Online impersonation in securities scams

For a con man to convince a target to part with his or her money, he first must gain the target's trust. In cyberspace, securities scammers gain the trust of unwitting investors through a variety of means. Perhaps the most insidious of these involves a form of online impersonation in which the perpetrator uses the Internet's technical capabilities to create bogus online content that mimics information from trusted sources. The author briefly explores the use of various impersonation techniques by persons charged in US Securities and Exchange Commission (SEC) enforcement actions alleging market manipulation and fraud.     

An optimal schedule for dollar cost averaging under different transaction costs

Dollar cost averaging is a popular habit adopted by investors who recognize the diffculty in consistently timing the market. Recent technological innovations allow for the direct deposit of a predetermined portion of each paycheck into a brokerage account for the purchase of equities. Since most payperiods are biweekly or monthly, the automatic and frequent purchase of small amounts of equity produces substantial transaction costs. If funds were allowed to accumulate for a time in a money market account prior to equity purchase, then transaction costs may be lowered. However, since equity returns are generally more than that earned in the money market, delayed purchases would forfeit higher returns. In this study, we determine the optimal transaction size to maximize returns. We use the classical economic order quantity framework of inventory management and extend that framework to deal with the special discounting structures commonly offered by brokerage firms. Numerical examples and sensitivity analysis of fixed and variable transaction cost structure models are presented.     

神经网络模型在股票投资中的应用

引入数据挖掘技术中的神经网络技术对上证综合指数进行分析,通过建立神经网络模型对次日收盘指数进行预测,经过多次实验和比较,得到精度较高的次日收盘指数预测模型,利用该模型,本文预测了2010年1至4月份的上证综合指数次日收盘点数,通过与真实值比较发现,神经网络模型在预测上证综合指数方面具有较好的效果,因此,在一定程度上解决股民入市时机选择问题。     

Trading With a Stock Chart Heuristic

The efficient market hypothesis (EMH) is a cornerstone of financial economics. The EMH asserts that security prices fully reflect all available information and that the stock market prices securities at their fair values. Therefore, investors cannot consistently ldquobeat the marketrdquo because stocks reside in perpetual equilibrium, making research efforts futile. This flies in the face of the conventional nonacademic wisdom that astute analysts can beat the market using technical or fundamental stock analysis. The purpose of this research is to partially assess whether technical analysts, who predict future stock prices by analyzing past stock prices, can consistently achieve a trading return that outperforms the stock market average return. This is tested using knowlege engineering experimentation with one price history pattern - the ldquobull flag stock chartrdquo - which signals technical analysts of a future stock market price increase. A recognizer for the stock chart pattern is built using a template-matching technique from pattern recognition. The recognizer and associated trading rules are then tested by simulating trading on over 35 years of daily closing price data for the New York stock exchange composite index. The experiment is then replicated using the horizontal rotation or mirror image pattern of the ldquobull flagrdquo (or ldquobear flagrdquo stock chart) that signals a future stock market decrease. Results are systematic, statistically significant, and fail to confirm the null hypothesis based on a corollary to the EMH: that profit realized from trading determined by this heuristic method is no better than what would be realized from trading decisions based on random choice.     

信号有噪声时的证券市场羊群行为模型

针对目前中国证券市场羊群行为重、市场主力的信号不确定的实际,通过假设市场封闭、简化交易过程和对象,建立市场信号有噪声时的羊群行为模型,并进行参数假设,求解模型的解,分析跟风行为对市场价格波动的影响。得出,羊群行为的存在增加了股票价格的波动。     

基于遗传算法的股票市场选择模型

为提高投资者在股票市场的收益,解决在证券投资中股票选择这一重要问题,提出一种基于遗传算法的股票选择模型。算法以上市公司的财务指标为样本特征,为克服K-means算法的不稳定性,采用基于遗传算法的K-means算法对同一板块股票进行聚类分析,剔除财务指标较差的一类中的股票。对筛选条件编码,为解决传统遗传算法处理复杂问题时存在的过早收敛现象,提出改进的遗传算子,利用改进的遗传算法寻找使股票市场投资收益最大化的选股模型参数。实验结果表明,该算法在股票选择上具有较好的效果,可供市场投资者借鉴。     

Pricing fuzzy vulnerable options and risk management

The assumption of unrealistic “identical rationality” in classic option pricing theory is released in this article to amend Klein’s [Klein, P. (1996). Pricing Black–Scholes options with correlated credit risk. Journal of Banking Finance, 1211–1129] vulnerable option pricing formula. Through this formula, default risk and liquidity risk are both well-explained when the investment behaviors and market expectations of the participants are heterogeneous. The numerical results show that when the investing decisions of each market participant come from their individual rationality and use their own subjective price to trade, the option price becomes a boundary. The upper boundary becomes an absolutely safe line and the lower boundary becomes an absolutely unsafe line for investors who want to invest in some financial securities with default risk. The proposed model suggests a more realistic pricing mechanism for the issuers and traders who want to value options with default risk.     

小波神经网络在股票技术分析中的应用

股票交易作为证券投资的一种,也越来越成为广大投资者的一个重要投资手段。股票市场是一个效益与风险并存的地方,没有分析的投资是盲目的投资。但股票市场受随机因素影响很大,利用线性很难分析。该文利用小波与神经网络作为一种大规模并行处理的非线性系统,依据数据本身的内在联系建模,具有良好的适应性与自学习能力、较强的抗干扰能力,在股票价格的短期预测中已取得了令人比较满意的成绩。     

Combined DEMATEL technique with a novel MCDM model for exploring portfolio selection based on CAPM

This research proposes a novel MCDM model, including DEMATEL, ANP, and VIKOR for exploring portfolio selection based on CAPM. We probe into the influential factors and relative weights of risk-free rate, expected market return, and beta of the security. The purpose of this research is to establish an investment decision model and provides investors with a reference of portfolio selection most suitable for investing effects to achieve the greatest returns. Taking full consideration of the interrelation effects among criteria/variables of the decision model, this paper examined leading semiconductor companies spanning the hottest sectors of integrated circuit (IC) design, wafer foundry, and IC packaging by experts. Empirical findings revealed that risk-free rate was affected by budget deficit, discount rate, and exchange rate; expected market return was affected by country risk, industrial structure, and macroeconomic factors; and beta of the security was affected by firm-specific risk and financial risk. Also, the factors of the CAPM possessed a self-effect relationship according to the DEMATEL technique. In the eight evaluation criteria, macroeconomic criterion was the most important factor affecting investment decisions, followed by exchange rate and firm-specific risk. In portfolio selection, leading companies in the wafer foundry industry outperformed those in IC design and IC packaging, becoming the optimal portfolio of investors during the time that this study was conducted.     

Smooth Transition Quantile Capital Asset Pricing Models with Heteroscedasticity

Capital asset pricing model (CAPM) has become a fundamental tool in finance for assessing the cost of capital, risk management, portfolio diversification and other financial assets. It is generally believed that the market risks of the assets, often denoted by a beta coefficient, should change over time. In this paper, we model timevarying market betas in CAPM by a smooth transition regime switching CAPM with heteroscedasticity, which provides flexible nonlinear representation of market betas as well as flexible asymmetry and clustering in volatility. We also employ the quantile regression to investigate the nonlinear behavior in the market betas and volatility under various market conditions represented by different quantile levels. Parameter estimation is done by a Bayesian approach. Finally, we analyze some Dow Jones Industrial stocks to demonstrate our proposed models. The model selection method shows that the proposed smooth transition quantile CAPM?CGARCH model is strongly preferred over a sharp threshold transition and a symmetric CAPM?CGARCH model.     

Government-Based Security Standards

Abstract

Dot.com companies began to collapse early in 2000. Before the crash, investors would purchase stock in new technical companies without even looking at a business plan. Companies established in a programmer's family room were offering stock within months and investors were buying, creating thousands of “paper millionaires.” Financiers never bothered to investigate, for example, how long the company would have to remain in business before they could expect to make a profit. When the bubble burst, investors lost huge amounts of money. Between March 2000 and October 2002, the NASDAQ Composite lost 78 percent of its value as it fell from 5046.86 to 1114.11. Law firms specializing in bankruptcy were busier than ever before.     

Planning stock portfolios by means of weighted frequent itemsets

Planning stock portfolios is a challenging task, because investors have to forecast stock market trends. To limit losses due to wrong forecasts a common strategy is diversification, which consists in buying stocks belonging to different sectors/markets to spread bets across different assets. Since the amount of stock market data is continuously growing, an appealing research strategy is to first apply data mining algorithms to discover significant patterns from potentially large stock datasets and then exploit them to support investor decision-making.This article presents an itemset-based approach to supporting buy-and-hold investors in technical analyses by automatically identifying promising sets of high-yield yet diversified stocks to buy. Specifically, it investigates the use of itemsets to generate stock portfolios from historical stock data and recommend them for buy-and-hold investments. To achieve this goal, it analyzes stock market datasets, which contain for each stock the closing prices on different trading days. Datasets are enriched with (analyst-provided) taxonomies, which are used to classify stocks as the corresponding sectors. Unlike previous approaches, it generates a model composed of a subset of potentially interesting itemsets, which are then used to support investors in decision-making. The selected itemsets represent promptly usable stock portfolios satisfying expert’s requirements on minimal average return and minimal level of diversification across sectors.The experiments performed on real stock datasets acquired under different market conditions demonstrate the effectiveness of the proposed approach compared to real stock funds.     

基于行为金融学视角的股票市场价格仿真研究

对股票价格走势及其主要影响因素(投资者心理行为)的研究,不仅有助于投资者理解股票市场的运行特点而且还可以给监管当局提供相关的政策建议.文中利用Multi-agents建模技术,对投资者心理行为进行了合理简化,综合考虑了交易制度、宏观经济因素、历史交易信息等因素的影响,构造出了具有自适应能力的投资者(Agent),动态模拟了真实股票市场的运行情况.文章的主要结论为:相对较多的资金投入致使股票价格在较高水平频繁波动;相对过多的投资者也导致股票价格的频繁波动;消极的投资态度引致较低的股票价格水平.文中的仿真方法可以应用到复杂金融衍生品价格形成机制的研究中.     

An approach to portfolio selection using an ARX predictor for securities’ risk and return

It is well known that every investment carries a risk associated, and depending on the type of investment, it can be very risky; for instance, securities. However, Markowitz proposed a methodology to minimize the risk of a portfolio through securities diversification. The selection of the securities is a choice of the investor, who counts with several technical analyzes to estimate investment’s returns and risks. This paper presents an autoregressive exogenous (ARX) predictor model to provide the risk and return of some Brazilian securities – negotiated at the Brazilian stock market, BOVESPA – to select the best portfolio, herein understood as the one with minimum expected risk. The ARX predictor succeeded in predicting expected returns and risks of the securities, which resulted in an effective portfolio. Additionally the Markowitz theory was confirmed, showing that diversification reduces the risk of a portfolio.     

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。     

基于PKI技术的网上证券信息系统的安全解决方案

随着互联网的发展,网上证券交易作为一种全新的交易模式,大幅度地降低了交易成本,提高了交易的透明度,打破了空间的限制,使投资者摆脱了固定场所的束缚。该文介绍了基于PKI技术的网上证券交易系统的设计与实现。系统体系结构包含四个重要实体:证书认证模块、身份认证模块、客户端模块和业务系统模块。该方案对用户的身份验证采用了数字证书管理,对关键数据进行数字签名,数据传输采用了安全连接,实现了数据真实性、完整性、不可否认性。     

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.     

Corporate financial distress diagnosis model and application in credit rating for listing firms in China

With the enforcement of the removal system for distressed firms and the new Bankruptcy Law in China’s securities market in June 2007, the development of the bankruptcy process for firms in China is expected to create a huge impact. Therefore, identification of potential corporate distress and offering early warnings to investors, analysts, and regulators has become important. There are very distinct differences, in accounting procedures and quality of financial documents, between firms in China and those in the western world. Therefore, it may not be practical to directly apply those models or methodologies developed elsewhere to support identification of such potential distressed situations. Moreover, localized models are commonly superior to ones imported from other environments.     

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.     

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