasing demand for relevant products from the population are presented. Conclusions. The intensification of the integration of Data Science in the medical field is due to the increase of digitized data (statistics, textual informa- tion, visualizations, etc.). Through the use of machine learning methods, doctors and other health professionals have new opportunities to improve the efficiency of the health care system as a whole. Key words: Data science, efficiency, information, machine learning, medicine, Python, healthcare.

scholarly journals Influence of Entrepreneurial Orientation on Venture Capitalists' Initial Trust

2021 ◽  
Vol 12 ◽  
Author(s):  
Hongtao Yang ◽  
Lei Zhang ◽  
Yenchun Jim Wu ◽  
Hangyu Shi ◽  
Shuting Xie
Keyword(s):  
Impression Management ◽  
Risk Taking ◽  
Entrepreneurial Orientation ◽  
Empirical Test ◽  
Management Strategies ◽  
Management Theory ◽  
Venture Capitalists ◽  
Initial Trust ◽  
Five Dimensions ◽  
The Relationship

The effectiveness of trust has been extensively investigated in entrepreneurship studies. However, compared to the outcomes of trust, we still lack knowledge about the mechanisms underlying venture capitalists' initial trust in entrepreneurs. Drawing from signal theory and impression management theory, this study explores an impression management motivational explanation for the influencing factors of venture capitalists' initial trust. An empirical test is based on 202 valid questionnaires from venture capitalists, and the results indicate that the signal of five dimensions of entrepreneurial orientation has a significant impact on the initial trust of venture capitalists and that a signal of entrepreneurial orientation of perseverance or passion positively influences venture capitalists' initial trust through acquired impression management strategies, while a signal of entrepreneurial orientation of risk-taking, innovation, or proactivity positively affects the initial trust of venture capitalists through defensive impression management strategies. The perceptions of entrepreneurs' hypocrisy by venture capitalists negatively moderate the relationship between acquired impression management strategies and the initial trust of venture capitalists and negatively moderate the relationship between defensive impression management strategies and the initial trust of venture capitalists.

scholarly journals How data science methods can improve the quality and efficiency of ICF and HEDP research

2020 ◽  
Author(s):  
Patrick Knapp ◽  
Michael Glinsky ◽  
Benjamin Tobias ◽  
John Kline
Keyword(s):  
Data Science ◽  
Science Methods

scholarly journals Closing the research-implementation gap using data science tools: a case study with pollinators of British Columbia

2020 ◽  
Author(s):  
Laura Melissa Guzman ◽  
Tyler Kelly ◽  
Lora Morandin ◽  
Leithen M’Gonigle ◽  
Elizabeth Elle
Keyword(s):  
British Columbia ◽  
Data Science ◽  
Academic Research ◽  
Science Methods ◽  
Research Implementation ◽  
Conservation Practice ◽  
Implementation Gap ◽  
Web App ◽  
Using Data

AbstractA challenge in conservation is the gap between knowledge generated by researchers and the information being used to inform conservation practice. This gap, widely known as the research-implementation gap, can limit the effectiveness of conservation practice. One way to address this is to design conservation tools that are easy for practitioners to use. Here, we implement data science methods to develop a tool to aid in conservation of pollinators in British Columbia. Specifically, in collaboration with Pollinator Partnership Canada, we jointly develop an interactive web app, the goal of which is two-fold: (i) to allow end users to easily find and interact with the data collected by researchers on pollinators in British Columbia (prior to development of this app, data were buried in supplements from individual research publications) and (ii) employ up to date statistical tools in order to analyse phenological coverage of a set of plants. Previously, these tools required high programming competency in order to access. Our app provides an example of one way that we can make the products of academic research more accessible to conservation practitioners. We also provide the source code to allow other developers to develop similar apps suitable for their data.

scholarly journals Collaboration between Government and Research Community to Respond to COVID-19: Israel’s Case

2021 ◽  
Vol 7 (4) ◽  
pp. 208
Author(s):  
Mor Peleg ◽  
Amnon Reichman ◽  
Sivan Shachar ◽  
Tamir Gadot ◽  
Meytal Avgil Tsadok ◽  
...  
Keyword(s):  
Health Policy ◽  
Data Science ◽  
Research Community ◽  
Remote Access ◽  
Data Driven ◽  
Multidisciplinary Teams ◽  
Science Methods ◽  
Research Environments ◽  
Future Data ◽  
The Government

Triggered by the COVID-19 crisis, Israel’s Ministry of Health (MoH) held a virtual datathon based on deidentified governmental data. Organized by a multidisciplinary committee, Israel’s research community was invited to offer insights to help solve COVID-19 policy challenges. The Datathon was designed to develop operationalizable data-driven models to address COVID-19 health policy challenges. Specific relevant challenges were defined and diverse, reliable, up-to-date, deidentified governmental datasets were extracted and tested. Secure remote-access research environments were established. Registration was open to all citizens. Around a third of the applicants were accepted, and they were teamed to balance areas of expertise and represent all sectors of the community. Anonymous surveys for participants and mentors were distributed to assess usefulness and points for improvement and retention for future datathons. The Datathon included 18 multidisciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5 presentation mentors, and 12 judges. The insights developed by the three winning teams are currently considered by the MoH as potential data science methods relevant for national policies. Based on participants’ feedback, the process for future data-driven regulatory responses for health crises was improved. Participants expressed increased trust in the MoH and readiness to work with the government on these or future projects.

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