Excel高效办公——财务函数的应用

随着计算机技术的迅速普及,财会人员已经逐步从繁杂的手工劳动中解脱出来。财务人员如果能够正确、灵活地使用Excel进行财务函数的计算,则能大大减轻日常工作中有关指标计算的工作量。财务函数大体上可分为四类:投资计算函数、折旧计算函数、偿还率计算函数、债券及其他金融函数。它们为财务分析提供了极大的便利。使用这些函数不必理解高级财务知识,只要填写变量值就可以了。下面举例说明各种不同的财务函数的应用。     

巧用Excel作理财规划

Excel2003强大的财务函数和电子方案表格为财务分析提供了相当便利的方法,本文结合Excel中的贷款计算模板和FV等财务函数,设计一个存款收益测算表,应用于个人或家庭的理财规划。     

巧用Excel作理财规划

Excel2003强大的财务函数和电子方案表格为财务分析提供了相当便利的方法，本文结合Excel中的贷款计算模板和FV等财务函数，设计一个存款收益测算表，应用于个人或家庭的理财规划。     

国际投资

国际证券投资国际证券投资是通过在国际间发行或买卖股票和债券的方式所进行的一种投资活动。证券一般分为债券和股票两种。持有某企业股票，就成为该企业的股东，就可以凭票定期分取股息和红利，也可出卖股票；持有某公司或政府的债券，就成为该公司或政府的债权人，可以凭券领取利息，到期收回本金。所以购买证券就是一种投资方式，相对应的发行证券则是一种集资方式。国际债券的发行方式分为公募与私募两种。公募是指承购公司将接受的新发行的债券向特定的投资者售出债券，采用公开制度。私募是债券发行者经过承购公司只向有限的投资者销…     

投资理财好助手：Excel5.0财务函数

提到Excel，许多用户将其奉为电子表格制作的王者，这是因为它可以出色地完成涉及各行业的有关数据处理的工作。Excel尤以“博大精深”的函数库、数据分析手段、VB宏语言在用户面前展示了其独特的轻力。其实，Excel不但在办公室中发挥着巨大作用，而且在家庭生活中也有一席用武之地。它所包含的投资函数、债券分析函数，不失为当前广大市民进行投资决策、投资分析的有力工具。什么是函数要知道什么是函数，首先得了解什么是公式。公式是在单元格中用运算符号连接数值或单元名称，最终产生运算结果的手段。如单元格A1中力数值100，单元格A…     

利用EXCEL进行财务预测的回归分析

财务预测是财务管理工作中的一项十分重要的工作,对企业投资、预算等决策非常重要。财务预测的回归分析,是利用一系列的历史资料求得各资产负债表项目和销售额的函数关系,据此预测计划销售额与资产、负债数量,然后预测融资需求。利用Excel能够有效地解决财务预测的回归分析的问题。本文以销售额的多元回归分析预测为例来说明Excel在财务预测回归分析中的应用。     

利用EXCEL进行财务预测的回归分析

财务预测是财务管理工作中的一项十分重要的工作,对企业投资、预算等决策非常重要。财务预测的回归分析,是利用一系列的历史资料求得各资产负债表项目和销售额的函数关系,据此预测计划销售额与资产、负债数量,然后预测融资需求。利用Excel能够有效地解决财务预测的回归分析的问题。本文以销售额的多元回归分析预测为例来说明Excel在财务预测回归分析中的应用。     

企业投融资组合的模糊模型与优化

以投资组合产出率及目前流行的风险价值VAR为目标函数，研究了在这两个目标下企业投融资组合管理的模糊模型和优化问题，说明了决策变量是财务杠杆和债务结构．给出了金融市场不确定性环境的构造过程，运用进化规划进行优化计算，对不同模糊程度下的债务结构、财务杠杆及其股东权益资本产出率进行了仿真．     

改进财务评价指标体系的筛选研究

为解决目前传统项目财务评价指标存在的意义重复、通用性欠佳等不足,提出了指标改进与技术筛选相结合的体系模型。首先根据传统指标的不足,提出新的改进财务评价指标,形成更加完善的指标体系;进而运用因子分析与聚类分析相结合的数据挖掘方法对改进的投资项目财务评价指标体系进行筛选,提高了财务评价的准确性及计算效率;最后应用实例验证了数据挖掘技术在投资项目财务评价指标研究中的有效性,为项目财务评价工作者提供一种改进的思路与方法。     

样条函数在构造债券收益率曲线中的应用

针对债券市场上芜杂的行情数据,提出将DBSCAN聚类算法应用于构造债券收益率曲线样条函数。通过运用DBSCAN算法对用于构造债券收益率曲线的行情数据进行聚类分析,能够有效地剔除市场上的异常交易数据。在聚类分析结果的基础上,再次应用DBSCAN算法于构造债券收益率曲线,根据市场上行情数据的密集区域对样条函数进行分段。此外,针对传统的依赖于经验进行债券收益率曲线样条函数分段点选取的缺点,使用DBSCAN算法可有效地提高债券收益率曲线和行情数据的符合程度。实验结果表明,将DBSCAN算法用于构建债券收益率曲线样条函数,可以提高收益率曲线反映利率期限结构波动及准确性的效果。     

Value of information based scheduling of cloud computing resources

Traditionally, heavy computational tasks were performed on a dedicated infrastructure requiring a heavy initial investment, such as a supercomputer or a data center. Grid computing relaxed the assumptions of the fixed infrastructure, allowing the sharing of remote computational resources. Cloud computing brought these ideas into the commercial realm and allows users to request on demand an essentially unlimited amount of computing power. However, in contrast to previous assumptions, this computing power is metered and billed on an hour-by-hour basis.In this paper, we are considering applications where the output quality increases with the deployed computational power, a large class including applications ranging from weather prediction to financial modeling. We are proposing a computation scheduling that considers both the financial cost of the computation and the predicted financial benefit of the output, that is, its value of information (VoI). We model the proposed approach for an example of analyzing real-estate investment opportunities in a competitive environment. We show that by using the VoI-based scheduling algorithm, we can outperform minimalistic computing approaches, large but fixedly allocated data centers and cloud computing approaches that do not consider the VoI.     

Robo-advisors and investment intention: A perspective of value-based adoption

Financial robo-advisors have been widely used to assist individuals in their investment decisions, making it important to reduce uncertainties in the assistance process. Existing empirical studies rarely explore uncertainty reduction strategies and their implications on users’ investment intentions in the context of financial robo-advisors; our study attempts to address this gap. We construct a model to explain how uncertainty reduction strategies affect users’ investment intention in using financial robo-advisors. By collecting and analyzing a sample of 307 financial robo-advisor users, we find that algorithmic interpretability, structural assurance, and interactivity as uncertainty reduction strategies are positively related to users’ investment intention through the value-based adoption mechanism. Our research extends the value-based adoption model and uncertainty reduction theory in the financial robo-advisor context. We provide insights to financial robo-advisor service providers about focusing on improving algorithmic transparency, third-party assurance, and interactivity of financial robo-advisors to enhance perceived value and investment intention.     

非结构化智能金融投研平台的开发与行业应用

当前针对非结构化数据处理的研究多集中于实验态的技术实现,对于其在金融投研业务中落地应用的整体架构与路径的研讨则较为缺乏.为此,提出将大数据、自然语言处理、知识图谱等技术结合起来进行智能化投研平台的研发设计,并实现其在真实金融投研场景的应用.该平台基于Hadoop分布式系统进行数据采集、存储与计算,集成了传统文本处理技术...     

Learning to trade via direct reinforcement

We present methods for optimizing portfolios, asset allocations, and trading systems based on direct reinforcement (DR). In this approach, investment decision-making is viewed as a stochastic control problem, and strategies are discovered directly. We present an adaptive algorithm called recurrent reinforcement learning (RRL) for discovering investment policies. The need to build forecasting models is eliminated, and better trading performance is obtained. The direct reinforcement approach differs from dynamic programming and reinforcement algorithms such as TD-learning and Q-learning, which attempt to estimate a value function for the control problem. We find that the RRL direct reinforcement framework enables a simpler problem representation, avoids Bellman's curse of dimensionality and offers compelling advantages in efficiency. We demonstrate how direct reinforcement can be used to optimize risk-adjusted investment returns (including the differential Sharpe ratio), while accounting for the effects of transaction costs. In extensive simulation work using real financial data, we find that our approach based on RRL produces better trading strategies than systems utilizing Q-learning (a value function method). Real-world applications include an intra-daily currency trader and a monthly asset allocation system for the S&P 500 Stock Index and T-Bills.     

Application of improved clustering algorithm in investment recommendation in embedded system

The user's investment behaviour is individual, and group-oriented, which can reflect the user's cognitive background and interest on a certain extent. The user investment group can help users to find similar investment partners. Users can view the investment or other related people's interests. With the development of the Internet financial industry, people's demand for Internet financial knowledge services has become increasingly strong. Accessing financial information and conducting financial transactions through online financial platforms has become normal for investors. As a popular research area, the recommendation system can help users to better use Internet information, improve user loyalty, and promote products. In this paper, an improved kernel cluster-based incremental clustering method is proposed, and the stock information of the Shanghai Stock Exchange is used as the experimental data for cluster mining. The experimental results show that the improved kernel-based incremental clustering algorithm proposed in this paper can complete the investment recommendation for financial users. For a certain extent, it reduces the risk of financial investment, enhances the stability of the financial market, and has a strong positive effect.     

R&D Investment Enhance the Financial Performance of Company Driven by Big Data Computing and Analysis

The application of computer technology, especially the emergence of some statistical software and graphic presentation technology, has enabled many areas of research that require a large amount of data analysis. This paper discusses the relationship between R&D investment and corporate financial performance, and further studies the effect of environmental regulations on this relationship through these technologies. The unbalanced panel data of listed companies from 2007 to 2016 were used as a sample, and then corresponding regression modelswere established through logical reasoning. Empirical analysis has found that there is an inverted U-shaped relationship between R&D investment and company financial performance, and there is a U-shaped relationship between the intensity of environmental regulations and companies’ investment in R&D. Another finding is that the inverted U-shaped relationship between companies’ R&D investment and financial performance is moderated by environmental regulations in such a way that greater environmental regulations is associated with a lower point of maximum efficiency in the inverted U-shaped curve. This indicates that the strengthening of environmental regulations will affect a company’s resource allocation, which will lead to a reduced investment in production, R&D and so on, thus reducing the peak value of financial performance.     

Creating investment scheme with state space modeling

This paper proposes a unified approach to creating investment strategies with various desirable properties for investors. Particularly, we provide a new interpretation and the resulting formulations for state space models to attain our investment objectives, which are possibly specified as generating additional returns over benchmark stock indexes or achieving target risk-adjusted returns.Our state space models with particle filtering algorithm are employed to develop expert systems for investment strategies in highly complex financial markets. More concretely, in our state space framework, we apply a system model to representing portfolio weight processes with various constraints, as well as the standard underlying state variables such as volatility processes. Further, we formulate an observation model to stand for target value processes with non-linear functions of observed and latent variables.Numerical experiments demonstrate the effectiveness of our methodology through creating excess returns over S&P 500 and generating investment portfolios with fine risk-return profiles.