The COVID-19 pandemic is influencing the Dutch economy heavily. More so, small and medium-sized enterprises, also known as SMEs, are notoriously unstable and as a result, could be even more heavily affected by the coronavirus outbreak. The first major lockdown in The Netherlands was instated on March 23, 2020, which introduced several new measures, such as the prohibition of gatherings, the closing of food and beverage outlets, and the prohibition of all contact-based professions.

In such a time of economic instability as caused by the coronavirus outbreak, it is very useful for a company to know in what financial state they are going to be such that they can actively take precautions, such as liquidating their assets or decreasing their expenses. The financial state of a company is often reflected using Key Performance Indicators, or KPIs for short. These KPIs include metrics like the revenue, cost, and cash flow of a company. The forecasting of these KPIs can help a company in informing in what financial state they are going to be and are usually done using historical data of the company. Whereas the decrease in economic activity of business partners of a company is not reflected in the historical KPI data of the company itself, it can be seen in a network of companies that indicates whether there exists a relationship between two companies by using data on monetary transactions between companies. For this reason, we think that enriching historical KPI data using node features extracted from a dynamic network of companies can help improve the quality of KPI predictions during a period of economic instability such as the COVID-19 pandemic.

This thesis answers the question of whether we can use utilize a dynamic network of SMEs to improve the quality of KPI predictions during the COVID-19 lockdown. To answer this question, we first focus on creating a dynamic network consisting of SMEs and the transactions between them out of unstandardized data by proposing a novel, lightweight entity resolution algorithm that is used to find a mapping between companies. The resulting network is analyzed, and we found that the effects of the coronavirus lockdown are visible in the network. Next, we examine several KPIs, such as the revenue or the cash flow of a company, and we found that we can also see the effects of the COVID-19 lockdown in several KPIs. Lastly, this thesis describes an analysis of whether node features can be used to improve the quality of the forecasting of several of these KPIs, where we found that node features such as the degree and clustering coefficient of a node can indeed help with improving KPI forecasting under certain conditions.