A C++ Platform for the Evolution of Trade Networks

This paper presents a general C++ platform for the implementation of a trade network game (TNG) that combines evolutionary game play with preferential partner selection. In the TNG, successive generations of resource constrained traders choose and refuse trade partners on the basis of continually updated expected payoffs, engage in risky trades modelled as two-person games, and evolve their trade strategies over time. The modular design of the TNG platform facilitates experimentation with alternative specifications for market structure, trade partner matching, trading, expectation formation, and trade strategy evolution. The TNG platform can be used to study the evolutionary implications of these specifications at three different levels: individual trader attributes, trade network formation, and social welfare.     

Platform design for large-scale artificial market simulation and preliminary evaluation on the K computer

Artificial market simulations have the potential to be a strong tool for studying rapid and large market fluctuations and designing financial regulations. High-frequency traders, that exchange multiple assets simultaneously within a millisecond, are said to be a cause of rapid and large market fluctuations. For such a large-scale problem, this paper proposes a software or computing platform for large-scale and high-frequency artificial market simulations (Plham: /pl\(\Lambda\)m). The computing platform, Plham, enables modeling financial markets composed of various brands of assets and a large number of agents trading on a short timescale. The design feature of Plham is the separation of artificial market models (simulation models) from their execution (execution models). This allows users to define their simulation models without parallel computing expertise and to choose one of the execution models they need. This computing platform provides a prototype execution model for parallel simulations, which exploits the variety in trading frequency among traders, that is, the fact that some traders do not require up-to-date information of markets changing in millisecond order. We evaluated a prototype implementation on the K computer using up to 256 computing nodes.     

Empowering Automated Trading in Multi-Agent Environments

Trading in the financial markets often requires that information be available in real time to be effectively processed. Furthermore, complete information is not always available about the reliability of data, or its timeliness—nevertheless, a decision must still be made about whether to trade or not. We propose a mechanism whereby different data sources are monitored, using Semantic Web facilities, by different agents, which communicate among each other to determine the presence of good trading opportunities. When a trading opportunity presents itself, the human traders are notified to determine whether or not to execute the trade. The Semantic Web, Web Services, and URML technologies are used to enable this mechanism. The human traders are notified of the trade at the optimal time so as not to either waste their resources or lose a good trading opportunity. We also have designed a rudimentary prototype system for simulating the interaction between the intelligent agents and the human beings, and show some results through experiments on this simulation for trading of the Chicago Board Options Exchange (CBOE) options.     

Online double auction mechanism for perishable goods

One-sided auctions are used for market clearing in the spot markets for perishable goods because production cost in spot markets is already “sunk.” Moreover, the promptness and simplicity of one-sided auctions are beneficial for trading in perishable goods. However, sellers cannot participate in the price-making process in these auctions. A standard double auction market collects bids from traders and matches the higher bids of buyers and lower bids of sellers to find the most efficient allocation, assuming that the value of unsold items remains unchanged. Nevertheless, in the market for perishable goods, sellers suffer a loss when they fail to sell their goods, because their salvage values are lost when the goods perish. To solve this problem, we investigate the suitable design of an online double auction for perishable goods, where bids arrive dynamically with their time limits. Our market mechanism aims at improving the profitability of traders by reducing trade failures in the face of uncertainty of incoming/departing bids. We develop a heuristic market mechanism with an allocation policy that prioritizes bids of traders based on their time-criticality, and evaluate its performance experimentally using multi-agent simulation. We find out that our market mechanism realizes efficient and fair allocations among traders with approximately truthful behavior in different market situations.     

Automated traders in commodities markets: Case of producer–consumer institution

Automatizing commodities’ price negotiation was hard to achieve in practice, mainly because of logistical complications. The purpose of our work is to show that it is possible to automatize thoroughly commodities’ trading in the futures market by replacing human traders with artificial agents. As a starting step, we designed a market institution, called producer–consumer, where only an automated seller and an automated buyer can trade on behalf of the producer and consumer, respectively. The producer and consumer periodically feed their trading agents with supply and demand (S&D) forecasts. We suggested a parameterizable trading strategy, called bands and frequencies, for the agents. To measure the overall efficiency of this trading system in terms of price stability and liquidity, we made some hypotheses on the benchmark price curve and its linkages to S&D curves and other relevant market variables. Then we proposed analytical tools to measure strategy performance. Finally, we conducted some computer simulations to prove the workability of this approach.     

Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals

This paper investigates whether trading volume and price distortion can be explained by the investor’s bounded rationality. Assuming that agents are bounded by their information access and processing, what are the consequences on market dynamics? We expose the result of simulations in an ABM that considers the liquidity as an endogenous characteristic of the market and allows to design investors as bounded rational. In a call auction market, where two risky assets are exchanged, traders are defined as a mix between fundamentalist and trend-follower outlook. Each one differs as to behaviour, order-placement strategy, mood, knowledge, risk-aversion and investment horizon. We place agents in a context of evolving fundamental values and order placement strategy; they perceive the fundamental but they also have some heterogeneous belief perseverance; and they adapt their orders to the market depth so as to maximise their execution probability and their profit. By adding bounded rationality in their information processing, we show that (1) usual features as trend-follower outlook and heterogeneous investment horizon are important features to generate excess volatility of asset prices and market inefficiency; (2) the learning fundamental value stabilises the market price and the trading volume; (3) the order-placement strategy increases trading volume, but reduces market efficiency and stability; (4) the agent’s mood prevents illiquid market and weakly increases the market volatility as classical noise trader agents; (5) the impatience to sell of traders is always present in the market: the market sell orders are always more numerous than the market buy orders.     

A multi‐agent system for analyzing the effect of information on prediction markets

Prediction markets have been shown to be a useful tool for forecasting the outcome of future events by aggregating public opinion about the event's outcome. In this paper, we investigate an important aspect of prediction markets—the effect of different information‐related parameters on the behavior of the traders in the market. We have developed a multi‐agent based system that incorporates different information‐related aspects including the arrival rate of information, the reliability of information, the penetration or accessibility of information among the different traders, and the perception or impact of information by the traders. We have performed extensive simulations of our agent‐based prediction market for analyzing the effect of information‐related parameters on the traders' behaviors expressed through their trading prices, and compared our agents' strategies with another agent‐based pricing strategy used in prediction markets called the zero intelligence strategy. Our results show that information‐related parameters have a significant impact on traders' beliefs about event outcomes, and, frequent, reliable information about events improves the utilities that the traders receive. Overall, our work provides a better understanding of the effect of information on the operation of prediction markets and on the strategies used by the traders in the market. © 2011 Wiley Periodicals, Inc.     

Coarse and fine identification of collusive clique in financial market

Collusive transactions refer to the activity whereby traders use carefully-designed trade to illegally manipulate the market. They do this by increasing specific trading volumes, thus creating a false impression that a market is more active than it actually is. The traders involved in the collusive transactions are termed as collusive clique. The collusive clique and its activities can cause substantial damage to the market's integrity and attract much attention of the regulators around the world in recent years. Much of the current research focused on the detection based on a number of assumptions of how a normal market behaves. There is, clearly, a lack of effective decision-support tools with which to identify potential collusive clique in a real-life setting. The study in this paper examined the structures of the traders in all transactions, and proposed two approaches to detect potential collusive clique with their activities. The first approach targeted on the overall collusive trend of the traders. This is particularly useful when regulators seek a general overview of how traders gather together for their transactions. The second approach accurately detected the parcel-passing style collusive transactions on the market through analysing the relations of the traders and transacted volumes. The proposed two approaches, on one hand, provided a complete cover for collusive transaction identifications, which can fulfil the different types of requirements of the regulation, i.e. MiFID II, on the other hand, showed a novel application of well-known computational algorithms on solving real and complex financial problem. The proposed two approaches are evaluated using real financial data drawn from the NYSE and CME group. Experimental results suggested that those approaches successfully identified all primary collusive clique scenarios in all selected datasets and thus showed the effectiveness and stableness of the novel application.     

Characterising trader manipulation in a limit-order driven market

Use of trading strategies to mislead other market participants, commonly termed trade-based market manipulation, has been identified as a major problem faced by present day stock markets. Although some mathematical models of trade-based market manipulation have been previously developed, this work presents a framework for manipulation in the context of a realistic computational model of a limit-order market. The Maslov limit order market model is extended to introduce manipulators and technical traders. We show that “pump and dump” manipulation is not possible with traditional Maslov (liquidity) traders. The presence of technical traders, however, makes profitable manipulation possible. When exploiting the behaviour of technical traders, manipulators can wait some time after their buying phase before selling, in order to profit. Moreover, if technical traders believe that there is an information asymmetry between buy and sell actions, the manipulator effort required to perform a “pump and dump” is comparatively low, and a manipulator can generate profits even by selling immediately after raising the price.     

On the effects of competition between agent-based double auction markets

Real market institutions, stock and commodity exchanges for example, do not occur in isolation. The same stocks and commodities may be listed on multiple exchanges, and traders who want to deal in those goods have a choice of markets in which to trade. While there has been extensive research into agent-based trading in individual markets, there is little work on this kind of multiple market scenario. Our work seeks to address this imbalance in the context of double auction markets. This paper examines how standard economic measurements, like allocative efficiency, are affected by the presence of multiple markets for the same goods, especially when the markets are competing for traders. We find that while dividing traders between several small markets typically leads to lower efficiency and worse convergence than grouping them into one large market, competition between markets for traders, can reduce these losses.     

Computational speed and high-frequency trading profitability: an ecological perspective

High-frequency traders (HFTs) account for a considerable component of equity trading but we know little about the source of their trading profits and to what extend IT based differentiators such as news processing power and ultra-low latency has contributed to competitive advantage within HFT realm. Given a fairly modest amount of empirical evidence on the subject, we study the effects of computational speed on HFTs’ profits through an experimental artificial agent-based equity market. Our approach relies on an ecological modelling inspired from the r/K selection theory, and is designed to assess the relative financial performance of two classes of aggressive HFT agents endowed with dissimilar computational capabilities. We use a discrete-event news simulation system to capture the information processing disparity and order transfer delay, and simulate the dynamics of the market. Through Monte Carlo simulation we obtain in our empirical setting robust estimates of the expected outcome.     

Information and Dynamics: Sequences of Call Markets

This paper is part of a wider research project with the objective of creating computational testbeds for designing and testing new mechanisms—new economic and political institutions. Here we illustrate the power of such an approach by testing two call market designs in a repeated demand-supply environment. We find there to be significant differences in performance depending on the information provided to the traders between calls. In particular, we find that both dynamic and static performance is better, less volitility and higher gains from trade, if traders receive less information between calls.     

Electronic multilateral trade of electricity

This paper presents a technical approach for electronic multilateral trade of electricity in competitive power industries. The trade involves strategic sharing of data among agents in an attempt to provide the opportunity to intelligently discover competitive behavior of peer suppliers. A trading logic is implemented as a specialized software module within the agent. The logic mimics intelligence of the human strategic trade. A time-bounded trade protocol has been introduced as a trading basis among rivalry trade agents in the market. The protocol limits the trade rounds in order to bind the trading process to specific deadlines. The protocol is coded as part of the automated trade server. The results of a generic 3-bus test system show that the electronic multilateral trade logic presented in this paper better distributes market sales, lowers prices and consequently provides higher social welfare compared to the standard Cournot economic model that may be used by the human decision-maker for market trading. Based on a set of test cases with different load profiles, it is noted that the electronic multilateral trade drives the market price closer to the marginal cost of generation supply and far away from the estimated Cournot price.     

P2P网络中基于随时间推移的风险值评估的信任管理模型

针对现有的对等(P2P)网络信任管理模型中存在的问题:忽视短期交易时间与长期交易时间对信任值的不同影响以及缺少对交易对象资源的具体风险分析,在现有信任管理模型的基础之上,以不同用户对同一种类资源所提供的资源品质和风险作为关注点,引入风险值评估的概念,建立了基于随时间推移的风险值评估的信任管理模型。仿真结果表明,该模型使得恶意节点的行为得到有效控制,对交易资源的分析量化更加深入,进一步有效地帮助用户筛选出最优的交易者。     

Adaptive stock trading with dynamic asset allocation using reinforcement learning

Stock trading is an important decision-making problem that involves both stock selection and asset management. Though many promising results have been reported for predicting prices, selecting stocks, and managing assets using machine-learning techniques, considering all of them is challenging because of their complexity. In this paper, we present a new stock trading method that incorporates dynamic asset allocation in a reinforcement-learning framework. The proposed asset allocation strategy, called meta policy (MP), is designed to utilize the temporal information from both stock recommendations and the ratio of the stock fund over the asset. Local traders are constructed with pattern-based multiple predictors, and used to decide the purchase money per recommendation. Formulating the MP in the reinforcement learning framework is achieved by a compact design of the environment and the learning agent. Experimental results using the Korean stock market show that the proposed MP method outperforms other fixed asset-allocation strategies, and reduces the risks inherent in local traders.     

Traders' Long-Run Wealth in an Artificial Financial Market

In this paper, we study the long-run wealth distribution of agents with different trading strategies in the framework of the Genoa Artificial Stock Market.The Genoa market is an agent-based simulated market able to reproduce the main stylised facts observed in financial markets, i.e., fat-tailed distribution of returns and volatility clustering. Various populations of traders have been introduced in a`thermal bath' made by random traders who make random buy and sell decisions constrained by the available limited resources and depending on past price volatility. We study both trend following and trend contrarian behaviour; fundamentalist traders (i.e., traders believing that stocks have a fundamental price depending on factors external to the market) are also investigated. Results show that the strategy alone does not allow forecasting which population will prevail. Trading strategies yield different results in different market conditions. Generally, in a closed market (a market with no money creation process), we find that trend followers lose relevance and money to other populations of traders and eventually disappear, whereas in an open market (a market with money inflows), trend followers can survive, but their strategy is less profitable than the strategy of other populations.     

The role of intelligence in time series properties

Although the influence of intelligence on market performance has long been discussed, in this paper we provide a broader scope for examining this issue. The performance in a market composed of zero-intelligence traders is compared with that in a market where traders are endowed with the simple adaptive learning method or GP-based learning algorithm both without and with Boolean functions. Market properties such as the price, return, trading volume, and heterogeneity among traders are provided to analyze the role of intelligence. We find that the influence of intelligence on the market crucially depends on the representation of intelligence or the learning method      

Gaming Prediction Markets: Equilibrium Strategies with a Market Maker

We study the equilibrium behavior of informed traders interacting with market scoring rule (MSR) market makers. One attractive feature of MSR is that it is myopically incentive compatible: it is optimal for traders to report their true beliefs about the likelihood of an event outcome provided that they ignore the impact of their reports on the profit they might garner from future trades. In this paper, we analyze non-myopic strategies and examine what information structures lead to truthful betting by traders. Specifically, we analyze the behavior of risk-neutral traders with incomplete information playing in a dynamic game. We consider finite-stage and infinite-stage game models. For each model, we study the logarithmic market scoring rule (LMSR) with two different information structures: conditionally independent signals and (unconditionally) independent signals. In the finite-stage model, when signals of traders are independent conditional on the state of the world, truthful betting is a Perfect Bayesian Equilibrium (PBE). Moreover, it is the unique Weak Perfect Bayesian Equilibrium (WPBE) of the game. In contrast, when signals of traders are unconditionally independent, truthful betting is not a WPBE. In the infinite-stage model with unconditionally independent signals, there does not exist an equilibrium in which all information is revealed in a finite amount of time. We propose a simple discounted market scoring rule that reduces the opportunity for bluffing strategies. We show that in any WPBE for the infinite-stage market with discounting, the market price converges to the fully-revealing price, and the rate of convergence can be bounded in terms of the discounting parameter. When signals are conditionally independent, truthful betting is the unique WPBE for the infinite-stage market with and without discounting.     

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.     

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。