Study On Early-warning System Of Chinese Grain Price

The objective to establish grain price early-warning system is to monitor and evaluate grain price so that the price can be timely adjusted and controlled when it is in abnormal state in order to guarantee the grain economy to develop in a sustaining, healthy and stable way. The research work in this paper follow four steps: define warning situation, seek warning sources, analyze warning omens ,foretell warning degree.First, we define the grain price fluctuation rate as situation indictor and its warning line in a systematic way.Second, we analyze the factors that influence grain price and divide them into eight categories which are product cost, grain supply, grain demand, real value of currency, market circulation, national policy, international factors and other factors.Third, basing on above result, we select 23 indictors as warning omens. They are agricultural productive materials price growth rate , sown area of grain crops growth rate, grain yield per area growth rate-, natural disaster covered grain areas growth rate, net grain import change rate , grain reserve change rate , population growth rate, per income growth rate, city and town population growth rate, food industry production value growth rate, year-end pig number growth rate, medical & pharmaceutical and textile industry production value growth rate, grain marketization degree, inflation rate using the previous year as base year (preceding year=100), public grain purchases price growth rate, investment in agricultural science and technology growth rate, investment in agricultural infrastructure growth rate, growth rate of graduates number from agriculture , forestry , science&technology universities and colleges and specialized secondary schools , government expenditure for agriculture and agricultural credit growth rate, international grain price growth rate, RMB exchange rate growth rate, last grain price growth rate, economic crop price growth rate,,Meanwhile, a new method is attempted to be used in this paper and the grain price early-warning problem is transformed into machine learning problem by introducing statistic learning theory and SVM method which are gaining popularity in machine learning field at present in the world. In the end, a grain price early warning model is established by using ordinal regression algorithm learning from historical data.