光伏发电投资决策的实证分析——基于ARMA-GARCH模型修正后的动态规划法

运用可靠方法评估项目的最优临界值和最大机会价值是光伏发电投资决策面临的关键问题。本研究选取了与某光伏企业发电投资项目价值“孪生”的一只股票的836个日收盘价格(从2012年1月4日至2015年6月24日)建立波动率预测模型,并在此基础上修正了该项目投资决策的动态规划法。然后给出了该投资的最优临界值、最大机会价值以及不同波动率下的这两个值的变化趋势。研究表明:该“孪生”股票价格的条件异方差使得最优临界值和最大机会价值对波动率的敏感程度不同——当波动率增大时,上述两个值虽然都增加,但增加的程度不同;当波动率增大到一定程度时,这两个值增加的程度都明显提高。因此,将波动率纳入光伏发电投资决策分析中有助于提高决策质量,减少企业损失。     

The three-factor model and artificial neural networks: predicting stock price movement in China

Since the establishment of the Shanghai Stock Exchange (SHSE) in 1990 and the Shenzhen Stock Exchange (SZSE) in 1991, China’s stock markets have expanded rapidly. Although this rapid growth has attracted considerable academic interest, few studies have examined the ability of conventional financial models to predict the share price movements of Chinese stock. This gap in the literature is significant, given the volatility of the Chinese stock markets and the added risk that arises from the Chinese legal and regulatory environment. In this paper we address this research gap by examining the predictive ability of several well-established forecasting models, including dynamic versions of a single-factor CAPM-based model and Fama and French’s three-factor model. In addition, we compare the forecasting ability of each of these models with that of an artificial neural network (ANN) model that contains the same predictor variables but relaxes the assumption of model linearity. Surprisingly, we find no statistical differences in the forecasting accuracy of the CAPM and three-factor model, a result that may reflect the emerging nature of the Chinese stock markets. We also find that each ANN model outperforms the corresponding linear model, indicating that neural networks may be a useful tool for stock price prediction in emerging markets.     

基于前景云的不确定语言多准则国际股指投资群决策

针对投资决策过程中语言评价值具有随机性及模糊性,以及投资者的决策容易受到其情绪的影响且不同投资者受到的影响程度不同,本文提出基于前景云的不确定语言多准则投资群决策方法,并将其运用在国际股指投资中。其中,前景理论模型用来刻画投资者情绪对决策的影响,而云模型用来刻画语言评价值模糊性和随机性之间的关联。更具体来说,论文首先解决传统文献云生成方法中云期望值超过论域或者无法区分语言评价标度等级等问题,然后构建了前景云模型并将该模型应用于多个专家共同进行的国际股指投资群决策。实证结果显示,该模型得出的决策结果比传统决策方法下的结果更直观、可靠,表现为决策依据不仅考虑方案的期望值大小及变动风险,而且还考虑了投资者情绪对决策的影响。由此可得出,本文所提出的模型更符合现实情景,也更能有效实现对投资群决策。     

Capital Investment — An Optimal Control Perspective

Many managers appear to have a mental model of how investment decisions should be carried out. This paper attempts to identify some of the characteristics of such a mental model by constructing an optimal control model of the process of authorizing an investment project. The key activities in the model are design, support generation and authorization. It is assumed that the effort that can be expended on each is limited. The objective function is the net present value of the cash flows associated with the project. The solution to the model is readily interpreted qualitatively. For viable projects, four different patterns of decision-making are found, each of which is optimum under appropriate circumstances. It is argued that in many actual investment decisions it should be possible for managers to approximate reasonably closely the optimal behaviour, and that therefore the optimal control model may shed light on managers' mental model.     

Improving weighted information criterion by using optimization

Although artificial neural networks (ANN) have been widely used in forecasting time series, the determination of the best model is still a problem that has been studied a lot. Various approaches available in the literature have been proposed in order to select the best model for forecasting in ANN in recent years. One of these approaches is to use a model selection strategy based on the weighted information criterion (WIC). WIC is calculated by summing weighted different selection criteria which measure the forecasting accuracy of an ANN model in different ways. In the calculation of WIC, the weights of different selection criteria are determined heuristically. In this study, these weights are calculated by using optimization in order to obtain a more consistent criterion. Four real time series are analyzed in order to show the efficiency of the improved WIC. When the weights are determined based on the optimization, it is obviously seen that the improved WIC produces better results.     

证券投资组合理论的一种新模型及其应用

马科维茨(Markowitz)以证券收益率的方差作为投资风险的测度建立了组合证券投资模型，本基于熵的概念，在研究马科维茨(Markowitz)证券投资组合模型的基础上，分析了该模型用方差度量风险的不足，进而提出一种新的证券投资组合优化模型，并以实例作了说明。     

股份制公司的综合投资方案决策模型

对股份制公司的综合投资方案的决策问题进行了研究.首先依据多个投资方案的风险与收益并存的实际情况,建立了最佳投资组合方案的多目标决策模型.然后,由董事会综合各股东所持股份和相互评价权值,利用群决策的方法得到一个最终投资方案,此方案在理论上能使公司获得最大收益.     

Diversity of firm’s life cycle adapted from the firm’s technology investment decision

The stylized model presented is an optimal control model of technology investment decision of a single product firm. The firm’s technology investment does not have only a long-run positive effect but also a short-run adverse effect on its sales volume. We examine the case of high adverse investment effects where the firm finally leaves the market but we have observed different life cycles till this happens. Depending on the firm’s initial technology stock and sales volume, we compute different firm’s life cycles, which are driven by a trade-off between two strategies: technology versus sales focus strategy. Indifference curves, where managers are indifferent to apply initially technology or sales focus strategies, separate founding conditions of the firm to various classes distinguishable because of the firm’s life cycle.     

实物期权在新药开发中的应用

实物期权作为一种新兴的投资评估工具，以能够解决传统分析方法中所无法克服的高度不确定性和管理灵活性而被广泛的应用。本文详细介绍了实物期权二叉树定价模型在一新药开发的定价分析中的应用，进而得出实物期权定价分析方法在具有初始投资额大、投资期限长、不确定性大等特点的新产品开发过程中的实际应用价值。     

Decision making in stock trading: An application of PROMETHEE

The key issue for decision making in stock trading is selection of the right stock at the right time. In order to select the superior stocks (alternatives) for investment, a finite number of alternatives have to be ranked considering several and sometimes conflicting criteria. Therefore, we are faced with a special multicriteria decision-making problem. The purpose of this paper is to develop a decision-making model for selecting superior stocks in stock exchange and a model is provided in order to structure this problem. The proposed model is structured around two pillars: Industry evaluation and Company evaluation. The preference ranking organization method for enrichment evaluation (PROMETHEE) has been used for solving the problem. The model has been applied at Tehran Stock Exchange (TSE) as a real case and a survey from the experts in order to determine the effective criteria for industry evaluation and company evaluation has been conducted.