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on Corporate Finance |
| By: | Rogelio A. Mancisidor |
| Abstract: | This research introduces for the first time the concept of multimodal learning in bankruptcy prediction models. We use the Conditional Multimodal Discriminative (CMMD) model to learn multimodal representations that embed information from accounting, market, and textual modalities. The CMMD model needs a sample with all data modalities for model training. At test time, the CMMD model only needs access to accounting and market modalities to generate multimodal representations, which are further used to make bankruptcy predictions. This fact makes the use of bankruptcy prediction models using textual data realistic and possible, since accounting and market data are available for all companies unlike textual data. The empirical results in this research show that the classification performance of our proposed methodology is superior compared to that of a large number of traditional classifier models. We also show that our proposed methodology solves the limitation of previous bankruptcy models using textual data, as they can only make predictions for a small proportion of companies. Finally, based on multimodal representations, we introduce an index that is able to capture the uncertainty of the financial situation of companies during periods of financial distress. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2211.08405&r=cfn |
| By: | Barrela, Rodrigo; Lopez-Garcia, Paloma; Setzer, Ralph |
| Abstract: | This paper analyses the implications of corporate indebtedness for investment following large economic shocks. The empirical analysis is based on a large Orbis-iBACH firm-level data set for euro area countries from 2005 to 2018. Our results suggest that investment of high-debt firms is significantly depressed for an extended period in the aftermath of economic crises. In the four years after a negative economic shock, the cumulative loss of capital of high-debt firms is around 15% higher than that of firms with lower debt burdens. The negative impact of high debt on investment is most evident for firms in Southern and Eastern Europe and for micro firms. These findings suggest a potentially significant negative impact of increased corporate indebtedness on investment in the post-COVID-19 recovery. JEL Classification: E22, F34, G31, G32 |
| Keywords: | corporate debt, COVID shock, investment, leverage, local projections |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20222751&r=cfn |
| By: | Wesley Mendes-da-Silva (São School of Business Administration of the Fundação Getulio Vargas); Israel José dos Santos Felipe (Federal University of Ouro Preto/Brazil and NIPE/Portugal); Cristiana Cerqueira Leal (School of Economics and Management & NIPE – Center for Research in Economics and Management, University of Minho); Marcelo Otone Aguiar (Federal University of Espirito Santo) |
| Abstract: | We study whether the tone of the daily news in mass media, used as a proxy for market sentiment, affects the typical daily pledge amount in reward crowdfunding campaigns. Based on unique data from over 350,000 pledges in reward crowdfunding campaigns in over 2,600 cities in Brazil, we find that market sentiment affects the willingness of backers to make larger pledges. Our unprecedented results reveal that good news induces pledges of larger amounts. The effect of tone over pledge amounts is inhibited by the geographic distance backer-entrepreneur, and it is reinforced by the income inequality in the pledger’s city. |
| Keywords: | Natural Language Processing, Crowdfunding, Media, Investor sentiment |
| JEL: | L26 G32 G41 O31 C41 I31 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:nip:nipewp:2/2022&r=cfn |
| By: | Norbäck, Pehr-Johan (Research Institute of Industrial Economics (IFN)); Persson, Lars (Research Institute of Industrial Economics (IFN)); Tåg, Joacim (Research Institute of Industrial Economics (IFN)) |
| Abstract: | The creation and scaling of startups are associated with risk-taking and different types of owners treat these risks differently. We show how an active venture capital (VC) market affects risk-taking in research and scaling decisions of startups. VC-backed startups will choose more high-risk, high-reward research and scaling strategies than independent startups. The reason is temporary ownership and the compensation structures used in the VC industry. These create ”exit costs” for VC-backed startups that imply that riskier strategies pay off. We also show that the presence of an active VC market may induce startups to take more risks initially since VC firms can help startups pivot in case of failure. |
| Keywords: | Entrepreneurship; Pivoting; Scaling; Venture capital |
| JEL: | G24 L25 M13 |
| Date: | 2022–11–18 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:iuiwop:1444&r=cfn |
| By: | Todd A. Gormley; Vishal K. Gupta; David A. Matsa; Sandra C. Mortal; Lukai Yang |
| Abstract: | In 2017, “The Big Three” institutional investors launched campaigns to increase gender diversity on corporate boards. We estimate that their campaigns led American corporations to add at least 2.5 times as many female directors in 2019 as they had in 2016. Firms increased diversity by identifying candidates beyond managers’ existing networks and by placing less emphasis on candidates’ executive experience. Firms also promoted more female directors to key board positions, indicating firms’ responses went beyond tokenism. Our results highlight index investors’ ability to effectuate broad-based governance changes and the important impact of investor buy-in in increasing corporate-leadership diversity. |
| JEL: | G34 J71 M12 M14 |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30657&r=cfn |
| By: | Imad Talhartit (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Finance, Audit and Organizational Governance Research); Sanae Ait Jillali (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Finance, Audit and Organizational Governance Research); Mounime El Kabbouri (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Finance, Audit and Organizational Governance Research) |
| Abstract: | Behavioral finance is the application of psychology to finance, dedicated to explaining anomalies in the financial market based on research and analysis of human behavior. This paper aims for studying from a conceptual side the main behavioral biases that impact traders operating in the financial market under uncertain circumstances. The current literature confirms the existence of cognitive and emotional biases, which could be caused by heuristics or framing faults impacting the decision-making process in investment and financing decisions alongside the performance of traders. In this vein, the findings affirm that although it is difficult to change people's emotions and control them completely, moreover the capacity for human introspection is limited, with the understanding of cognitive biases based on the knowledge and beliefs of the trader, the possibility of modifying or changing the individuals' way of reasoning is more or less feasible in order to moderate their behaviors within the market. Behavioral finance admitting a certain degree of inefficiency in the markets, and the existence of factors that influence the behavior of the trader, is calling for a precise set of rules and trading plans (such as money management), besides the mental and psychological control essential to succeed in the financial market. This theoretical informative paper enters into a series of works that challenge investors' rationality assumption and inferences about the efficiency of financial market information. |
| Keywords: | Financial markets,Behavioral finance,Behavioral biases,Investment decisions,Traders' performance |
| Date: | 2022–10–31 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-03844737&r=cfn |
| By: | Bonacorsi, Laura; Cerasi, Vittoria; Galfrascoli, Paola; Manera, Matteo |
| Abstract: | We study the relationship between the risk of default and Environmental, Social and Governance (ESG) factors using Supervised Machine Learning (SML) techniques on a cross-section of European listed companies. Our proxy for credit risk is the z-score originally proposed by Altman (1968). We consider an extensive number of ESG raw factors sourced from the rating provider MSCI as potential explanatory variables. In a first stage we show, using different SML methods such as LASSO and Random Forest, that a selection of ESG factors, in addition to the usual accounting ratios, helps explaining a firm’s probability of default. In a second stage, we measure the impact of the selected variables on the risk of default. Our approach provides a novel perspective to understand which environmental, social responsibility and governance characteristics may reinforce the credit score of individual companies. |
| Keywords: | Financial Economics, Productivity Analysis, Research Methods/ Statistical Methods |
| Date: | 2022–11–29 |
| URL: | http://d.repec.org/n?u=RePEc:ags:feemwp:329521&r=cfn |