Hilbert spectrum support vector regression for public companies’ sales forecast
Abstract
Accurate and timely revenue forecasting is of critical importance to investors in public companies, who base their investment decisions on the Discounted Cash Flow (DCF) model. Market practice dictates that financial analysts develop financial models by focusing on revenue growth projections for the first year of the forecast horizon, which holds the highest weighting in discounted cash flow calculations. These projections are typically derived through expert assessment of publicly available quarterly reporting. However, the potential of contemporary data science tools, particularly machine learning approaches that employ linear approximation techniques to model nonlinear patterns to enhance current quarter revenue analysis is largely underutilized. The present study focused on developing source code to apply the Hilbert Spectrum Support Vector Regression (Hilbert spectrum SVR, HSVR) method for sales prediction. Testing HSVR on data of companies listed on the Moscow Exchange and comparing its performance metrics with those of classical Support Vector Regression (SVR), led to the conclusion that HSVR can be deployed in industrial settings for revenue forecasting of public companies using news vectors – vector representations (embeddings) of news articles.
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References
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