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添加4字节 、 2021年1月22日 (五) 19:52
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Another good example is random matrix theory, which can be used to identify the noise in financial correlation matrices.  One paper has argued that this technique can improve the performance of portfolios, e.g., in applied in portfolio optimization.
 
Another good example is random matrix theory, which can be used to identify the noise in financial correlation matrices.  One paper has argued that this technique can improve the performance of portfolios, e.g., in applied in portfolio optimization.
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Another good example is [[random matrix theory]], which can be used to identify the noise in financial correlation matrices.  One paper has argued that this technique can improve the performance of portfolios, e.g., in applied in [[Modern portfolio theory|portfolio optimization]].<ref>{{cite journal |author1=Vasiliki Plerou |author2=Parameswaran Gopikrishnan |author3=Bernd Rosenow |author4=Luis Amaral |author5=Thomas Guhr |author6=H. Eugene Stanley |title=Random matrix approach to cross correlations in financial data |journal=Physical Review E|volume= 65|page= 066126 |year=2002 |doi=10.1103/PhysRevE.65.066126 |pmid=12188802 | issue = 6|arxiv = cond-mat/0108023 |bibcode = 2002PhRvE..65f6126P |s2cid=2753508 }}</ref>
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另一个很好的例子是随机矩阵理论,它可以用来识别金融相关矩阵中的噪声。一篇论文认为,这种技术可以改善投资组合的性能,例如,应用于投资组合优化。
 
另一个很好的例子是随机矩阵理论,它可以用来识别金融相关矩阵中的噪声。一篇论文认为,这种技术可以改善投资组合的性能,例如,应用于投资组合优化。
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There are, however, various other tools from physics that have so far been used, such as fluid dynamics, classical mechanics and quantum mechanics (including so-called classical economy, quantum economics and quantum finance), and the path integral formulation of statistical mechanics.
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Another good example is [[random matrix theory]], which can be used to identify the noise in financial correlation matrices.  One paper has argued that this technique can improve the performance of portfolios, e.g., in applied in [[Modern portfolio theory|portfolio optimization]].<ref>{{cite journal |author1=Vasiliki Plerou |author2=Parameswaran Gopikrishnan |author3=Bernd Rosenow |author4=Luis Amaral |author5=Thomas Guhr |author6=H. Eugene Stanley |title=Random matrix approach to cross correlations in financial data |journal=Physical Review E|volume= 65|page= 066126 |year=2002 |doi=10.1103/PhysRevE.65.066126 |pmid=12188802 | issue = 6|arxiv = cond-mat/0108023 |bibcode = 2002PhRvE..65f6126P |s2cid=2753508 }}</ref>
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There are, however, various other tools from physics that have so far been used, such as fluid dynamics, classical mechanics and quantum mechanics (including so-called classical economy, quantum economics and quantum finance), and the path integral formulation of statistical mechanics.
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There are, however, various other tools from physics that have so far been used, such as [[fluid dynamics]], [[classical mechanics]] and [[quantum mechanics]] (including so-called [[classical economy]], [[quantum economics]] and [[quantum finance]]),<ref name="AK">{{Cite book|title = Probabilistic Economic Theory|last = Anatoly V. Kondratenko|work = Nauka |year = 2015|isbn = 978-5-02-019121-1}}</ref> and the [[path integral formulation]] of statistical mechanics.<ref name=":0">{{Cite book|title = The Unity of Science and Economics: A New Foundation of Economic Theory|last = Chen|first = Jing|publisher = Springer|year = 2015|isbn = |location = https://www.springer.com/us/book/9781493934645|pages = }}</ref>
    
然而,到目前为止,还有其他各种各样的物理学工具被使用,例如流体动力学、经典力学和量子力学(包括所谓的古典经济学、量子经济学和量子金融学) ,以及路径积分表述统计力学。
 
然而,到目前为止,还有其他各种各样的物理学工具被使用,例如流体动力学、经典力学和量子力学(包括所谓的古典经济学、量子经济学和量子金融学) ,以及路径积分表述统计力学。
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The concept of economic complexity index, introduced by the MIT physicist Cesar A. Hidalgo and the Harvard economist Ricardo Hausmann and made available at MIT's Observatory of Economic Complexity, has been devised as a predictive tool for economic growth. According to the estimates of Hausmann and Hidalgo, the ECI is far more accurate in predicting GDP growth than the traditional governance measures of the World Bank.
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There are, however, various other tools from physics that have so far been used, such as [[fluid dynamics]], [[classical mechanics]] and [[quantum mechanics]] (including so-called [[classical economy]], [[quantum economics]] and [[quantum finance]]),<ref name="AK">{{Cite book|title = Probabilistic Economic Theory|last = Anatoly V. Kondratenko|work = Nauka |year = 2015|isbn = 978-5-02-019121-1}}</ref> and the [[path integral formulation]] of statistical mechanics.<ref name=":0">{{Cite book|title = The Unity of Science and Economics: A New Foundation of Economic Theory|last = Chen|first = Jing|publisher = Springer|year = 2015|isbn = |location = https://www.springer.com/us/book/9781493934645|pages = }}</ref>
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The concept of [[economic complexity index]], introduced by the MIT physicist [[Cesar A. Hidalgo]] and the Harvard economist [[Ricardo Hausmann]] and made available at MIT's [[The Observatory of Economic Complexity|Observatory of Economic Complexity]], has been devised as a [[List of countries by future GDP (based on ECI) estimates|predictive tool for economic growth]]. According to the estimates of Hausmann and Hidalgo, the ECI is far more accurate in predicting GDP growth than the traditional governance measures of the [[World Bank]].<ref>{{cite web |url=http://atlas.media.mit.edu/atlas/ |title= The Atlas of Economic Complexity |author1=Ricardo Hausmann |author2=Cesar Hidalgo |publisher= The Observatory of Economic Complexity (MIT Media Lab) |accessdate=26 April 2012|display-authors=etal}}</ref>
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The concept of economic complexity index, introduced by the MIT physicist Cesar A. Hidalgo and the Harvard economist Ricardo Hausmann and made available at MIT's Observatory of Economic Complexity, has been devised as a predictive tool for economic growth. According to the estimates of Hausmann and Hidalgo, the ECI is far more accurate in predicting GDP growth than the traditional governance measures of the World Bank.
      
经济复杂性指数的概念,由麻省理工学院的物理学家 Cesar a. Hidalgo 和哈佛大学的经济学家 Ricardo Hausmann 提出,并在麻省理工学院的经济复杂性观察站提供,已经被设计成经济增长的预测工具。根据 Hausmann 和 Hidalgo 的估计,与世界银行的传统治理措施相比,出口信贷保险在预测 GDP 增长方面要准确得多。
 
经济复杂性指数的概念,由麻省理工学院的物理学家 Cesar a. Hidalgo 和哈佛大学的经济学家 Ricardo Hausmann 提出,并在麻省理工学院的经济复杂性观察站提供,已经被设计成经济增长的预测工具。根据 Hausmann 和 Hidalgo 的估计,与世界银行的传统治理措施相比,出口信贷保险在预测 GDP 增长方面要准确得多。
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The concept of [[economic complexity index]], introduced by the MIT physicist [[Cesar A. Hidalgo]] and the Harvard economist [[Ricardo Hausmann]] and made available at MIT's [[The Observatory of Economic Complexity|Observatory of Economic Complexity]], has been devised as a [[List of countries by future GDP (based on ECI) estimates|predictive tool for economic growth]]. According to the estimates of Hausmann and Hidalgo, the ECI is far more accurate in predicting GDP growth than the traditional governance measures of the [[World Bank]].<ref>{{cite web |url=http://atlas.media.mit.edu/atlas/ |title= The Atlas of Economic Complexity |author1=Ricardo Hausmann |author2=Cesar Hidalgo |publisher= The Observatory of Economic Complexity (MIT Media Lab) |accessdate=26 April 2012|display-authors=etal}}</ref>
      
There are also analogies between finance theory and diffusion theory. For instance, the Black–Scholes equation for option pricing is a diffusion-advection equation (see however  for a critique of the Black–Scholes methodology). The Black–Scholes theory can be extended to provide an analytical theory of main factors in economic activities. Other economists, including Mauro Gallegati, Steve Keen, Paul Ormerod, and Alan Kirman have shown more interest, but also criticized some trends in econophysics. More recently, Vernon L. Smith, one of the founders of experimental economics and Nobel Memorial Prize in Economic Sciences laureate, has used these techniques and claimed they show a lot of promise. Also several scaling laws have been found in various economic data.
 
There are also analogies between finance theory and diffusion theory. For instance, the Black–Scholes equation for option pricing is a diffusion-advection equation (see however  for a critique of the Black–Scholes methodology). The Black–Scholes theory can be extended to provide an analytical theory of main factors in economic activities. Other economists, including Mauro Gallegati, Steve Keen, Paul Ormerod, and Alan Kirman have shown more interest, but also criticized some trends in econophysics. More recently, Vernon L. Smith, one of the founders of experimental economics and Nobel Memorial Prize in Economic Sciences laureate, has used these techniques and claimed they show a lot of promise. Also several scaling laws have been found in various economic data.
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There are also analogies between finance theory and [[diffusion]] theory. For instance, the [[Black–Scholes equation]] for [[option (finance)|option]] pricing is a [[diffusion equation|diffusion]]-[[advection]] equation (see however <ref name="autogenerated2003">{{cite book |author1=Jean-Philippe Bouchaud |author2=Marc Potters |title=Theory of Financial Risk and Derivative Pricing |url=https://archive.org/details/theoryoffinancia0000bouc |url-access=registration |publisher=Cambridge University Press|year= 2003 |accessdate=|work=}}</ref><ref>{{cite journal|doi=10.1080/713665871 | volume=1 | issue=5 | title=Welcome to a non-Black-Scholes world | year=2001 | journal=Quantitative Finance | pages=482–483 | last1 = Bouchaud | first1 = J-P. | last2 = Potters | first2 = M.| s2cid=154368053 }}</ref> for a critique of the Black–Scholes methodology). The Black–Scholes theory can be extended to provide an analytical theory of main factors in economic activities.<ref name=":0" />
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在金融理论和扩散理论之间也有相似之处。例如,期权定价的布莱克-斯科尔斯方程是一个扩散-对流方程(见对布莱克-斯科尔斯方法论的批判)。布莱克-斯科尔斯理论可以扩展为经济活动中主要因素的分析理论。其他经济学家,包括毛罗 · 加勒盖蒂,史蒂夫 · 基恩,保罗 · 奥默罗德和艾伦 · 基尔曼对此表现出了更多的兴趣,但也批评了经济物理学的一些趋势。最近,实验经济学创始人之一、诺贝尔经济学奖得主弗农 · l · 史密斯使用了这些技术,并声称它们显示了很大的希望。在各种经济数据中也发现了一些标度律。
 
在金融理论和扩散理论之间也有相似之处。例如,期权定价的布莱克-斯科尔斯方程是一个扩散-对流方程(见对布莱克-斯科尔斯方法论的批判)。布莱克-斯科尔斯理论可以扩展为经济活动中主要因素的分析理论。其他经济学家,包括毛罗 · 加勒盖蒂,史蒂夫 · 基恩,保罗 · 奥默罗德和艾伦 · 基尔曼对此表现出了更多的兴趣,但也批评了经济物理学的一些趋势。最近,实验经济学创始人之一、诺贝尔经济学奖得主弗农 · l · 史密斯使用了这些技术,并声称它们显示了很大的希望。在各种经济数据中也发现了一些标度律。
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There are also analogies between finance theory and [[diffusion]] theory. For instance, the [[Black–Scholes equation]] for [[option (finance)|option]] pricing is a [[diffusion equation|diffusion]]-[[advection]] equation (see however <ref name="autogenerated2003">{{cite book |author1=Jean-Philippe Bouchaud |author2=Marc Potters |title=Theory of Financial Risk and Derivative Pricing |url=https://archive.org/details/theoryoffinancia0000bouc |url-access=registration |publisher=Cambridge University Press|year= 2003 |accessdate=|work=}}</ref><ref>{{cite journal|doi=10.1080/713665871 | volume=1 | issue=5 | title=Welcome to a non-Black-Scholes world | year=2001 | journal=Quantitative Finance | pages=482–483 | last1 = Bouchaud | first1 = J-P. | last2 = Potters | first2 = M.| s2cid=154368053 }}</ref> for a critique of the Black–Scholes methodology). The Black–Scholes theory can be extended to provide an analytical theory of main factors in economic activities.<ref name=":0" />
       
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