基于马尔可夫决策法的企业市场占有率预测

马尔可夫决策法是一种对随机过程未来状况进行预测的有效方法.本文简要介绍了马尔可夫过程和马尔可夫链,并举例说明了如何利用马尔可夫决策法对企业市场占有率进行预测.     

市场占有率预测——基于马尔可夫链的研究

<正>当前,国内家用电器市场已经进入成熟阶段,国际品牌与国内的各路诸侯杀的不可开交,市场竞争已经趋于白热化。企业想在市场中站稳脚跟就必须运用科学方法,为提高产品市场占有率做出准确预测并做出相关决策。利用马尔可夫决策可以有效的对市场占有率做出预测。     

马尔可夫链在股票价格预测中的应用

在企业的生产、经营、管理、决策等工作中 ,经常会遇到这样的情况 ,事物未来的发展及演变状态仅仅受事物现状的影响 ,而与过去的状态无关 ,也就是具有马尔可夫性。运用马尔可夫模型 ,对具有马尔可夫性的股票价格进行分析和预测 ,为马尔可夫模型应用的拓广和股票价格的概率估计预测提供理论依据和实际应用的参考。     

Markov链在股票市场近期走势的预测分析

在企业的生产、经营、管理、决策等工作中,经常会遇到这样的情况,事物未来的发展及演变状态仅仅受事物现状的影响,而与过去的状态无关,也就是具有马尔可夫性。运用马尔可夫模型,对具有马尔可夫性的股票价格进行分析和预测,为马尔可夫模型应用的拓广和股票价格的概率估计预测提供理论依据和实际应用的参考。     

马尔科夫过程模型在股指预测中的应用

在企业的生产、经营、管理、决策等工作中,经常会遇到这样的情况,事物未来的发展及演变状态仅仅受事物现状的影响,而与过去的状态无关,也就是具有马尔可夫性。运用马尔可夫模型,对具有马尔可夫性的股票价格指数进行分析和预测,为马尔可夫模型应用的拓广和股票价格的概率估计预测提供理论依据和实际应用的参考。     

基于马尔可夫链的股价预测

在企业的生产、经营、管理、决策等工作中,经常会遇到这样的情况：事物未来的发展及演变状态仅仅受事物现状的影响,而与过去的状态无关,也就是具有马尔可夫性。本文运用马尔科夫理论预测股票价格,建立其随机过程模型,使决策的长期效益趋于最优,通过实例检验,证明了此模型的可行性和实用性。运用马尔可夫过程理论,对未来股价走势和股指未来的突破方向进行了研究,对其他预测方法作了有益的补充。     

基于马尔可夫链的股份预测

在企业的生产、经营、管理、决策等工作中,经常会遇到这样的情况：事物未来的发展及演变状态仅仅受事物现状的影响,而与过去的状态无关,也就是具有马尔可夫性。本文运用马尔科夫理论预测股票价格,建立其随机过程模型,使决策的长期效益趋于最优,通过实例检验,证明了此模型的可行性和实用性。运用马尔可夫过程理论,对未来股价走势和股指未来的突破方向进行了研究,对其他预测方法作了有益的补充。     

基子马尔可夫链的股价预测

在企业的生产、经营、管理、决策等工作中,经常会遇到这样的情况:事物未来的发展及演变状态仅仅受事物现状的影响,而与过去的状态无关,也就是具有马尔可夫性.本文运用马尔科夫理论预测股票价格,建立其随机过程模型,使决策的长期效益趋于最优,通过实例检验,证明了此模型的可行性和实用性.运用马尔可夫过程理论,对未来股价走势和股指未来的突破方向进行了研究,对其他预测方法作了有益的补充.     

企业市场决策中的占有率优化

本文通过对马尔可夫过程理论中用于分析随机过程方法的研究,提出了将转移概率矩阵法应用于企业产品的市场占有率分析当中,认为该理论的无后效性和稳定性特点能够帮助企业在纵向和横向资讯不够充分的情况下克服预测的误差和决策的盲目性,并给出了均衡状态下的市场占有率模型,以期通过不同方案的模拟分析,帮助企业优化决策。     

吸收态马尔可夫链及其应用

吸收态马尔可夫链在经济系统中有着广泛的应用,许多经济现象可以用吸收态马尔可夫链进行描述,通过对现象变化过程中各种状态的研究,利用吸收态马尔可夫链的推导结果,对事物的发展作出预测,进而为决策提供依据。可以说,吸收态马尔可夫链的实际应用价值比较高。     

商品住宅需求量灰色系统预测模型研究

论文运用灰色系统理论,建立了有效的商品住宅市场短期需求预测模型,并以沈阳市为例对模型进行运用,结果证明该方法具有科学性,能为政府进行商品住宅市场短期平衡和控制提供决策依据。     

公路交通运输量GM-Markov综合预测模型研究

为了提高公路交通运输量的预测精度,在介绍一般模型的基础上,建立了GM-Markov预测模型,它是将灰色预测方法与Markov预测模型优化组合,用灰色预测模型GM(1,1)预测随机时间序列数据的总体发展趋势,而用Markov模型预测各数据在总体趋势下的随机波动性变化,得到随机时间序列数据趋势预测模型的解。通过公路货运量的实际数据进行了验证,结果表明:GM-Markov预测模型既能预测参数随机数据序列的总体趋势,又能适应波动性较大的随机序列变化,其预测精度高于GM(1,1)模型的预测精度。     

Impact of Wal-Mart on Market Share in a Rural Grocery Market: An Application of the Markov Chain Model

With Americans spending up to 13% of their income on grocery shopping, smart shopping can help a typical family save. Many scholars have studied the effects on market share and profits from changing market share using the Markov model, and also studied the competitive strategies of grocery retailers under these conditions. While the forecast of the steady state is highly useful, examining the transition periods before the steady state is reached is also useful. In these transition periods, a retailer may opt for a new competitive strategy; therefore, the transition periods may provide greater insight into the dynamics of the competitive response. More attention should be paid to the revenues for groceries in transition periods before a steady state is reached—especially in markets dominated by retail giants, such as Wal-Mart. This study attempts to model that phenomenon and to explore appropriate competitive strategies.     

企业流动型客户占有率及其效益的马尔科夫预测

客户关系管理是目前各界探讨的一个热点问题。从适应客户关系管理对客户数据深入挖掘的需要出发,将客户分类为流动型客户和稳定型客户,流动型客户可分为高利润客户和低利润客户。对企业产品对流动型客户的占有率以及由该客户创造的效益选用马尔科夫预测模型进行预测和效益评估,有利于指导企业的运作,也有利于预测结果的准确。     

Forecast information and traditional retailer performance in a dual-channel competitive market

Independent firms in a dual-channel competitive market are expected to have their own information about the nature of the market. In this research, we develop a game-theoretic model to examine the value of forecast information about consumers' willingness to pay. The model is based on a simultaneously played Bertrand game. Our results indicate that the profits of online as well as traditional retailers always increase with forecast accuracy, and that forecast accuracy has a greater effect on the performance of the traditional retailer than on that of the online retailer. Our results also show that the difference in profit between that of the traditional retailer and the online retailer increases with forecast accuracy. In addition we find that forecast accuracy is much more valuable to the traditional retailer when there is an increasing volatility in the market, an increasing level of consumer valuation of the product, and an increasing intensity in market competition. Based on our results, we derive optimal market strategies and identify directions of future research.     

Using diffusion models to forecast market size in emerging markets with applications to the Chinese car market

Marketing managers have to forecast the market size and this forecast guides strategic decisions whether to continue exporting, open new factories or expand existing production operations. Forecasting sales and the market size is a challenging task; even more so in emerging markets where data is limited and the market demand is changeable. This research proposes a novel approach that applies diffusion models using car ownership data to forecast car sales. Car ownership data may be easier to access than sales data in emerging markets but marketing managers are more interested in the sales forecast. Researchers propose using diffusion models to forecast the adoption of new products or products which are new to consumers in a market. This research demonstrates that marketing managers can use diffusion models to predict car sales in China where cars are new products to most consumers in this market. Since the majority of car buyers in China are first time buyers, car manufacturers and retailers must also forecast when the market composition will change. This effectively means predicting when first time car buying will start to slow down and repeat/replacement purchase or second hand car purchase will become more important. To forecast both sales and market composition change, marketing managers must choose a robust model. Managers want insights from models that have been tested robustly especially in less stable market conditions. In this context, this study illustrates the value of using a rolling forecast instead of a fixed horizon approach when comparing and choosing which model to use to forecast both sales and market composition change for the Chinese car market.     

海运运价指数研究综述

海运运价指数反映航运市场水平的动态变化,对贸易成本有重要影响。结合近些年海运运价指数的文献研究进展,首次对波罗的海干散货运价指数(BDI)、波罗的海原油运价指数(BDTI)以及中国出口集装箱运价指数(CCFI)三个海运运价指数的影响因素、波动周期性和指数预测方法研究领域的成果进行归纳整理和对比分析,为航运公司、船舶中间商的经营决策提供参考。影响因素主要从供需关系下的运输成本、航运市场、经济环境三个因素展开,波动周期结合各航运市场的特征并分析原因,预测方法则围绕线性模型与非线性模型进行比较,在文献述评的基础上指出目前研究的不足与未来的研究方向。     

Recent advances in reinforcement learning in finance

The rapid changes in the finance industry due to the increasing amount of data have revolutionized the techniques on data processing and data analysis and brought new theoretical and computational challenges. In contrast to classical stochastic control theory and other analytical approaches for solving financial decision-making problems that heavily reply on model assumptions, new developments from reinforcement learning (RL) are able to make full use of the large amount of financial data with fewer model assumptions and to improve decisions in complex financial environments. This survey paper aims to review the recent developments and use of RL approaches in finance. We give an introduction to Markov decision processes, which is the setting for many of the commonly used RL approaches. Various algorithms are then introduced with a focus on value- and policy-based methods that do not require any model assumptions. Connections are made with neural networks to extend the framework to encompass deep RL algorithms. We then discuss in detail the application of these RL algorithms in a variety of decision-making problems in finance, including optimal execution, portfolio optimization, option pricing and hedging, market making, smart order routing, and robo-advising. Our survey concludes by pointing out a few possible future directions for research.     

我国水电工程咨询企业跨国供应链管理决策

水电工程咨询行业的周期性特征以及国内水电工程咨询市场发展的有限性是我国水电工程咨询企业必须走出国门的最重要的驱动因素。水电工程咨询企业在跨国经营过程中必须认真搭建供应链管理模式框架,进行区位选择决策、战略定位决策和组织模式选择的决策,尽可能控制风险,发挥优势,保障跨国经营的成功。     

基于Markov过程的银行不良资产风险分析

把具有吸收态的马尔可夫链理论 ,运用于银行不良资产风险管理的可行性 ,从而建立相应的分析模型 ,设计出评价指标 ,对制定处理银行不良资产的政策和方案有一定的参考价值