随机利率与随机波动率环境下的DC型养老金计划

为实现养老金的保值增值,基金管理人将养老金投资于金融市场.假设金融市场包含一种无风险资产、一种股票和一种零息票债券,其中,利率期限结构满足随机仿射利率模型,而股票价格波动率满足Heston随机波动率模型.基金管理人希望寻找一种最优投资组合以最大化其终端财富的期望效用.假设基金管理人对风险的偏好满足幂效用或指数效用,运用随机动态规划原理和变量替换方法,得到幂效用和指数效用下最优投资策略的显式解.最后,通过数值算例分析主要模型参数对最优投资策略的影响.研究结果表明,利率风险、股市波动风险以及缴费率都对缴费确定(DC)型养老金的投资决策产生较大的影响.     

Clustering stock price time series data to generate stock trading recommendations: An empirical study

Predicting the stock market is considered to be a very difficult task due to its non-linear and dynamic nature. Our proposed system is designed in such a way that even a layman can use it. It reduces the burden on the user. The user's job is to give only the recent closing prices of a stock as input and the proposed Recommender system will instruct him when to buy and when to sell if it is profitable or not to buy share in case if it is not profitable to do trading. Using soft computing based techniques is considered to be more suitable for predicting trends in stock market where the data is chaotic and large in number. The soft computing based systems are capable of extracting relevant information from large sets of data by discovering hidden patterns in the data. Here regression trees are used for dimensionality reduction and clustering is done with the help of Self Organizing Maps (SOM). The proposed system is designed to assist stock market investors identify possible profit-making opportunities and also help in developing a better understanding on how to extract the relevant information from stock price data.     

Network Development Under a Strict Self-Financing Constraint

This paper offers a simplified model in which an agency is in charge of investing in road capacity and maintain it but cannot use the capital market so that the only sources of funds are the toll revenues. We refer to this requirement as the ’strict self-financing constraint’ to distinguish it from the traditional form for self-financing that allows borrowing from the capital market. Two small test problems are analysed: the one link problem and the problem of two parallel links with one link untolled. The numerical illustrations show the cost of the strict self-financing constraint as a function of the importance of the initial infrastructure stock, the rate of growth of demand, the price elasticity of demand and the flexibility in the pricing instruments.     

Bayesian forecaster using class-based optimization

Suppose that several forecasters exist for the problem in which class-wise accuracies of forecasting classifiers are important. For such a case, we propose to use a new Bayesian approach for deriving one unique forecaster out of the existing forecasters. Our Bayesian approach links the existing forecasting classifiers via class-based optimization by the aid of an evolutionary algorithm (EA). To show the usefulness of our Bayesian approach in practical situations, we have considered the case of the Korean stock market, where numerous lag-l forecasting classifiers exist for monitoring its status.     

Continuous-time mean variance portfolio with transaction costs: a proximal approach involving time penalization

This paper proposes a new continuous-time optimization solution that enables the computation of the portfolio problem (based on the utility option pricing and the shortfall risk minimization). We first propose a dynamical stock price process, and then, we transform the solution to a continuous-time discrete-state Markov decision processes. The market behavior is characterized by considering arbitrage-free and assessing transaction costs. To solve the problem, we present a proximal optimization approach, which considers time penalization in the transaction costs and the utility. In order to include the restrictions of the market, as well as those that imposed by the continuous-time space, we employ the Lagrange multipliers approach. As a result, we obtain two different equations: one for computing the portfolio strategies and the other for computing the Lagrange multipliers. Each equation in the portfolio is an optimization problem, for which the necessary condition of a maximum/minimum is solved employing the gradient method approach. At each step of the iterative proximal method, the functional increases and finally converges to a final portfolio. We show the convergence of the method. A numerical example showing the effectiveness of the proposed approach is also developed and presented.     

一个风险敏感最优控制问题的随机最大值原理及在投资选择中的应用

在本文, 我们主要研究了一类产生于金融市场中投资选择问题的风险敏感最优控制问题. 用经典的凸变分技术, 我们得到了该类问题的最大值原理. 最大值原理的形式相似于风险中性的情形. 但是, 对偶方程和变分不等式明显地依赖于风险敏感参数 γ. 这是与风险中性情形的主要区别之一. 我们用该结果解决一类最优投资选择问题. 在投资者仅投资国内债券和股票的情况下, 前人用贝尔曼动态规划原理所得的最优投资策略仅是我们结果的特殊形式. 我们也给了一些数值算例和图, 他们显式地解释了最大期望效用和模型中参数的关系.     

High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates

High utility itemset mining considers the importance of items such as profit and item quantities in transactions. Recently, mining high utility itemsets has emerged as one of the most significant research issues due to a huge range of real world applications such as retail market data analysis and stock market prediction. Although many relevant algorithms have been proposed in recent years, they incur the problem of generating a large number of candidate itemsets, which degrade mining performance. In this paper, we propose an algorithm named MU-Growth (Maximum Utility Growth) with two techniques for pruning candidates effectively in mining process. Moreover, we suggest a tree structure, named MIQ-Tree (Maximum Item Quantity Tree), which captures database information with a single-pass. The proposed data structure is restructured for reducing overestimated utilities. Performance evaluation shows that MU-Growth not only decreases the number of candidates but also outperforms state-of-the-art tree-based algorithms with overestimated methods in terms of runtime with a similar memory usage.     

A reinforcement learning approach to competitive ordering and pricing problem

This study analyses simultaneous ordering and pricing decisions for retailers working in a multi‐retailer competitive environment for an infinite horizon. Retailers compete for the same market where the market demand is uncertain. The customer selects the winning agent (retailer) in each term on the basis of random utility maximization, which depends primarily on retailer price and random error. The complexity of the problem is increased by competitiveness, necessity for simultaneous decisions and uncertainty in the nature of increases, and is not conducive to examination using standard analytical methods. Therefore, we model the problem using reinforcement learning (RL), which is founded on stochastic dynamic programming and agent‐based simulations. We analyse the effects of competitiveness and performance of RL on three different scenarios: a monopolistic case where one retailer employing a RL agent maximizes its profit, a duopolistic case where one retailer employs RL and another utilizes adaptive pricing and ordering policies, and a duopolistic case where both retailers employ RL.     

Imperfect Evolutionary Systems

In this paper, we propose a change from a perfect paradigm to an imperfect paradigm in evolving intelligent systems. An imperfect evolutionary system (IES) is introduced as a new approach in an attempt to solve the problem of an intelligent system adapting to new challenges from its imperfect environment, with an emphasis on the incompleteness and continuity of intelligence. We define an IES as a system where intelligent individuals optimize their own utility, with the available resources, while adapting themselves to the new challenges from an evolving and imperfect environment. An individual and social learning paradigm (ISP) is presented as a general framework for developing IESs. A practical implementation of the ISP framework, an imperfect evolutionary market, is described. Through experimentation, we demonstrate the absorption of new information from an imperfect environment by artificial stock traders and the dissemination of new knowledge within an imperfect evolutionary market. Parameter sensitivity of the ISP framework is also studied by employing different levels of individual and social learning     

Multi-criteria inventory classification with reference items

The ABC method is a well-known approach to classify inventory items into ordered categories, such as A, B and C. As emphasized in the literature, it is reasonable to evaluate the inventory classification problem in the multi-criteria context. From this point of view, it corresponds to a sorting problem where categories are ordered. Here, one important issue is that the weights of the criteria and categorization preferences can change from industry to industry. This requires the analysis of the problem in a specific framework where the decision maker (expert)’s preferences are considered. In this study, the preferences of the decision maker are incorporated into the decision making process in terms of reference items into each class. We apply two utility functions based sorting methods to the problem. We perform an experiment and compare results with other algorithms from the literature.     

Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine

Stock trend prediction is regarded as one of the most challenging tasks of financial time series prediction. Conventional statistical modeling techniques are not adequate for stock trend forecasting because of the non-stationarity and non-linearity of the stock market. With this regard, many machine learning approaches are used to improve the prediction results. These approaches mainly focus on two aspects: regression problem of the stock price and prediction problem of the turning points of stock price. In this paper, we concentrate on the evaluation of the current trend of stock price and the prediction of the change orientation of the stock price in future. Then, a new approach named status box method is proposed. Different from the prediction issue of the turning points, the status box method packages some stock points into three categories of boxes which indicate different stock status. And then, some machine learning techniques are used to classify these boxes so as to measure whether the states of each box coincides with the stock price trend and forecast the stock price trend based on the states of the box. These results would support us to make buying or selling strategies. Comparing with the turning points prediction that only considered the features of one day, each status box contains a certain amount of points which represent the stock price trend in a certain period of time. So, the status box reflects more information of stock market. To solve the classification problem of the status box, a special features construction approach is presented. Moreover, a new ensemble method integrated with the AdaBoost algorithm, probabilistic support vector machine (PSVM), and genetic algorithm (GA) is constructed to perform the status boxes classification. To verify the applicability and superiority of the proposed methods, 20 shares chosen from Shenzhen Stock Exchange (SZSE) and 16 shares from National Association of Securities Dealers Automated Quotations (NASDAQ) are applied to perform stock trend prediction. The results show that the status box method not only have the better classification accuracy but also effectively solve the unbalance problem of the stock turning points classification. In addition, the new ensemble classifier achieves preferable profitability in simulation of stock investment and remarkably improves the classification performance compared with the approach that only uses the PSVM or back-propagation artificial neural network (BPN).     

基于Agent的股票交易模拟及应用

股票市场是市场经济的重要组成部分。但是现有的基于演绎推理的理论分析方法在处理股市这类复杂性系统时遇到了很多困难,因此基于归纳推理的实验经济学方法成为一种可行的选择。论文基于多Agent系统,采用再励学习算法模拟交易者行为特征,实现了一个股票市场的模拟系统,并且应用这一系统研究了涨跌停板交易机制对于股市的影响。     

High utility pattern mining over data streams with sliding window technique

Processing changeable data streams in real time is one of the most important issues in the data mining field due to its broad applications such as retail market analysis, wireless sensor networks, and stock market prediction. In addition, it is an interesting and challenging problem to deal with the stream data since not only the data have unbounded, continuous, and high speed characteristics but also their environments have limited resources. High utility pattern mining, meanwhile, is one of the essential research topics in pattern mining to overcome major drawbacks of the traditional framework for frequent pattern mining that takes only binary databases and identical item importance into consideration. This approach conducts mining processes by reflecting characteristics of real world databases, non-binary quantities and relative importance of items. Although relevant algorithms were proposed for finding high utility patterns in stream environments, they suffer from a level-wise candidate generation-and-test and a large number of candidates by their overestimation techniques. As a result, they consume a huge amount of execution time, which is a significant performance issue since a rapid process is necessary in stream data analysis. In this paper, we propose an algorithm for mining high utility patterns from resource-limited environments through efficient processing of data streams in order to solve the problems of the overestimation-based methods. To improve mining performance with fewer candidates and search space than the previous ones, we develop two techniques for reducing overestimated utilities. Moreover, we suggest a tree-based data structure to maintain information of stream data and high utility patterns. The proposed tree is restructured by our updating method with decreased overestimation utilities to keep up-to-date stream information whenever the current window slides. Our approach also has an important effect on expert and intelligent systems in that it can provide users with more meaningful information than traditional analysis methods by reflecting the characteristics of real world non-binary databases in stream environments and emphasizing on recent data. Comprehensive experimental results show that our algorithm outperforms the existing sliding window-based one in terms of runtime efficiency and scalability.     

An integrated portfolio optimisation procedure based on data envelopment analysis,artificial bee colony algorithm and genetic programming

Portfolio optimisation is an important issue in the field of investment/financial decision-making and has received considerable attention from both researchers and practitioners. However, besides portfolio optimisation, a complete investment procedure should also include the selection of profitable investment targets and determine the optimal timing for buying/selling the investment targets. In this study, an integrated procedure using data envelopment analysis (DEA), artificial bee colony (ABC) and genetic programming (GP) is proposed to resolve a portfolio optimisation problem. The proposed procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market for 4 years. The potential average 6-month return on investment of 9.31% from 1 November 2007 to 31 October 2011 indicates that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans, and thus making profits in the Taiwan stock market. Moreover, it is a strategy that can help investors to make profits even when the overall stock market suffers a loss.     

Adaptive Fuzzy-GARCH model applied to forecasting the volatility of stock markets using particle swarm optimization

Fluctuations in the stock market follow the principle of volatility clustering in which changes are cataloged by similarity; as such, large changes tend to follow large changes, and small changes tend to follow small changes. This clustering is one of the major reasons why many generalized autoregression conditional heteroscedasticity (GARCH) models do not forecast the stock market well. In this paper, an adaptive Fuzzy-GARCH model with particle swarm optimization (PSO) is proposed to solve this problem.The adaptive Fuzzy-GARCH model refers to both GARCH models and the parameters of membership functions, which are determined by the characteristics of market itself. Here, we present an iterative algorithm based on PSO to estimate the parameters of the membership functions. The PSO method aims to achieve a global optimal solution with a rapid convergence rate. The three stock markets of Taiwan, Japan, and Germany were analyzed to illustrate the performance of the proposed method.     

An integrated TOPSIS crow search based classifier ensemble: In application to stock index price movement prediction

Predicting future stock index price movement has always been a fascinating research area both for the investors who wish to yield a profit by trading stocks and for the researchers who attempt to expose the buried information from the complex stock market time series data. This prediction problem can be addressed as a binary classification problem with two class labels, one for the increasing movement and other for the decreasing movement. In literature, a wide range of classifiers has been tested for this application. As the performance of individual classifier varies for a diverse dataset with respect to different performance measures, it is impractical to acknowledge a specific classifier to be the best one. Hence, designing an efficient classifier ensemble instead of an individual classifier is fetching increasing attention from many researchers. Again selection of base classifiers and deciding their preferences in ensemble with respect to a variety of performance criteria can be considered as a Multi Criteria Decision Making (MCDM) problem. In this paper, an integrated TOPSIS Crow Search based weighted voting classifier ensemble is proposed for stock index price movement prediction. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), one of the popular MCDM techniques, is suggested for ranking and selecting a set of base classifiers for the ensemble whereas the weights of the classifiers used in the ensemble are tuned by the Crow Search method. The proposed ensemble model is validated for prediction of stock index price over the historical prices of BSE SENSEX, S&P500 and NIFTY 50 stock indices. The model has shown better performance compared to individual classifiers and other ensemble models such as majority voting, weighted voting, differential evolution and particle swarm optimization based classifier ensemble.     

Systematic analysis and review of stock market prediction techniques

Prediction of stock market trends is considered as an important task and is of great attention as predicting stock prices successfully may lead to attractive profits by making proper decisions. Stock market prediction is a major challenge owing to non-stationary, blaring, and chaotic data, and thus, the prediction becomes challenging among the investors to invest the money for making profits. Several techniques are devised in the existing techniques to predict the stock market trends. This work presents the detailed review of 50 research papers suggesting the methodologies, like Bayesian model, Fuzzy classifier, Artificial Neural Networks (ANN), Support Vector Machine (SVM) classifier, Neural Network (NN), Machine Learning Methods and so on, based on stock market prediction. The obtained papers are classified based on different prediction and clustering techniques. The research gaps and the challenges faced by the existing techniques are listed and elaborated, which help the researchers to upgrade the future works. The works are analyzed using certain datasets, software tools, performance evaluation measures, prediction techniques utilized, and performance attained by different techniques. The commonly used technique for attaining effective stock market prediction is ANN and the fuzzy-based technique. Even though a lot of research efforts, the current stock market prediction technique still have many limits. From this survey, it can be concluded that the stock market prediction is a very complex task, and different factors should be considered for predicting the future of the market more accurately and efficiently.     

An early warning system for financial crisis using a stock market instability index

Abstract: This paper proposes to utilize a stock market instability index (SMII) to develop an early warning system for financial crisis. The system focuses on measuring the differences between the current market conditions and the conditions of the past when the market was stable. Technically the system evaluates the current time series against the past stable time series modelled by an asymptotic stationary autoregressive model via artificial neural networks. Advantageously accessible to extensive resources, the system turns out better results than the conventional system which detects similarities between the conditions of the current market and the conditions of previous markets that were in crisis. Therefore, it should be considered as a more advanced tool to prevent financial crises than the conventional one. As an empirical example, an SMII for the Korean stock market is developed in order to demonstrate its potential usefulness as an early warning system.     

一维下料的改进遗传算法优化

针对一维下料优化问题,在对一维下料方案数学模型分析的基础上,提出了基于改进遗传算法的优化求解方案。主要思想是把零件的一个顺序作为一种下料方案,定义了遗传算法中的关键问题:编码、解码方法、遗传算子和适应度函数的定义。该算法设计了一种新颖的遗传算子,包括顺序交叉算子、线性变异算子、扩展选择算子。根据这一算法开发出了一维下料方案的优化系统。实际应用表明,该算法逼近理论最优值,而且收敛速度快,较好地解决了一维下料问题。     

股指期货套利管理系统

针对我国股指期货市场存在的问题——理论与实际应用脱节,造成股指期货市场的发展需求和实际发展条件不平衡,提出构建股指期货套利管理系统的策略. 依据实地调研资料进行系统设计,使用C#实现股指期货套利管理系统（SIFAM-System）. 系统实现了基于基差的跨期套利和基于无套利区间的跨期套利,以及与套利相关的信息管理功能. SIFAM-System利用计算机实现模型计算、行情监控、套利机会的判断及开平仓,达到了辅助投资者决策的目的,也为股指期货市场的发展问题提供了从理论到实现的解决策略.