The role of algorithmic trading systems on stock market efficiency

The rapid development of information technology has changed the dynamics of financial markets. The main purpose of this study is laid on examining the role of IT based stock trading on financial market efficiency. This research specifically focused on algorithmic trading. Algorithmic trading enables investors to trade stocks through a computer program without the need for human interventions. Based on an empirical analysis of the Korean stock market, this study discovered the positive impact of algorithmic trading on stock market efficiency at three-fold. First, the study results indicate that algorithmic trading contributes to the reduction in asymmetric volatility, which causes inefficiency of information in a stock market. Second, an algorithmic trading also increases the operation efficiency of a stock market. Arbitrage trading contributes on the equilibrium between the spot market and futures market as well as on the price discovery. Third, algorithmic trading provides liquidity for market participants contributing to friction free transactions. The research results indicate that stock exchanges based on electronic communications networks (ECNs) without human intervention could augment a financial market quality by increasing trading share volumes and market efficiency so that it can eventually contribute to the welfare of market investors.     

Price efficiency in futures and spot trading: The role of information technology

During the last years information technology has had a profound impact on financial markets. The speed of trading and the amount of available information has increased substantially. Nearly all exchanges have upgraded their trading systems to meet the demand of investors and enhance their competitive position. However, the impact on liquidity and price efficiency remains unclear. In this paper we present an event study to examine the effects of an infrastructure change at the Deutsche Börse in Germany. On April 23, 2007, Deutsche Börse released an upgraded version of their electronic trading system Xetra. We study the impact that this upgrade had on the efficiency of prices, measured as the pricing gaps between the observed futures prices and their theoretical values based on the underlying cash market. Our results suggest that the system upgrade reduced the pricing gapand thus improved price efficiency.     

Using computational intelligence to forecast carbon prices

European Union has introduced the European Trading System (ETS) as a tool for developing and implementing international treaties related to climate changes and to identify the most cost-effective methods for reducing greenhouse gas emissions, in particular carbon dioxide (CO₂), which is the most substantial. Companies producing carbon emissions must effectively manage associated costs by buying or selling carbon emission futures. Viewed from this perspective, this paper provides a model for managing the risk by buying and selling carbon emission futures by implementing techniques that leverage computational intelligence. Three computational intelligence techniques are proposed to provide accurate and timely forecasts for changes in the price of carbon: a novel hybrid neuro-fuzzy controller that forms a closed-loop feedback mechanism called PATSOS; an artificial neural network (ANN) based system; an adaptive neuro-fuzzy inference system (ANFIS). Results are based on 1074 daily carbon price observations collected to comprise a useful time-series dataset and for evaluation of the proposed techniques. The extra-sample performance of the proposed techniques is calculated. Analysis results are compared with those produced by other models. Comparison studies reveal that PATSOS is the most accurate and promising methodology for predicting the price of carbon. It is stated that this paper registers a first attempt to apply a hybrid neuro-fuzzy controller to forecasting carbon prices.     

Automated traders in commodities markets: Case of producer–consumer institution

Automatizing commodities’ price negotiation was hard to achieve in practice, mainly because of logistical complications. The purpose of our work is to show that it is possible to automatize thoroughly commodities’ trading in the futures market by replacing human traders with artificial agents. As a starting step, we designed a market institution, called producer–consumer, where only an automated seller and an automated buyer can trade on behalf of the producer and consumer, respectively. The producer and consumer periodically feed their trading agents with supply and demand (S&D) forecasts. We suggested a parameterizable trading strategy, called bands and frequencies, for the agents. To measure the overall efficiency of this trading system in terms of price stability and liquidity, we made some hypotheses on the benchmark price curve and its linkages to S&D curves and other relevant market variables. Then we proposed analytical tools to measure strategy performance. Finally, we conducted some computer simulations to prove the workability of this approach.     

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.     

Modeling and Simulation of an Artificial Stock Option Market

Since their introduction in 1973, options have become an important and very popular financial instrument. However, despite much research performed on the subject, the effects of option trading on the underlying asset market are still debated. Both empirical and theoretical studies have failed to point out how price volatility and volumes of the underlying asset are affected. In this paper we present the first study on the effects of an option market related to an underlying stock market, using an artificial financial market based on heterogeneous agents. We modeled a realistic European option using two market models. The microstructure of the first model is kept as simple as possible, being composed only of random traders. The second model is more complex and realistic, involving the presence of various kinds of trading strategies (random, fundamentalist and chartist). We show that the introduction of options, in the proposed models, tends to decrease the volatility of the underlying stock price. Moreover, the traders’ wealth can be strongly affected by the use of option hedging.     

Developing a rule change trading system for the futures market using rough set analysis

Many technical indicators have been selected as input variables in order to develop an automated trading system that determines buying and selling trading decision using optimal trading rules within the futures market. However, optimal technical trading rules alone may not be sufficient for real-world application given the endlessly changing futures market. In this study, a rule change trading system (RCTS) that consists of numerous trading rules generated using rough set analysis is developed in order to cover diverse market conditions. To change the trading rules, a rule change mechanism based on previous trading results is proposed. Simultaneously, a genetic algorithm is employed with the objective function of maximizing the payoff ratio to determine the thresholds of market timing for both buying and selling in the futures market. An empirical study of the proposed system was conducted in the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. The proposed trading system yields profitable results as compared to both the buy-and-hold strategy, and a system not utilizing a genetic algorithm for maximizing the payoff ratio.     

Intraday dynamic relationships between CSI 300 index futures and spot markets: a high-frequency analysis

Based on intraday 5-min high-frequency dataset, this paper empirically analyzes the intraday dynamic relationships between China’s CSI 300 index futures and spot markets with vector autoregression (VAR) and multivariate GARCH (MGARCH) models. By comparing four VAR–MGARCH models (dynamic conditional correlation, constant conditional correlation, diagonal and BEKK), the VAR–DCC–MGARCH model is found to fit the data the best and be preferred over the other models. The results of this model show that although there are bidirectional price causal relationships between the CSI 300 index futures and spot markets, the index futures return shock affects the spot market more severely than the spot return shock affects the futures market, indicating that the index futures market dominates the price discovery process between the two markets. There are bidirectional volatility spillovers effects between the CSI 300 index futures and spot markets, and the spillovers effects from index futures to spot almost equal to that from index spot to futures. The time-varying conditional correlations between the CSI 300 index futures and spot markets change from 0.4787 to 0.9594 across time, showing there is a strong positive correlation and linkage effect between the two markets. These results indicate that after a period of time of development, the price discovery performance of the CSI 300 index futures market has begun to function well, and the impact of the CSI 300 index futures market on its underlying spot market has strengthened.     

期货市场创新的模型和决策

本文研究了期货市场创新（Ｉｎｎｏｖａｔｉｏｎ）的最优设计问题，分析了保值者和投资者市场结构和交易均衡条件，建立了具有保值性，流动性，价格引导和交易费用的模型，设计了期货市场的单个创新，并发创新（Ｓｉｍｕｌｔａｎｅｏｕｓ Ｉｎｎｏｖａｔｉｏｎ）和顺序创新（Ｓｅｑｕｅｎｔｉａｌ Ｉｎｎｏｖａｔｉｏｎ）的最优决策，运用模拟退火方法，进行了期货市场创新的仿真研究。     

Entrepreneurship and the carbon market: opportunities and challenges for South African entrepreneurs

The Kyoto Protocol and its implementation brought forward issues of climate change and its mitigation strategy by national measures through the creation of market mechanisms in carbon trading. The trading of emission certificates has become an important trade commodity worldwide, and its markets have diversified. While this opportunity has created new markets for entrepreneurs and actors that range from farmers to brokers, unequal involvement in most developing countries is noted. This has been mostly observed in those countries where entrepreneurship is often regarded as the cornerstone of economic growth and social improvement. South Africa has spearheaded other African countries in its implementation of Clean Development Mechanism (CDM) projects leading to carbon trading. Based on our research on South African entrepreneurship and its involvement in the carbon market, we conclude that albeit a number of opportunities, the biggest challenge for entrepreneurial participation in the carbon market remains in the nature and processes of CDM project implementation, the lack of a clear supportive system, limited access to financing and—more importantly—general ignorance of the trading opportunities by entrepreneurs. The complex nature of CDM projects themselves limits participation due to lack of the necessary skills on the national level leading to uneven distribution of CDM projects on provincial levels in South Africa. Recommendations are provided to overcome the obstacles and to promote entrepreneurial activity in the carbon market.     

Bridging the divide in financial market forecasting: machine learners vs. financial economists

Financial time series forecasting is a popular application of machine learning methods. Previous studies report that advanced forecasting methods predict price changes in financial markets with high accuracy and that profit can be made trading on these predictions. However, financial economists point to the informational efficiency of financial markets, which questions price predictability and opportunities for profitable trading. The objective of the paper is to resolve this contradiction. To this end, we undertake an extensive forecasting simulation, based on data from thirty-four financial indices over six years. These simulations confirm that the best machine learning methods produce more accurate forecasts than the best econometric methods. We also examine the methodological factors that impact the predictive accuracy of machine learning forecasting experiments. The results suggest that the predictability of a financial market and the feasibility of profitable model-based trading are significantly influenced by the maturity of the market, the forecasting method employed, the horizon for which it generates predictions and the methodology used to assess the model and simulate model-based trading. We also find evidence against the informational value of indicators from the field of technical analysis. Overall, we confirm that advanced forecasting methods can be used to predict price changes in some financial markets and we discuss whether these results question the prevailing view in the financial economics literature that financial markets are efficient.     

Neural network forecasting of an opening cash price index

The study investigates the information content of SGX-DT Nikkei 225 futures prices during the non-cash-trading (NCT) period using an artificial neural network model. The cash market closing index, the futures prices from a period in the same trading day and on the following trading day are utilized to determine the appropriate input nodes of a back propagation neural network model in forecasting the opening cash price index. Sensitivity analysis is first employed to address and solve the issue of finding the appropriate network topology. Extensive studies are then performed on the robustness of the constructed network by using different training and testing sample sizes. The effectiveness of the method is demonstrated on data from a 6-month historical record (1998-99). Analytic results demonstrate that the proposed neural network model outperforms a neural network model with the previous day's closing index as the input node and the random walk model forecasts. It, therefore, indicates that there is valuable information involved in futures prices during the NCT period that can be used to forecast the opening cash market price index.     

基于区块链和博弈论的碳交易多方定价机制研究与系统设计

随着全球气候变化问题的日益突出,碳交易作为一种重要的环保手段逐渐受到广泛关注。在碳交易中,多方定价机制被广泛应用。本文探讨了基于区块链和博弈论的碳交易多方定价机制设计。通过使用区块链技术记录碳交易的信息和智能合约实现自动化执行和多方协作,该机制可以增加碳交易的透明度和可信度。通过基于博弈论的多方定价机制帮助找到最优的定价策略,以实现碳交易的公平和合理。同时,还采用机器学习和数据分析技术,根据历史交易数据对碳交易单价及市场未来趋势进行预测,为交易者带来更精确的参考建议。此外,本文还设计并实现了一个基于区块链和博弈论的碳交易系统,并且对该系统的可行性进行了验证,证明了最优的定价策略在该系统中是可行的。已确认并按要求修改     

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。     

The forecasting ability of Internet-based virtual futures market

Internet-based virtual futures markets (VFMs) have been used in predicting election results and movie ticket sales. We construct an Internet-based VFM to predict an underlying stock price. While the virtual futures market has received much attention, questions remain as to the ideal number of participants. Results of Granger causality tests and analysis of directional accuracy show that a VFM with only a small number of participants (75) is able to generate informative futures prices useful in the prediction of the underlying stock price. Moreover, the participants were not professional investors but merely undergraduate finance students with only a cursory introduction to futures trading. Our results provide additional evidence supporting the use of VFMs in forecasting and show that VFMs are powerful forecasting tools.     

基于agent行为的供应链低碳化建模与仿真

从供应链低碳化出发,分析了企业碳配额、碳交易市场中的碳价格以及消费者主观购买行为等因素对企业利润的影响。采用Repast Simphony实验仿真平台和Groovy开发语言,在复杂的不确定市场环境下,对供应链参与方行为进行仿真,建立了引入碳交易因素以后的供应链模型。模型中包含消费者对产品的选择、低碳化运输和采购、供应商选择等市场行为。仿真结果表明,企业碳配额和消费者主观购买行为对企业利润的影响较大,对碳交易市场中的碳价格影响较小,初步验证了模型的有效性。     

基于探针的动态期货交易监控系统设计与实现

随着分布式系统的发展,系统复杂性大大的增加,为了分析和了解分布式系统的运行情况以及分析系统的瓶颈,对于分布式系统的监控变得重要起来。期货交易系统是一种典型的分布式系统,它在期货经济发展中具有重要的意义。为了保证期货交易系统能够安全、稳定的运行,分析系统的瓶颈,对于其运行状态进行监控,是十分必要的。本文把表达式解析应用到监控的数据分析中,从灵活性、可配置性和扩展性等角度出发,设计并实现了基于探针的动态的期货交易监控系统。并且对于系统的特点进行了深入的分析。     

Using multi-agent simulation to understand trading dynamics of a derivatives market

A fundamental question that arises in derivative pricing is why investors trade in a particular derivative at a “fair” price supplied by Arbitrage Pricing Theory (APT). APT establishes a price that is fair for a disinterested investor with a particular set of beliefs about market evolution and attributes trading to differences in those beliefs entertained by the opposite sides of the transaction.We present a model for an investor in a frictionless market that combines investors’ incentives in the form of pre-existing liability structures with derivatives pricing procedure tailored for a particular investor. This model enables us to show, through a series of experiments, that investors trade even when their belief structures are identical and accurate.More generally, our study suggests that multi-agent simulation of a financial market can provide a mechanism for conducting experiments that shed light on fundamental properties of the market. As all processes in financial markets (including decision making) become automated, it becomes crucial to have a mechanism by which we can observe the patterns that emerge from a variety of possible investor behaviors. Our simulator, designed as a dealer’s market, provides such a mechanism within a certain range of models.     

Using rough set to support investment strategies of real-time trading in futures market

Finding proper investment strategies in futures market has been a hot issue to everyone involved in major financial markets around the world. However, it is a very difficult problem because of intrinsic unpredictability of the market. What makes things more complicated is the advent of real-time trading due to recent striking advancement of electronic communication technology. The real-time data imposes many difficult tasks to futures market analyst since it provides too much information to be analyzed for an instant. Thus it is inevitable for an analyst to resort to a rule-based trading system for making profits, which is usually done by the help of diverse technical indicators. In this study, we propose using rough set to develop an efficient real-time rule-based trading system (RRTS). In fact, we propose a procedure for building RRTS which is based on rough set analysis of technical indicators. We examine its profitability through an empirical study.     

Hilbert Spectra and Empirical Mode Decomposition: A Multiscale Event Analysis Method to Detect the Impact of Economic Crises on the European Carbon Market

Exploring the effect of an economic crisis on the carbon market can be propitious to understand the formation mechanisms of carbon pricing, and prompt the healthy development of the carbon market. Through the ensemble empirical mode decomposition (EEMD), a multiscale event analysis approach is proposed for exploring the effect of an economic crisis on the European carbon market. Firstly, we determine the appropriate carbon price data of the estimation and event windows to embody the impact of the interested economic crisis on carbon market. Secondly, we use the EEMD to decompose the carbon price into simple modes. Hilbert spectra are adopted to identify the main mode, which is then used to estimate the strength of an extreme event on the carbon price. Thirdly, we perform a multiscale analysis that the composition of the low-frequency modes and residue is identifying as the main mode to capture the strength of the interested economic crisis on the carbon market, and the high-frequency modes are identifying as the normal market fluctuations with a little short-term effect on the carbon market. Finally, taking the 2007–2009 global financial crisis and 2009–2013 European debt crisis as two cases, the empirical results show that contrasted with the traditional intervention analysis and event analysis with the principle of “one divides into two”, the proposed method can capture the influences of an economic crisis on the carbon market at various timescales in a nonlinear framework.