Machine learning embedded automation in financial forecasting : A quantitative case study at Ericsson

Abstract: In today’s increasingly data-driven world, time series forecasting is becoming a prevalent practice. Business executives can make better decisions aided by insights from financial forecasts. Modern markets value accurate financial performance data, and strong forecasting capabilities within the area can help corporate management ensure timely, precise, and accurate informational transparency to investors and other key stakeholders. Rather than utilizing standard statistical time series models to address this forecasting problem, another option would be to use a learning technique from the field of artificial intelligence (AI), namely the RNN models of LSTM and GRU, as well as automatingand embedding the forecasting process. In this research paper, the author investigates how neural models perform compared to traditional statistical models in internal financial time series forecasting, at a case company, with limited access to historical financial data. The research dives into the potential and usage of machine learning, or neural network embedded automation within financial forecasting are investigated and demonstrated. Over 2000 models were generated from custom software built for the case company. The paper finds that neural models (in the form of the recurrent neural network variants LSTM and GRU) outperform traditional statistical models when only using sales data. If the neural prophet model too is considered an accurate neural model, the neural models consistently beat the traditional statistical time series forecasting models on average within the scope and delimitation of this study - despite only using small sample sizes. These results show the usability of neural models. With the speed at which neural models are improving in new research, knowing that they can forecast actual business events with small sample sizes and better use large amounts of data when it is available, neural networks are increasingly useful for company internal financial performance forecasting. While the results do yield value for the case company, they cannot be generalized without broader experimentation using multiple datasets and business cases. Furthermore, no quantitative or technical analysis should be expected to fully explain a complex phenomenon such as business sales, alone. It can ultimately be both powerful and helpful, but should be accompanied by sustainable fundamental analysis and insights.