Employee happiness is not merely a workplace concern—it constitutes a strategic intangible asset that significantly influences firm performance and shareholder value. Firms with a satisfied and motivated workforce consistently outperform their peers, exhibiting superior stock returns, enhanced profitability, and greater long-term resilience (Edmans, 2011; Bellet et al., 2024). Despite growing empirical evidence, financial markets often fail to fully account for the value of employee well-being, resulting in persistent mispricing and abnormal returns. This inefficiency challenges the efficient market hypothesis ( EMH ) (Fama, 1970), which asserts that stock prices should fully reflect all available information.

Behavioral finance provides alternative explanations for this phenomenon. Investor biases may delay the recognition of human capital advantages, creating a prolonged market correction period. Prospect theory (Kahneman and Tversky, 1979) posits that investors tend to underreact to gradual, long-term value creation while overreacting to salient, short-term signals due to cognitive distortions such as loss aversion. Similarly, the risk-as-feelings hypothesis (Loewenstein et al., 2001) suggests that sentiment-driven judgments often override rational analysis: individuals in positive moods tend to make more optimistic financial decisions, whereas those in negative moods exhibit greater pessimism.

A growing body of literature links investor mood and sentiment to market performance (Frieder and Subrahmanyam, 2004; Edmans et al., 2007; Hong and Kacperczyk, 2009; Edmans, 2011; Bialkowski et al., 2012; Choi et al., 2016; Fang et al., 2021; Bellet et al., 2024; Basar, 2024). For instance, Hirshleifer and Shumway (2003) find that sunshine correlates with improved investor mood and higher stock returns. Frieder and Subrahmanyam (2004) observe significantly higher stock returns during Rosh HaShanah, a Jewish holiday linked to positive mood, and markedly lower returns during Yom Kippur, which is associated with a sombre reflection and negative mood. Similarly, Edmans et al. (2007) show that losses in international soccer tournaments negatively affect national sentiment in the losing country, leading to measurable declines in stock market performance. Bialkowski et al. (2012) report abnormally high returns in predominantly Muslim countries during Ramadan, a period characterized by elevated collective mood, communal bonding, and spiritual upliftment.

At the firm level, employee mood and satisfaction have been shown to directly influence individual productivity and aggregate organizational performance (Edmans, 2011; Bellet et al., 2024; Basar, 2024). Fang et al. (2021) argue that investments in employee well-being generate a self-reinforcing cycle: increased revenues support greater employee satisfaction, which in turn fosters loyalty and sustainable growth. However, the Easterlin paradox implies that the marginal utility of happiness declines as income rises (Easterlin, 2001; Kim, 2024), with Edmans (2011) estimating that this saturation point occurs after approximately 54 months in the U.S. stock market context.

Employee happiness constitutes an intangible asset that lacks direct visibility and is inherently difficult to measure. Unlike physical assets, it is absent from corporate balance sheets, often resulting in its undervaluation by the market and exclusion from stock price considerations (Peters and Taylor, 2017). Over the past two decades, organizations have attempted to capture this intangible value through employee surveys, proprietary analytics, and the publication of workplace rankings. Indices such as “The Best American Companies to Work For” have been widely employed in empirical studies evaluating the relationship between employee satisfaction and firm performance. Edmans (2011), for instance, finds that companies on the list earned an average monthly excess return of 29 basis points (3.5% annualized). Other studies report similar patterns, though with variations in timing and magnitude (Fulmer et al., 2003; Best, 2008). Fulmer et al. (2003) find that listed firms outperformed the market over the long term (1995–2000), while Fillbeck and Preece ( 2003 ) document excess returns limited to the first two years following inclusion. Best (2008) attributes this outperformance to compensation structures and benefit programs that enhance employee motivation and retention.

In addition to such rankings, alternative proxies for sentiment and organizational climate—such as holidays (Bialkowski et al., 2012), corporate culture (Ylinen and Ranta, 2023), psychological rewards (Sadiq et al., 2022), and mindfulness (Khan and Abbas, 2022)—have been linked to firm outcomes. Sociocultural diversity across dimensions such as gender, ethnicity, religion, and lifestyle has also been identified as a key driver of employee satisfaction and organizational effectiveness (Tan et al., 2019; Guest, 2019; Amin et al., 2022; Frijns et al., 2024; Wu et al., 2024; Adam and Alfawaz, 2025). Inclusive work environments tend to foster greater employee engagement, whereas workplace overcrowding or lack of representation can diminish morale and productivity (Murphy, 2001).

This study examines how financial markets value employee happiness by analysing monthly stock performance of firms in the United Kingdom listed in the annual “Best 100 British Companies to Work For” ( BC ) rankings published by the Great Place to Work Institute between 2001 and 2020. We assess both short-term market reactions to changes in list inclusion and long-term stock performance of listed firms. Consistent with previous studies (Edmans, 2011; Fang et al., 2021), our results show that happy firms generate a statistically significant alpha of 32 basis points per month (3.86% annualized). When firms are weighted by their rankings, the monthly alpha further increases to 34 basis points (4.10% annualized), relative to the benchmark.

To ensure robustness, we employ the Fama–MacBeth (1973) regression framework, incorporating a dummy variable for inclusion on the BC list, along with a comprehensive set of firm-level controls, including size, beta, market-to-book ratio, age, leverage, tangible-to-intangible asset ratio, and share turnover. We also introduce employee-specific controls such as average salary, gender and ethnic diversity, and workplace density. Prior literature suggests that gender diversity enhances employee satisfaction (Adam and Alfawaz, 2025), whereas overcrowded workspaces negatively impact morale and performance due to reduced comfort and poor environmental fit (Murphy, 2001).

Our findings underscore that favorable workplace conditions—including higher wages, greater diversity, and reduced density—positively correlate with firm performance. Conversely, negative factors such as employee aging and overcrowding are associated with underperformance. These results highlight the critical role of employee happiness and workplace quality in driving firm-level success.

The remainder of this paper is structured as follows: Data & Methodology describes the data and methodology, Analysis and Findings presents the empirical findings and robustness checks, and Conclusion concludes with a discussion of the implications and contributions of this research.

The primary data source for this study is the annual BC lists that have been published by Sunday Times since 2001. The list itself is compiled by the Great Place to Work Institute, a global research and consulting organization founded in 1981 by Robert Levering and Milton Moskowitz. As of today, the institute collaborates with over 5,500 organizations worldwide, representing more than 10 million employees, and reaches an estimated 25 million readers through affiliated publications.

The credibility and influence of the institute have been widely acknowledged. For instance, Eamon Fitzgerald, CEO of Naked Wines, describes it as: “What differentiates Best Companies is the reputation and trust. It’s the number one employee engagement survey”.¹

The BC list is released annually on the first Sunday of March, which this study adopts as the portfolio formation date each year. Accordingly, March 1^st is used as the event date for all years from 2001 to 2020.

In 2001, the inaugural list featured 50 firms, whereas in subsequent years the list was expanded to include 100 firms annually. Across 20 years, this results in a cumulative total of 1,950 firm-year observations. After excluding duplicate entries and firms that are not publicly traded, the final sample comprises 483 unique publicly listed firms, referred to as Sample-F.

To facilitate further analysis, two additional sub-samples are constructed:

Table 1 presents the year-by-year distribution of firms across these three samples ( Sample-F, Sample-N, and Sample-D ).

In addition to forming baseline samples, we construct weighted portfolios to evaluate the performance of listed firms under different weighting schemes. Specifically, three weighting methods are applied: equal-weighting, size-weighting, and rank-weighting. These are implemented on Sample-F, Sample-N, and Sample-D, where applicable.

First, we assign equal weights to all firms within each sample, resulting in the equal-weighted portfolios:

Second, we construct size-weighted portfolios, where weights are based on each firm’s market capitalization. Larger firms receive higher weights using the formula: $$W_{it}=\frac{M{C}_{it}}{M{C}_t}$$ where $W_{it}$ and $M{C}_{it}$ represent the weight and market capitalization of firm i at year t, respectively. $M{C}_{it}$ is the total market capitalization of the corresponding sample in year t. Size-weighted portfolios are created only for Sample-F and Sample-N, denoted as FS and NS, respectively.

Third, we construct rank-weighted portfolios, where weights are determined by each firm’s position in the BC list. Since Sample-D firms are no longer listed and lack ranking data, this method is applied only to Sample-F and Sample-N, resulting in portfolios FR and NR ( see Table 2 ). The weights are calculated as follows: $$W_{it}=\frac{(L{R}_t+1)-R_{it}}{\sum _{j=1}^k[(L{R}_t+1)-R_{jt}]}$$ where $R_{it}$ is the rank of firm i in year t, $L{R}_t$ is the lowest rank in the list for year t, and k is the number of firms in the respective sample. The transformation $L{R}_t+1-R_{it}$ inverts the ranking order so that higher-ranked firms (i.e., lower numerical ranks) receive greater weights Table 2.

This study examines the impact of employee happiness on firms’ financial performance, employing Carhart’s (1997) four-factor model. Results indicate that employee happiness significantly enhances stock performance, as evidenced by our equal-weighted portfolio of the full list (Sample-F) achieving a 32-basis point monthly alpha (3.86% annualized) over the benchmark. By employing ranking-based portfolio weighting, this alpha increases to 34 basis points per month (4.10% annualized), highlighting the influence of company rankings on profitability. Our findings suggest that firms ranked higher on the “Best Companies” list achieve superior performance relative to lower-ranked firms.

We further observe that portfolios of newly listed companies generate substantial abnormal returns, with a 34-basis point monthly alpha (4.12% annualized), demonstrating that initial inclusion on the list correlates with stronger returns compared to ongoing members until the announcement of a new list. Ranking-based weighting enhances this alpha to 38 basis points per month (4.66% annualized), significant at the 1% level. In contrast, portfolios of delisted companies perform within expected ranges, recording a smaller, statistically insignificant alpha, indicating that bad news travels fast, with stock prices responding promptly to delisting.

For robustness, the Fama-MacBeth (1973) analysis confirms these results, showing that “happy” stocks, net of the risk-free rate, outperform the market by 33 basis points monthly (4.05% annually). It also highlights three key findings: ( 1 ) firms with less crowded workplace for employees tend to perform better financially than those firms with overcrowded ones; ( 2 ) higher average salaries positively impact stock performance; and ( 3 ) a younger average employee age correlates with improved financial outcomes.

Disaggregated industry-level analysis reveals that the technology sector yields the highest alpha (33.24 bps), reflecting the stronger role of employee satisfaction in knowledge-intensive, innovation-driven environments. In contrast, the industrial sector exhibits more modest but still significant alphas (25.79 bps and 27.43 bps), likely due to its capital-intensive structure, physically demanding roles, and greater heterogeneity across sub-industries. These findings underscore the varying extent to which employee happiness translates into financial performance across different sectors.

Finally, a longevity analysis indicates that newly listed firms continue to deliver abnormal returns for 36 months after first appearing on the list, aligning with Edmans’ (2011) findings of a drift that dissipates within five years. This suggests that stock markets recognize employee happiness-related intangibles only after an extended period, when tangible outcomes begin to materialize, corroborating Edmans’ explanation of market inefficiency in the valuation of intangibles.

While these findings offer valuable insights, the study is limited to publicly traded firms due to stock price data availability, potentially omitting effects observed in private companies. Additionally, unobservable factors such as leadership style, corporate culture, or firm-specific HR policies may influence both employee satisfaction and performance, making causal interpretations challenging. Future research could address these limitations by incorporating private firms or employing natural experiments to better isolate the drivers of the observed relationships.