Finding attractive technical patterns in cryptocurrency markets

The cryptographic currency market is an emerging venue for traders looking to diversify their investments. We investigate the use of genetic programming (GP) for finding attractive technical patterns in a cryptocurrency market. We decompose the problem of automatic trading into two parts, mining useful signals and applying them to trading strategies, and focus our attention on the former. Extensive experiments are performed to analyze the factors that affect the quality of the solutions found by the proposed GP system. With the introduction of domain knowledge through extended function sets and the inclusion of diversity preserving mechanism, we show that the proposed GP system successfully finds attractive technical patterns. Out-of-sample performance of the patterns indicates that the GP consistently finds signals that are profitable and frequent. A trading simulation with the generated patterns suggests that the captured signals are indeed useful for portfolio optimization.     

Stock timing using genetic algorithms

In this paper, a genetic algorithm to search a set of technical trading rules which gives buying and selling advices about individual stocks is proposed. This approach is tested out of a sample of 24 French stocks among the most important stocks traded on the French market. We show that in most cases, the method outperforms a simple buy and hold strategy. However, we also illustrate the fact that the near‐optimal set of rules varies through time and across stocks. Copyright © 2002 John Wiley & Sons, Ltd.     

Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets

The development of new models that would enhance predictability for time series with dynamic time-varying, nonlinear features is a major challenge for speculators. Boundedly rational investors called “chartists” use advanced heuristics and rules-of-thumb to make profit by trading, or even hedge against potential market risks. This paper introduces a hybrid neurofuzzy system for decision-making and trading under uncertainty. The efficiency of a technical trading strategy based on the neurofuzzy model is investigated, in order to predict the direction of the market for 10 of the most prominent stock indices of U.S.A, Europe and Southeast Asia. It is demonstrated via an extensive empirical analysis that the neurofuzzy model allows technical analysts to earn significantly higher returns by providing valid information for a potential turning point on the next trading day. The total profit of the proposed neurofuzzy model, including transaction costs, is consistently superior to a recurrent neural network and a Buy & Hold strategy for all indices, particularly for the highly speculative, emerging Southeast Asian markets. Optimal prediction is based on the dynamic update and adaptive calibration of the heuristic fuzzy learning rules, which reflect the psychological and behavioral patterns of the traders.     

A Forex trading system based on a genetic algorithm

In this paper, a genetic algorithm will be described that aims at optimizing a set of rules that constitute a trading system for the Forex market. Each individual in the population represents a set of ten technical trading rules (five to enter a position and five others to exit). These rules have 31 parameters in total, which correspond to the individuals’ genes. The population will evolve in a given environment, defined by a time series of a specific currency pair. The fitness of a given individual represents how well it has been able to adapt to the environment, and it is calculated by applying the corresponding rules to the time series, and then calculating the ratio between the profit and the maximum drawdown (the Stirling ratio). Two currency pairs have been used: EUR/USD and GBP/USD. Different data was used for the evolution of the population and for testing the best individuals. The results achieved by the system are discussed. The best individuals are able to achieve very good results in the training series. In the test series, the developed strategies show some difficulty in achieving positive results, if you take transaction costs into account. If you ignore transaction costs, the results are mostly positive, showing that the best individuals have some forecasting ability.     

Fitting the control parameters of a genetic algorithm: An application to technical trading systems design

This paper studies the problem of how changes in the design of the genetic algorithm (GA) have an effect on the results obtained in real-life applications. In this study, focused on the application of a GA to the tuning of technical trading rules in the context of financial markets, our tentative thesis is that the GA is robust with respect to design changes. The optimization of technical trading systems is a suitable area for the application of the GA metaheuristic, as the complexity of the problem grows exponentially as new technical rules are added to the system and as the answer time is crucial when applying the system to real-time data. Up to now, most of GAs applications to this subject obviated the question of possible “design dependence” in their results. The data we report, based on our experiments, do not allow us to refute the hypothesis of robustness of the GA to design implementation, when applying to technical trading systems tuning.     

A micro-movement model with Bayes estimation via filtering: Application to measuring trading noises and costs

We highlight a general hybrid system as the micromovement model for asset price using counting processes recently introduced with its Bayes estimation via filtering. We construct a new simple micromovement model and apply it to analyze trade-by-trade stock price data in the light of the series of works initiated by Christie and Schultz [Why do NASDAQ market makers avoid odd-eighth quotes?, Finance 49 (1994) 1813–1840]. Through the new model, we propose more reasonable, but computationally intensive measures for trading noise including clustering noise and non-clustering noise, and for trading cost. We employ Bayes estimation via filtering to obtain parameter estimates of the new model and to provide numerical measures of trading noise and trading cost for three stocks from four chosen periods. Our empirical results support the important findings in [Christie, Harris, Schultz, Why did NASDAQ market makers stop avoiding odd-eighth quotes?, Finance 49 (1994) 1841–1860; Barclay, Christie, Harris, Kandel, Schultz, The effects of market reform on the trading costs and depths of NASDAQ stocks, J. Finance 54(1) (1999) 1–34].     

Speculative dynamics with bounded rationality learning

We study a linear model for a future market characterized by the presence of different classes of traders. In the market there are three classes of traders: rational traders, feedback traders and fundamentalist traders. Each class of traders is described by a trading strategy and by an information set about the fundamental. The analysis is developed under bounded rationality, rational traders forming expectations do not know the “true” model but believe in a misspecified model. The convergence of the learning activity to the Rational Expectations Equilibria of the model is analyzed. Two different learning mechanisms are studied: the Ordinary Least Squares algorithm and the Least Mean Squares algorithm. The main goal of the study is to analyze how the presence of different classes of traders in the market affects the robustness of the Rational Expectations Equilibria of the model with respect to bounded rationality learning. Moreover we verify the claim that bubbles and erratic behavior in the stock price dynamics may arise because of learning non-convergence to Rational Expectations Equilibria. The results show that if the Ordinary Least Squares algorithm is used by the agents to update beliefs, convergence to one of the two Rational Expectations Equilibria of the model is ensured only if there are positive feedback traders in the market. On the contrary, the Least Mean Squares algorithm guarantees convergence to the Rational Expectations Equilibria given an appropriate initial belief.     

基于Dempster-Shafer证据理论的外汇交易策略研究

自动化交易是现代金融领域的研究热点,而交易策略是其核心。技术指标分析中,每一种指标都有其优势和劣势,单一的指标经常产生导致亏损的虚假信号。为了提高交易信号的质量和可靠性,针对外汇市场的多变性和存在诸多不确定性的客观事实,本文引入证据理论来处理不同技术指标分析方法结论存在的差异;将不同的指标作为独立的证据源,用D-S合成规则对各个指标分析方法的结果予以融合,建立了基于证据理论的多指标融合外汇交易模型,给出了基于证据理论的交易框架。根据技术指标的特点及交易原理,构造了指标证据的基本概率分配函数。最后,通过实例分析验证了该方法的科学性和有效性。     

Fuzzy relations for the analysis of traders’ preferences in an information market game

In this paper we focus on preference and decision data gathered during a computer-supported information market game in which 35 students participated during seven consecutive trading sessions. The participants’ individual preferences on the market shares are collected to calculate a collective preference ranking using the Borda social choice method. Comparing this preference ranking to the shares’ actual market ranking resulting from the participants’ trading, we find a statistically significant difference between both rankings. As the preferences established by market behavior cannot be adequately explained through a social choice rule, we propose an alternative explanation based on the herd behavior phenomenon where traders imitate the most successful trader in the market. Using a decision analysis technique based on fuzzy relations, we study the participants’ rankings of the best share in the market during 7 weeks and compare the most successful trader to the other traders. The results from our analysis show that a substantial number of traders is indeed following the market leader.     

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.      

具有增益的概率网络金融计划模型

本文绘出一类具有增益的概率网络金融计划模型．许多多阶段金融计划问题可纳入这类模型．在这类模型中,随机变量的分布函数与Alexander过滤交易规则密切联系在一起,金融市场交易信号由神经网络产生,目标函数的最优值按其期望值计算．文中提出临界流和临界路的概念,给出目标函数下界等于其期望值的充分必要条件和期望最优解的求解方法．     

投资者情绪对科创板市场收益率的影响——基于文本数据挖掘视角

本文以科创板市场为主要研究对象,基于文本数据挖掘方法探究了新冠疫情发生前和疫情期间投资者情绪对市场收益率的影响及其作用机制。利用东方财富股吧2019年7月至2020年3月的日度科创板股票评论数据,基于Bi-LSTM深度学习技术对文本数据情感倾向进行分类,建立投资者情绪指数。通过构建双向固定效应的联立方程模型,采用2SLS方法估计投资者情绪对科创板市场收益率的作用,并检验在经济平稳运行和受新冠疫情冲击期间该作用的差异性。实证分析及稳健性检验的结果均表明,投资者情绪通过影响交易量进而影响科创板股票市场收益率,这种正向作用在1%的置信水平下显著。此外,投资者情绪对科创板收益率的影响在经济平稳运行和受新冠疫情冲击期间均保持稳健,且在新冠疫情期间作用更强。本研究成果对于新冠疫情期间我国证券市场监管层完善科创板交易机制,以及对中小投资者优化投资战略具有重要意义。     

夜盘交易对我国贵金属期货市场的影响研究

对比了我国贵金属期货市场推出夜盘交易制度前后流动性、波动性与联动性的变化,并研究了该交易制度带来的长期与短期不同的影响。首先,采用经过修正的流动性比率衡量市场流动性,并引入虚拟变量纳入回归模型,进一步研究夜盘交易对市场流动性的贡献;接下来,将每日收益率分解为隔夜收益率和日内收益率,通过建立EGARCH模型,分别考察夜盘交易对隔夜波动率和日内波动率长期与短期的影响;最后,通过建立VAR-BEKK-GARCH模型,从均值和方差两个层面研究了国内外贵金属期货市场间的联动性。研究发现:我国推出的夜盘交易制度显著提高了贵金属期货市场的流动性,降低了贵金属期货市场的波动性,尤其是降低了隔夜收益率的波动性,日内收益率的波动性并没有显著减弱;夜盘交易的推出同时增强了国内外期货市场之间的联动性,提升了我国贵金属期货市场对信息的吸收与传递效率。     

On the pricing of American contingent claims under transaction costs and multiple risky assets

This paper addresses the hedging problem of American Contingents Claims (ACCs) in the framework of continuous-time Itô models for financial market. The special feature of this paper is that in the financial market the investor has to face fixed and proportional transaction costs when trading multiple risky assets. By using the auxiliary martingale approach and extending the results of Cvitanic and Karatzas [Cvitanic J, Karatzas I. Hedging and portfolio optimization under transaction costs: a martingale approach. Math Finance 1996;6:135–65] on pricing European contingent with transaction costs in the single-stock market, an arbitrage-free interval [h_low, h_up] is identified, and the end points are characterized by auxiliary martingales and stopping times in terms of auxiliary stochastic control problems. Here h_up and h_low are so-called the upper hedging price and the lower hedging price.     

基于机器学习的智能TWAP和VWAP算法的研究及应用

TWAP与VWAP算法为两类较常见的经典交易算法.传统的VWAP算法在TWAP算法的基础上,大多使用预测日内成交量分布的方法指导算法下单.传统成交量分布的预测效果严重依赖于市场交易惯性,但交易量分布受到日内诸多突发因素的影响,导致算法对市场突发状况的应对能力较弱.本文对传统TWAP与VWAP算法进行改进,利用滚动的1分钟粒度高频实时资金博弈数据,基于Logistic分类器训练量价模型,以该预测结果为入参构建最优化期望执行均价模型,求出当下各个价格档位对应委托数量的最优解.通过相对高频的分钟级价格预测机制,保证算法实时跟踪市场行情走势并寻求相对优势的交易机会.该算法经测试可以稳定地跑赢市场均价,具备推广应用的可行性.     

夜盘交易对我国农产品期货市场影响的实证研究

随着我国农产品期货与国际市场的联动性进一步加强,为防止相关期货产品的隔夜风险和价格跳水问题,对部分农产品期货实行夜盘交易制度。为测度夜盘交易制度是否有益于农产品期货市场朝着稳定、理性的方向发展,本文采用了适合刻画金融序列波动性的GARCH族模型,实证检验得出GARCH、GARCH-M和EGARCH模型能够高度拟合农产品期货的价格序列并显著衡量夜盘交易对于我国农产品期货市场的影响。研究结论如下:第一、基于GRACH模型实证结果,夜盘交易制度变量的回归结果显著,该制度能减轻农产品期货的价格波动,且其影响是显著的;第二、EGARCH模型的回归结果同样显著,分别对比不同样本期的EGARCH模型实证结果可以得到,夜盘交易的开放减少了农产品期货市场的非对称性,使得市场趋向于理性的方向发展。     

Resonant Excitation of a Composite Beam

Phase–locked loops are widely used within communication technology for synchronizing signals by tracking their phases. Their functional principle can be applied for maintaining the resonant excitation of a beam whose eigenfrequency changes over time and, thus, representing an alternative excitation method for the adaptronic strut for machine tools shown in [1]. In this contribution an experimental set–up for resonant excitation of a composite beam is introduced. A particle is mounted on the beam, its position and mass are are adjustable to modify the eigenfrequency of the beam. The simulation results of the analytical examination of the test–rig are compared with the experimental results. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Unit commitment in electricity pool markets

We consider an electricity generator making offers of energy into an electricity pool market over a horizon of several trading periods (typically a single trading day). The generator runs a set of generating units with given start-up costs, shut-down costs and operating ranges. At the start of each trading period the generator must submit to the pool system operator a new supply curve defining quantities of offered energy and the prices at which it wants these dispatched. The amount of dispatch depends on the supply curve offered along with the offers of the other generators and market demand, both of which are random, but do not change in response to the actions of the generator we consider. After dispatch the generator determines which units to run in the current trading period to meet the dispatch. The generator seeks a supply function that maximizes its expected profit. We describe an optimization procedure based on dynamic programming that can be used to construct optimal offers in successive time periods over a fixed planning horizon.     

Hedging unit‐linked life insurance contracts in a financial market driven by shot‐noise processes

We consider the risk‐minimizing hedging problem for unit‐linked life insurance in a financial market driven by a shot‐noise process. Because the financial market is incomplete, the insurance claims cannot be hedged completely by trading stocks and bonds only, leaving some risk to the insurer. The theory of ((pseudo) locally) risk‐minimization is applied after a change of measure. Then the risk‐minimizing trading strategies and the associated intrinsic risk processes are determined for two types of unit‐linked contracts represented by the pure endowment and the term insurance. Copyright © 2009 John Wiley & Sons, Ltd.     

基于我国期货市场的跨期套利研究

不同于股票市场,期货市场存在天然的做空机制,非常方便进行套利操作。本文以我国的期货市场为研究对象,主要针对跨期套利进行实证研究。首先介绍了跨期套利,并提出了一套完善的、可操作的套利交易策略。其次,在此基础上,我们选取郑州商品交易所的期货的棉花的15种组合(不同交割月份)所有历史数据进行测试,最后,我们分析了该策略下跨期套利机会出现次数、收益率、对冲平仓次数、实物交割次数、仓位管理、保证金追加等方面。实证结果表明,本文所提出的跨期套利模型以及相应的策略在我国的期货市场是可行的。