Genetic Algorithms for Small Enterprises Default Prediction: Empirical Evidence from Italy

Introduction

Bankruptcy prediction has been extensively studied since the late 1960s (Altman, 1968, 1993, 2004; Altman et al., 2005; Altman & Sabato, 2005; Beaver, 1967, 1968; Berger, 2006; Berger & Frame, 2007; Blum, 1974). Analysis of the financial position of the firms is very useful for both the firms and banks alike, such a task is used for credit risk assessment and to maintain the stability of financial markets and general economic prosperity. The numbers of defaulting firms is, in fact, an important issue for the economy of every country and it can be considered as an index of the development and robustness of the economy of a country (Zopounidis & Dimitras, 1998). Entrepreneurs, employees and labor organizations, policy makers, industry participants, investors, creditors, auditors, shareholders, banks are all interested in bankruptcy prediction because it affects all of them alike (Etemadi et al., 2009; O’Leary, 1998; Ravi et al., 2008). Therefore, timely identification of firms’ impending failure is highly desirable (Jones, 1987).

Prediction models may be used by the shareholders in choosing between the opportunities open to them (i.e. divestment of the company, merging with, or takeover by other companies). If the results are not satisfactory, then shareholders can choose the option to sell out the company at a price over real value, because the company still has a bargaining power to be sold over real value. If shareholders wait in the hope of restructuring the company they may face substantially low market values (Aktan, 2011). Moreover, company default prediction models can be considered by firm operating in a district or in a cluster. Since, failure of a such firm would ruin the reputation of the entire district and will affect investment decisions and credit decisions; therefore, the other firms in the district can monitor what is going on through estimation tools and of course can takeover other distressed firms that would be beneficial for them.

Labor organizations can use default prediction models to reveal information about the financial health of the company, from which they can define the pay rise and insist on other labor rights (Aktan, 2011). Moreover, employees of financially distressed predicted company can work harder to recover the company or seek another job for themselves.

Investors could utilize prediction models to maximize their investment decisions. In fact, an investor may not have enough time for and information in evaluation of investment tools. In such a situation, wrong decisions would be taken according to poor foresight and therefore the expected return on investment cannot be achieved. Failure prediction models help the investors providing necessary time an investor needs and resolving the experience/information deficits. With the help of the prediction tools, investors can identify the poor stock in their portfolios and take actions to sell them before these stocks’ value evaporate. According to Aktan (2011) “an investor can reshape his portfolio by prediction tools, i.e. if the financially distressed company is expected to recover from its problems, then investing on this company’s low priced stocks can bring high returns later, when the company resolves. Thus, investors achieve their objectives by investing in the right areas, and companies have the opportunity to be strong by rational distribution of funds. Resolution of difficulties in funding lets the opportunities for new investments and provides companies to obtain competitive advantages”.