Forecasting technological trends based on the heterogeneous data analysis

To achieve competitiveness, enterprises need to exploit a development strategy by forecasting promising technologies using limited resources. Numerous previous studies have shown that unexpected changes in R&D and patenting of intellectual property are associated with large changes in the market value of an enterprise. In fact, there is a strong correlation between the volatility of market shares, stock prices in the early stages of high-tech enterprise development, and the period when the innovative technology is not yet defined. Thus, we propose that if company's share prices continue to trend upward, then the developed technologies are likely to become promising innovations in the future. This paper proposes a method for forecasting technology trends by analyzing web news to identify high-tech enterprises, predicting stock price trends for selected enterprises and analyzing the clusters of patent applications. Unlike other studies, our method advances the idea of predicting technology trends by forecasting the stock price trend using univariate and multivariate data preparation approaches, and using Bayesian optimization to explore the best hyperparameters for machine and deep learning models. The study uses the developed software system for analyzing word frequency burst detection and stock price prediction. In particular, the proposed method is adopted to predict the price dynamics of Tesla and Samsung shares as case studies.