Predicting new firm survival and growth: The power of alternative data

Abstract

This paper demonstrates that the alternative data of manager turnover can provide investors and policymakers with a more timely and available predictor of new firm performance beyond the traditional financial information. This paper constructs a comprehensive alternative dataset of manager turnover that covers a near-population sample of new firms in the United Kingdom. It shows that manager departures and appointments can predict new firms’ survival and growth, even after controlling for firm financials. In addition to the within-firm prediction, the average manager turnover in other firms of the same industry can cross-predict individual firm performance. The within-firm prediction is more pronounced for non-family firms, smaller firms and firms incorporated during or after the Great Recession, and the cross-firm prediction is stronger for younger firms. This paper sheds light on the power of alternative data in the prediction of firm performance, particularly for new firms that often do not have available information.

Keywords:

• Alternative data
• new firms
• manager turnover
• firm performance

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 There is a wide range of literature focussing on the stock return predictability of public firms. See the literature review in Ang and Bekaert (2007), and McLean and Pontiff (2016). Meanwhile, bankruptcy prediction attracts attention from investors, academic researchers and policymakers. See, for example, the studies of Bharath and Shumway (2008), Chava and Jarrow (2004) and Shumway (2001) on how to predict firm distress. Campbell, Hilscher, and Szilagyi (2008) provide a detailed literature review on predicting firm bankruptcy. Most of the empirical applications in this literature require detailed financial reports and stock price information.

2 For instance, there were 2.6 million private businesses in the UK in 2017 (see https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/663235/bpe_2017_statistical_release.pdf), representing 76% of the country’s employment, and 92% of the total revenue (ONS 2018). In addition to representing a predominant proportion of the economy, private and young firms differ remarkably from large and mature firms in several dimensions, such as investment (Gilje and Taillard 2016; Asker, Farre-Mensa, and Ljungqvist 2011), cash holdings (Farre-Mensa 2017), and CEO compensation (Gao and Li 2015). Therefore, it is difficult for us to simply extrapolate the existing evidence from mature firms to young firms.

3 The practitioners have been well aware of the power of alternative data in predicting public firm performance and stock returns. There is no clear definition of alternative data, but it is generally believed that alternative data are different from traditional data that are readily available. Examples of alternative data include customer/investor sentiment derived from social media posts, crop yields obtained from satellite imagery, and mobile phone app usage patterns. These alternative data have been intensively used to predict public firm performance and stock returns. For example, CargoMetrics, an alternative data provider, uses satellite intel on ships for its security investment. Eagle Alpha, among numerous alternative data platforms, utilises invoice data from a logistics company to offer early access to global trade information, surpassing the release of official monthly data from government sources (Wigglesworth 2016).

4 In the UK, each firm is required to file financials and management changes, but the reporting requirements vary with firm size. Small firms are only required to report abbreviated balance sheet accounts, which include detailed information on assets, capital and shareholder funds. Only large companies are required to file detailed financial statements.

5 Hellmann and Puri (2002) collect the survey data of 170 start-ups in Silicon Valley and show that venture capital (VC)-backed firms are more likely to replace founder managers with professional managers. Wasserman (2003) shows that founders are more likely to be immediately replaced after the venture achieves a particular milestone. Kaplan, Sensoy, and Strömberg (2009) find a positive correlation between founder replacements and subsequent initial public offering (IPO) events by analysing 50 VC-backed start-ups. Ewens and Marx (2018) examine the role of venture capital in founder replacements using over 22,000 VC-funded start-ups in the US.

6 The conventional growth rate is defined as $\frac{\mathit{\text{Total Asset}}{s}_{t+2}}{\mathit{\text{Total Asset}}{s}_t}-1\equiv 2\mathrm{ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}*}\mathit{\text{Asset growt}}{h}_{i,t+2}/(2-\mathit{\text{Asset growt}}{h}_{i,t+2}).$

7 The web-version of the FAME database maintains records of historical accounting information for a period of ten years, so I need to combine the archive disk with the web-version data. In consideration of potential concerns regarding Brexit’s impact on newly established firms, the sample ends in the year 2017.

8 Robb and Robinson (2014) use the data from the Kauffman Firm Survey and find an average leverage ratio of 0.64 for a sample of US start-ups, which includes sole proprietorships and partnerships as well as corporations. González-Uribe and Paravisini (2019) study a sample of firms established between 2009 and 2011 in the UK and find the equity ratio to be 0.34.

9 Kaplan, Sensoy, and Strömberg (2009) use a sample of 48 VC-backed companies and report an average of 1.9 founders. Beckman (2006) reports 2.2 founders based on a sample of 173 companies in Silicon Valley. Wasserman (2003) finds an average of 2.2 founders using a sample of 202 start-ups, including both VC-backed and non-VC-backed companies.

10 The report ‘The State of UK Competition’ was published by the Competition and Markets Authority of the UK in 2020. The competitiveness evaluation is based on the market share of the largest ten firms at a given level of aggregated activity ratios between the years 1998 and 2008. Please refer to Figures 2.2 and A.3 for the most and least competitive sectors. Available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/939636/State_of_Competition_Report_Nov_2020_Final.pdf.