农为邦本，固本兴邦。作为国民经济的基础性支柱，农业经济的现代化进程与国家战略发展深度耦合。在“科技兴农”战略指引下，我国将农业科技创新提升至国家创新体系建设的关键层级。农业科技企业作为农业现代化的重要实施主体，承担着技术成果转化、产业模式革新的双重使命。随着农业供给侧结构性改革的深化，资本市场对农业科技领域的关注度持续升温，并购重组、股权投资等市场行为日趋活跃。然而受制于行业发展特性——企业成长周期普遍较短、行业可比样本稀缺、财务数据与技术创新价值关联度存疑等估值瓶颈，传统估值体系难以精准捕捉生物育种、数字农业等新兴业态的农业科技企业价值创造逻辑，导致农业科技企业普遍面临融资渠道受阻的困境。本研究聚焦这一核心痛点，着力构建适配农业科技企业特征的估值方法论体系，为破解“技术价值显性化、无形资产资本化”的行业难题提供解决方案，助力农业科技企业在资本市场实现价值发现与战略升级。

由于农业科技企业存在收入增长的非线性特征、季节性周期效应显著、研发资本密集度高等经营特性，导致企业现金流与成本收益关系存在高度不稳定性，使得传统估值模型面临着适用性瓶颈。本研究通过系统性文献研究确立方法论框架，继而对企业价值评估理论体系展开解构，通过分析对比传统价值评估方法及其局限性，以及实物期权法的理论基础与适用性后，本研究认为基于实物期权框架下的Schwartz-Moon模型通过集成收入增长弹性、成本波动系数等动态风险参数体系，有效契合农业科技企业的技术迭代特征与市场波动属性，系统考察产业技术转化特征与价值驱动要素后，确立该模型在农业科技企业估值中的评估优势。研究选取农业科技行业的农机流通领域标杆企业吉峰科技作为实证对象，通过行业集中度分析与企业创新度评估的双重验证确立其样本代表性。在模型应用中，针对产品研发周期、智能农机渗透率等产业特性对长期收入波动率、离散化时间增量等核心参数实施场景化校准，同时构建B-S期权模型作为对照组实施横向估值比较，结合二级市场实际股价波动区间验证模型有效性，最终通过参数敏感性溯源揭示收入相关参数、成本控制参数等价值敏感因子，并以此为基础为吉峰科技提供价值提升对策，最后根据Schwartz-Moon模型应用情况分析其局限性并提出相关应用建议。理论推导表明，Schwartz-Moon模型与农业科技行业的经营特性高度适配，其动态随机估值框架为农业科技企业开辟了创新性估值路径。在案例应用环节，本文对Schwartz-Moon 模型中成本波动率、行业增长率、超额收益时间等参数的获取机制进行调整，实现对模型本土化应用的参数优化，从而提升模型适配精度。

Agriculture is the foundation of the country, solid foundation. As the basic pillar of the national economy, the modernization process of the agricultural economy is deeply coupled with the national strategic development. Under the guidance of the strategy of " invigorating agriculture through science and technology, " China has promoted agricultural science and technology innovation to the key level of national innovation system construction. As an important implementation subject of agricultural modernization, agricultural science and technology enterprises undertake the dual mission of technological achievements transformation and industrial model innovation. With the deepening of the supply-side structural reform of agriculture, the capital market has continued to pay more attention to the field of agricultural science and technology, and market behaviors such as mergers and acquisitions and equity investment have become increasingly active. However, due to the valuation bottlenecks such as the short growth cycle of enterprises, the scarcity of comparable samples in the industry, and the doubtful correlation between financial data and technological innovation value, the traditional valuation system is difficult to accurately capture the value creation logic of agricultural science and technology enterprises in emerging formats such as biological breeding and digital agriculture, which leads to the difficulty of financing channels for agricultural science and technology enterprises. Focusing on this core pain point, this study focuses on constructing a valuation methodology system that adapts to the characteristics of agricultural science and technology enterprises, and provides solutions to solve the industry problem of ' explicit technical value and capitalization of intangible assets ', so as to help agricultural science and technology enterprises realize value discovery and strategic upgrading in the capital market.

Due to the non-linear characteristics of income growth, significant seasonal cycle effect and high R & D capital intensity of agricultural science and technology enterprises, the relationship between cash flow and cost-benefit of enterprises is highly unstable, which makes the traditional valuation model face the bottleneck of applicability. This study establishes a methodological framework through systematic literature research, and then deconstructs the theoretical system of enterprise value evaluation. After analyzing and comparing the traditional value evaluation methods and their limitations, as well as the theoretical basis and applicability of the real option method, this study believes that the Schwartz-Moon model based on the real option framework effectively fits the technical iteration characteristics and market fluctuation attributes of agricultural science and technology enterprises by integrating dynamic risk parameter systems such as income growth elasticity and cost fluctuation coefficient. After systematically examining the characteristics of industrial technology transformation and value-driven factors, the evaluation advantages of the model in the valuation of agricultural science and technology enterprises are established. The research selects Ji Feng Technology, a benchmark enterprise in the field of agricultural machinery circulation in the agricultural science and technology industry, as the empirical object, and establishes its sample representativeness through the dual verification of industry concentration analysis and enterprise innovation evaluation. In the application of the model, the core parameters such as long-term income volatility and discrete time increment are calibrated according to the industrial characteristics such as product development cycle and intelligent agricultural machinery penetration rate. At the same time, the B-S option model is constructed as the control group to implement horizontal valuation comparison, and the validity of the model is verified by combining the actual stock price fluctuation range of the secondary market. Finally, the value-sensitive factors such as income-related parameters and cost control parameters are revealed through parameter sensitivity traceability, and based on this, the value promotion countermeasures are provided for Ji feng Technology. Finally, according to the application of Schwartz-Moon model, its limitations are analyzed and relevant application suggestions are put forward. Theoretical derivation shows that the Schwartz-Moon model is highly compatible with the operating characteristics of the agricultural science and technology industry, and its dynamic random valuation framework opens up an innovative valuation path for agricultural science and technology enterprises. In the case application link, this paper adjusts the acquisition mechanism of parameters such as cost volatility, industry growth rate, and excess return time in the Schwartz-Moon model to optimize the parameters of the localized application of the model, thereby improving the model adaptation accuracy.

[1] lrving Fisher. The Nature of Capital and Income[M]. New York: The Macmillan Co, 1906:15-20.

[2] lrving Fisher. The Rate of Interest: Its Nature, Determination and Relation to Economic Phenomena[M]. New York: The Macmillan Co, 1930.

[3] Modigliani F, Miller M H. The Cost of Capital Corporation Finance and The Theory of Investment[J]. American Economic Review, 1959, 48(4):443-453.

[4] William F. Sharpe. Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk[J]. The Journal of Finance, 1964, 19(3):425-442.

[5] Fischer Black and Myron Scholes. The Pricing of Options and Corporate Liabilities[J]. Journal of Political Economy, 1973, 81(3):637-659.

[6] 刘淑莲.企业价值评估与价值创造战略研究——两种价值模式与六大驱动因素[J].会计研究,2004,(09):67-71.

[7] 朱建娣.风险企业价值评估模型的改进围[J].统计与决策,2005(08):152-153.

[8] 邹纪元,王阳.江苏连云港港口股份有限公司企业价值评估研究[J].现代商业,2019,(08):121-123.DOI:10.14097/j.cnki.5392/2019.08.064.

[9] 高娜.收益法下高新技术企业价值评估案例分析[J].财会学习,2020,252(07):98-99.

[10] 张喆,颜菁,徐剑刚,等.物联网生态企业的价值评估模型及应用案例[J].研究与发展管理,2023,35(06):184-194.DOI:10.13581/j.cnki.rdm.20221505.

[11] 郝晋宏.企业价值评估中利用审计报告的关注事项[J].财务与会计,2024,(23):44-46.

[12] 周盛.高新技术企业价值影响因素的实证研究[J].企业家天地,2012,477(07):36-37.

[13] 刘宏岩.高新技术企业的投资价值评估[J].企业改革与管理,2018,(05):4-5.DOI:10.13768/j.cnki.cn11-3793/f.2018.0534.

[14] 范风珂,孙如妨.高新技术企业价值评估存在的问题及方法研究[J].中国资产评估,2020,246(09):24-27.

[15] 赵振洋,张渼.科创板生物医药企业价值评估研究-基于修正的FCFF 估值模型[J].中国资产评估,2019,236(11):8-16.

[16] 李涛.基于自由现金流量模型对科创板上市公司的估值分析——以嘉元科技为例[J].商业会计,2020,677(05):45-48.

[17] 胡晓明,胡辰阳,姚思嘉.基于Schwartz-Moon模型的科创企业值评估——以睿创微纳为例[J].市场周刊,2022,35(10):5-8+12.

[18] 田庆锋,徐朗.基于轻量级卷积神经网络的科技型中小企业估值研究[J].科技管理研究,2023,43(14):49-57.

[19] 金燕,刘健,何召滨.独角兽企业培育体系与估值模型探析——基于国家电投氢能科技的案例分析[J].管理会计研究,2024,(01):85-94.

[20] Schwartz ES, Moon M. Rational Pricing of Internet Companies[J]. Financial Analysts Journal, 2000, 56(3): 62-75.

[21] Schwartz E S, Moon M. Rational Pricing of Internet Companies Revisited[J]. The Financial Review, 2001, 36(4):7-26.

[22] Klobucnik J, Sievers S. Valuing High Technology Growth Firms[J]. Journal of Business Economics, 2013.83(9):947-984.

[23] Mchawrab S, Dubreuille S, Lleo S. Schwartz and Moon Valuation Model: Evidence from IT Companies[J]. Ssrn Electronic Journal, 2011, 56(7):127-139

[24] Baek C, Dupoyet B V, Prakash A J. Debt and Equity Valuation of it Companies: A Real Option Approach[J]. Social Science Electronic Publishing, 2004.

[25] Schosser J, Heiko Ströbele. What is the value of Facebook? Evidence from the Schwartz/Moon model[J]. Journal of Risk Finance, 2019, 20(3):267-290.

[26] Doffou A. The Valuation of Internet Companies[J]. Journal of Applied Financial Research,2014, volume i 2014.

[27] 郑建明,范黎波.不确定性、实物期权与企业价值—基于简约Schwartz-Moon模型的分析[J].数学的实践与认识,2008,(05):42-48.

[28] 戴晓凤,李检华.高科技上市公司内在价值评估:基于连续型实物期权模型的实证研究[J].财经理论与实践,2009,30(01):41-45.

[29] 邢凯华.S-M期权定价模型在价值评估中的应用研究[J].新西部,2011(06):84-85.

[30] 王治,李馨岚.互联网企业价值评估模型比较研究[J].财经理论与实践,2021,42(05):75-82.DOI:10.16339/j.cnki.hdxbcjb.2021.05.010.

[31] 权忠光,阮咏华,叶陈刚,等.基于Schwartz-Moon模型的大数据企业价值评估及其影响因素研究[J].中国注册会计师,2022,(11):53-58.DOI:10.16292/j.cnki.issn1009-6345.2022.11.024.

[32] 袁庆庆,钟美.基于S-M模型的“专精特新”企业价值评估研究——以芯海科技为例[J].现代营销(下旬刊),2024,(07):59-61.DOI:10.19932/j.cnki.22-1256/F.2024.07.059.

[33] 陈文浩,李明.基于Schwartz-Moon模型的新能源汽车企业价值评估[J].河北企业,2024,(10):108-111.DOI:10.19885/j.cnki.hbqy.2024.10.025.

[34] Black, Fischer, Scholes, Myron S. The Pricing of Options and Corporate Liabilities[J]. Journal of Political Economy, 1973, 81(3):637-654.

[35] Myers SC. The Determinants of Corporate Borrowing[J]. Journal of Financial Economics, 1977, 5(2):147-175.

[36] Merton RC. Theory of rational option pricing[J]. World entific Book Chapters, 2005, 6(5):73-87.

[37] Sudhakar A, Srikanth P, Determinants of In-the-money Expiration of Call Option Contracts-An Empirical Evidence from Call Options on Nifty 50 Index[J]. Global Business Review, 2016, 17(6):1373-1387.

[38] Kazemi S, Dehghan M, Bastani A F, et al. Asymptotic expansion of solutions to the Black-Scholes equation arising from American option pricing near the expiry[J]. Journal of Computational and Applied Mathematics, 2017, 81(3):637-654.

[39] Frino A, Lodh S C, Khan E, et al. The Black Scholes Call Option Pricing Modeland the Australian Options Market: Where are We after 15 Years[J]. The international journal of accounting and business society, 2019, 1(1): 40-57.

[40] 张玉行,杨红.Black-Scholes 及其优化模型在企业价值评估中的分析研究[J].生产力研究,2012(11):225-226+234.

[41] 王未卿,洪贤,邓静涛.对Black-Scholes期权定价模型的修正及检验[J].财会月刊,2012(33):56-59.

[42] 彭兆东.实物期权理论在企业价值评估中的运用——基于对乐视网的价值评估[J].市场周刊(理论研究),2015(08):12-13.

[43] 代雪珍,曹高飞,乔亚琴.带红利的 Black-Scholes 期权定价公式的简化推导[J].科技经济导刊,2019,27(10):168.

[44] 王曦,耿长波,马宝玲,郜峰.现金流折现与实物期权模型在石油公司估值中的对比研究——以中海油收购 Nexen 公司为例[J].资源与产业,2013,15:150-156.

[45] 彭迪,郭化林.基于复合实物期权模型的PPP项目价值评估[J].中国资产评估,2019(10):37-40.

[46] 罗艾筠.基于实物期权法的文化创意类公司价值评估[J].财会通讯,2020,(12):131-133+146.DOI:10.16144/j.cnki.issn1002-8072.2020.12.027.

[47] 谷锦锦,李志刚.基于模糊实物期权的独角兽企业价值评估[J].科技创业月刊,2021,34(03):9-12.

[48] 魏雪莉.基于EVA和改进的B-S模型的科创板企业价值评估研究[J].中国物价,2022,(02):63-66.

[49] 陈希颖,骆公志.基于DCF与B-S定价模型的人工智能企业价值评估研究[J].中国物价,2024,(08):21-26.

[50] 赖玥,高瑞笛.基于FCFF模型和B-S模型的MCN企业价值评估研究——以遥望科技为例[J].现代商业,2025,(05):176-180.DOI:10.14097/j.cnki.5392/2025.05.004.

[51] 祝兰芳,王征兵.农业企业价值评估方法的选择与完善[J].财会月刊,2006(35):40-41.DOI:10.19641/j.cnki.42-1290/f.2006.35.018.

[52] 谢非,刘婷婷.农业上市公司股价与企业价值比较研究[J].价格理论与实践,2018,414(12):139-142DOI:10.19851/j.cnki.cn11-1010/f2018.12.032.50.

[53] 于琪,王颖驰.浅析经济增加值(EVA)在企业价值提升中的应用——以L农业公司为例[J]..山西农经,2022(15):144-146.DOI:10.16675/j.cnki.cn14-1065/f.2022.15.046.

[54] 杜彦坤,安同良.农业科技企业的界定及管理现代化演进分析[J].经济研究参考,2002(75):14-18.DO1:10.16110/j.cnki.issn2095-3151.2002.75.003.

[55] 程春,杨立社.农业科技企业知识产权质押融资问题思考[J].科技管理研究,2015,35(03):126-129+141.

[56] 王京雷,陈欣,李晓阳.乡村产业振兴科技园区技术诱致模式研究[J].农业经济问题,2023,(06):135-144.DOI:10.13246/j.cnki.iae.20221110.002.

[57] 李佳聪,李隆方舟,李炜淼,等.实物期权法在农业企业价值评估中的应用研究[J].中国市场,2020,(09):88-89.DOI:10.13939/j.cnki.zgsc.2020.09.088.

[58] 杜甜甜.基于蒙特卡洛模拟的EVA企业价值评估——以中芯国际为例[J].天津经济,2025,(03):98-100.Hafeez. Corporate governance and firm efficiency: an application of internet companies of China[J].Emerging Markets Finance and Trade,2020,56（12）:2874-2890

[59] 张嘉伦,宋夏云.基于蒙特卡洛模拟—实物期权法的医药专利价值评估案例研究——以天士力为例[J].财务管理研究,2024,(08):18-28.

[60] 孙丽君,冯斌斌,陈天飞.基于鞅论的灰狼优化算法全局收敛性分析[J].控制与决策,2022,37(11):2839-2848.DOI:10.13195/j.kzyjc.2021.0645.

[61] 邢小玉,王东立.可违约金融市场中具有有限预期损失约束的最优投资策略[J].南开大学学报(自然科学版),2025,58(01):1-11.