Salvatore Ferri

Purpose: This paper aims to investigate the role of stock liquidity in valuation metrics through market multiples by assessing how liquidity affects key valuation metrics such as enterprise value to sales (EV/Sales), enterprise value to EBITDA (EV/EBITDA), price to book value (P/BV) and price to earnings (P/E) in the European market. Furthermore, the moderating role played by the entrepreneurial context – represented by the micro, small and medium-sized enterprise (MSME) status – is tested. Design/methodology/approach: The study applies an empirical analysis to a sample of European listed firms. Multivariate analysis is used to assess the direct impact of liquidity (proxied by the bid-ask spread) on valuation multiples, thereby incorporating both company fundamentals and market behaviour. LASSO regression is employed to confirm the robustness. Findings: Results confirm a positive relationship between liquidity and market multiples, indicating that higher stock liquidity leads to an increase in overall valuation. Moreover, the moderation analysis indicates that this “positive liquidity effect” is most amplified among listed MSMEs, suggesting a potential distorting effect of market metrics among this category of companies. The analysis particularly highlights how entrepreneurial ventures may display stronger liquidity-valuation links. Research limitations/implications: Findings advance knowledge on the liquidity-value debate by adding insights based on the entrepreneurial light. They also provide practical implications for investors, managers and financial analysts, regarding potential liquidity-distorting effects on valuation metrics. Originality/value: This paper contributes to the business and finance fields, considering for the first time the moderating role played by MSME status.

Purpose: Considering the relevance of “green” as well as of “intellectual capital” in the current economy, scholars and practitioners are increasingly interested into the so-called Green Intellectual Capital (GIC). Against this backdrop, as the research is at an early stage with still no clear evidence, this article intends to investigate the relationship between GIC disclosure and firm’s performance, also analyzing the moderation role of CEO gender. Design/methodology/approach: Taking into account the theoretical background, the quali-quantitative analysis focuses on a sample of Italian listed companies during the period 2020–2022. To check the relationships, a content analysis has been conducted to measure the level of GIC disclosed and then generalized least squares, ordinary least squares and lasso regression have been employed to test the hypotheses. Findings: Results show that GIC disclosure produces a negative relationship with financial performance. In the meantime, findings point out that a female CEO moderates this relationship, boosting financial outcomes. Research limitations/implications: The study offers actionable managerial implications thanks to the proposed set of items to monitoring GIC. At the same time, it underlines how leadership styles can contribute to leverage green knowledge. Moreover, it seeks to contribute to the current literature and offers intriguing insights for policymakers. Originality/value: This paper addresses the still unclear relationship between GICD and performance, providing new cues regarding the role of women in CEO positions. Furthermore, in doing so, it focuses on Italian listed companies, a context still uncharted. Finally, the methodology represents a novelty in approaching these topics.

The purpose of this study is to provide a structured literature review (SLR) aimed at identifying the primary areas of auditing where machine learning (ML) has been applied in recent years. Additionally, the study seeks to outline emerging research trends, offering a preliminary framework that highlights which streams are developing more rapidly (Agnew, 2016). This contributes to a better understanding of the potential future impact of ML on auditing practices. The SLR is motivated by the theoretical and practical interest in ML applied to audit, which is still in the early stage (Bertomeu, 2020).

Using a and a unique set of Italian non-listed Unlikely to Pay (UTP) positions, that consist in the phase that precedes the insolvency but where it is still possible for the company to succeed in restructuring, this paper aims to analyze the relationships between corporate governance characteristics and financial distress status. We compare the performance of corporate governance variables in predicting corporate defaults, using both the Logit and Random Forest models, which previous researchers have deemed to be the most efficient machine learning techniques. Our results show that the use of corporate governance variables – especially with regards to CEO renewal and stability in the composition of the board of directors – increases the accuracy of the Random Forest technique and influences the success of the turnaround process. This paper also confirms the Random Forest technique’s ability to significantly outperform the Logit model in terms of accuracy.

The Z-Score model developed by Altman in 1968 is considered one of the more reliable predictors of bankruptcy. In contraposition to the existing literature, the paper aims to investigate the Z’-Score and Z’’-Score ability to predict unlike-to-pay (UTP) loans, which is an event far earlier than insolvency. To investigate this relation, the study uses a unique sample of UTP loans, provided by a major Italian bank, and applies, as a predictive model, the Logit model, well known in academics. Final results confirm that the Z’-Score and the Z’’-Score are able to forecast UTP loans. Furthermore, the findings of the papers reveal the importance of corporate governance variables in predicting financial failures.

Cash flows analysis plays an increasingly important role in the study of business dynamics since cash flows play a key role in the company's economic performance, not only from a standpoint but also in predictive terms. The literature on the subject is poor in number and depth of research, the samples analyzed so far are limited and the statistical tools are weak. Retracing the steps of past research, we studied the relationships between cash flows of several management areas and economic performance, using a complete sample of Italian listed companies in the 2008-2017 period with more solid statistical tools compared to previous studies. The database used to collect all the balance sheet data necessary to conduct our research is Amadeus of the Bureau Van Dijk platform, which already shows reclassified and easily comparable financial statements. Correlation and multiple regression analysis were used to assess if our cash flow proxies could be strong predictors of future cash flow and, consequently, of business performance. The flows for investments and the ability to generate cash, where the latter is positively correlated with future profitability, manage to explain, together with the net cash generation of the company, a large part of the variability of the operating income produced in subsequent periods. The flows from investments seem to be the most suitable for correctly classifying the most profitable companies in the medium-long term, while cash generation, deriving from the characteristic activity, contributes to providing answers, about corporate profitability, on shorter time horizons.

Purpose: The purpose of this paper is to analyze the impact of patenting on the performance of academic spin-off firms (ASOs) in the post-creation stage. Specifically, our study analyses how the combination of knowledge transfer mechanisms by ASOs and patents can foster ASOs’ early growth performance.Design/methodology/approach: The authors explored the relations between patenting processes and spin-off performance through econometric methods applied to a broad sample of Italian ASOs. The research adopts a deductive approach, and the hypotheses are tested using panel data models by considering the sales growth rate as the dependent variable regressed over measures of patenting activity and quality and assuming that firm-specific unobservable drivers of growth are captured by random effects.Findings: The empirical analysis shows that the incorporation of knowledge transferred by the parent university and academic founders through patents affects the performance of ASOs. Specifically, the authors find that the number of patents is a positive driver of ASOs’ performance, whilst patent age does not have a significant impact on growth. Moreover, spin-offs with a larger endowment of patents obtained before foundation, surprisingly, grow less on average.Practical implications: The findings have implications for ASO founders by suggesting that patenting processes reap benefits. However, in the trade-off of external knowledge access vs internal knowledge protection, it may be better to begin patenting after the foundation of ASOs.Originality/value: The authors enrich the on-going debate about the connections between knowledge transfer and organizational performance. This paper combines the concepts of patents and ASOs by providing evidence on the role of patenting processes as a transfer mechanism of explicit knowledge in ASOs. Furthermore, the authors contribute to the literature on costs and benefits of patents by hinting at unexpected findings.