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We examine corporate security choice by simulating an economy populated by adaptive agents who learn about the structure of security returns and prices through experience. Through a process of evolutionary selection, each agent gr...
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We examine corporate security choice by simulating an economy populated by adaptive agents who learn about the structure of security returns and prices through experience. Through a process of evolutionary selection, each agent gravitates toward strategies that generate the highest payoffs. Despite the fact that markets are perfect and agents maximize value, a financing hierarchy emerges in which straight debt dominates other financing choices. Equity and convertible debt display significant underpricing. In general, the smaller the probability of loss to outside investors, the more likely the firm is to issue the security and the smaller the security's underpricing.
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We present a dynamic model of corporate risk management and managerial career concerns. We show that managers with low (high) initial reputation have high (low) career concerns about keeping their jobs and receiving all future inc...
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We present a dynamic model of corporate risk management and managerial career concerns. We show that managers with low (high) initial reputation have high (low) career concerns about keeping their jobs and receiving all future income. These managers are more likely to speculate (hedge) early in their careers. In the later stage of their careers when managers have less career concerns, there is no speculative motive for self interested managers. On the other hand, highly reputable managers have minimal career concerns and they engage in neither hedging nor speculation early in their careers, but they may choose to hedge after poor early performance.
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A one-layer recurrent neural network with a discontinuous activation function is proposed for linear programming. The number of neurons in the neural network is equal to that of decision variables in the linear programming problem...
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A one-layer recurrent neural network with a discontinuous activation function is proposed for linear programming. The number of neurons in the neural network is equal to that of decision variables in the linear programming problem. It is proven that the neural network with a sufficiently high gain is globally convergent to the optimal solution. Its application to linear assignment is discussed to demonstrate the utility of the neural network. Several simulation examples are given to show the effectiveness and characteristics of the neural network.
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Purpose - The paper aims to test the rational-expectations hypothesis using data from the Chinese stock market.
Design/methodology/approach - The rational-expectations hypothesis plays a critical role in economic and financial st...
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Purpose - The paper aims to test the rational-expectations hypothesis using data from the Chinese stock market.
Design/methodology/approach - The rational-expectations hypothesis plays a critical role in economic and financial studies. However, it is unclear whether this hypothesis is consistent with real-world decision making since existing empirical results are mixed. This paper tests the hypothesis directly using survey data from China's stock market by developing a technique to analyze discrete or limited independent-variable models.
Findings - The paper shows that in China's stock market survey forecasts are overly optimistic, especially with positive information, and can be improved slightly using past information. Originality/value - The paper develops a technique to analyze the discrete or limited independent-variable model. Testing with Chinese stock market data provides some insights into the characteristics of emerging markets.
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This paper presents new stability results for recurrent neural networks with Markovian switching. First, algebraic criteria for the almost sure exponential stability of recurrent neural networks with Markovian switching and withou...
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This paper presents new stability results for recurrent neural networks with Markovian switching. First, algebraic criteria for the almost sure exponential stability of recurrent neural networks with Markovian switching and without time delays are derived. The results show that the almost sure exponential stability of such a neural network does not require the stability of the neural network at every individual parametric configuration. Next, both delay-dependent and delay-independent criteria for the almost sure exponential stability of recurrent neural networks with time-varying delays and Markovian-switching parameters are derived by means of a generalized stochastic Halanay inequality. The results herein include existing ones for recurrent neural networks without Markovian switching as special cases. Finally, simulation results in three numerical examples are discussed to illustrate the theoretical results.
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The digital hardware realization of a recurrent neural network for solving the assignment problem is presented. The design is based on an analog neural network and is mapped to a one-dimensional systolic array for parallel process...
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The digital hardware realization of a recurrent neural network for solving the assignment problem is presented. The design is based on an analog neural network and is mapped to a one-dimensional systolic array for parallel processing. The processing elements are connected with a ring topology that reduces the overhead in controlling the pipeline. The design was simplified by exploiting regularities in the data to eliminate the need for multipliers and dividers in hardware implementation. The results of implementation and verification based on field programmable gate array device show the feasibility of the digital neural network approach to the assignment problem.
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A typical squall line with damaging wind and hailstones occurred on 28 April 2006 in Shandong Province, middle eastern China, and caused great economic loss. The characteristics of cloud-to-ground lightning (CG) in the squall line...
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A typical squall line with damaging wind and hailstones occurred on 28 April 2006 in Shandong Province, middle eastern China, and caused great economic loss. The characteristics of cloud-to-ground lightning (CG) in the squall line were studied in detail by combining the data from the ground-based CG location network, two Doppler radars and the Lightning Imaging Sensor on the TRMM satellite. Results show that positive CG flashes accounted for 54.7% of the total CG flashes. During the initial developing stage, the CG flash rate was lower than 0.5 fl min~(-1) and most of the CG flashes were positive. It increased significantly, up to 4.5fl min~(-1), along with the rapid development of the squall line, and the percentage of positive CG was more than 75% during this period. The CG flash rate began to decrease but the percentage of negative CG flash increased gradually and exceeded that of positive CG during the mature and dissipating stages. Positive CG flashes tended to occur on the right flank and negative ones on the left flank. Strong wind at the surface occurred in or near the regions with dense positive CG flashes. Almost all positive CG flashes occurred near the strong radar echo regions, in the front parts of the squall line. However, the negative CG flashes almost exclusively occurred in the regions with weak and uniform radar echoes. The total flash rate in the storm was very high, up to 136fl min~(-1), and its ratio of intracloud flashes (IC) to CG flashes was 35:1. Dense positive CG flashes corresponded to updraft regions, they did not occur in the core of the updraft, but just behind and close to the main updraft instead. The rear inflow jet, between 3 and 6 km, played an important role in the formation of the bow echo and very strong wind at surface. The CG distribution features in the squall line were obviously different from that of an ordinary MCS. The charge structure could be roughly described as an inverted charge structure.
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We have produced a draft sequence of the rice genome for the most widely cultivated subspecies in China, Oryza sativa L. Spp. indica, by whole-genome shotgun sequencing. The genome was 466 megabases in size, with an estimated 46,0...
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We have produced a draft sequence of the rice genome for the most widely cultivated subspecies in China, Oryza sativa L. Spp. indica, by whole-genome shotgun sequencing. The genome was 466 megabases in size, with an estimated 46,022 to 55,615 genes . Functional coverage in the assembled sequences was 92.0/100. About 42.2/100 of the genome was in exact 20-nucleotide oligomer repeats, and most of the transposons were in the intergenic regions between genes. Although 80.6/100 of predicted Arabidopsis thaliana genes had a homolog in rice, only 49.4/100 of predicted rice genes had a homolog in A. thaliana. The large proportion of rice genes with no recognizable homologs is due to a gradient in the GC content of rice coding sequences.
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The semidefinite programming problem is an important optimization problem that has been extensively investigated. A real-time solution method for solving such a problem, however, is still not yet available. The paper proposes a re...
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The semidefinite programming problem is an important optimization problem that has been extensively investigated. A real-time solution method for solving such a problem, however, is still not yet available. The paper proposes a recurrent neural network for this purpose. First, an auxiliary cost function is introduced to minimize the duality gap between the admissible points of the primal problem and the corresponding dual problem. Then a dynamical system is constructed to drive the duality gap to zero exponentially along any trajectory by modifying the gradient of the auxiliary cost function. Furthermore, a subsystem is developed to circumvent the computation of matrix inverse, so that the resulting overall dynamical system can be realized using a recurrent neural network. The architecture of the resulting neural network is discussed. The operating characteristics and performance of the proposed approach are demonstrated by means of simulation results.
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We present a general methodology for designing optimization neural networks. We prove that the neural networks constructed by using the proposed method are guaranteed to be globally convergent to solutions of problems with bounded...
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We present a general methodology for designing optimization neural networks. We prove that the neural networks constructed by using the proposed method are guaranteed to be globally convergent to solutions of problems with bounded or unbounded solution sets, in contrast with the gradient methods whose convergence is not guaranteed. We show that the proposed method contains both the gradient methods and nongradient methods employed in existing optimization neural networks as special cases. Based on the theoretical results of the proposed method, we study the convergence and stability of general gradient models in the case of unisolated solutions. Using the proposed method, we derive some new neural network models for a very large class of optimization problems, in which the equilibrium points correspond to exact solutions and there is no variable parameter. Finally, some numerical examples show the effectiveness of the method.
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