基于模糊推理系统的股票买卖策略研究

金融交易指标是分析股票等金融产品买入和卖出策略的普遍选择，但由于指标描述交易策略的模糊性，使得非专业人士很难掌握交易的时间和数量。针对上述问题，本文首先分析随机指标的交易策略。其次根据交易策略和模糊集理论建立模糊推理系统模型，此模型可以给出具体的股票交易策略，包括买入以及卖出的时间和数量等，并通过给定时间段内的获利情况来判断模型的有效性。最后以沪深A股市场的15只股票价格作为实验数据，对模型的有效性进行检验。实验表明，在没有人为因素参与的情况下，大多数股票由系统进行模拟交易都可以在一段时间内获取更大的利润。     

股指期货冲击成本测算与实证研究

本文首先对国外测算冲击成本常用的一种方法基于中国市场的实际进行了改进,并将其运用到沪深300股指期货合约中,对永久冲击成本和暂时冲击成本分别建模并运用高频交易数据加以测算模拟和实证分析,对线性和非线性两种形式的冲击函数模拟了投资者冲击成本最小的交易策略。本文的结论可直接用于投资者交易冲击成本估算、以及程序化和算法交易研究中的冲击成本模型输入。从模拟结果来看,中国市场的冲击成本显著高于更加发达成熟的美国市场,侧面显示了中国市场的流动性和有效性要弱于美国市场。     

基于Dempster-Shafer证据理论的外汇交易策略研究

自动化交易是现代金融领域的研究热点,而交易策略是其核心。技术指标分析中,每一种指标都有其优势和劣势,单一的指标经常产生导致亏损的虚假信号。为了提高交易信号的质量和可靠性,针对外汇市场的多变性和存在诸多不确定性的客观事实,本文引入证据理论来处理不同技术指标分析方法结论存在的差异;将不同的指标作为独立的证据源,用D-S合成规则对各个指标分析方法的结果予以融合,建立了基于证据理论的多指标融合外汇交易模型,给出了基于证据理论的交易框架。根据技术指标的特点及交易原理,构造了指标证据的基本概率分配函数。最后,通过实例分析验证了该方法的科学性和有效性。     

基于熵权灰色关联分析的高校技术交易行为评价——以A高校为例

首先建立了针对高校技术交易评价的指标体系.结合所要研究的问题,说明了如何使用熵值法对评价指标进行赋权,并通过灰色关联度方法进行评价.然后,应用建立的评价方法结合A高校2006-2013年间技术交易的统计数据,对A高校技术交易的总体情况进行了客观评价,为高校技术交易管理提供了有价值的参考.     

2008年中国足球超级联赛成绩与技术统计的因子分析

通过查阅资料,收集、并整理了2008年中超联赛15支队伍210场比赛的技术统计.运用因子分析法对比赛成绩与技术统计的关系进行了研究,命名并分析影响比赛成绩的5个因子.旨在揭示统计指标与比赛成绩的特点与规律,为我国足球运动的理论与训练提供科学依据。     

一种新型粗糙集的指标筛选模型及实证分析

针对现有指标筛选方法不能将指标客观数据和指标实际含义同时处理的弊端,研究提出了一种新型粗糙集指标筛选方法,并对绿色经济下的社会可持续发展评价指标体系构建进行了实证分析.方法同时将粗糙集决策表的相对约简理论与信息表的属性约简理论相结合,既保证了筛选的客观性又兼顾了指标的实际含义,其主要做法一是通过布尔推理算法,保证了连续型指标离散化过程中候选断点的最优组合,为粗糙集约简提供了高准确率的信息表数据;二是考虑了指标的实际含义,通过对有因果关系的指标构成的决策表进行相对约简,删除了指标信息间的冗余指标;三是通过对无实际联系的指标构成的信息表进行属性约简,删除了研究指标客观数据中的冗余指标.     

基于排序预测的带交易费用在线投资组合策略

在线投资组合决策过程中频繁调整资产头寸会产生较多的交易费用。本文提出了一个综合考虑预期收益和交易费用的在线投资组合策略。通过预测资产的排序计算组合的预期收益,利用相对熵距离衡量交易费用,构造了一个极大化预期收益和极小化交易费用的优化模型,从而得到了一个在线投资组合更新策略。然后,从理论上证明了该策略具有BH泛证券性,即该策略与离线的最优购买并持有策略具有相同的渐近平均指数收益率。最后,采用中美股票市场实际数据,对该策略进行了数值分析。结果表明,该策略的表现优于已有的在线投资组合策略,且对模型的参数不敏感。     

基于流动性约束和价格趋势的最优交易策略

股票市场中当交易规模较大时,投资者不仅要预判未来的股票价格趋势从而降低风险成本,也要考虑大额交易指令对市场冲击的成本.在投资者预判股票未来价格趋势的同时,考虑交易量的线性价格冲击,建立一个控制最小交易成本的策略模型,采用随机动态规划方法得出总体执行成本最小的交易策略解析解,进一步利用数值算例对最优交易策略进行了比较静态分析.     

数字图书馆评价指标体系的设计

数字图书馆评价是一个复杂的多属性决策问题,探讨了整个评价环节中的指标遴选问题.通过对相关评价指标的综合,借助于Delphi问卷获取了指标筛选的基础数据,并借助差异性分析、鉴别力分析、模糊隶属度、主成分分析、指标竞优评价、信度与效度检验等技术得到了较为合理的评价指标体系.     

混合电力系统合作博弈规划的分配策略研究

在市场环境下,博弈论是描述混合电力系统(由风电、光伏发电和储能设备组成)电源规划决策过程的有力工具.已有研究表明风、光、储通过合作组成总联盟能够实现最大的经济效益,但如何制定有约束力的协议(或分配策略)使得合作最终能够实现是合作博弈的关键问题,也是研究重点.首先论文给出混合电力系统合作博弈规划中具有约束力的分配策略应满足的条件,即满足个体理性、整体理性和联盟理性,以使得联盟中的所有参与者均有意愿参与合作.此外,论文提出一类改进的DP (Disruption Propensity)指标用于定量描述各参与者对不同分配策略的接受程度,即特定分配策略下的合作强度.最后,提出并分析比较了四种典型的分配策略,即均分策略、按容分配策略、Shapley值以及基于改进DP指标的分配策略,进一步证明后两种分配策略属于核心的集合,是有约束力的分配策略.     

Application of differential evolution algorithm for optimization of strategies based on financial time series

An approach for optimization of trading strategies (algorithms) based on indicators of financial markets and evolutionary computation is described. A new version of differential evolution algorithm for the search for optimal parameters of trading strategies for maximization of trading profit is used. The experimental results show that this approach can improve several times the profitability of the trading strategies.     

基于动态交易量预测的VWAP算法交易卖出策略

传统的VWAP交易策略通过预测区间交易量分布进行拆单交易,对于交易量区间分布的预测是基于区间成交量占总成交量比例进行的,这一预测方法没有考虑股票价格变动因素。因此,本文首先通过时间序列因素分解方法进行区间交易量分布预测,进而根据股票价格变化对区间交易量分布进行动态调整,并构建了基于动态区间交易量分布的股票卖出策略,最后通过实证检验了本文给出的动态区间交易量分布预测的有效性和交易策略的有效性。数值结果表明,本文所给动态区间交易量分布预测方法比传统VWAP方法预测结果更加接近于实际的交易量分布,且本文所给交易策略与传统VWAP交易策略相比,具有更大的收益。     

影子价格思想在中原经济区各地市经济发展集团排序中的应用

随着中原经济区发展战略的提出,如何根据各地市经济发展的实际情况制定差异化的发展战略是摆在决策者面前的一个重要问题.从传统的经济分析指标出发,基于集团排序方法,以国内生产总值和固定资产投出为基础,运用影子价格的思想,通过确定不同的级内级差得到了满意的排序结果.这一结果不仅为中原经济区差异化发展战略的制定提供了理论依据,也为相关研究提供了新的方法和思路.     

On discrete time hedging errors in a fractional Black-Scholes model

In this paper we investigate asymptotic behavior of error of a discrete time hedging strategy in a fractional Black-Scholes model in the sense of Wick-Ito-Skorohod integration.The rate of convergence of the hedging error due to discrete-time trading when the true strategy is known for the trader,is investigated.The result provides new statistical tools to study and detect the effect of the long-memory and the Hurst parameter for the error of discrete time hedging.     

Risk-Sensitive Dynamic Asset Management

This paper develops a continuous time portfolio optimization model where the mean returns of individual securities or asset categories are explicitly affected by underlying economic factors such as dividend yields, a firm's return on equity, interest rates, and unemployment rates. In particular, the factors are Gaussian processes, and the drift coefficients for the securities are affine functions of these factors. We employ methods of risk-sensitive control theory, thereby using an infinite horizon objective that is natural and features the long run expected growth rate, the asymptotic variance, and a single risk-aversion parameter. Even with constraints on the admissible trading strategies, it is shown that the optimal trading strategy has a simple characterization in terms of the factor levels. For particular factor levels, the optimal trading positions can be obtained as the solution of a quadratic program. The optimal objective value, as a function of the risk-aversion parameter, is shown to be the solution of a partial differential equation. A simple asset allocation example, featuring a Vasicek-type interest rate which affects a stock index and also serves as a second investment opportunity, provides some additional insight about the risk-sensitive criterion in the context of dynamic asset management. Accepted 10 December 1997     

More than a second channel? Supply chain strategies in B2B spot markets

The emergence of B2B spot markets has greatly facilitated spot trading and impacted supply chain structures as well as the way commercial transactions take place between firms in many industries. While providing new opportunities, the B2B spot market also exposes participants to a price risk. This new business landscape raises some important questions on how the supplier and manufacturer should change their sales channel and procurement strategies and tailor their decisions to this changing environment. In this paper, we study the channel-choice, pricing and ordering/production decisions of the risk-averse supplier and manufacturer in a two-tier supply chain with a B2B spot market. Our analysis shows that, to benefit from the B2B spot market and control the risk exposure, the upstream supplier should develop an integrated channel-choice and pricing strategy. When the supplier adopts a dual-channel strategy, the equilibrium contract price decreases in the supplier’s risk attitude, but increases in the demand uncertainty. However, it first decreases and then increases in the manufacturer’s risk attitude and spot price volatility. We conclude that rather than simply being a second channel, the B2B spot market provides a strategic tool to supply chain members to achieve an advantageous position in their contract trading.     

Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets

The development of new models that would enhance predictability for time series with dynamic time-varying, nonlinear features is a major challenge for speculators. Boundedly rational investors called “chartists” use advanced heuristics and rules-of-thumb to make profit by trading, or even hedge against potential market risks. This paper introduces a hybrid neurofuzzy system for decision-making and trading under uncertainty. The efficiency of a technical trading strategy based on the neurofuzzy model is investigated, in order to predict the direction of the market for 10 of the most prominent stock indices of U.S.A, Europe and Southeast Asia. It is demonstrated via an extensive empirical analysis that the neurofuzzy model allows technical analysts to earn significantly higher returns by providing valid information for a potential turning point on the next trading day. The total profit of the proposed neurofuzzy model, including transaction costs, is consistently superior to a recurrent neural network and a Buy & Hold strategy for all indices, particularly for the highly speculative, emerging Southeast Asian markets. Optimal prediction is based on the dynamic update and adaptive calibration of the heuristic fuzzy learning rules, which reflect the psychological and behavioral patterns of the traders.     

基于在线理论的股票算法交易策略研究

运用在线理论研究多支股票算法交易策略。在El-Yaniv等人研究基础上,构造了单支股票买入问题的在线策略,证明该策略为最优在线策略;将构造的单支股票交易策略应用到多支股票交易策略问题中,设计了多支股票交易策略算法,并以每支股票收益加权进行投资组合;最后选择上证A股二十支股票从2009年到2012年的交易时间价格数据验证本文所提策略有效性。将20支股票随机抽取10支组成一组,选4组分别进行验证,结果表明本文所给策略对于任意选择的多支股票有较好收益。对交易周期分别选取10个偶数长度进行验证,发现交易周期为18天时平均收益最大,平均收益率为5.2%。     

Optimal Security Liquidation Algorithms

This paper develops trading strategies for liquidation of a financial security, which maximize the expected return. The problem is formulated as a stochastic programming problem that utilizes the scenario representation of possible returns. Two cases are considered, a case with no constraint on risk and a case when the risk of losses associated with trading strategy is constrained by Conditional Value-at-Risk (CVaR) measure. In the first case, two algorithms are proposed; one is based on linear programming techniques, and the other uses dynamic programming to solve the formulated stochastic program. The third proposed algorithm is obtained by adding the risk constraints to the linear program. The algorithms provide path-dependent strategies, i.e., the fraction of security sold depends upon price sample-path of the security up to the current moment. The performance of the considered approaches is tested using a set of historical sample-paths of prices.