Distributed Stock Exchange Scenario using Artificial Emotional Knowledge Daniel Cabrera-Paniagua1, Tiago Thompsen Primo2, Claudio Cubillos3, Rosa Vicari4 1
Escuela de Ingeniería Comercial, Universidad de Valparaíso Pasaje La Paz 1301, Viña del Mar, Chile
[email protected] 2 Samsung Research Institute Brasil Av. Cambacica 1200, Prédio 01, Campinas - SP, Brasil
[email protected] 3 Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso Av. Brasil 2241, Valparaíso, Chile
[email protected] 4 Instituto de Informatica, Universidade Federal do Rio Grande do Sul Av. Bento Gonçalves 9500, Porto Alegre, Brasil
[email protected]
Abstract. The current globalization and distribution of markets has meant that companies and organizations frequently need to adapt their internal structure and operating processes, in order to demonstrate efficiency and effectiveness for every customers and users, specially when considering aspects that are beyond logical definitions, such as the emotions. To cope with that, this work proposes an alternative to model the distributed Stock Exchange Scenario with ontologies. The proposed model considers that each investor can invest using information obtained by communication with different traders or investors. Each investor has its own knowledge represented by ontologies, which is composed by technical knowledge together with internal emotional states. Our preliminary results, shows the possibility to use ontologies as knowledge representation mechanisms for domains that consider the human emotional dimension for decision-making processes. Keywords: Investor, emotions, ontology.
1 Introduction People make decisions in different scenarios: individually or in interactive environments, either cooperating or negotiating. Despite of the contextual problem (coordination or negotiation), effective communication must be supported on three main aspects: knowledge of the domain (general vocabulary and specific technical concepts), knowledge of the language used (in terms of syntax, semantic, and timing), and assertiveness, that is, the ability to express own or personal ideas with clarity, without showing aggressiveness or passiveness. Any problem existing over these
three main aspects will probably affect negatively the accomplishment of goals. Which leads us to the following question: Would be emotions a central component of assertiveness, and how could it be used by intelligent agents? The capital market domain represents a very interesting study area, mainly because the behavior of global stock markets has a highly impact in the world economy. The investment can be addressed thought different risk instruments (for example, stocks, venture capital funds, bonds, among others). In a real scenario, each investor, considering several aspects (knowledge available, own technical investment criteria, own emotional states, only to mention a few) can buy, maintain, or sell investment instruments in each period, a rich environment to explore the usage of intelligent agents. The usage of agents was explored by Kendall and Su where they presented a multiagent based model of a simulated stock market within which active stock traders are modeled as heterogeneous adaptive artificial agents, considering an approach of integrating individual learning and social learning to co-evolve these artificial agents with the aim of evolving successful trading strategies [1], a concept that was used in this work, but, based on the use of ontologies. This idea was also explored by, Pu Qiu Mei et al. [5] where they presented an enegotiation model based on multiagent and ontology. In particular, their work presented a novel agent construction model that enables agents to communicate in the Semantic Web [13]. Our proposal considers that each stock market has an independently context (knowledge, specific vocabulary, own process), so, it is important for each agent investor to have access to all available information (in our case represented by an ontology) in order to perform its own decision-making process and share such knowledge among other agents. Thus, considering an electronic stock market, each investor agent must consider, firstly, internal information and investment preferences, and secondly, the information associated with each specific stock market in order to make a decision. This work proposes to simulate a distributed scenario of stock exchange market, where each investor can invest using internal information, general knowledge of stock market, and specific knowledge. This allows to incorporate the notion of globality of the market in an environment of electronic trading stock market. Each investor has its own knowledge represented by ontologies, and also can to interact with other investors or traders (for example, to increase own knowledge), allocated in any stock exchange market. Thus, each investor can move from a stock market to another one. The remaining of the paper is organized as: Section 2 presents some selected works associated with study area; section 3 presents concepts of human decision-making processes, concepts of stock market domain and a subsection about ontologies and knowledge representation; section 4 explains the use of an ontology model for the stock exchange market scenario; and finally, section 5 presents conclusions of the developed work.
2 Related Work In 2008, Boer-Sorbán provided a computational agent-based continuous-time simulation approach that supports a flexible representation of stock market organizations and traders’ variable behavior [2]. Kodia et al. [3] describe the behavioral and cognitive attitudes of the investor at the micro level and explains their effects to his decision-making. The authors used an agent-based simulation identifing three types of investors: novice investors; expert investors and market intermediary. Outkin [4] presents an agent-based model of a dealer-mediated market, similar to Nasdaq. The author does not include an agent-based diagram, or another type of explanation to their model. Smith et al. [6] defined an ontology for coordination, in which agents dynamically manage the interdependencies that arise during their interactions. A proof-of-concept implementation in the insurance domain is described and empirically evaluated. The proposed ontology has several concepts: agent, resources (viable, consumable, shareable, cloneable, owner), activity (coordinableActivity, nonCoordinableActivity), interdependency, and operationaRelationships. The evaluation of the ontology proposal was based on study case using a sample scenario, taken from the domain of car insurance fraud, to which a centralized coordination mechanism could be applied to successfully coordinate a number of activities. Hai Dong et al. [7] presented a brief overview on the current negotiation ontology researches. Additionally, the authors presented a unique ontology notation system, unifying the notations used in some ontologies, to maximally promote knowledge sharing outcome in this field. This notation system is derived of UML [8]. Their results suggest that, in general, ontologies models have as their main domain to ecommerce applications. Furthermore, the proposed ontologies available in the literature do not have evaluation results. This raises the need to formally develop the scope of the use of ontologies in multiagent negotiation. An example of integrated electronic stock exchange markets is MILA (Integrated Latin American Market) [15], composed of the stock exchanges markets of Chile, Colombia and Peru. MILA supports electronic transactions for buy/sale of stocks, by providing real-time information of the above three markets. Our work proposes the use of integrated knowledge of different stock exchange markets in a distributed environment of different stock exchange markets with the support of a domain ontology. This allows us to conceive an unique electronic platform for trading stocks. In addition, each investor can incorporate an emotional dimension when deciding to invest in a determinate market, such aspect is not present in the previous related works.
3 Theoretical Background With the aim to design a mechanism to facilitate stock market business, with distributed investors, it is necessary to consider some theoretical human aspects: decision-making process; knowledge about the stock markets, and a knowledge
representation of stock information and investor information (knowledge and emotions). 3.1 Human Decision-Making Process A decision encompasses several options to be done. Usually, it requires an evaluation of the specific characteristics and scope of each alternative to determine the fittest option. This explanation made sense if the entity that must take a decision has all necessary information. Usually, the decisions are made on uncertainty scenarios, with partial information and limited available time. Thus, humans developed internal mechanisms to take decisions quickly. Sometimes, the decision that is taken does not represent the best option, but, may be a "satisfactory solution". Two major aspects are crucial in a decision-making process: the probably result, and the value that each option represents for the person [12]. The expected utility model offers a rational perspective: the expected utility corresponds to utility of a specific result, considering the probability that this results may be obtained. However, risk aversion (for example, prefer an option with a minor but sure gain) or loss aversion (for example, prefer an option with minor utility, but less likely to lose investment) constitutes two cases of evidence that humans, usually, make decisions on uncertainty environments, and under a ratio-emotional schema. 3.2 Stock Market Domain To invest is to put money into financial schemes, stocks, property, or a commercial venture with the expectation of achieving a profit [10]. An investment corresponds to the acquisition of an asset on which is possible to allocate funds, with the intention to protect or increase their value to generate positive returns [11]. A market corresponds to a physical or nonphysical space where suppliers and demanders interact over any good or service. When talking about financial markets, the instruments, which are traded, correspond, for example, to financial stocks. Stocks represent a property title over a company, that is, represent a “fragment” of company, allowed to stocks’ owns to be company’s owns. The investor is transformed in partner, and shares company earnings. The figure 1 presents a general process of stock market exchange. The Investor role corresponds to person or entity, which buys stocks in a Stock Exchange Market. In the same sense, the trader role is the entity that acts as intermediary between different investors. 3.3 Ontologies and Knowledge Representation Ontologies are formal representations of a consensual knowledge [14]. It can be used as a mechanism to represent information and knowledge, forming a structured knowledge base, which allows to model concepts, relationships and properties. An ontology can be understood as a hierarchy of concepts. Each concept may have attributes and relationships. The hierarchy defines an agreed terminology associated
with a specific domain or environment, allowing a common knowledge vocabulary to share information between agents.
Fig. 1. General process of stock exchange.
The use of domain ontology allows the agents of a negotiation to share the knowledge necessary to communicate its intentions, comprehend the intentions of other agents, and in general, perform all actions associated with a negotiation process.
In multiagent system scenarios, the automated negotiation is typically based on the assumption that agents can only participate to a negotiation if they commit to a shared protocol [9]. In most traditional negotiation scenarios, the protocol is fixed and implicitly assumed. A traditional negotiation schema has a set of constraints on the type of interactions that can take place among agents: number of participants, interaction protocols allowed in negotiation process, technology used in agents implementation, for to mention just a few. In this sense, a very important aspect to consider corresponds to knowledge that each agent has about negotiation environment, that is, communication protocols and languages used agent society, structure messages, and business model knowledge.
4 Stock Exchange Market Scenario When an investor decides to invest, it is necessary to consider the information related to different senses. Firstly, each investor has a personal and individual investment profile, which is composed by: a personality dimension (that defines essential aspects of human behavior); an emotional dimension (associated with emotional states); and specifically information of the investment (investment capital available, time of investment, number of periods in which investment returns can be negative, among others). Secondly, it is necessary to have general knowledge of capital market domain (for example, know the concepts of profitability, risk, or probability of loss, just to name a few).
Fig. 2. Global Stock Exchange Markets.
And finally, it is necessary to have knowledge of each specific stock exchange market in which an investor intends to invest, that is, knowledge of locally normative and legal aspects: normative over investment capital (tax for new foreign investment capital, and tax for withdrawal of investment); country risk index; and market volume (quantity of company traded, and number of transactions associated with each company). Table 1. General terms of stock exchange market. Name
Description
Type
Investor
Corresponds to person or entity which buys stocks in a Stock Exchange Market.
Concept
Investment Capital
It is the risk capital used by an investor to buy stocks.
Concept
Company
Stock
The Company is the entity which offers stocks of it, in the Stock Exchange Market. These stocks are purchased by different investors (this is called “Primary Market”). Then, an investor can sell its stocks to others investors (this is called “Secondary Market”). It is the unit that is sold by a company (first time) or by an investor “X”, and purchased by an investor “Y”. Represents “a fragment” of the company.
Class
Class Concept
It is the place where stocks are buyed / selled.
Concept
Country
It is the country where each Stock Exchange Market is available for investors
Concept
Business Rule
Corresponds to specific rule or policy that is applied in each Stock Exchange Market.
Concept
Tax Trader
Class
Concept
Stock Exchange Market
Risk Index
Ontology Type Class
Class
Corresponds to valuation of several dimensions of a country. While most highest is it value, more risk represents this country for investment. It is a legal tax, defined in each country, which can be applied to both incoming or outbound investment capital. Usually, the tax is defined as a percentage of investment capital value. The trader corresponds to entity who acts as intermediary between different investors.
Instance attribute
Class Class, Data Property Or Axiom Class Data Property Class
Concept Concept
Class Class
Transaction
Every time a stock is bought or sold, a transaction is generated
Concept
Portfolio
An investment portfolio contains a set of stocks, usually, of different companies.
Concept
Stock Price
It is the price defined by company (and then, by market) required to obtained one stock.
Instance attribute
Class Data Property
Profitability
It is a variation of stock value, represented in a percentage of stock price. Can be positive, zero, or negative.
Instance attribute
Class Data Property
Risk
Represents the standard deviation associated with a stock.
Instance attribute
Probability of Represents the probability of profitability can be negative, that is, the Loss probability that a stock can lose value.
Instance attribute
It is each instant of Stock Exchange Market calendar. Usually, the U.S. Stock Exchange Market has 250 to 252 periods per year.
Concept
Period (Date)
Class
Class Data Property Class Data Property Class
A stock exchange market with more companies, and in addition, with a highest number of transactions by market period, forms an interesting market with high liquidity. The proposed scenario assumes that an investor decides on which market is
more attractive for investment, that is, defines an investment portfolio. For this, the investor uses information about your investment profile, general knowledge of capital markets, and specific knowledge of each market. At any time, the investor can check the performance of its investment, and then, he decides to modify the investment portfolio, or invest in another market (see figure 2).
Fig. 3. Abstract model of stock exchange concepts.
Concepts can be identified within this scenario. The table 1 presents a list of concepts, with a brief description and its corresponding type. In the same sense, the figure 3 shows an abstract model of terms associated with table 1. In order to build an ontology to represent such domain, we have chose to use the OWL language. This decision is grounded in the fact that such language is able to build a decidable knowledge representation (based in description logics), and, it is quite popular when we consider the Semantic Web domain. To demonstrate our scenario we present the description of an Agent Investor. This agent, for the purpose of this article, has associated emotional characteristics like Tranquility, Influence Factors, Risk Influence, Trust, Timeframe for Low Selectivity and his Joy and Sadness. Figure 4, presents an overall view of the developed ontology. Each aspect is represented by a numerical representation. The number 1, is the class hierarch for the class auction domain; the number 2 is the Data Properties defined to describe the individuals that will compose this model; the number 3 is an example of the hasRiskValue characteristics, meaning that it belongs to the class Behaviour and the acceptable values are float; the number 4 is a the representation of four individuals, the number five are a set of object properties that were defined to describe relationships between the individuals and the number 6 represents the Data Properties of the AgentInvestor1 individual.
Fig. 4. Ontologies Classes, Properties and Individuals.
The potential of this representation is mainly related to the potential to describe different contexts in which it is involved the stock market actors. Also, we can explore the usage of reasoning rules and derive knowledge to cope with the agent decision process. As usage example, we could make use of ontologies to cope with a transaction between two agent investors and two agent traders. In this situation, we could consider a few aspects such as the local context, un-emotional aspects and the same country. With this proposal we can envision most complex situations, were the emotional state of the investor is an important variable to consider. It is important to
mention, that we are not proposing the usage of such emotional representation by any ontological reasoning method, but, to provide an alternative to engage and share such information among the agents‘ beliefs at a standardized matter. To describe the emotional characteristics from an investor, we could use properties from the EmotionalProfile class, and, with such information the trader agent can determine, in cases where two stocks have the same price, which one would be more appropriated to the investor profile. An example of communication between an investor and two different traders (associated with different stocks exchange markets), that could be benefit from the proposed ontology, is presented in the figure 5.
Fig. 5. Illustrative sequence of communication between investor and traders.
The presented scenario was build to illustrate the potential to use an ontology with emotional aspects to cope with a multiagent model for stock market. This proposal advances the state of the art by incorporating the possibility to decide, not only, by logical factors, but also, by the investor emotional state. Those aspects can be further extended within the proposed ontology, or, by adding new ontologies that are build using the same ontology engineering approach.
5 Conclusion An approach of an ontology for knowledge representation of a distributed scenario of stock exchange market has been presented along with an multiagent model for stock exchange. The real human investors make their decisions considering both technical as internal emotional state information. Thus, the proposed ontology incorporates two dimensions (technical and emotional) with the idea to incorporate among agents the possibility to consider emotional aspects for human decisions. The future work will address, firstly, to increase the proposed scenario, in terms of concepts, relationships, and interactivity between actors, and secondly, to test the ontology representation on a functional prototype.
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