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.     

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.     

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Agent的我国股票市场卖空机制仿真研究

针对我国股市现有的卖空约束缺陷,用复杂适应系统的思想和方法分析股市的复杂性,运用Swarm平台,构建基于Agent的直接计算机模型。设定模型由“交易事件”驱动,给定Agent理性和非理性的行为规则,让Agent通过对有关数据分析后建立有偏预期或无偏预期的交易策略,从而导致资产价格和交易量的变化,以此分析引入卖空机制对股市的影响。仿真模拟表明,卖空机制可以把价格风险降低,增加市场的流动性,对改变股市的资产结构有积极影响,成为投资者和市场规避风险的工具。     

基于元胞自动机的股票市场价格行为的研究

构建了基于元胞自动机的股票市场模型，用于模拟和分析股票市场价格行为的特点和原因，区分了股票市场上基于基本面因素和模仿者两类投资者，对于不同的投资者有不同的投资策略，由简到繁通过3个步骤建立最终的模型，刻画了股票市场的基本结构和行为。利用Matlab．通过计算机模拟发现，此模型能够反映股票市场中尖蜂厚尾、波动聚集的特点。通过仿真结果分析得出模仿者对尖峰厚尾现象有较强的影响，波动聚集与投资者交易行为有关。     

A Multiagent Approach to Q-Learning for Daily Stock Trading

The portfolio management for trading in the stock market poses a challenging stochastic control problem of significant commercial interests to finance industry. To date, many researchers have proposed various methods to build an intelligent portfolio management system that can recommend financial decisions for daily stock trading. Many promising results have been reported from the supervised learning community on the possibility of building a profitable trading system. More recently, several studies have shown that even the problem of integrating stock price prediction results with trading strategies can be successfully addressed by applying reinforcement learning algorithms. Motivated by this, we present a new stock trading framework that attempts to further enhance the performance of reinforcement learning-based systems. The proposed approach incorporates multiple Q-learning agents, allowing them to effectively divide and conquer the stock trading problem by defining necessary roles for cooperatively carrying out stock pricing and selection decisions. Furthermore, in an attempt to address the complexity issue when considering a large amount of data to obtain long-term dependence among the stock prices, we present a representation scheme that can succinctly summarize the history of price changes. Experimental results on a Korean stock market show that the proposed trading framework outperforms those trained by other alternative approaches both in terms of profit and risk management.     

The Strategic Exploitation of Limited Information and Opportunity in Networked Markets

This paper studies the effect of constraining interactions within a market. A model is analysed in which boundedly rational agents trade with and gather information from their neighbours within a trade network. It is demonstrated that a trader’s ability to profit and to identify the equilibrium price is positively correlated with its degree of connectivity within the market. Where traders differ in their number of potential trading partners, well-connected traders are found to benefit from aggressive trading behaviour. Where information propagation is constrained by the topology of the trade network, connectedness affects the nature of the strategies employed.      

The Interplay Between Two Stock Markets and a Related Foreign Exchange Market: A Simulation Approach

There is still much that is unknown about the interactions among financial markets, and about the relationships between stock prices and exchange rates. This topic gains attention during financial crises, and many papers try to find empirical regularities emerging from financial data, or to study contagion processes. In this paper we present a study on the interplay between two stock markets and one foreign exchange market extending the framework provided by the Genoa Artificial Stock Market. There are four different trading strategies, and the agents are divided into two groups: those who trade in the stock markets and those who trade in the FOREX. We studied three market conditions: the FOREX dynamics, the behavior of the two stock markets together with the FOREX, and finally we conducted a what-if analysis for testing the effects of a inflationary monetary shock of one currency affecting all of the three markets.     

Correlation and hierarchies in financial markets

As an emerging financial market, the trading value of carbon emission trading market has definitely increased in recent years. The carbon emission is not only trading in carbon emitters but also has become an important investment target. To determine the mechanism of this growing market, we analyzed the EU allowances (EUA) price series in European Climate Exchange (ECX) that is the leading European emissions futures market. As other financial market, the absolute value of price change (volatility) in carbon emission trading market also shows long-term power-law correlations. Our analysis shows that definite cross correlations exist between EUA and many other markets. These cross correlations exist in wild-range fields, stock market index, futures of crude, sugar, cocoa, etc., suggesting that in this new carbon emission trading market the speculation behavior had already become a main factor that can affect the price change.     

Self-organization of complex adaptive systems as a society of rational agents

In this paper, we aim at providing a general theoretical framework for designing complex adaptive systems as a society of rational agents. We term entities with their own interest agents. They are also rational in the sense that they only do what they want to do and what they think is in their own best interest. We formulate the dynamic interaction among those rational agents as competitive and cooperative problems. We obtain the equilibrium behavior in the long-run, and characterize the collective behavior of these rational agents. We show how complex collective behavior can emerge from the locally optimal behavior of each agent. We also describe why and how they organize themselves into a multilevel hierarchical organization with nesting structures. This work was presented, in part, at the Second International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1997     

Simple Agents,Intelligent Markets

Attainment of rational expectations equilibria in asset markets calls for the price system to disseminate agents’ private information to others. Markets populated by human agents are known to be capable of converging to rational expectations equilibria. This paper reports comparable market outcomes when human agents are replaced by boundedly-rational algorithmic agents who use a simple means-end heuristic. These algorithmic agents lack the capability to optimize; yet outcomes of markets populated by them converge near the equilibrium derived from optimization assumptions. These findings point to market structure (rather than cognition or optimization) being an important determinant of efficient aggregate level outcomes.     

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.     

An intelligent stock trading system using comprehensive features

The aim of this study is to predict automatic trading decisions in stock markets. Comprehensive features (CF) for predicting future trend are very difficult to generate in a complex environment, especially in stock markets. According to related work, the relevant stock information can help investors formulate objects that may result in better profits. With this in mind, we present a framework of an intelligent stock trading system using comprehensive features (ISTSCF) to predict future stock trading decisions. The ISTSCF consists of stock information extraction, prediction model learning and stock trading decision. We apply three different methods to generate comprehensive features, including sentiment analysis (SA) that provides sensitive market events from stock news articles for sentiment indices (SI), technical analysis (TA) that yields effective trading rules based on trading information on the stock exchange for technical indices (TI), as well as the trend-based segmentation method (TBSM) that raises trading decisions from stock price for trading signals (TS). Experiments on the Taiwan stock market show that the results of employing comprehensive features are significantly better than traditional methods using numeric features alone (without textual sentiment features).     

Research on the Effects of Institutional Liquidation Strategies on the Market Based on Multi-agent Model

Based on the multi-agent model, an artificial stock market with four types of traders is constructed. On this basis, this paper focuses on comparing the effects of liquidation behavior on market liquidity, volatility, price discovery efficiency and long memory of absolute returns when the institutional trader adopts equal-order strategy, Volume Weighted Average Price (VWAP) strategy and Implementation Shortfall (IS) strategy respectively. The results show the following: (1) the artificial stock market based on multi-agent model can reproduce the stylized facts of real stock market well; (2) among these three algorithmic trading strategies, IS strategy causes the longest liquidation time and the lowest liquidation cost; (3) the liquidation behavior of institutional trader will significantly reduce market liquidity, price discovery efficiency and long memory of absolute returns, and increase market volatility; (4) in comparison, IS strategy has the least impact on market liquidity, volatility and price discovery efficiency, while VWAP strategy has the least impact on long memory of absolute returns.

     

Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system

The increasing reliance on Computational Intelligence techniques like Artificial Neural Networks and Genetic Algorithms to formulate trading decisions have sparked off a chain of research into financial forecasting and trading trend identifications. Many research efforts focused on enhancing predictive capability and identifying turning points. Few actually presented empirical results using live data and actual technical trading rules. This paper proposed a novel RSPOP Intelligent Stock Trading System, that combines the superior predictive capability of RSPOP FNN and the use of widely accepted Moving Average and Relative Strength Indicator Trading Rules. The system is demonstrated empirically using real live stock data to achieve significantly higher Multiplicative Returns than a conventional technical rule trading system. It is able to outperform the buy-and-hold strategy and generate several folds of dollar returns over an investment horizon of four years. The Percentage of Winning Trades was increased significantly from an average of 70% to more than 92% using the system as compared to the conventional trading system; demonstrating the system’s ability to filter out erroneous trading signals generated by technical rules and to preempt any losing trades. The system is designed based on the premise that it is possible to capitalize on the swings in a stock counter’s price, without a need for predicting target prices.     

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.     

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.     

Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model.     

Trend following deep Q-Learning strategy for stock trading

Computers and algorithms are widely used to help in stock market decision making. A few questions with regards to the profitability of algorithms for stock trading are can computers be trained to beat the markets? Can an algorithm take decisions for optimal profits? And so forth. In this research work, our objective is to answer some of these questions. We propose an algorithm using deep Q-Reinforcement Learning techniques to make trading decisions. Trading in stock markets involves potential risk because the price is affected by various uncertain events ranging from political influences to economic constraints. Models that trade using predictions may not always be profitable mainly due to the influence of various unknown factors in predicting the future stock price. Trend Following is a trading idea in which, trading decisions, like buying and selling, are taken purely according to the observed market trend. A stock trend can be up, down, or sideways. Trend Following does not predict the stock price but follows the reversals in the trend direction. A trend reversal can be used to trigger a buy or a sell of a certain stock. In this research paper, we describe a deep Q-Reinforcement Learning agent able to learn the Trend Following trading by getting rewarded for its trading decisions. Our results are based on experiments performed on the actual stock market data of the American and the Indian stock markets. The results indicate that the proposed model outperforms forecasting-based methods in terms of profitability. We also limit risk by confirming trading actions with the trend before actual trading.     

Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction

Stock market price is one of the most important indicators of a country's economic growth. That's why determining the exact movements of stock market price is considerably regarded. However, complex and uncertain behaviors of stock market make exact determination impossible and hence strong forecasting models are deeply desirable for investors' financial decision making process. This study aims at evaluating the effectiveness of using technical indicators, such as simple moving average of close price, momentum close price, etc. in Turkish stock market. To capture the relationship between the technical indicators and the stock market for the period under investigation, hybrid Artificial Neural Network (ANN) models, which consist in exploiting capabilities of Harmony Search (HS) and Genetic Algorithm (GA), are used for selecting the most relevant technical indicators. In addition, this study simultaneously searches the most appropriate number of hidden neurons in hidden layer and in this respect; proposed models mitigate well-known problem of overfitting/underfitting of ANN. The comparison for each proposed model is done in four viewpoints: loss functions, return from investment analysis, buy and hold analysis, and graphical analysis. According to the statistical and financial performance of these models, HS based ANN model is found as a dominant model for stock market forecasting.