Model for Predicting Financial Performance of Development and Construction Corporations

A Grey System Theory‐Based Default Prediction Model for Construction Firms

“…Researchers usually consider the values of symptoms at the level of year t (in most of the cases, this year is the one before the company's distress) without attempting to link these values with the changes that have occurred in the indicator over a greater period, for example, 3 to 5 years before the problem. For example, Chen () and Chen () used quarterly financial reports, although Pompe and Feelders () and Tserng et al () used a 1‐year data sequence and a 2‐year data sequence, respectively. These periods are not enough to determine a trend (Neves and Vieira, ).…”

“…This study suggests that the proposed model uses fewer ratios than the models of the most recent previous studies in the construction industry. For example, Pompe and Feelders (), Chen (), and Chen () use 10, 36, and 25 variables, respectively, although Tserng et al () proposed a model with seven variables; however, that group's model has an accuracy rate of only 80.31%.…”

“…Chava and Jarrow (2004) stated that different industries have different accounting conventions and different levels of competition; therefore, the probability of default may differ for companies in different industries with otherwise identical balance sheets. Many scholars, such as Tserng et al (2011aTserng et al ( , 2011b, Chen (2012), Chen (2009), Kangari et al (1992), Lee et al (2011), Wang et al (2008), and Cruz and Marques (2012) have shown that the special characteristics and financial risks of the construction industry are significantly different from other industries in many ways. First, construction firms face a high degree of uncertainty and high operational risks, due to technical, human, and natural factors.…”

“…The construction industry has been one of the largest contributors in the economy with a global economy investment total of approximately 10% (Construction Industry Development Board, 2004). As a result of the aforementioned factors, many studies have developed models for predicting the probability of construction firm default, such as Mason and Harris (1979), Abidali (1990), Kangari et al (1992), Russell and Jaselskis (1992), Severson et al (1994), Abidali and Harris (1995), Russell and Zhai (1996), Pompe and Feelders (1997), Chen (2009), Tserng et al (2011b), and Chen (2012).…”

A Big Data Analytics Approach for Construction Firms Failure Prediction Models

“…[6], [12]- [16] among others) use a small number of construction firms' data, normally below 100, to build their models. A few [17]- [19] have used much higher number of construction firms' data.…”