Gabriele Sampagnaro

Financial Distress Risk in Initial Public Offerings: How Much Do Venture Capitalists Matter?

Using a sample of 1,593 US firms that go public between 1990 and 2007, we find that VC-backed IPOs experience less financial distress risk post-offering than do comparable non-VC-backed IPOs. After controlling for endogeneity, we find this is related to the screening done by VC-investors, who select firms with lower risk of financial distress and by VCs reducing risks when they finance portfolio firms. We find companies backed by more reputable VCs exhibit higher levels of financial distress risk even when they show superior operating performance, due their highly levered capital structure and investment in relatively illiquid assets.

Competition, specialization and bank–firm interaction: what happens in credit crunch periods?

This article empirically investigates the relationship between interbank competition, bank orientation and credit availability for a sample of more than 30 000 loans granted by a large banking group operating in the Italian credit market. We test whether and how, during a credit crunch period, competition affects bank orientation and how relationship lending and interbank competition can mitigate the credit crunch problem, for financially distressed firms. Using a unique and large bank–firm level data set, the main results show that an increase in competition is associated with a stronger relationship in terms of the length of the bank–borrower interaction, whereas the distance bank branch-headquarter negatively affects it. Moreover, a strong lender–borrower relationship, in terms of length and exclusivity, is found positively significant in determining the change in the amount of credit granted. Nonlinearity and sector specialization effects are tested, too, and report interesting results, supporting the crucial role of relationship lending during a financial crisis.

Predicting the growth of high-growth SMEs: evidence from family business firms

Purpose The purpose of this paper is to arrive at high-growth firm (HGF) and predict the growth of rapid-growth firms using the set of balance-sheet ratios. Design/methodology/approach The source of data came from the AIDA database, a commercial database provided by Bureau van Dijk. A total of 45,000 family business small- and medium-scale enterprises of Italy were selected for the study. Liquidity ratio, solvency ratio, firm age, cash flow, and working capital are considered as predictors of the firm growth. Probit regression is used for predicting the growth of the firms. Findings The result of the study indicated that the most important financial indicators were the liquidity ratio, solvency ratio, firm age, cash flow, and working capital are most important predictors of firm growth. The ROC of the model is 70.78, which shows that the model is fair. Originality/value The present study considers an innovative approach that considers balance sheet issued the year prior to the observation of rapid growth as predictors of firm growth (similar to the credit-scoring models, i.e. the Z-score model, to measure the probability of default).

Macroeconomic indicators and their impact on stock markets in ASIAN 3: A pooled mean group approach

Abstract The objective of this paper is to examine the long-run and the short-run relationship between India, China and Japanese stock markets and key macroeconomic variables such as exchange rates and inflation (proxied by consumer price index) of ASIAN 3 economies (India, China and Japan). Monthly time series data spanning the period from 2008 January to November 2016 has been used. The unit root test, the cointegration test, Granger causality test and pooled mean group estimator have been applied to derive the long-run and short-run statistical dynamics. The findings of pooled estimated results of ASIAN 3 countries show that exchange rate has a positive and significant long-run effect on stock markets while the inflation has a negative and insignificant long-run effect. In the short run, there is no statistically significant relationship between macroeconomic variables and stock markets. This study emphasises on the impact of macroeconomic variables on the stock market performance of a developing economy (India and China) and developed economy (Japan).

Firm age and liquidity ratio as predictors of firm growth: evidence from Indian firms

ABSTRACT The result of the study shows that liquidity ratio and firm age increases the probability of firm becoming high growth or low growth. However, the result indicates that the chances of being high-growth firm are higher for young firms. Quantile results show that the coefficient of liquidity ratio switches from negative in lower quantiles to become positive in upper quantile with the strong positive effect and firm age coefficients are largest in the lower quantiles. These results also confirm the probit result as per which firm age is negatively significant with the growth of the firm. The present study considers an innovative approach that considers balance sheet issued the year prior to the observation of rapid growth as predictors of firm growth (similar to the credit scoring models, i.e. the Z-score model, to measure the probability of default).

The Determinants and the Effect of Soft Information 'Loss' in Bank Lending.

In this paper, we examine who holds the decision-making power for credit approval in small business lending and the determinants of shifting this power from a local branch to an upper organizational level. Since soft information is usually collected by local branches, shifting the decision-making power may lead to a loss of soft information because it is difficult to transmit soft information across organizational levels. We discover that when the local bank operates with a relationship lending strategy, it tends to handle loan requests, and soft information loss is less likely to occur. Furthermore, we observe that the organizational level at which loan requests are handled has a significant effect on the approval/rejection of a loan request.

The Shadow Economy and Banks’ Lending Technology

Is there a relationship between bank monitoring models and the level of shadow economy? This paper develops a model of optimal lending technology to study the relationship between local underground economic activity and banks’ lending choices. In turn, as the aggregate level of informality and tax evasion increase, it becomes more profitable for banks to screen and supervise borrowers using more costly in-depth monitoring technologies. A large dataset of regional Italian data confirms these conjectures.

Competition and Relationship Lending: What Happens in Credit Crunch Periods?

This paper empirically investigates the relationship between interbank competition, bank orientation and credit availability for a sample of more than 30,000 loans granted by a large banking group operating in the Italian credit market. We test whether and how, during a credit crunch period, competition affects bank orientation and how relationship lending and interbank competition can mitigate the credit crunch problem, for financially distressed firms. Using a unique and large bank-firm level dataset, the main results show that an increase in competition is associated with a stronger relationship in terms of the length of bank-borrower interaction, whereas the distance bank branch-headquarter negatively affect it. Moreover, a strong lender-borrower relationship, in terms of length and exclusivity, is found positively significant in determining the change in the amount of credit granted. Non-linearity and sector specialization effects are tested, too, and report interesting results, supporting the crucial role of relationship lending during a financial crisis.

The Quality of Real Estate Data: The Italian Case

The chapter1 discusses issues concerning the quality of property data from different sources and resulting implications for market participants. It is divided into two sections. In the first section, we discuss the nature and availability of property data for the Italian market. Our initial exploration of the quality and accessibility of some domestic data documents the presence of many property data sources, each of which uses different methods of data collection. The high number of data sources and their methodological heterogeneity produce excessive data discrepancies, hardly compatible with efficient research and professional investment processes. Using a set of longitudinal aggregated property values, we proceed to estimate the level of uniformity of data using correlation and cointegration analysis.

Identifying High Growth SMEs Through Balance Sheet Ratios

In this paper we trying to discover the balance sheet ratios that enable us to predict which firms are better candidates for a high-growth path. Following the intuition behind the credit scoring model (i.e., Z-score model), we consider the idea that the balance sheet in the period preceding the high growth affects the balance sheet at the time of the exceptional growth. To this end, we used a quantile regression and a TOBIT analysis that discriminates, according to a defined threshold, among the financial data of two groups of firms (High growth and Non-High Growth firms) selected from a population of approximately 21,000 firms. The results of the analysis demonstrate the relevance of firm size, firm age, and, most importantly, internal cash flows (despite bank loans), to the growth and success of a firm.