Investment decisions of companies in financial distress

López-Gutiérrez, Carlos; Azofra, Sergio Sanfilippo; Torre-Olmo, Begoña

doi:10.1016/j.brq.2014.09.001

Cited by 49 publications

(42 citation statements)

References 39 publications

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“…Using the generalized method of moments (GMM) system, from panel data, the results showed that the influence of financial distress on investment is distinct according to the investment opportunities available to companies. So, companies in difficulties with fewer opportunities have the highest propensity to underinvest, while firms in problems with better opportunities do not present different investment behaviour than healthy companies (López-Gutiérrez et al 2015).…”

Section: Earnings Management and Bankruptcy Predictionmentioning

confidence: 98%

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Ogachi

Ndege

Gaturu

et al. 2020

JRFM

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Predicting bankruptcy of companies has been a hot subject of focus for many economists. The rationale for developing and predicting the financial distress of a company is to develop a predictive model used to forecast the financial condition of a company by combining several econometric variables of interest to the researcher. The study sought to introduce deep learning models for corporate bankruptcy forecasting using textual disclosures. The study constructed a comprehensive study model for predicting bankruptcy based on listed companies in Kenya. The study population included all 64 listed companies in the Nairobi Securities Exchange for ten years. Logistic analysis was used in building a model for predicting the financial distress of a company. The findings revealed that asset turnover, total asset, and working capital ratio had positive coefficients. On the other hand, inventory turnover, debt-equity ratio, debtors turnover, debt ratio, and current ratio had negative coefficients. The study concluded that inventory turnover, asset turnover, debt-equity ratio, debtors turnover, total asset, debt ratio, current ratio, and working capital ratio were the most significant ratios for predicting bankruptcy.

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Section: Earnings Management and Bankruptcy Predictionmentioning

confidence: 98%

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Ogachi

Ndege

Gaturu

et al. 2020

JRFM

View full text Add to dashboard Cite

show abstract

“…The System GMM methodology has been commonly used to control endogeneity problems (e.g. Beck et al, 2000; López-Gutiérrez et al, 2015; Uotila et al, 2009) and seems to be superior to the other option to control the endogeneity using panel data: Diff GMM. Finally, according to Heid et al (2012), System GMM is more adequate when data show high persistence as it is in our case.…”

Section: Research Design and Samplementioning

confidence: 99%

Does gender diversity on corporate boards reduce information asymmetry in equity markets?

Abad

Lucas‐Pérez

Mínguez‐Vera

et al. 2017

BRQ Business Research Quarterly

154

134

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We examine the relation between the gender diversity on boards of corporations and the levels of information asymmetry in the stock market. Prior evidence suggests that the presence of women on director boards increases the quantity and quality of public disclosure by firms, and we therefore expect firms with higher gender diversity on their boards to show lower levels of information asymmetry in the market. Using a Spanish sample, proxies for information asymmetry estimated from high-frequency data along with system GMM panel methodology, we find that the gender diversity on boards is negatively associated with the level of information asymmetry in the stock market. Our findings support the changes in the laws that have been introduced in several countries to increase the proportion of female company directors by providing evidence that gender diverse boards have beneficial effects on stock markets.

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“…In a recent study, Khanam (2017) also reported the influence of demographic factors such as age, level of education, occupation, trading experience, and level of investor income. In contrast, behavioral finance researchers argue that investors tend to behave irrationally in the investment decision-making process due to the influence of several psychological factors (Kiyilar & Acar, 2013;López-Gutiérrez et al, 2015;Chen et al, 1995).…”

mentioning

confidence: 99%

The Financial Behavior of Investment Decision Making Between Real and Financial Assets Sectors

Hala¹,

Abdullah²,

Andayani³

et al. 2020

JAFEB

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This research was conducted to achieve several objectives and focus research was based on financial behavior theory and prospect theory as grounded theory e.g., investigate the financial decision-making behavior between financial and real assets investment, and confirm the relationship existing between herding behavior and overconfidence factors to the level of loss and regret aversion, and financial literacy into real assets investment decisions. The study used 220 real estate auction respondents as investor samples at the State Assets and Auction Service Office Makassar, South Sulawesi, Indonesia. Data was collected through the use of a questionnaire consisting of 23 questions to measure the variables. Moreover, the research data passed through several feasibility tests like the inner and outer modeling by Partial Least Square-Structural equation model (PLS-SEM) while the hypotheses formulated were also tested to determine the magnitude of the variable relationship. Through the use of the direct and intervening test, loss and regret aversion variables have a positive and significant effect while financial literacy variables have no significant effect. There is a slight difference in the decision-making process for real assets and financial assets investors. Investment decision making behavior in the financial assets sector requires less complicated decisions compared to the decisions related to real assets investments.

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Investment decisions of companies in financial distress

Cited by 49 publications

References 39 publications

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Does gender diversity on corporate boards reduce information asymmetry in equity markets?

The Financial Behavior of Investment Decision Making Between Real and Financial Assets Sectors

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