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1.
We analyze the institutional determinants of U.S. financial market regulation with a general model of the policy-making process in which legislators delegate authority to regulate financial risk at both the firm and systemic levels. The model explains changes in U.S. financial regulation leading up to the financial crisis. We test the predictions of the general model with a novel, comprehensive data set of financial regulatory laws enacted specifically between 1950 and 2009. The theoretical and empirical analysis finds that economic and political factors impact Congress’ decision to delegate regulatory authority to executive agencies, which in turn impacts the stringency of financial market regulation, and our estimation results indicate that political factors may have been stronger and resulted in inefficiencies.  相似文献   
2.
中国银保监会出台联合授信管理制度旨在抑制企业过度融资行为,提高资金使用效率。为检验该制度是否达到预期效果,采用2014—2018年沪深A股上市公司样本,运用非平衡面板数据回归实证考察联合授信机制对企业非效率投资的影响,发现联合授信对企业的非效率投资(包含投资不足和投资过度)呈现显著抑制效应,其中联合授信对投资过度的抑制效应最大。进一步研究发现,相较于国有、大规模、低负债、高盈利等异质属性的企业,联合授信对非国有、小规模、高负债、低盈利等属性企业的非效率投资(尤其是投资过度)抑制效应更为显著。  相似文献   
3.
We suggest that the distortion of the positive risk–return relation in the ICAPM is a consequence of trading by informed investors to exploit mispricing. We hypothesize and demonstrate that a non-positive (strongly positive) risk–return relation following positive (negative) market returns is attributed to short-selling (purchasing) of overpriced (underpriced) stocks along with optimistic (pessimistic) expectations conditional on good (bad) market news. We verify this asymmetry in the risk–return relation through the indirect risk–return relation conditional on good (bad) market news. We also find that the attenuation (reinforcement) of the positive risk–return relation is more profound in high- (low-) sentiment periods.  相似文献   
4.
Using data on Brazil, Colombia, Mexico, the Philippines, Russia and Turkey, our empirical results show that the exchange rates of their currencies have adequate explanatory power in explaining their US dollar-denominated sovereign bonds, particularly in the post-global financial crisis period. We develop a two-factor pricing model with closed-form solutions for the sovereign bonds in which the correlated factors are foreign exchange rates and US risk-free interest rates that follow a double square-root process relevant in the low interest rate environment. The numerical results and associated error analysis show that the model credit spreads can broadly track the market credit spreads.  相似文献   
5.
A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.  相似文献   
6.
The advance of cryptocurrencies has sparked wide concern over their interplay with the existing global financial market. This paper analyzes the risk spillover relation between cryptocurrencies and major financial assets, and unravels how cryptocurrencies could influence global financial systemic risk. We find that cryptocurrencies function as a separate risk source from traditional assets. Major legislative, financial and technological events in the cryptocurrency market may affect risk spillover dynamics. Although the overall penetration of cryptocurrencies is not yet deep, introducing cryptocurrency can significantly increase the systemic risk to traditional markets during low risk level episodes.  相似文献   
7.
8.
Companies increasingly face the need for transformation in today’s rapidly changing business environment, characterized by major shifts in technology, regulation, and customer behavior. A lack of strategic risk insight and foresight leaves many incumbents insufficiently prepared in the face of such deep uncertainty. We argue that traditional risk management falls short because it predominantly focuses on strategy execution while leaving strategy formulation largely untouched. Moreover, an administrative-heavy risk management process can create strategic inertia and a misleading sense of control. In today’s dynamic business context, companies must not only increase the speed and impact of their strategy execution but also continuously explore the development of new strategies in response to disruptive events or emerging opportunities. Our research shows how leading companies develop a strategic risk management (SRM) capability to increase their resilience and agility in response to deep uncertainty. SRM takes a strategic, forward-looking perspective and focuses on strengthening processes, people, and practices for purposefully integrating risk into the strategy formulation process. This article offers a framework with three proven configurations of content and timing integration, risk management roles, and leading practices that enable effective SRM.  相似文献   
9.
The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.  相似文献   
10.
The purpose of the paper is to propose an original proprietary proxy of a firm's litigation risk. We extend the scope of litigation risk outside of the conflicts with shareholders and the domain of security litigation. We demonstrate that the source of the risk of litigation can be found in the firm's policies and in its management's operational or strategic decisions, even if a sector conditioning effect exists. Based on a sample of 465 US M&A transactions between 2000 and 2014, we provide evidence that the level of litigation risk, at the acquirer's level, has a positive and significant impact on the takeover premium. We also provide evidence that a significant relationship exists between the acquirer's litigation risk and the means of payment. An extension of the sample to international transactions is used as a robustness check; it confirms the previous results.  相似文献   
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