基于高斯核支持向量机和遗传算法的优化组合研究

选用2008~2015共8年数据,首先基于高斯核的支持向量机在沪市A股上构建周期性的投资组合,并通过误差图和评价指标与BP神经网络、广义回归神经网络进行比较,结果表明了支持向量机在股票预测上更具有优势.再将改进遗传算法运用于上证股票市场构建最优投资组合,以上证指数作为基准进行比较,得出混合遗传算法优化组合的模型相比单一模型更为有效.     

知情交易者和不知情交易者的策略互动

本文利用EEOW模型结合高频交易数据,对中国股票市场上知情交易者和不知情交易者的交易策略互动进行了实证研究.研究发现:在中国股票市场上,不知情交易者的交易行为对知情交易者没有影响,但是知情交易者的交易行为却对不知情交易者有显著的影响.当预期到市场上有众多知情交易者时,不知情交易者就减少交易以降低与知情交易者发生交易的概率.这一点与传统模型中的"噪声"交易者假设是不一致的.本文的结论对于今后构建更加贴近实际情况的交易策略模型有很好的指导意义.     

基于改进的支持向量回归机的金融时序预测

金融市场是一个复杂、演化、非线性的动态变化的系统.金融数据往往带有噪声,非平稳且时常是混沌的.本文基于时序数据的先验知识——近期数据对于预测未来走势提供了更多的信息,对于传统的支持向量机的回归模型做出了一定的改进,即对于近期的数据预测错误施以更严重的惩罚,构建了改进的支持向量回归机模型.使用该改进模型对中国股票市场指数时间序列进行了预测,结果显示,本文改进的模型较之传统的支持向量回归机模型和神经网络模型有较好的预测效果.     

一种改进二叉树支持向量机在故障诊断中的应用

考虑到构建二叉树支持向量机时样本的分布情况对分类器推广能力具有较大影响,提出一种改进的二叉树支持向量机层次结构构建方法.以类间样本距离和带权值的类内样本距离与其标准差的比值作为类的分类度.将类间距离大且类内样本平均分布广的类最先分离.利用标准数据集,通过与不同多类分类算法比较,验证了改进的二叉树支持向量机的优越性.对双转子涡喷发动机气路部件进行应用改进的算法进行故障诊断,得到了较好的故障识别率.     

基于模拟退火算法的知情交易研究北大核心CSCD

为了更准确地估计知情交易概率水平(PIN),文章主要使用模拟退火算法并结合交易量时间概念对知情交易模型进行参数估计.算法改进后的知情交易模型已可适用于较高频的交易订单数据,从而更好地捕捉微观市场中PIN的变化.参数估计方面,文章主要使用模拟退火算法对EKOP-PIN、EHO-PIN以及DY-PIN进行带约束的最大似然估计,并将结果与无约束优化的间接估计算法进行了比较,基本解决了以往算法存在的局部最优和数值溢出等问题.数值模拟显示,使用模拟退火算法并运用模式搜索法进行末端优化的混合算法在计算PIN时能够进一步提高估计精度.使用沪深300股指期货高频交易数据的实证结果显示,重新估计的EHO-PIN对波动率有着较高的解释和预测能力,可作为一种有效的市场波动率预警指标.     

基于模拟退火算法的知情交易研究

为了更准确地估计知情交易概率水平(PIN),文章主要使用模拟退火算法并结合交易量时间概念对知情交易模型进行参数估计.算法改进后的知情交易模型已可适用于较高频的交易订单数据,从而更好地捕捉微观市场中PIN的变化.参数估计方面,文章主要使用模拟退火算法对EKOP-PIN、EHO-PIN以及DY-PIN进行带约束的最大似然估计,并将结果与无约束优化的间接估计算法进行了比较,基本解决了以往算法存在的局部最优和数值溢出等问题.数值模拟显示,使用模拟退火算法并运用模式搜索法进行末端优化的混合算法在计算PIN时能够进一步提高估计精度.使用沪深300股指期货高频交易数据的实证结果显示,重新估计的EHO-PIN对波动率有着较高的解释和预测能力,可作为一种有效的市场波动率预警指标.     

股票市场涨跌停板设置的微模拟研究

本文构建了一个基于订单驱动的人工模拟股票市场模型,并对投资者在连续竞价交易时的投资行为进行了刻画,数值模拟所得的股票对数收益率序列具有尖峰、胖尾等非正态分布特征并显示出明显的波动率聚集现象。通过在模拟实验中设定股票价格的不同涨跌幅限制范围,研究了涨跌停板设置对股票市场波动性的影响,分析结果表明,在忽略诸如恶意操纵等其他因素情况下,适当扩大涨跌幅限制并不会使股票收益的波动增大,相反还会使收益的波动率有较为明显的降低。     

从投资情绪角度看股票市场流动性——来自B股向境内居民开放的研究

我们借鉴行为方面的模型,即投资情绪的变化会影响股票市场的流动性,从而影响股票的未来收益。在卖空限制条件下市场高流动性预示市场充斥着非理性投资者。本文通过中国股市具备卖空限制和2001年2月B股向境内居民开放前后B股投资主体(投资情绪)发生变化这一自然事件,实证分析了B股市场向境内居民开放后,投资者情绪变化引起市场流动性增加,对股票预期收益的流动性溢价影响也由开放前的不显著变化为显著。     

时态支持向量机模型在股票操纵模式发现上的研究

基于原始时间属性下的时态数据难以发现规律的特点,文章构建了时态支持向量机模型,该模型通过对输入时态数据的粒度变换,获得多个分类模型,从而能够发现多种规律.在此基础上,结合时态型操纵特征构建了股票操纵模式发现模型,最后在证监会披露的操纵股票真实数据上进行数值实验,实验发现细时态粒度数据的分类模型在识别一般操纵和严重操纵上效果较好,粗时态粒度数据的分类模型在识别未被操纵或轻微操纵上效果较好.在未知数据集上实验,该模型可以有效识别不同程度操纵股票的模式,其中1个时态粒度数据下添加市场差异特征的模型表现最好,识别准确率达到了98.25%.文章验证了在不同时态粒度输入下,时态支持向量机模型能够发现在原始数据上不能发现的模式特征,这对解决一些复杂规律在原始特征下难以被发现的问题具有重要借鉴意义.     

果蝇优化最小二乘支持向量机混合预测模型—以我国物流需求量预测为例

为解决最小二乘支持向量机参数设置的盲目性,利用果蝇优化算法对其参数进行优化选择,进而构建了果蝇优化最小二乘支持向量机混合预测模型.以我国物流需求量预测为例,验证了该模型的可行性和有效性.实例验证结果表明:与单一最小二乘支持向量机和模拟退火算法优化最小二乘支持向量机预测模型相比,该模型不仅能够有效选择参数值,而且预测精度更高.     

上海股票市场波动不对称性研究—GJR-与VS-GARCH模型的比较

分析了两个不对称的GARCH模型:G JR-GARCH和VS-GARCH,并对VS-GARCH进行了修正。使用上证综合指数对两个模型进行了实证比较,发现修正的VS-GARCH更适合中国证券市场,能更好的捕捉到波动的不对称性。同时实证结果显示在沪市上,好消息引发的波动大于坏消息引发的波动,并从交易规则和投资者心理方面进行了解释。     

The informational content of insider trading disclosures: empirical results for the Polish stock market

In this paper, we try to answer the question as to whether insider trading disclosures convey valuable information to market participants, valuable in the sense of the profitability of an investment strategy that faithfully mirrors insider behaviour. Our interest in this subject is limited to the case of announcements concerning insider transactions issued over a 6 year-period on the Warsaw Stock Exchange (WSE). Initially, we use event study methodology to check whether insider trading disclosures are accompanied by a performance of stock returns as well as trading volume. Two different models generating expected returns (expected volume) are employed to verify the robustness of our results. The first of these is the regime switching model, with the results then being recalculated by using a GARCH-type model which seem to be most useful for dealing with some of the inconvenient statistical properties of stock return and trading volume data. Afterwards, a technique based on the reference return strategies is used to examine whether or not outsiders who imitate insider behaviour are able to profit from it. The major findings are as follows: firstly, announcements about the sale of stocks by insiders convey no information to market participants. Secondly, a statistically significant market response to insider disclosures of purchases of stocks in their own company can be observed in the three days prior to the announcement release for both return as well as trading volume series, and finally, outsiders who purchased stocks previously bought by insiders experience negative returns whereas outsiders disposing of stocks previously sold by insiders earned a return of 8.57% over the 6 month-period.      

基于成交量分解模型的改进VWAP策略

传统VWAP(交易量加权平均价格)策略通过拆分大额委托订单,跟踪市场成交均价,达到最小化冲击成本的目的,而准确预测成交量日内分布是运用VWAP策略的关键。通过详细考察现有的改进VWAP策略中成交量预测模型的建模方式和预测结果,发现由于无法分离成交量日内周期结构,现有模型样本依赖性较大且难以适用于多数股票。因此,本文从个股与市场成交量变化趋势的关系角度出发,推导个股成交量与市场趋势的关系,通过构造个股成交量关于市场因素的因子载荷,将日内成交量分解为市场共同部分和个股特殊部分,预测成交量日内分布并构建动态VWAP策略。实证结果表明新的成交量分解模型可以有效分离个股的成交量日内周期结构,在此基础上构造的改进VWAP策略不仅具有较为广泛的适用性,且跟踪误差减少幅度比现阶段同类型的改进VWAP策略更大,能更好的降低市场冲击成本。     

注意力视角下媒体报道对股价的影响模型研究

鉴于中国A股市场个人投资者比例相对较高但其理性程度相对较低的实际情况,在拓展Tetlock模型的基础上构建了一个两资产三阶段的理性期望模型。研究发现:首先,被报道股票的价格受到注意力效应的影响,存在正向注意力溢价;其次,注意力交易者对被报道股票的交易量在媒体报道后放大,且存在买卖不平衡性;最后,作为对有限理性的补偿,注意力受媒体报道的影响程度越高,其收益越低。论文在统一的分析框架之内考察了媒体报道对缓解信息不对称和引发投资者注意力效应两方面的影响,有助于在理论上为进一步探索媒体报道对股票市场的影响机理、在实践中为监管层更好地利用媒体力量促进股市健康发展提供理论启示。     

Thin trading and estimation of systematic risk: An application of an error-correction model

Financial economics literature indicates that estimates for securities' systematic risk, i.e. the beta coefficients, are highly affected by infrequent trading. This is an especially serious problem in small security markets. In this study, the applicability of an error-correction model is investigated for modeling the risk behavior of thinly traded securities. The empirical results from a small stock market, i.e. the Helsinki Stock Exchange, indicate the estimated error-correction term to be highly dependent on the underlying trading frequency of the stock, while the direct effect is dependent merely on the market value of the firm. The model thus appears to produce useful information about the risk characteristics of thinly traded stocks.     

Modelling Asset Prices for Algorithmic and High-Frequency Trading

ABSTRACT

Algorithmic trading (AT) and high-frequency (HF) trading, which are responsible for over 70% of US stocks trading volume, have greatly changed the microstructure dynamics of tick-by-tick stock data. In this article, we employ a hidden Markov model to examine how the intraday dynamics of the stock market have changed and how to use this information to develop trading strategies at high frequencies. In particular, we show how to employ our model to submit limit orders to profit from the bid–ask spread, and we also provide evidence of how HF traders may profit from liquidity incentives (liquidity rebates). We use data from February 2001 and February 2008 to show that while in 2001 the intraday states with the shortest average durations (waiting time between trades) were also the ones with very few trades, in 2008 the vast majority of trades took place in the states with the shortest average durations. Moreover, in 2008, the states with the shortest durations have the smallest price impact as measured by the volatility of price innovations.     

中国证券市场知情交易概率的动态马尔科夫状态转移模型研究

对指令驱动市场知情交易的研究是近年来的热点问题。常用的EKOP模型存在一些缺陷,本文放宽了EKOP模型关于日内信息均匀释放以及交易者行为独立性的假设,用动态的马尔科夫状态转移模型对该模型进行了改进,并检验了改进后的知情交易概率模型在中国证券市场的适用性。通过模拟数据以及对中国证券市场交易数据的实证研究发现动态的马尔科夫状态转移模型克服了EKOP模型受买卖方数据影响而产生的系统偏误,估计的知情交易概率更符合事后检验。     

The geometry of relative arbitrage

Consider an equity market with n stocks. The vector of proportions of the total market capitalizations that belong to each stock is called the market weight. The market weight defines the market portfolio which is a buy-and-hold portfolio representing the performance of the entire stock market. Consider a function that assigns a portfolio vector to each possible value of the market weight, and we perform self-financing trading using this portfolio function. We study the problem of characterizing functions such that the resulting portfolio will outperform the market portfolio in the long run under the conditions of diversity and sufficient volatility. No other assumption on the future behavior of stock prices is made. We prove that the only solutions are functionally generated portfolios in the sense of Fernholz. A second characterization is given as the optimal maps of a remarkable optimal transport problem. Both characterizations follow from a novel property of portfolios called multiplicative cyclical monotonicity.     

金融系统的非线性分析:交易量对股价波动的非线性影响

如何研究股价波动和成交量之间的关系一直是金融系统研究中感兴趣的话题.Lamoureux 和 Lastrapes 认为选择日交易量度量每天流入市场的信息量是合理的,但他们假定交易量对波动率的影响是线性的.提出部分非线性GARCH模型分析交易量对股票市场波动率的影响,基于GARCH模型局部线性化非参数似然估计方法,对中国证券市场股票价格和交易量数据进行实证研究.结果表明,交易量对股价波动的影响具有显著的非线性性.     

Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting

Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified. Support vector machine (SVM) have been proposed as a complimentary volatility model that is capable to extract information from multiscale and high-dimensional market data. Presented results for SP500 index suggest that SVM can efficiently work with high-dimensional inputs to account for volatility long-memory and multiscale effects and is often superior to the main-stream volatility models. SVM-based framework for volatility forecasting is expected to be important in the development of the novel strategies for volatility trading, advanced risk management systems, and other applications dealing with multi-scale and high-dimensional market data.