Comparison of Prediction Models Applied in Economic Recession and Expansion

Abstract

As a rule, the economy regularly undergoes various phases, from a recession up to expansion. This paper is focused on models predicting corporate financial distress. Its aim is to analyze impact of individual phases of the economic cycle on final scores of the prediction models. The prediction models may be used for quick, inexpensive evaluation of a corporate financial situation leading to business risk mitigation. The research conducted is drawn from accounting data extracted from the prepaid corporate database, Albertina. The carried-out analysis also highlights and examines industry specifics; therefore, three industry branches are under examination. Enterprises falling under Manufacture of metal products, Machinery, and Construction are categorized into insolvent and healthy entities. In this study, 18 models are selected and then applied to the business data describing recession and expansion. The final scores achieved are summarized by the main descriptive statistics, such as mean, median, and trimmed mean, followed by the absolute difference comparing expansion and recession. The results confirm the expectations, assuming that final scores with higher values describe better corporate financial standing during the expansion phase. Similar results are achieved for both healthy and insolvent enterprises. The paper highlights exceptions and offers possible interpretations. As a conclusion, it is recommended that users need to respect the current phase of the economic cycle when interpreting particular results of the prediction models.

Composite Financial Performance Index Prediction – A Neural Networks Approach

“…Predictions of business indicators, mostly forecasting corporate bankruptcy, represented a center of major interest in corporate finance and accounting research since the 1960s (Camska & Klecka, 2020). Back then, financial prediction models described a particular economic reality (Camska & Klecka, 2020), and were based on multivariate discriminant analysis and logistic regression (Balcaen & Ooghe, 2006).…”

“…Predictions of business indicators, mostly forecasting corporate bankruptcy, represented a center of major interest in corporate finance and accounting research since the 1960s (Camska & Klecka, 2020). Back then, financial prediction models described a particular economic reality (Camska & Klecka, 2020), and were based on multivariate discriminant analysis and logistic regression (Balcaen & Ooghe, 2006). In the last decades with the development of artificial intelligence, more and more studies have begun to use machine learning to predict financial indicators, also to predict the business performance of companies (Hu et al, 1999;Ahn et al, 2000;Huang et al, 2005;Min & Lee, 2005;West et al, 2005;Estrella & Mishkin, 1996;Hsu et al, 2009;Ribeiro & Lopes, 2011;Lin et al, 2011;Li & Sun, 2013;Shen et al, 2015).…”

Financial Situation of Pre-Packed Insolvencies

“…It has serious consequences for creditors, especially low satisfactory rates during the insolvency proceedings discussed by Smrčka and Čámská (2017). Corporate bankruptcy does not only influence direct creditors but it also influences many other internal and external stakeholders (Branch, 2002) and the bankruptcy itself is influenced by economic cycle (Čámská & Klečka, 2020).…”

Forecasting Economic Recession through Share Price in the Logistics Industry with Artificial Intelligence (AI)

“…Another closely related study adopted a probit framework to predict economic recessions by the yield curve and stock market liquidity [11]. However, with the development of machine learning and artificial intelligence, more and more economic recession models have been proposed with these latest technologies [12]. Due to the advance in artificial intelligence (AI) technology development nowadays, AI tools are now extensively used in forecasting, prediction, and other intelligence applications [13], including stock market investment [14].…”

“…Another closely related study adopted a probit framework to predict economic recessions by the yield curve and stock market liquidity [11]. However, with the development of machine learning and artificial intelligence, more and more economic recession models have been proposed with these latest technologies [12].…”