The Empirical Research of Price Fluctuation Rules and Influence Factors with Fresh Produce Sequential Auction

Research the law of price fluctuations under the fresh agricultural sequential auction based on the auction theory; use Kunming International Flower Auction Centre (KIFA) as an example for empirical analysis. After appoint the study object which is the rose of 80 cm length for the B level (network) named Corolla and the study period, the price trends. Establish some reasonable assumptions factors, and then use the multiple linear regressions for analysis these variables. Eventually find that the previous auction price, auction duration, the buyer type (size) and the seller types (size) and other factors have a certain degree of influence on the auction price fluctuations. Introduction Sequential auction belongs to multi-object auctions that are multi objects being traded in sequence with an open price auction mechanism which have a rapid trading speed and a lower trading cost. Along with the developing of auction practice and auction theory, the market of western countries first found that sequential auction is very suit for trading fresh produce. By importing sequential auction can solve a lot of problems such as lower marketing efficiency, higher marketing cost and so on. It has many benefits for agricultural industrialization. However, because of the character that last transaction price will affect current transaction in sequential auction; the transaction price will generate a great fluctuate with other factors over time. Obviously, this transaction price fluctuate is disadvantageous for the auction participant. How to analysis the factors of influencing price formation and price fluctuate using the rule of price fluctuate to adopt reasonable measures control the price fluctuate or made it tend to smooth and steady is the mainly problem to be solved in this paper. The Fluctuation Rules of Fresh Produce Price in Sequential Auction The fluctuation rules of fresh produce price in sequential auction in this article are based on the long term auction practice of Kunming International Flower Auction Market which is short for KIFA. The KIFA was set up and put into operation at 2002; it is a large professional flower trading market and it engage in fresh flowers auction. Nowadays, the KIFA is become the biggest flower auction and trading market in China even in Asia. In this section, we will analyze the auction price fluctuation phenomenal in the KIFA. All the auction data are come from the transaction history database of KIFA, 5,621,604 transaction data from Dec 1st 2007 to Feb 2nd 2010. According to the preliminary analysis of these almost six million data, we find there is a superfine rose named Carola in KIFA has the biggest transaction frequency and best trading volume and largest transaction amount while the trading class of Carola is class B and the length is 80cm. So this article chose the Carola with 80cm length and B class as the mainly research object. And we choose the whole year data of 2008 and 2009 of KIFA for a deeply analyze in order to find out the rules and the influence of price fluctuation. 6th International Conference on Sensor Network and Computer Engineering (ICSNCE 2016) © 2016. The authors Published by Atlantis Press 601 Figure 1. The average price fluctuation contrast of Carola with 80cm length and B class between 2008 and 2009 The Fig. 1 directly shows that in every year, from May to August, the price fluctuation became stable. Analyze the data further; we calculate the mean value and standard deviation of Carola with 80cm length and B class transaction price during every moth in the year of 2008 and 2009. Table 1 The average price and standard deviation of Carola with 80cm length and B class in each month of 2008 Jan. Feb. Mar. Apr. May. June. July. Aug. Sept. Oct. Nov. Dec. Avg. Price 0.87 1.04 0.74 0.37 0.22 0.58 0.24 0.35 0.41 0.57 1.43 1.44 SD 0.23 0.59 0.19 0.13 0.11 0.19 0.12 0.12 0.15 0.13 0.27 0.17 Table 2 The average price and standard deviation of Carola with 80cm length and B class in each month of 2009 Jan. Feb. Mar. Apr. May. June. July. Aug. Sept. Oct. Nov. Dec. Avg. Price 1.26 0.96 1.28 0.25 0.30 0.30 0.40 0.67 0.47 0.84 1.04 1.10 SD 0.31 0.50 0.47 0.12 0.10 0.12 0.21 0.36 0.17 0.19 0.27 0.19 Table 3 and Table 4 show the classification of daily transaction price fluctuation trends of Carola with 80cm length and B class in May 2008 and May 2009. Among them, the data of May 1st 2008, May 1st and May 28th 2009 are missing. Table 3 The classification of daily price fluctuation trends of Carola with 80cm length and B class in each day of May 2008 Trends Date Total days Up May 5th, May 9th, May 13th, May 17th, May 18th, May 20th, May 21st, May 22nd, May 28th, May 30th 10 Down May 2nd, May 3rd, May 6th, May 8th, May 10th, May 11th, May 12th, May 15th, May 16th, May 24th, May 25th, May 26th, May 27th 13 Mix May 4th, May 7th, May 14th, May 19th, May 23rd, May 29th, May 31st 7 0 1 2 3 1 . 2 . 3 . 4 . 5 . 6 . 7 . 8 . 9 . 1 1 1 Average price of 2008