Firm performance and market responses to firms publicly recognized for remote-work opportunities: the case of virtual vocations top 100 companies to watch for fully remote jobs

Purpose This study aims to examine the market reaction to, and the performance of companies recognized for extensive remote-work opportunities. To identify firms that offer extensive remote work prospects for potential employees, the authors use the Virtual Vocations “Top 100 Companies to Watch for Fully Remote Jobs” list, published annually since 2016, to determine whether top remote work firms outperform unranked companies on both short-term and long-term bases. Design/methodology/approach This paper uses event study methodology including calculating abnormal returns and cumulative abnormal returns, and raw- and just-adjusted returns for the sample compared to market returns and matched samples. In addition, regression-based analysis for the Carhart four-factor and Fama–French five-factor models, a buy and a hold abnormal returns model. Profitability measures [return on equity (ROE) and return on assets (ROA)] are included. Findings The authors find that the announcement effect for listed firms is negative. The authors find limited evidence that long-term raw and risk-adjusted returns outperform matched samples, and in some cases, the S&P 500 and Russell 2000 indices. ROA and ROE experience decreases of 3.55 and 8.48 percentage points, respectively, in the year following the ranking. The authors do find that smaller firms that offer extensive remote work opportunities may achieve superior long-term performance, as evidenced by both raw and risk-adjusted returns. The more positive returns associated with remote work may be due to better monitoring, control and the ability to manage the performance of fully remote workers in smaller companies. Originality/value These mixed results are promising for remote work and additional research. The COVID-19 pandemic changed work dynamics and showed that many companies can function effectively in a remote work environment, particularly those in the Information Technology, Health care and Financial sectors. As technological developments advance, the work landscape will likely continue to evolve. The findings indicate some support for superior long-term performance in raw and risk-adjusted returns for those firms embracing remote work, which bodes well for the future.

Authors

Institutions

Publication Details

Journal
Studies in Economics and Finance
Published
2026-03-17
DOI
https://doi.org/10.1108/sef-12-2024-0894
Primary Topic
AI and HR Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Firm performance and market responses to firms publicly recognized for remote-work opportunities: the case of virtual vocations top 100 companies to watch for fully remote jobs

Greg Filbeck, Patrick Lovely, Dianna Preece, Xin Zhao
Studies in Economics and Finance
AI and HR Technologies
article

Firm performance and market responses to firms publicly recognized for remote-work opportunities: the case of virtual vocations top 100 companies to watch for fully remote jobs

Greg Filbeck, Patrick Lovely, Dianna Preece, Xin Zhao
article en

Abstract

Purpose This study aims to examine the market reaction to, and the performance of companies recognized for extensive remote-work opportunities. To identify firms that offer extensive remote work prospects for potential employees, the authors use the Virtual Vocations “Top 100 Companies to Watch for Fully Remote Jobs” list, published annually since 2016, to determine whether top remote work firms outperform unranked companies on both short-term and long-term bases. Design/methodology/approach This paper uses event study methodology including calculating abnormal returns and cumulative abnormal returns, and raw- and just-adjusted returns for the sample compared to market returns and matched samples. In addition, regression-based analysis for the Carhart four-factor and Fama–French five-factor models, a buy and a hold abnormal returns model. Profitability measures [return on equity (ROE) and return on assets (ROA)] are included. Findings The authors find that the announcement effect for listed firms is negative. The authors find limited evidence that long-term raw and risk-adjusted returns outperform matched samples, and in some cases, the S&P 500 and Russell 2000 indices. ROA and ROE experience decreases of 3.55 and 8.48 percentage points, respectively, in the year following the ranking. The authors do find that smaller firms that offer extensive remote work opportunities may achieve superior long-term performance, as evidenced by both raw and risk-adjusted returns. The more positive returns associated with remote work may be due to better monitoring, control and the ability to manage the performance of fully remote workers in smaller companies. Originality/value These mixed results are promising for remote work and additional research. The COVID-19 pandemic changed work dynamics and showed that many companies can function effectively in a remote work environment, particularly those in the Information Technology, Health care and Financial sectors. As technological developments advance, the work landscape will likely continue to evolve. The findings indicate some support for superior long-term performance in raw and risk-adjusted returns for those firms embracing remote work, which bodes well for the future.

Studies in Economics and Finance
Pennsylvania State University (US), University of Louisville (US)
Decent work and economic growth
Openalex Percentile: Top 20%
AI and HR Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.