证券集中交易系统探析

证券交易系统从分散走向集中是信息技术在证券行业发展的必然趋势 ,同时也是证券公司集中化管理和金融业务创新的必然结果。本文首先提出了证券集中交易系统的体系架构以及实现模式 ,论述了证券集中交易系统应该满足的一些基本要求以及集中交易方式与以营业部为中心的分布式交易方式相比所具有的优势。最后 ,对集中交易系统所面临的一些主要风险进行了分析     

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.     

证券公司集中监控系统设计

证券市场是完整的市场体系的重要组成部分，对整个经济的运行具有重要影响.证券交易系统的安全稳定运行是证券业界非常关注的课题.本文设计一个可以快速实时响应、灵活监控证券公司各个交易系统的网络连通和业务功能运行情况的监控系统（RSCMS），以提高部门的实时监控效率，并为系统的运行情况提供数据评估的依据.本文研究了监控系统关键技术的解决方法，并提供了在证券公司运行的成功案例     

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.     

证券数据中心网络系统

数据中心作为证券等金融机构关键数据平台,其网络系统地位极为重要。针对某证券公司在实施数据中心建设过程,根据需求提出了网络中心设计原则和建设目标,阐述了其数据中心网络系统的核心业务网络、交易与办公系统网络、广域网等不同网络系统的设计方案。新的网络系统设计经运行表明,满足了系统业务需求,实现了建设目标。     

一种计算机交易系统风险监督体系

随着计算机交易系统在金融领域的深入运用,交易呈现出越来越多的风险.从计算机及网络技术的角度,提出了一种计算机交易系统风险监督体系.基于该体系,能够为银行、保险、证券、邮政、电信等行业的计算机交易系统提供"独立、实时、灵活"的风险监督管理系统,从而有效地保障了计算机交易系统在风险监督中的良性化运行.     

网上证券交易系统安全机制的研究

网上证券交易作为当前证券公司最便捷的交易渠道之一正在迅速发展,如何保障股民账户信息和交易信息的安全是当务之急。由于网上证券交易活动交互性强的特点,需要客户端和服务器通过中间传输网络进行频繁的通信,而用户群体的多样性使得客户端存在着很多不确定因素,中间传输网络的开放性也为攻击者提供了一个很方便的攻击平台。所以本文根据网上证券交易的特点,分析了网上证券交易面临的安全风险,从客户端、服务器以及通信网络三个方面,提出了贯穿网上交易行为的各个环节的多个安全技术手段和管理手段,为网上交易安全构建了一个全局协同的整体安全防护体系。     

Economic theory and the financial crisis

I want to probe in the role of the market in allocating resources in this very preliminary essay. One does not have to see study deeply to that the failure of markets for various kinds of derivative securities to perform properly is an essential element of the current financial crisis. Actually, financial crises are not a new phenomenon. The history of capitalism has been marked by repeated collapses of the financial system, situations in which the ??markets?? for loans disappear for extensive periods of time. The 18th century saw some bubbles, but these might not be quite modern. But from 1819 on, there have a succession of failures of banks and other financial institutions. These have typically been unpredicted and did not correspond in time to any particular exogenous event (e.g., wars). Economists from Joho Stuart Mill (1848) on did recognize the phenomenon. But the discussion was and is not at all integrated with the general exposition of classical economics. No one could be a more vigorous advocate of unrestrained markets than Milton Friedman; yet, to my reading, the account that he and Anna Schwartz gave of monetary developments in the United States and particularly with regard to the Great Depression emphasizes, not prices, not even interest rates, but the supply of money, and, by inference, of liquidity. (Friedman and Schwartz (1963)). I start with the neoclassical general equilibrium framework, to which I have given a good deal of attention and effort. I seek to identify a possible point at which it fails to supply a coherent theory of securities markets and so might possibly lead to some understanding of the repeated crises of the financial system underlying the development of capitalism.     

人工神经网络在证券价格预测中的应用

证券市场中成功的交易模式是可以模仿及学习的.证券价格走势实质是一种复杂时序函数.人工神经网络是在模仿人脑处理问题过程中发展起来的新型智能信息处理系统,人工神经网络可以通过调节连接权值以任意精度逼近任何连续函数,因此也可以逼近证券价格随时间变换这种函数.文中采用基于BP模型的神经网络,用BP算法和遗传算法来训练网络权值,同时也采用了动量法和学习率自适应调整相结合的策略,对证券市场的价格进行建模和预测,结果表明,此模型具有较好的学习、泛化能力,对股票市场或其他类似的非线性经济系统的走势预测决策具有较好的效果.     

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     

Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach

The detrimental effects of insider trading on the financial markets and the economy are well documented. However, resource-constrained regulators face a great challenge in detecting insider trading and enforcing insider trading laws. We develop a text analytics framework that uses machine learning to predict ex-ante potentially opportunistic insider trading (using actual insider trading allegation by shareholders as the proxy) from corporate textual disclosures. Distinct from typical black-box neural network models, which have difficulty tracing a prediction back to key features, our approach combines the predictive power of deep learning with attention mechanisms to provide interpretability to the model. Further, our model utilizes representations from a business proximity network and incorporates the temporal variations of a firm’s financial disclosures. The empirical results offer new insights into insider trading and provide practical implications. Overall, we contribute to the literature by reconciling performance and interpretability in predictive analytics. Our study also informs the practice by proposing a new method for regulators to examine a large amount of text in order to monitor and predict financial misconduct.     

规避网络钓鱼给证券行业带来的安全风险

网上交易作为证券行业进行业务开展的主要手段之后,网上交易的安全性就成了人们日益关注的话题。在网上交易过程中,钓鱼网站攻击是一种重要的攻击方式。由于此方式的特殊性,导致被害用户损失严重。所以做好对钓鱼网站的防范,对于证券行业具有很大现实意义。本文从钓鱼网站的实施过程、社会危害、防范方法等几个角度,对网络钓鱼进行全面地阐述。详细的描述了恶意份子如何引导用户去访问恶意网站。详细的描述了网络钓鱼的主要防范技术,如黑白名单检测技术、页面相似度检查技术。从技术角度和用户的上网行为角度,提出了相关的方法,尽量规避网络钓鱼技术给证券行业带来的安全风险。     

A hybrid option pricing model using a neural network for estimating volatility

The Black–Scholes (BS) model is the standard approach used for pricing financial options. However, although being theoretically strong, option prices valued by the model often differ from the prices observed in the financial markets. This paper applies a hybrid neural network which preprocesses financial input data for improving the estimation of option market prices. This model is comprised of two parts. The first part is a neural network developed to estimate volatility. The second part is an additional neural network developed to value the difference between the BS model results and the actual market option prices. The resulting option price is then a summation between the BS model and the network response. The hybrid system with a neural network for estimating volatility provides better performance in terms of pricing accuracy than either the BS model with historical volatility (HV), or the BS model with volatility valued by the neural network.     

Trading Financial Derivatives on the Web—An Approach Towards Automating Negotiations on OTC Markets

Derivative instruments have become increasingly important to financial institutions, institutional investors, traders and private individuals throughout the world, both as risk-management tools and as a source of revenue. The volume of over-the-counter (OTC) traded derivatives has increased enormously over the past decade, because institutional investors have often had a need for special derivative products which are not traded on organized exchanges. An important feature of OTC trading is the bargaining on multiple attributes of a contract such as price, strike price and contract maturity. Negotiation on multiple attributes of a deal is currently not supported by electronic trading floors. In this paper we describe an approach of how to automate the multi-attribute multilateral negotiations using a Web-based trading system. First, we will give an overview of various approaches to supporting or automating negotiations on multiple attributes. Then we will introduce multi-attribute auctions, an extension of single-sided auction theory and analyze preliminary game-theoretic results. Finally, we will show a Web-based electronic trading system for OTC derivatives, based on multi-attribute auctions.     

Weather, Information Security, and Markets

The most common examples of financial variables are traded assets such as stocks and bonds. Derivatives have greatly facilitated financial management, the most useful technique of which is hedging, a trading activity that aims to reduce exposure to risk. The weather derivatives market has similarities to both the potential evolution and pricing of information security derivatives. Financial markets and investors often consider a cyberattack on a corporation's information systems to be a serious management problem and thus a threat to overall corporate value. This perception intensifies if the attack also involves direct theft of personal client data. In such a scenario, companies not only lose customer trust, but they're also subject to costly legal actions, both from customers and government regulators. Information security derivatives can provide a solution. The first is related to investors and brand name: a company that uses appropriate financial instruments to hedge its risks is likely to have investor trust and confidence. Prediction markets could assist in determining security attacks and their related risks as well as preferences.     

Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach

Financial volatility refers to the intensity of the fluctuations in the expected return on an investment or the pricing of a financial asset due to market uncertainties. Hence, volatility modeling and forecasting is imperative to financial market investors, as such projections allow the investors to adjust their trading strategies in anticipation of the impending financial market movements. Following this, financial volatility trading is the capitalization of the uncertainties of the financial markets to realize investment profits in times of rising, falling and side-way market conditions. In this paper, an intelligent straddle trading system (framework) that consists of a volatility projection module (VPM) and a trade decision module (TDM) is proposed for financial volatility trading via the buying and selling of option straddles to help a human trader capitalizes on the underlying uncertainties of the Hong Kong stock market. Three different measures, namely: (1) the historical volatility (HV), (2) implied volatility (IV) and (3) model-based volatility (MV) of the Hang Seng Index (HSI) are employed to quantify the implicit volatility of the Hong Kong stock market. The TDM of the proposed straddle trading system combines the respective volatility measures with the well-established moving-averages convergence/divergence (MACD) principle to recommend trading actions to a human trader dealing in HSI straddles. However, the inherent limitation of the MACD trading rule is that it generates time-delayed trading signals due to the use of moving averages, which are essentially lagging trend indicators. This drawback is intuitively addressed in the proposed straddle trading system by applying the VPM to compute future projections of the volatility measures of the HSI prior to the activation of the TDM. The VPM is realized by a self-organising neural-fuzzy semantic network named the evolving fuzzy semantic memory (eFSM) model. As compared to existing statistical and computational intelligence based modeling techniques currently employed for financial volatility modeling and forecasting, eFSM possesses several desirable attributes such as: (1) an evolvable knowledge base to continuously address the non-stationary characteristics of the Hong Kong stock market; (2) highly formalized human-like information computations; and (3) a transparent structure that can be interpreted via a set of linguistic IF–THEN semantic fuzzy rules. These qualities provide added credence to the computed HSI volatility projections. The volatility modeling and forecasting performances of the eFSM, when benchmarked to several established modeling techniques, as well as the observed trading returns of the proposed straddle trading system, are encouraging.     

新技术在证券交易系统中的应用研究

数据通信技术、网络技术等在证券行业的广泛应用,为实现证券异地交易,获取实时交易行情提供了有效的手段,也极大地推动了证券行业的发展,在此基础上,针对当前证券交易系统设计和实现中已经使用的和将要使用的一些新技术进行了研究与探讨,并结合这些新技术的应用提出了证券交易系统今后的发展趋势。     

The use of data mining and neural networks for forecasting stock market returns

It has been widely accepted by many studies that non-linearity exists in the financial markets and that neural networks can be effectively used to uncover this relationship. Unfortunately, many of these studies fail to consider alternative forecasting techniques, the relevance of input variables, or the performance of the models when using different trading strategies. This paper introduces an information gain technique used in machine learning for data mining to evaluate the predictive relationships of numerous financial and economic variables. Neural network models for level estimation and classification are then examined for their ability to provide an effective forecast of future values. A cross-validation technique is also employed to improve the generalization ability of several models. The results show that the trading strategies guided by the classification models generate higher risk-adjusted profits than the buy-and-hold strategy, as well as those guided by the level-estimation based forecasts of the neural network and linear regression models.     

Information technology and securities market

This paper examines the use of information technology (IT) in the financial securities market. These markets have seen a proliferation of IT applications in the past decade, and as a result the very structure and nature of competition in the industry has changed. Various regulatory agencies are attempting to keep up with this progression and ensure fair and efficient markets. One of the directives of the Securities and Exchange Commission (SEC) was for a National Market System (NMS) to overcome the problems of fragmentation. Although a true NMS does not exist, huge gains have been made towards it, however, it is unlikely to become the dominant system. An outgrowth of IT use and the NMS directive has been a movement towards fully automated exchanges. These efforts have not met with much success as the existing automated exchanges are mainly small and experimental. A second outgrowth of the NMS is program trading. Pure in its intention, this application has since grown beyond control and was found to be responsible for much of the large decline in.stock prices in the October 1987 market crash. Limits on the use of program trades are appearing daily, and their future seems limited at best.     

Exchange market for complex commodities: search for optimal matches

The Internet has led to the development of online markets, and computer scientists have designed various auction algorithms, as well as automated exchanges for standardized commodities; however, they have done little work on exchanges for complex non-standard goods. We present an exchange system for trading complex goods, such as used cars or non-standard financial securities. The system allows traders to represent their buy and sell orders by multiple attributes; for example, a car buyer can specify a model, options, colour, and other desirable features. Traders can also provide complex price constraints, along with preferences among acceptable trades; for instance, a car buyer can specify dependency of an acceptable price on the model, year of production, and mileage. We describe the representation and indexing of orders, and give algorithms for fast identification of matches between buy and sell orders. The system identifies the most preferable matches, which maximize trader satisfaction, and it allows control over the trade-off between speed and optimality of matching. It supports markets with up to 300?000 orders, and processes hundreds of new orders per second.