Abstract
At present, the mortgage loan of farmland contractual management right is in the pilot and exploration stage. In this overall environment, how to formulate an appropriate pricing model according to the particularity of farmland property right and the actual situation is the theoretical support to be solved. On the basis of theoretical analysis, model construction and analysis of existing problems, this paper establishes a binary tree pricing method of farmland management right mortgage based on machine learning and complex network algorithm. This paper analyzes the basic theory of rural land management right mortgage pricing and constructs the pricing model. Based on the Markov decision process model of dynamic pricing problem, this paper proposes the solution algorithm and proposes the dynamic pricing solution for this paper from two aspects of value function and strategy gradient reinforcement learning algorithm. This paper analyzes the basic principle of A3C algorithm and expounds the algorithm design and neural network architecture for the specific dynamic pricing problem in this paper. The dynamic pricing method of rural land mortgage based on deep reinforcement learning designed in this paper can well predict the real price of rural land management right mortgage pricing and effectively optimize the mortgage loan environment of rural contracted land management right, which is the innovation of financial system in the current new rural construction.
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References
Xu C, Sun Y, Liao L (2017) Pricing and risk analysis of reverse mortgage based on stochastic interest rate. J Tongji Univ 45(1):144–151
Matthias E, Mareike L (2020) Update diagnosis and therapeutic management of pulmonary embolism. Dtsch Med Wochenschr 145(11):710–715
Xue R, Wang C et al (2018) A new method for soil health assessment based on analytic hierarchy process and meta-analysis. Sci Total Environ 650(Pt 2):2771–2777
Liu Q, Yang F, Pu Y et al (2016) Segmentation of farmland obstacle images based on intuitionistic fuzzy divergence. J Intell Fuzzy Syst 31(1):163–172
Wang C, Zhao S, Kalra A et al (2019) Webpage depth viewability prediction using deep sequential neural networks. IEEE Trans Knowl Data Eng 31(3):601–614
Hu GB, Zhang RJ et al (2015) The long correlation option pricing based on binary tree model. APR 3:765–770
Lee Y, Lee J (2015) Binary tree optimization using genetic algorithm for multiclass support vector machine. Expert Syst Appl 42(8):3843–3851
Han X, Feng B, Pu X (2015) Modelling decision behaviours in pricing game of closed-loop supply chains. J Oper Res Soc 66(6):1052–1060
Kiran R, Shekhar S, Rau R et al (2016) List price optimization using customized decision trees. Lect Notes Comput Sci 9728:438–444
Souravlas S, Sifaleras A (2017) Binary-tree based estimation of file requests for efficient data replication. IEEE Tran Parallel Distrib Syst 1839–1852
Liu Y, Hao P, Zhang P et al (2018) Dense convolutional binary-tree networks for lung nodule classification. IEEE Access 6:49080–49088
Simiyu HM, Waititu AG, Akinyi JA (2019) Comparative analysis of the artificial neural networks options pricing model under constant and time-variant volatilities. J Math Stat 15(1):158–175
Coon JP, Badiu MA, Liu Y et al (2019) Binary-tree encoding for uniform binary sources in index modulation systems. IEEE J Sel Top Signal Process 13(6):1270–1285
Stubnova M, Marta U, Hudakova J et al (2020) Estimation of residential property market price: comparison of artificial neural networks and hedonic pricing model. Emerg Sci J 4(6):530–538
Persis KH, Jasmine L, Shoba SJG (2018) A reversible approach for data concealment using binary tree algorithm in JPEG bitstream. Int J Pure Appl Math 119(12):14413–14419
Zhao H, Zhao Z, Chatterjee R et al (2017) Pricing variance, gamma and corridor swaps using multinomial trees. Soc Sci Electron Publ 25(2):7–21
Atkins R, Mcdiarmid C (2015) Extremal distances for subtree transfer operations in binary trees. Ann Comb 2(4):27–33
Zhang M, Liu X, Liu Y (2019) Study on loan pricing model of commercial banks based on artificial neural network. J Math Finance 09(4):667–674
Acknowledgements
The authors acknowledge the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Grant: 20YJCZH030), the Guangdong Basic and Applied Basic Research Foundation (Grant: 2020A1515011382), the 13th five year planning project of philosophy and Social Sciences in Guangdong Province (Grant: GD20CYJ03).
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Fu, Z., Hu, S. Binary tree pricing method of farmland management right mortgage based on machine learning and complex network algorithm. Neural Comput & Applic 34, 6625–6636 (2022). https://doi.org/10.1007/s00521-021-06071-x
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DOI: https://doi.org/10.1007/s00521-021-06071-x