Nasdaq-100 Companies’ Hiring Insights - A Topic-based Classification Approach to the Labor Market

Sep 14, 2026

The document addresses labor-market uncertainty driven by new and disruptive technologies and argues for labor market intelligence grounded in data analysis. It examines how online job vacancies, especially those on LinkedIn, can be hard to interpret at scale due to the volume of postings. It proposes a structural topic modeling approach using NASDAQ-100 companies’ LinkedIn job vacancies, revealing the most frequently posted job categories. The results support stakeholders with clearer labor-market trend understanding for better decisions.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages19

Reading Time7 min

AccessFree to download

Introduction

Background

Motivation

Methodology and Data

Results and Job Category Prevalence

Conclusion

Nasdaq-100 Companies’ Hiring Insights: A Topic-based Classification Approach to the Labor Market Seyed Mohammad Ali Jafari 1[0000-0002-4315-7812] and Ehsan Chitsaz2[0000-0002-9656-418X] 1 PhD candidate, Technological Entrepreneurship Department, Faculty of Entrepreneurship, University of Tehran, Iran [Sma_jafari@ut.ac.ir](Sma_jafari@ut.ac.ir) 2Assistant Professor, Faculty of Entrepreneurship, University of Tehran, Iran, [Chitsaz@ut.ac.ir](Chitsaz@ut.ac.ir), corresponding author Abstract. The emergence of new and disruptive technologies makes the economy and labor market more unstable. To overcome this kind of uncertainty and to make the labor market more comprehensible, we must employ labor market intelligence techniques, which are predominantly based on data analysis. Companies use job posting sites to advertise their job vacancies, known as online job vacancies (OJVs) . LinkedIn is one of the most utilized websites for matching the supply and demand sides of the labor market; companies post their job vacancies on their job pages, and LinkedIn recommends these jobs to job seekers who are likely to be interested. However, with the vast number of online job vacancies, it becomes challenging to discern overarching trends in the labor market. In this paper, we propose a data mining-based approach for job classification in the modern online labor market. We employed structural topic modeling as our methodology and used the NASDAQ-100 indexed companies' online job vacancies on LinkedIn as the input data. We discover that among all 13 job categories, Marketing, Branding, and Sales; Software Engineering; Hardware Engineering; Industrial Engineering; and Project Management are the most frequently posted job classifications. This study aims to provide a clearer understanding of job market trends, enabling stakeholders to make informed decisions in a rapidly evolving employment landscape. Keywords: Labor market intelligence, topic modeling, artificial intelligence, online job postings, NASDAQ-100 companies. 1 Introduction 1.1 Background The labor market's structure is significantly influenced by various factors, including digitalization, often termed digital disruption, which fundamentally changes employment rules and the competencies required of employees (Chinoracký &Čorejová, 2019) . Environmental challenges linked to global population growth can be addressed through innovative technologies such as fully electric machines (HEV/EV), which catalyze the emergence of new industries necessitating novel types of employees (López Ibarra, Matallana, Andreu, & Kortabarria, 2019) . Moreover, demographic shifts alter labor market demands and firm strategies, influencing the dynamics of employment across sectors (Hopenhayn, Neira, & Singhania, 2022) . This complex and unpredictable labor market environment underscores the need for Human Resource (HR) managers, policymakers, and job seekers to gain a nuanced understanding of labor market trends. Recent research has shown that the adoption of machine learning and big data analytics can significantly enhance the analysis of labor market dynamics, providing essential insights for policy makers and HR professionals (Mršić, Jerkovic, & Balkovic, 2020) . LinkedIn is primarily a social network focused on facilitating the hiring process for both job seekers and employers. Many high-tech companies rely on LinkedIn as a tool for talent acquisition and hiring employees. Although LinkedIn is an effective hiring tool (Karakatsanis et al., 2017), it lacks certain features that could help human resource managers, policymakers, and job seekers understand labor market trends and patterns. For instance, it is difficult to discern which job categories companies are most interested in, or to analyze the prevalence of job categories in comparison to one another. 1.2 Motivation Companies during digital transformation and technological improvement should consider answering this question: “

FAQs

Why is labor market intelligence needed in the context of disruptive technologies?

Disruptive technologies increase instability and unpredictability, making labor market trends harder to interpret. Labor market intelligence helps transform data into comprehensible insights.

What data source and method does the study use to analyze hiring trends?

The study uses NASDAQ-100 companies’ online job vacancies from LinkedIn and applies structural topic modeling for job classification.

Which job categories are found most frequently among the NASDAQ-100 companies?

Marketing, Branding, and Sales; Software Engineering; Hardware Engineering; Industrial Engineering; and Project Management are reported as the most frequently posted categories.

Sep 14, 2026

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