Using news to predict Chinese medicinal material price index movements

Purpose The purpose of this paper is to propose an approach for predicting the movements of Chinese medicinal material price indexes using news based on text mining. Design/methodology/approach A research framework and three major methods, namely, domain dictionary construction, market convergence time calculation and dimensionality reduction integrating semantic analysis, are proposed for the approach. The proposed approach is applied in practice for predicting the price index movements of the top ten Chinese medicinal materials that receive the greatest media attention. Findings A set of experiments performed herein show that a predictive relationship exists between the news and the commodity market and that each of the three major methods improves the forecasting performance. Research limitations/implications Because the field of Chinese medicinal materials lacks a corpus that can be used for sentiment analysis, the accuracy of a trained automatic sentiment classifier is lower than obtained by a manual method, which can cause the calculated convergence result to be inaccurate, thus affecting the final prediction model. The manual method of having people label news decreases the proposed method's aspects of being intelligent and automatic. Practical implications Using the method proposed herein to predict the trends in Chinese medicinal materials prices helps farmers arrange a reasonable planting plan to pursue their best interests. Social implications The method proposed herein to predict the trends in the prices of Chinese medicinal materials is conducive to the government arranging planned drug availabilities in order to avoid disasters in which herbs are looted. Originality/value The produced prediction result is meaningful in supporting farmers and investors to make better decisions in growing and trading Chinese medicinal material, which leads to financial returns on investments and the avoidance of severe losses.