面向组合投资预测的大数据生成算法

为满足组合投资预测对数据的需求,提出一种基于增量式贝叶斯网络模型的大数据生成方法.使用时间序列生成算法对未来各项数据进行部分生成;结合新生成数据对历史数据训练的贝叶斯网络模型进行更新,使更新后的贝叶斯网络能够体现该时间段内新旧金融数据中各项变量之间的关系及蕴含的规律;在贝叶斯网络中通过路径搜索算法生成投资组合路径的集合...     

A hybrid algorithm for constrained portfolio selection problems

Since Markowitz’s seminal work on the mean-variance model in modern portfolio theory, many studies have been conducted on computational techniques and recently meta-heuristics for portfolio selection problems. In this work, we propose and investigate a new hybrid algorithm integrating the population based incremental learning and differential evolution algorithms for the portfolio selection problem. We consider the extended mean-variance model with practical trading constraints including the cardinality, floor and ceiling constraints. The proposed hybrid algorithm adopts a partially guided mutation and an elitist strategy to promote the quality of solution. The performance of the proposed hybrid algorithm has been evaluated on the extended benchmark datasets in the OR Library. The computational results demonstrate that the proposed hybrid algorithm is not only effective but also efficient in solving the mean-variance model with real world constraints.     

A heuristic genetic algorithm for product portfolio planning

Product portfolio planning has been recognized as a critical decision facing all companies across industries. It aims at the selection of a near-optimal mix of products and attribute levels to offer in the target market. It constitutes a combinatorial optimization problem that is deemed to be NP-hard in nature. Conventional enumeration-based optimization techniques become inhibitive given that the number of possible combinations may be enormous. Genetic algorithms have been proven to excel in solving combinatorial optimization problems. This paper develops a heuristic genetic algorithm for solving the product portfolio planning problem more effectively. A generic encoding scheme is introduced to synchronize product portfolio generation and selection coherently. The fitness function is established based on a shared surplus measure leveraging both the customer and engineering concerns. An unbalanced index is proposed to model the elitism of product portfolio solutions.     

基数约束投资组合问题的一种混合元启发式算法求解

针对资产数目和投资资金比例受约束的投资组合选择这一NP难问题,基于混沌搜索、粒子群优化和引力搜索算法提出了一种新的混合元启发式搜索算法。该算法能很好地平衡开发能力和勘探能力,有效抑制了算法早熟收敛现象。标准测试函数的测试结果表明混合算法与标准的粒子群优化和引力搜索算法相比具有更好的寻优效率;实证分析进一步对混合算法与遗传算法及粒子群优化算法在求解这类投资组合选择问题的性能进行了比较。数值结果表明,混合算法在搜索具有高预期回报的非支配投资组合方面表现更好,取得了更为满意的结果。     

基于分支回溯的NAE-3SAT问题求解算法

NAESAT问题是可满足性问题的一个重要扩展,在集合分裂、最大割集等NP完全问题中有着重要的应用.针对NAESAT问题的泛化NAE-3SAT问题,提出了一个基于分支回溯的精确算法NAE.算法给出了多种化简规则,这些化简规则很好地提高了算法的时间效率.最后证明了算法在最坏情况下的时间复杂度上界为O(1.618n),其中n为公式中的变量数目.     

Solving SAT problem by heuristic polarity decision-making algorithm

This paper presents a heuristic polarity decision-making algorithm for solving Boolean satisfiability (SAT). The algorithm inherits many features of the current state-of-the-art SAT solvers, such as fast BCP, clause recording, restarts, etc. In addition, a preconditioning step that calculates the polarities of variables according to the cover distribution of Karnaugh map is introduced into DPLL procedure, which greatly reduces the number of conflicts in the search process. The proposed approach is implemented as a SAT solver named DiffSat. Experiments show that DiffSat can solve many "real-life" instances in a reasonable time while the best existing SAT solvers, such as Zchaff and MiniSat, cannot. In particular, DiffSat can solve every instance of Bart benchmark suite in less than 0.03 s while Zchaff and MiniSat fail under a 900 s time limit. Furthermore, DiffSat even outperforms the outstanding incomplete algorithm DLM in some instances.     

风险效用投资组合模型的遗传算法求解

给出一个折衷考虑风险最小化和收益最大化的单目标决策方法,以单位风险收益最大化为决策目标建立了投资组合的非线性分式规划模型,考虑到分式规划问题的求解难度,利用遗传算法求解模型,并给出算法步骤。最后,给出了数值算例,结果表明该算法是简单有效的。     

An efficient algorithm for stochastic capacity portfolio planning problems

This study investigates capacity portfolio planning problems under demand, price, and yield uncertainties. We model this capacity portfolio planning problem as a Markov decision process. In this research, we consider two types of capacity: dedicated and flexible capacity. Among these capacity types, flexible capacity costs higher but provides flexibility for producing different products. To maximize expected profit, decision makers have to choose the optimal capacity level and expansion timing for both capacity types. Since large stochastic optimization problems are intractable, a new heuristic search algorithm (HSA) is developed to reduce computational complexity. Compare to other algorithms in literature, HSA reduces computational time by at least 30% in large capacity optimization problems. In addition, HSA yields optimal solution in all numerical examples that we have examined.     

复杂最优消费组合问题的数值逼近解算法

针对由现有理论和方法求不到显式解的复杂最优消费组合问题,提出基于参数待定法以及遗传算法的数值逼近解算法。算法的可行性及通用性,在求解基准的复杂最优消费组合问题上得到了检验。     

Fuzzy portfolio selection using genetic algorithm

This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method.     

基于频次的SAT问题学习子句混合评估算法

为了有效管理学习子句,避免学习子句规模呈几何级增长,减少冗余学习子句对系统内存占用,从而提高布尔可满足性问题SAT求解器的求解效率,需要对学习子句进行评估,然后删减学习子句。传统的评估方式是基于学习子句的长度,保留较短的子句。当前主流的做法一个是变量衰减和VSIDS的子句评估方式,另外一个是基于文字块距离LBD的评估方式,也有将二者结合使用作为子句评估的依据。通过对学习子句参与冲突分析次数与问题求解的关系进行分析,将学习子句使用频率与LBD评估算法混合使用,既反映了学习子句在冲突分析中的作用,也充分利用了文字与决策层之间的信息。以Syrup求解器（GLUCOSE 4.1并行版本）为基准,在评估算法与并行子句共享策略方面做改进测试,通过实验对比发现,混合评估算法比LBD评估算法有优势,求解问题个数明显增多。     

Portfolio algorithm based on portfolio beta using genetic algorithm

The portfolio beta β_p is quite an important coefficient in modern portfolio theory since it efficiently measures portfolio volatility relative to the benchmark index or the capital market. β_p is usually employed for portfolio evaluation or prediction but scarcely for portfolio construction process. The main objective of this paper is to propose a portfolio algorithm that engages β_p in its portfolio construction process and studies its strengths. Our portfolio algorithm termed as β-G portfolio algorithm selects stocks based on their market capitalization and optimizes them in terms of the standard deviation of β_p. The optimizing process or finding optimal weights is done by the genetic algorithm. Our major findings on β-G portfolio algorithm are: (i) its performance depends on market volatility, i.e. it is expected to work well for a stable market whether it is bullish or bearish (ii) it tends to register outstanding performance for short-term applications.     

基于不完全算法的并行FPGA SAT求解器

可满足性问题是计算机理论与应用的核心问题。在FPGA上提出了一个基于不完全算法的并行求解器pprobSAT+。使用多线程的策略来减少相关组件的等待时间,提高了求解器效率。此外,不同线程采用共用地址和子句信息的数据存储结构,以减少片上存储器的资源开销。当所有数据均存储在FPGA的片上存储器时,pprobSAT+求解器可以达到最佳性能。实验结果表明,相比于单线程的求解器,所提出的pprobSAT+求解器可获得超过2倍的加速比。     

A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs

A memetic approach that combines a genetic algorithm (GA) and quadratic programming is used to address the problem of optimal portfolio selection with cardinality constraints and piecewise linear transaction costs. The framework used is an extension of the standard Markowitz mean–variance model that incorporates realistic constraints, such as upper and lower bounds for investment in individual assets and/or groups of assets, and minimum trading restrictions. The inclusion of constraints that limit the number of assets in the final portfolio and piecewise linear transaction costs transforms the selection of optimal portfolios into a mixed-integer quadratic problem, which cannot be solved by standard optimization techniques. We propose to use a genetic algorithm in which the candidate portfolios are encoded using a set representation to handle the combinatorial aspect of the optimization problem. Besides specifying which assets are included in the portfolio, this representation includes attributes that encode the trading operation (sell/hold/buy) performed when the portfolio is rebalanced. The results of this hybrid method are benchmarked against a range of investment strategies (passive management, the equally weighted portfolio, the minimum variance portfolio, optimal portfolios without cardinality constraints, ignoring transaction costs or obtained with L₁ regularization) using publicly available data. The transaction costs and the cardinality constraints provide regularization mechanisms that generally improve the out-of-sample performance of the selected portfolios.     

求解多目标投资组合优化模型的遗传算法

针对考虑最小交易量、交易费用,以及单项目最大投资上限约束的多目标投资组合模型,对目标函数添加惩罚函数项来处理约束条件的方法.本文通过对交叉算子、变异算子的改进,设计了一种遗传算法进行求解.实验算例表明,该算法是有效的.     

基于DCA-PSO算法的均值-VaR投资组合优化

投资组合决策面临现实证券市场中的大量数据,是一个复杂的组合优化问题,属于NP难问题,传统的算法难以有效求解。文化算法和粒子群算法是新近出现的两种仿生智能算法,将新提出的动态文化粒子群算法用于求解均值-VaR模型,用罚函数方法处理模型中的不等式约束,选取沪市和深市的十六支股票作为备选股票进行实证分析,数值结果表明该算法可以高效、合理地解决投资组合优化问题。     

基于动态奖惩的CDCL SAT求解器分支启发式算法

分支启发式算法在CDCL SAT求解器中有着非常重要的作用,传统的分支启发式算法在计算变量活性得分时只考虑了冲突次数而并未考虑决策层和冲突决策层所带来的影响。为了提高SAT问题的求解效率,受EVSIDS和ACIDS的启发,提出了基于动态奖惩DRPB的分支启发式算法。每当冲突发生时,DRPB通过综合考虑冲突次数、决策层、冲突决策层和变量冲突频率来更新变量活性得分。用DRPB替代VSIDS算法改进了Glucose 3.0,并测试了SATLIB基准库、2015年和2016年SAT竞赛中的实例。实验结果表明,与传统、单一的奖励变量分支策略相比,所提分支策略可以通过减少搜索树的分支和布尔约束传播次数来减小搜索树的规模并提高SAT求解器的性能。     

基于QPSO-SA混合算法的多目标投资组合优化

多目标投资组合优化就是决定每个具有特定风险、回报、交易费用等特征的资产在总投资价值中的投资比例,即选择那些资产投资以及寻找每个投资资产的最佳投资比例,使得总投资的风险最小、交易费用最小、回报最大等等。该问题是典型的NP难解问题,通常方法很难达到全局最优。研究如何把基于量子行为的微粒群优化算法（QPSO算法）和模拟退火算法(SA算法)结合起来解决多目标投资组合优化问题。利用美国标准普尔指数100的股票历史数据进行验证,纯QPSO算法与QPSO-SA混合算法的运行结果比较表明在解决多目标投资组优化问题中,QPSO-SA混合算法是一种高效的、可靠的优化算法,具有一定的实用价值。     

基于加强概率控制策略的SAT局部搜索算法

局部搜索算法是目前求解SAT问题比较有效的方法,而Sattime算法是在SAT国际大赛中获得大奖的一种典型局部搜索算法。在Sattime算法的求解过程中,记录变元翻转事件流数据库,通过数据分析与模式挖掘,发现Sattime算法的局部搜索行为中会出现相邻搜索步选择同一个变元的现象,即所谓的回环现象,从而降低了求解效率。为解决此问题,提出两种概率控制策略：加强子句选择策略和加强变元选择策略,并将这两种策略应用到Sattime算法中,形成新的局部搜索算法Sattime-P。实验结果表明,与Sattime算法相比,改进后的Sattime-P算法求解效率有显著的提升。该方法也对其他局部搜索算法的改进具有参考价值。     

Solving SAT by Algorithm Transform of Wu s Method

Recently algorithms for solving propositional satisfiability problem, or SAT,have aroused great interest,and more attention has been paid to transformation problem solving.The commonly used transformation is representation transform,but since its intermediate computing procedure is a black box from the viewpoint of the original problem,this approach has many limitations.In this paper,a new approach called algorithm transform is proposed and applied to solving SAT by Wu‘s method,a general algorithm for solving polynomial equations.B y establishing the correspondence between the primitive operation in Wu‘s method and clause resolution is SAT,it is shown that Wu‘s method,when used for solving SAT,,is primarily a restricted clause resolution procedure.While Wu‘s method introduces entirely new concepts.e.g.characteristic set of clauses,to resolution procedure,the complexity result of resolution procedure suggests an exponential lower bound to Wu‘s method for solving general polynomial equations.Moreover,this algorithm transform can help achieve a more efficient implementation of Wu‘s method since it can avoid the complex manipulation of polynomials and can make the best use of domain specific knowledge.