It is generally accepted that herding behavior and overconfidence behavior are unrelated or even mutually exclusive.However,these behaviors can both lead to some similar market anomalies,such as excessive trading volu...It is generally accepted that herding behavior and overconfidence behavior are unrelated or even mutually exclusive.However,these behaviors can both lead to some similar market anomalies,such as excessive trading volume and volatility in the stock market.Due to the limitation of traditional time series analysis,we try to study whether there exists network relevance between the investor’s herding behavior and overconfidence behavior based on the complex network method.Since the investor’s herding behavior is based on market trends and overconfidence behavior is based on past performance,we convert the time series data of market trends into a market network and the time series data of the investor’s past judgments into an investor network.Then,we update these networks as new information arrives at the market and show the weighted in-degrees of the nodes in the market network and the investor network can represent the herding degree and the confidence degree of the investor,respectively.Using stock transaction data of Microsoft,US S&P 500 stock index,and China Hushen 300 stock index,we update the two networks and find that there exists a high similarity of network topological properties and a significant correlation of node parameter sequences between the market network and the investor network.Finally,we theoretically derive and conclude that the investor’s herding degree and confidence degree are highly related to each other when there is a clear market trend.展开更多
A model to explain the dynamic characters of earnings management was developed based on the interactionamong several firms’ disclosure policies. Under the condition of incomplete information, each firm’s earnings ma...A model to explain the dynamic characters of earnings management was developed based on the interactionamong several firms’ disclosure policies. Under the condition of incomplete information, each firm’s earnings man-agement will be influenced by the earnings disclosure policies of other firms. It can lead to "herd behavior" of earningsmanagement. This paper studies the relationship between earnings manipulation and rights issue policy based on thedistribution of earnings after management. The results indicate that Chinese listed companies trend towards controllingROE in the narrow ranges just above 6% and 10% .Therefore, "herd behavior" exists in the earnings management.展开更多
The high energy cosmic-radiation detection(HERD)facility is planned to launch in 2027 and scheduled to be installed on the China Space Station.It serves as a dark matter particle detector,a cosmic ray instrument,and a...The high energy cosmic-radiation detection(HERD)facility is planned to launch in 2027 and scheduled to be installed on the China Space Station.It serves as a dark matter particle detector,a cosmic ray instrument,and an observatory for high-energy gamma rays.A transition radiation detector placed on one of its lateral sides serves dual purpose,(ⅰ)calibrating HERD's electromagnetic calorimeter in the TeV energy range,and(ⅱ)serving as an independent detector for high-energy gamma rays.In this paper,the prototype readout electronics design of the transition radiation detector is demonstrated,which aims to accurately measure the charge of the anodes using the SAMPA application specific integrated circuit chip.The electronic performance of the prototype system is evaluated in terms of noise,linearity,and resolution.Through the presented design,each electronic channel can achieve a dynamic range of 0–100 fC,the RMS noise level not exceeding 0.15 fC,and the integral nonlinearity was<0.2%.To further verify the readout electronic performance,a joint test with the detector was carried out,and the results show that the prototype system can satisfy the requirements of the detector's scientific goals.展开更多
随着分布式光伏发电在有源配电网中的规模化应用,对光伏功率进行准确预测成为提高电网运行效率和可靠性的关键问题。然而,由于不同区域的光照条件、天气变化等因素的差异,传统的功率预测方法在多区域分布式光伏发电中存在一定局限性。...随着分布式光伏发电在有源配电网中的规模化应用,对光伏功率进行准确预测成为提高电网运行效率和可靠性的关键问题。然而,由于不同区域的光照条件、天气变化等因素的差异,传统的功率预测方法在多区域分布式光伏发电中存在一定局限性。为解决这一问题,文章提出了一种基于时序迁移学习算法的有源配电网多区域分布式光伏功率预测方法。首先,文章对分布式光伏时序功率进行预测采用了长短期记忆网络(Long Short Term Memory Network,LSTM)算法,该算法能够有效捕捉时序数据的长期依赖关系,适用于光伏功率的时序预测。其次,针对不同区域光伏发电数据的差异性,本文引入迁移学习的思想,将训练数据较多的区域的分布式光伏模型迁移至训练数据较少的区域,以提高预测模型的泛化能力和准确性。文章利用算例仿真分析验证了时序迁移学习算法在预测有源配电网多区域分布式光伏功率的有效性和实用性。展开更多
基金Project supported by the Youth Program of the National Social Science Foundation of China(Grant No.18CJY057)。
文摘It is generally accepted that herding behavior and overconfidence behavior are unrelated or even mutually exclusive.However,these behaviors can both lead to some similar market anomalies,such as excessive trading volume and volatility in the stock market.Due to the limitation of traditional time series analysis,we try to study whether there exists network relevance between the investor’s herding behavior and overconfidence behavior based on the complex network method.Since the investor’s herding behavior is based on market trends and overconfidence behavior is based on past performance,we convert the time series data of market trends into a market network and the time series data of the investor’s past judgments into an investor network.Then,we update these networks as new information arrives at the market and show the weighted in-degrees of the nodes in the market network and the investor network can represent the herding degree and the confidence degree of the investor,respectively.Using stock transaction data of Microsoft,US S&P 500 stock index,and China Hushen 300 stock index,we update the two networks and find that there exists a high similarity of network topological properties and a significant correlation of node parameter sequences between the market network and the investor network.Finally,we theoretically derive and conclude that the investor’s herding degree and confidence degree are highly related to each other when there is a clear market trend.
文摘A model to explain the dynamic characters of earnings management was developed based on the interactionamong several firms’ disclosure policies. Under the condition of incomplete information, each firm’s earnings man-agement will be influenced by the earnings disclosure policies of other firms. It can lead to "herd behavior" of earningsmanagement. This paper studies the relationship between earnings manipulation and rights issue policy based on thedistribution of earnings after management. The results indicate that Chinese listed companies trend towards controllingROE in the narrow ranges just above 6% and 10% .Therefore, "herd behavior" exists in the earnings management.
基金supported by the National Natural Science Foundation of China(Nos.12375193,11975292,11875304)the CAS“Light of West China”Program+1 种基金the Scientific Instrument Developing Project of the Chinese Academy of Sciences(No.GJJSTD20210009)the CAS Pioneer Hundred Talent Program。
文摘The high energy cosmic-radiation detection(HERD)facility is planned to launch in 2027 and scheduled to be installed on the China Space Station.It serves as a dark matter particle detector,a cosmic ray instrument,and an observatory for high-energy gamma rays.A transition radiation detector placed on one of its lateral sides serves dual purpose,(ⅰ)calibrating HERD's electromagnetic calorimeter in the TeV energy range,and(ⅱ)serving as an independent detector for high-energy gamma rays.In this paper,the prototype readout electronics design of the transition radiation detector is demonstrated,which aims to accurately measure the charge of the anodes using the SAMPA application specific integrated circuit chip.The electronic performance of the prototype system is evaluated in terms of noise,linearity,and resolution.Through the presented design,each electronic channel can achieve a dynamic range of 0–100 fC,the RMS noise level not exceeding 0.15 fC,and the integral nonlinearity was<0.2%.To further verify the readout electronic performance,a joint test with the detector was carried out,and the results show that the prototype system can satisfy the requirements of the detector's scientific goals.
文摘随着分布式光伏发电在有源配电网中的规模化应用,对光伏功率进行准确预测成为提高电网运行效率和可靠性的关键问题。然而,由于不同区域的光照条件、天气变化等因素的差异,传统的功率预测方法在多区域分布式光伏发电中存在一定局限性。为解决这一问题,文章提出了一种基于时序迁移学习算法的有源配电网多区域分布式光伏功率预测方法。首先,文章对分布式光伏时序功率进行预测采用了长短期记忆网络(Long Short Term Memory Network,LSTM)算法,该算法能够有效捕捉时序数据的长期依赖关系,适用于光伏功率的时序预测。其次,针对不同区域光伏发电数据的差异性,本文引入迁移学习的思想,将训练数据较多的区域的分布式光伏模型迁移至训练数据较少的区域,以提高预测模型的泛化能力和准确性。文章利用算例仿真分析验证了时序迁移学习算法在预测有源配电网多区域分布式光伏功率的有效性和实用性。
