Analysis of the Risk of Bankruptcy of Tomato Processing Companies Operating in the Inter-Regional Interprofessional Organization “OI Pomodoro da Industria Nord Italia”

Enhancing plant defense using rhizobacteria in processing tomatoes: a bioprospecting approach to overcoming Early Blight and Alternaria toxins

Plant growth-promoting rhizobacteria (PGPR) with antagonistic activity toward plant pathogenic fungi are valuable candidates for the development of novel plant protection products based on biocontrol activity. The very first step in the formulation of such products is to screen the potential effectiveness of the selected microorganism(s). In this study, non-pathogenic rhizobacteria were isolated from the rhizosphere of tomato plants and evaluated for their biocontrol activity against three species of mycotoxin-producing Alternaria. The assessment of their biocontrol potential involved investigating both fungal biomass and Alternaria toxin reduction. A ranking system developed allowed for the identification of the 12 best-performing strains among the initial 85 isolates. Several rhizobacteria showed a significant reduction in fungal biomass (up to 76%) and/or mycotoxin production (up to 99.7%). Moreover, the same isolates also demonstrated plant growth-promoting (PGP) traits such as siderophore or IAA production, inorganic phosphate solubilization, and nitrogen fixation, confirming the multifaceted properties of PGPRs. Bacillus species, particularly B. amyloliquefaciens and two strains of B. subtilis, showed the highest efficacy in reducing fungal biomass and were also effective in lowering mycotoxin production. Isolates such as Enterobacter ludwigii, Enterobacter asburiae, Serratia nematodiphila, Pantoea agglomerans, and Kosakonia cowanii showed moderate efficacy. Results suggest that by leveraging the diverse capabilities of different microbial strains, a consortium-based approach would provide a broader spectrum of effectiveness, thereby signaling a more encouraging resolution for sustainable agriculture and addressing the multifaceted nature of crop-related biotic challenges.

Does Firm Life Cycle Impact Corporate Investment Efficiency?

Corporate investment efficiency (CIE) is an imperative factor influencing the smooth functioning and financial sustainability of an enterprise. The role of a firm life cycle on risk and performance fundamentals has been extensively explored in the literature. However, it remains unclear as to whether the life cycle stages of a firm have any impact on corporate investment efficiency. This paper investigates the role of firm life cycle stages (FLCS) in determining the investment efficiency of 351 Pakistani non-financial listed firms over the course of 12 years (2005–2016). It used panel data fixed effects and ordinary least squares (OLS) techniques to empirically examine the proposed relationship. By employing Dickinson’s FLCS measure, we found that CIE was lower during the introduction and decline stages and higher at the growth and maturity stages of a firm’s life cycle. Moreover, the results of regression analysis revealed that mature firms enjoyed the highest level of investment efficiency followed by the growth firms. Overall, CIE exhibited an inverted U-shaped trend across FLCS. In addition, the findings corroborated the idea that the sample firms could not sustain their investment efficiency when they moved along different stages of the life cycle. Thus, policymakers are suggested to customize their investment policies for each stage of FLC to attain sustainable financial performance throughout the life of a firm.

Is Bankruptcy Risk Tied to Corporate Life-Cycle? Evidence from Pakistan

In this paper we analyze the relationship between bankruptcy risk and the corporate life cycle in Pakistan from 2005 to 2014. For this purpose, we run a Hierarchical Linear Mixed Model (HLM) for a sample of 301 non-financial listed firms in 12 different sectors. The empirical outcomes reveal that firms during introduction, growth and, decline stages (mature stage) of life-cycle experience higher (lower) bankruptcy risk. Moreover, in juxtaposition with growth stage, bankruptcy risk is higher at the introduction stage of life-cycle. These findings suggest that financial managers should be cautious about the financial fragility of the firm at each stage of corporate life-cycle. The results also entail that Pakistani firms do not follow a sequential pattern in their life-cycle, rather they have the tendency to revert to a previous stage or jump to the next stage of life-cycle. This is the first study that empirically examines the association between firm life-cycle stage and corresponding bankruptcy risk and asserts that managers must incorporate the life-cycle effects into their financial planning and decision making for the sustainable working of an enterprise.

Predicting Corporate Financial Sustainability Using Novel Business Analytics

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

Pursuing Firm Economic Sustainability through Debt Restructuring Agreements in Italy: An Empirical Analysis

Corporate restructuring has become a central topic for both academics and practitioners, particularly following the global financial crisis. In particular, there is increasing interest in understanding the effectiveness of turnaround strategies, which are defined as attempts to restore the performance of firms after periods of downfall. However, despite the relevance of this issue, there is a shortage of empirical evidence regarding the effectiveness of turnaround strategies related specifically to financial interventions. Through the support of an empirical analysis among Italian firms, this paper seeks to fill this significant gap in the available literature. In particular, we conducted an in-depth analysis of 262 debt restructuring agreement (DRA) plans that occurred between 2005 and 2013 in 16 bankruptcy courts. Our study confirms the positive effect of changes in the top management team. This measure can be both a symbolic signal of genuine willingness to modify the strategy of the firm, and a real manifestation of the necessity to have new skills to complete the turnaround. In addition, the adoption of operational and strategic/asset measures increase the likelihood of turnaround success.