基于多Agent的碳排放权交易机制建模与仿真

以供应链的视角建立不同的碳排放权交易机制模型,研究不同机制的指标对企业运营决策的影响。采用Repast实验仿真平台进行建模,模型中考虑了供应链上原材料供应商、制造商、零售商和消费者等主体,分别研究了不同主体行为下制造商利润和产品价格变化情况。仿真结果表明了碳排放有偿分配机制以及公开拍卖机制对碳排放价格和企业决策的有效影响,初步验证了模型的有效性。     

基于碳交易的已存供应链网络优化研究

针对碳交易环境下的已存供应链物流网络优化问题,提出了一个混合整数线性规划模型。该模型综合考虑了已存工厂/物流中心的关闭或继续开设,新增候选工厂/物流中心,以包含供应链运营成本和碳交易成本在内的总成本最小化为目标。随后设计了数据生成准则,在Java中调用Cplex求解,通过算例验证了模型的有效性和求解方法的可行性。最后灵敏度分析为决策制定者提供了一些有意义的启示：较高的单位碳交易价格将迫使企业增加运营成本以降低实际碳排放量,那些注重碳减排的优秀企业将从碳交易中获得收益;碳排放配额不会对供应链运营产生影响,也不会改变供应链的实际碳排放量,但较低的碳排放配额会增加企业的碳交易成本,从而增加供应链的总成本。     

考虑处理基金和回收品质量的闭环供应链决策

为解决废弃电子产品处理基金政策和回收品质量不确定性对闭环供应链决策及协调的影响问题,运用博弈论和MATLAB数值仿真的方法,构建合作与非合作决策博弈模型,研究了两种情况下处理基金和回收品质量对销售及回收价格、批发价格、回收数量、供应链节点企业及整体利润的影响。结果表明,处理基金能够以价格形式传递到消费者,并提高销售价格、回收价格、回收数量和供应链整体利润;回收品质量提升对批发价格和销售价格无影响,但能增加回收数量和供应链利润;合作决策比非合作决策对企业、消费者及生态保护都更有利;价格契约和利润分享机制结合可以有效实现供应链协调。     

低碳背景下考虑批发价格契约供需网企业决策

在全球绿色低碳的视角下,构建高效、可循环的绿色供需体系,基于报贩模型针对低碳供需网系统进行研究。在低碳背景下分别建立不考虑碳排放、碳税、碳配额、碳配额与交易下的供需网批发价格契约模型,并运用Stackelberg博弈进行求解,探讨不同的碳排放政策对低碳供需网契约协调以及收益的影响。通过模型求解验证了无论在何种政策下传统的批发价格契约均无法实现低碳供需网系统协调;且通过数值仿真分析了四种不同的碳减排政策下不同参数的变化对供需网系统的影响。研究发现：各方收益随着碳税价格的升高而降低;当企业的碳排放量超额时,收益随着碳配额的减少而减少;而碳配额与交易下,制造商的收益随着碳交易价格的增加呈现先增后降的趋势。     

VMI下供应链库存模型研究

分别建立了供应链在实行VMI模式前后的供应链整体、供应商及下游企业的定价、成本及利润模型,通过前后的比较分析了VMI模式的实施前期和实施一段时间后对供应链整体、供应商及其下游企业成本、利润、购买价格、订购量的影响。     

基于区块链的游戏软件产品版权保护研究

目前关于区块链应用场景和理念设计的研究较多,但基于供应链协调视角揭示区块链应用的影响仍较缺乏。以游戏软件产品供应链为例,探讨了区块链场景下在线渠道消费者购买效用折扣系数和盗版用户效用折扣系数对均衡价格、均衡利润的影响。结果表明,供应商的利润提高幅度最大,提高量弥补了零售商的损失量,因此供应链整体利润得到优化。此外,通过构建并求解区块链场景下某类仿制软件供应链均衡价格和均衡利润模型,发现仿制软件供应商的价格优势不再明显,甚至消失。最后,考虑到零售商在区块链场景下均衡利润减少的事实,针对性地提出各成员基于供应链协调分配利润角度的相关对策建议。     

政府补贴和增值税退税政策的闭环供应链决策

基于政府补贴和增值税退税政策,通过Stackelberg博弈方法,建立由制造商、回收商和消费者组成的4种闭环供应链决策模型,得出相应的最优价格、补贴和退税政策.进一步,比较并分析4种不同模型下的回收价格、回收量、企业利润以及社会福利,得出政府和企业的最优决策选择.研究发现:1)4种情形下的社会福利均随着消费者环保意识的增加而递增.对政策制定者而言,若消费者环保意识较低,则政府补贴政策最优;若消费者环保意识较高,则政府补贴和增值税退税并存政策最优.2)在绿色消费者市场上,制造商选择高低两种不同定价策略受绿色细分市场规模和政府政策力度大小两方面因素的影响.     

基于区块链技术的碳排放机制设计

作为全球第二大碳交易体系,我国目前有七个试点碳市场,而试点碳市场之间各自独立运行造成信息不对称,不利于为全国范围内统一碳市场建立协同。碳排放流程的五大环节中若出现企业碳排放数据造假等将不利于地方政府掌握地方企业的真实数据、建立碳信息披露制度。如果政府给企业的配额分配不当,也将会在一定程度上造成市场的扭曲,最后导致企业集体不合作或者形成黑市经济。碳交易中信任机制缺乏、信息不对称拖慢了碳交易效率,提高了交易成本。针对上述碳排放环节中的痛点问题,文章采用具有公开透明、可追溯、可共享的优点的区块链技术,利用共识机制和智能合约,建设了数据共享平台,建立起碳生态系统的协同和信任机制,实现碳交易的可追溯、可共享,提高交易效率,降低交易成本,为全国范围内统一碳市场做好准备工作。     

差评量及差评影响因子对网络零售销量的影响

网络商品零售销量直接决定了该网上店铺在同行竞争中能否取胜,而消费者购买商品后给出的差评对网络潜在消费者的购买产生了一定的影响.文章通过模拟仿真,提出了一种基于差评影响因子、商品性价比和主观购买因子的网络零售销量影响因素模型.研究发现,主观购买因子及性价比的大小直接影响商品整体销量的多少,而已购买商品的消费者给出的差评量在达到临界值时,就会导致整体商品停滞.     

一种考虑随机性的多主体创新资源配置仿真模型

针对二元创新资源配置难题,基于主体建模方法,构建考虑随机性的多主体创新资源配置仿真模型,并制定多主体交互中的企业行为规则和消费者行为规则.利用Netlogo软件开展模拟仿真,进行可靠性检验和灵敏度分析.研究发现,资源柔性过大不利于资源配置,降低企业利润.能力柔性的增强有利于提高资源配置效率,进而提升企业创新绩效.两种柔性对资源配置有效性并未表现出协同效应,企业应优先提升能力柔性.该仿真模型综合考虑了消费者和市场变化、竞争条件、策略调整等因素,更符合创新实际,可以为企业制定二元创新资源配置决策提供参考.     

Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model.     

A computational approach to modeling commodity markets

We build an agent based computational framework to study large commodity markets. A detailed representation of the consumers, producers and the market is used to study the micro level behavior of the market and its participants. The user can control players’ preferences, their strategies, assumptions of the model, its initial conditions, market elements and trading mechanisms. The first part of the paper describes the computational framework and its three main modules. The later part describes a case study that examines the decentralized market in detail, specifically the computational options available for matching the buyers and suppliers in a synthetic market. The study illustrates the sensitivity of the outcome of various economic variables, such as clearing price, quantity, profits and social welfare, to different matching schemes in a bilateral computational setting. Based on seven different matching orders for the buyers and suppliers, our study shows that the results can vary dramatically for different pairing orders.     

Correlation and hierarchies in financial markets

As an emerging financial market, the trading value of carbon emission trading market has definitely increased in recent years. The carbon emission is not only trading in carbon emitters but also has become an important investment target. To determine the mechanism of this growing market, we analyzed the EU allowances (EUA) price series in European Climate Exchange (ECX) that is the leading European emissions futures market. As other financial market, the absolute value of price change (volatility) in carbon emission trading market also shows long-term power-law correlations. Our analysis shows that definite cross correlations exist between EUA and many other markets. These cross correlations exist in wild-range fields, stock market index, futures of crude, sugar, cocoa, etc., suggesting that in this new carbon emission trading market the speculation behavior had already become a main factor that can affect the price change.     

PRICE DYNAMICS, INFORMATIONAL EFFICIENCY, AND WEALTH DISTRIBUTION IN CONTINUOUS DOUBLE-AUCTION MARKETS

This paper studies the properties of the continuous double-auction trading mechanism using an artificial market populated by heterogeneous computational agents. In particular, we investigate how changes in the population of traders and in market microstructure characteristics affect price dynamics, information dissemination, and distribution of wealth across agents. In our computer-simulated market only a small fraction of the population observe the risky asset's fundamental value with noise, while the rest of the agents try to forecast the asset's price from past transaction data. In contrast to other artificial markets, we assume that the risky asset pays no dividend, thus agents cannot learn from past transaction prices and subsequent dividend payments. We find that private information can effectively disseminate in the market unless market regulation prevents informed investors from short selling or borrowing the asset, and these investors do not constitute a critical mass. In such case, not only are markets less efficient informationally, but may even experience crashes and bubbles. Finally, increased informational efficiency has a negative impact on informed agents' trading profits and a positive impact on artificial intelligent agents' profits.     

考虑消费者环保意识的WEEE 双渠道回收模型

在考虑消费者环保意识的基础上,建立基于不同消费者群体行为的WEEE双渠道回收模型,采用博弈论比较两条渠道在竞争情形与合作情形下的决策.研究表明,在不同的市场环境下,消费者的环保意识以及环保消费者的比例对两条渠道的合作策略有不同程度的影响.当两条渠道相互合作时,供应链的利润随着普通消费者环保意识的增加而增大,随环保消费者比例的增大而减小.在一般情况下,两条渠道合作时的回收价格和回收量小于竞争情形时的回收量和回收价格,但网络回收平台的单位期望收益远大于流动商贩的单位期望收益的情形除外.当消费者的环保意识、环保消费者的比例以及两条渠道的回收产品的单位利润满足一定条件时,双方合作才会对整个回收产业起到积极作用.最后,对模型中的各参数进行了敏感性分析,并用算例验证了模型的有效性.     

基于非结构数据流行学习的碳价格多尺度组合预测

碳交易价格的有效预测对制定符合国情的碳金融市场政策以及碳金融市场的风险管理都具有重要意义.对此,提出一种基于非结构数据流行学习的碳价格多尺度组合预测方法.首先,利用网络搜索指数提取碳价格相关的非结构化数据,基于等度量映射流行学习对其进行降维;然后,对降维后的非结构化数据、其他影响因素结构化数据、碳交易价格分别进行经验模态分解(Empirical mode decomposition,EMD),得到不同个数的本征模函数(Intrinsic mode function,IMF),并采用Fine-to-coarse方法对IMF进行重构,得到高频序列、低频序列和趋势项;最后,利用自回归积分滑动平均模型(Autoregressive integrated moving average model,ARIMA)、偏最小二乘(Partial least squares,PLS)回归和神经网络对高频数据、低频数据和趋势项进行预测,将3种预测结果进行集成,得到最终预测值.仿真实验结果表明,所提出的方法可以有效利用多源信息,具有较高的预测精度和良好的适用性.     

Short-Term Price Overreactions: Identification,Testing, Exploitation

This paper examines short-term price reactions after one-day abnormal price changes and whether they create exploitable profit opportunities in various financial markets. Statistical tests confirm the presence of overreactions and also suggest that there is an “inertia anomaly”, i.e. after an overreaction day prices tend to move in the same direction for some time. A trading robot approach is then used to test two trading strategies aimed at exploiting the detected anomalies to make abnormal profits. The results suggest that a strategy based on counter-movements after overreactions does not generate profits in the FOREX and the commodity markets, but in some cases it can be profitable in the US stock market. By contrast, a strategy exploiting the “inertia anomaly” produces profits in the case of the FOREX and the commodity markets, but not in the case of the US stock market.     

Research on the Effects of Institutional Liquidation Strategies on the Market Based on Multi-agent Model

Based on the multi-agent model, an artificial stock market with four types of traders is constructed. On this basis, this paper focuses on comparing the effects of liquidation behavior on market liquidity, volatility, price discovery efficiency and long memory of absolute returns when the institutional trader adopts equal-order strategy, Volume Weighted Average Price (VWAP) strategy and Implementation Shortfall (IS) strategy respectively. The results show the following: (1) the artificial stock market based on multi-agent model can reproduce the stylized facts of real stock market well; (2) among these three algorithmic trading strategies, IS strategy causes the longest liquidation time and the lowest liquidation cost; (3) the liquidation behavior of institutional trader will significantly reduce market liquidity, price discovery efficiency and long memory of absolute returns, and increase market volatility; (4) in comparison, IS strategy has the least impact on market liquidity, volatility and price discovery efficiency, while VWAP strategy has the least impact on long memory of absolute returns.