Trading Agent Research Articles

New technologies, Chinese goods and trade agents. GECEM database to study Jesuit networks between Asia and Latin American (17th and 18th centuries)

This article shows that the use new technology and computer programs sheds light on the role that Jesuits played in the commercial networks of the Iberian Empires during the early modern period. A number of studies have focused on the evangelical and missionary contribution by the members of the Society of Jesus in the Latin American and Asian frontiers, but this article puts forward a different point of view. I use the GECEM Database to compare two Jesuit mission areas (Macau and Paraguay) and their role in the global commercial routes. So, this article further departs from earlier scholarship on Jesuits by emphasising the local, by combining local scale with an analysis of inter-regional processes on a continental scale. My main sources are Jesuit letters, regulations, reports, accounts, and stories, and I also used the GECEM Database to analyse this empirical data. ResumenEste artículo muestra que el uso de las nuevas tecnologías y los programas informáticos arroja luz sobre el papel que los jesuitas desempeñaron en las redes comerciales de los imperios ibéricos durante la época moderna. Diversos estudios se han centrado en los aportes evangelizadores y misioneros de los miembros de la Compañía de Jesús en las fronteras latinoamericanas y asiáticas, pero este artículo plantea un punto de vista diferente. Utilizo la base de datos GECEM para comparar dos áreas de misión jesuita (Macao y Paraguay) y su papel en las rutas comerciales globales. De esta manera, el presente artículo se aleja de los estudios anteriores sobre los jesuitas al hacer hincapié en lo local, combinando la escala local con un análisis de los procesos interregionales a escala continental. Mis fuentes principales son las cartas, reglamentos, informes, relatos e historias de los jesuitas, y también utilicé la base de datos GECEM para analizar todos estos datos empíricos.

A Decision Theoretic Foundation for Noise Traders and Correlated Speculation

Noise traders are a central idea in the modern theory of asset markets, yet there is not a standard model of such agents in contrast to the well-established representation of rational agents as expected utility maximizers. We propose the Hurwicz criterion, a classical criterion in decision analysis for choice under uncertainty, as a foundation for noise traders in asset markets. Hurwicz agents trade on optimism and pessimism and do not trade on information. A binary asset market is introduced with asymmetric information and heterogeneity both in rationality and in ambiguity attitudes. In this environment, noise trader behavior is endogenously positively correlated, the market is more efficient in low sentiment periods, and the favorite-longshot bias holds in equilibrium. The analysis demonstrates that aggregate market properties such as positive trading volume and the favorite longshot bias can be derived from the micro behavior of individual agents that have an axiomatic foundation.

Catastrophe by Design in Population Games: A Mechanism to Destabilize Inefficient Locked-in Technologies

In multi-agent environments in which coordination is desirable, the history of play often causes lock-in at sub-optimal outcomes. Notoriously, technologies with significant environmental footprint or high social cost persist despite the successful development of more environmentally friendly and/or socially efficient alternatives. The displacement of the status quo is hindered by entrenched economic interests and network effects. To exacerbate matters, the standard mechanism design approaches based on centralized authorities with the capacity to use preferential subsidies to effectively dictate system outcomes are not always applicable to modern decentralized economies. What other types of mechanisms are feasible? In this article, we develop and analyze a mechanism that induces transitions from inefficient lock-ins to superior alternatives. This mechanism does not exogenously favor one option over another; instead, the phase transition emerges endogenously via a standard evolutionary learning model, Q-learning, where agents trade off exploration and exploitation. Exerting the same transient influence to both the efficient and inefficient technologies encourages exploration and results in irreversible phase transitions and permanent stabilization of the efficient one. On a technical level, our work is based on bifurcation and catastrophe theory, a branch of mathematics that deals with changes in the number and stability properties of equilibria. Critically, our analysis is shown to be structurally robust to significant and even adversarially chosen perturbations to the parameters of both our game and our behavioral model.

An Exploratory Study of the Perception of Peer-to-Peer Energy Trading within the Power Distribution Network in the UAE

The introduction of Smart Grid resulted in the development of various applications that are built upon the concept of bi-directional flow of electricity and data. One of the Smart Grids pillars is the Distributed Generation (DG) technologies, where customers turn to be prosumers with power generation capability. Another pillar is the Demand Side Management (DSM), which helps control the energy consumption by changing the power usage slots among other peers. DG and DSM have facilitated the sharing of excess power by customers to the grid, and then to their peers through the grid as a trading agent. Although the concept of integrating Peer-to-Peer energy trading with DSM has been explored by scholars and relatively established trading frameworks, there are very limited research performed in respect to the UAE market in terms of its acceptance and readiness towards this energy trading market. This research aims to explore the perception of Peer-to-Peer electricity trading within the Power Distribution Network in the United Arab Emirates. The study will review the Smart Grid network in the UAE and will obtain insights on people’s perception of the transition from classical electricity network to Smart Grid. It will also look into peoples’ perception regarding the transition from being electricity consumers to being electricity producers that trade among peers through semi-structured interviews. This will enhance the understanding of the energy trading market between self-generated power producers connected to a network grid, where the consumer will be utilizing the excess power available in the form of electricity trading, by importing and exporting power, without adding any additional power to the grid. The outcome of the study will provide an insight on the UAE electricity market by designing an electricity trading model that is built upon the following vital factors: power quality, supply reliability, type of integration, peers, and trading time. Furthermore, the study will provide a foundation base to the utilities, as well as individuals, when dealing with the changes in the electricity market structure.

Bidding to procure supply in newsvendor networks

We develop a model to determine the optimal bids a trade agent submits to sellers in a B2B market. The trade agent bids at multiple supply locations and allocates the goods procured after bidding to fulfill the demand of multiple clients. We analyze the structural properties of the trade agent's expected profit function and the optimal bids.

The International Monetary System Reform: Refusal to Use the Dollar and Fair Exchange Rate

The purpose of the study is to develop recommendations aimed at eliminating shortcomings of the modern international monetary system. The author suggests a draft of its reform. The draft provides, firstly, an objective reflection of the economic state of the issuing state in compliance with the exchange rate of the national currency and, secondly, allows eliminating disproportionate use of the US dollar in international trade. When developing the draft of the reform, the author proceeded from the assumption that the reform of the international monetary system based on consensus is impossible: «beneficiaries of the modern monetary order» will actively defend their national interests to the detriment of the interests of the majority of members of the international community. Under the current conditions, the only way to transform the international monetary system is to take unilateral actions of individual States. At the same time, the «engine of the reform» should include the commercial benefit of private participants of foreign economic activity: it is unrealistic to adjust the international monetary system by establishing new rules that do not ensure additional (at least minimal) profit for international trade agents. Based on these prerequisites, the author formulates a model of unilateral modernization of the settlement system and the system of customs and tariff regulation when importing goods that can be applied by an individual state. Implementation of this model should generate demand on the domestic foreign exchange market for the national currency of the importing state proportional to its share in international trade. A consistent increase in the number of states using this model will allow, based on market principles, creating a new international monetary system, eliminating its main drawbacks, including reducing the role of the US dollar in international trade to an adequate value reflecting the current state of the American economy. The author relied in the study on analytical and statistical methods, the method of economic and legal modeling.

Innovations in foreign trade management under pre-war neo protectionism conditions

Methods. To study the risks of neo protectionism for Ukrainian exporters there has been used the following methods: analysis and synthesis, induction and deduction – to identify prerequisites for the formation of neo protectionism in the conditions of globalization. The statistical methods have been used to develop the model of the dependence of the export of products (goods and services) on the indirect methods of neo protectionism to test the hypothesis about the influence of indirect financial support on the stimulation of exports and the creation of new jobs by exporting enterprises. Results. This study offers recommendations for overcoming the risks of foreign economic activity, in particular the relocation of jobs, changes in trade and payment balances on the example of individual countries, as well as the use of discriminatory measures against exporting firms from Ukraine. It is demonstrated that before the war, Ukrainian firms that exported their products and services to foreign markets faced fierce competition for both customers and investments. As most of the world prioritizes trade liberalization, many so-called «developed countries» suffer from the social and political consequences of the global distribution of wealth and labor, which include the movement of jobs abroad and rising unemployment. It was revealed that more and more countries are applying neo-protectionism measures to protect local markets and strengthen the competitive positions of national businesses, so the management of Ukrainian firms should take these risks into account when developing their own foreign trade strategies. Novelty. The article summarizes the arguments and counter-arguments in the framework of the scientific debate on the problem of the revival of neo-protectionism and economic nationalism in the conditions of global economic integration and trade liberalization. Innovative recommendations for minimizing threats to Ukrainian exporting firms from the policy of neo-protectionism in the foreign markets of unfriendly countries in the post-war period are proposed. Practical value. The recommendations provide for reorientation from the markets of unfriendly countries with high non-commercial risks to the markets of partner countries and insurance of commercial risks under the contract using the mechanisms of Public JSC «Export Credit Agency of Ukraine» in the post-war period. The results of the research can be useful for Ukrainian exporting firms and government bodies that regulate the foreign economic activity of trade agents that export dual-use goods.

Self-Fulfilling Prophecies, Quasi Nonergodicity, and Wealth Inequality

We construct a model of an exchange economy in which agents trade assets contingent on an observable signal, the probability of which depends on public opinion. The agents in our model are replaced occasionally, and each person updates beliefs in response to observed outcomes. We show that the distribution of the observed signal is described by a quasi-nonergodic process and that people continue to disagree with each other forever. These disagreements generate large wealth inequalities that arise from the multiplicative nature of wealth dynamics, which makes successful bold bets highly profitable.

Survival through Pragmatic Thinking: Revisiting the Untold Stories behind the Canada–China Grain Trade, 1960–63

:This article examines the untold stories behind the Canada–China grain trade in the 1960–63 period. According to some archived records of Canadian business proceedings that have yet barely been examined, non-state agents in both nations took joint initiatives to deliver these grain sales, demonstrating pragmatic attitudes to de-escalate political tensions. Despite intensive disputes that persistently sabotaged trade normalisation, many actors devoted tireless efforts consistent with the future trend of public diplomacy. Acting on behalf of local civil societies and business associations, Canadian wheat trade agents facilitated effective two-way communication with their Chinese peers. The emphasis on common interests, rather than on ideological assertion, helped to contain political distrust with "de-othering" narratives. Increased engagement by civilian stakeholders thus served to hedge against political risk exposures.

Stock Market Prediction using Reinforcement Learning with Sentiment Analysis

This work creates a new Deep Q-learning model with augmented sentiment analysis and stock trend labelling (DQS model). The novelty of this study is as following. We form the stock price prediction problem as trend prediction instead of predicting its accurate price. By benchmarking multiple machine learning methods, stock market trend label is proven to be effective and can be predicted accurately. We use news titles and apply Valence Aware Dictionary for Sentiment Reasoning (VADER) to project the sentiment of the news about stock under study. The input feature to a customized Deep Q-learning model incorporates stock market trend label and sentiment analysis score label. Our study shows that a trading agent using DQS model achieved 83% more portfolio value than a DQ model using only stock technical indicators. The trading agent based on DQS model achieved a Sharpe ratio of 3.65 comparing with 1.6 achieved by a traditional DQ model-based trading agent. This indicates the DQS model combining with input features proposed by our study can achieve excellent risk-free investment portfolio.

The impact of COVID-19 pandemic on business risks and potato commercial model

Abstract This study was aimed (1) to analyze the productivity, cost, and income of potato farming; (2) to analyze the risk of potato farming; and (3) to analyze the potato trade system at the level before and during COVID-19 pandemic. This study used a descriptive-quantitative research type. It was conducted in Banjarnegara Regency, Jawa Tengah Province, Indonesia. The data were collected through surveys, observations, and Focus Group Discussions. The unit of analysis is the farmers who plant potatoes. Data analysis was done descriptively. The results showed that there is no difference between the two marketing channels before and during pandemic. There are two channels of the trading system, namely farmer–collector–traders–wholesaler–exporter partners and farmer–collector–traders–wholesalers–retailers. However, the trading model maximizes the Agribusiness Sub Terminal (AST) as a potato trading agent that can provide direct price information, attract traders, and facilitate transactions and trading contacts. The trading model allows potato trading agents to provide direct price information, attract traders, and facilitate transactions and trading contacts. The implication of this study is to anticipate productivity risk and potato farming income risk through the AST function. This study contributes to the condition of farming before and during COVID-19 pandemic by comparing differences in productivity, costs, income, productivity risk, and income risk as well as the potato grading model.

When Yield is Not the Only Supply Uncertainty: Newsvendor Model of a Trade Agent

When Yield is Not the Only Supply Uncertainty: Newsvendor Model of a Trade Agent

An adaptive regression for agent-based modeling

The article discusses an algorithm, which that can be used to implement adaptive behavior of agents in agent-based models (ABM). It is assumed that an agent has some internal parametric model of the surrounding world, which motivates a likelihood function for the information about the world received by the agent. The process of adaptive learning of an agent via changing parameters is presented as filtering in a general state space model. By using a linear Gaussian transition density and a quadratic approximation for the log-likelihood function, an algorithm is obtained, which is called SQ filter in the article. This algorithm is a modification of the classical Kalman filter. It is applied to the linear regression with time-varying parameters. When an agent receives new information, the parameter estimates, which include both the regression coefficients and the error variance, are adjusted adaptively by taking into account possible outliers. The performance of the proposed adaptive regression was tested on two economic ABM. The algorithm showed good results both in an artificial stock market model where trader agents predict the market price and in a model of the Russian economy where firms predict demand for their output. With its help, it is possible to endow agents with plausible behavior without using overly complex calculations.

Heterogeneous Agent Trade

Heterogeneous Agent Trade

Effects of the East African Community Single Customs Territory (EACSCT) on the Movement of Goods along the Rwandan Customs Posts

Despite the factors making cross-border movement of goods complex and unpredictable, the latter plays a critical role in international trade. However, the EAC has introduced single customs territory to respond to the factors affecting the movement of goods and enhance trade within the region. This study then assesses the extent to which EACSCT affects the movement of goods across the Rwandan customs posts. The study used primary data collected on the perceptions of various trading agents on the implementation of the EACSCT, aspects of trade facilitation and movement of goods. Descriptive statistics and Multi-Level Model were used to analyse data and for estimates. The findings indicate that the EACSCT is successfully implemented along the Rwandan customs posts as rated good by various groups of trading agents. The level of trade facilitation is rated very high and the movement of goods has, to a large extent, been facilitated along the Rwandan customs posts. The findings also reveal that the EACSCT has significantly and positively affected the movement of goods along the Rwandan customs posts. These findings give an understanding that the EACSCT has significantly reduced or removed the barriers to the movement of goods by enhancing the level of trade facilitation. It is expected that 1-unit increase in the level of trade facilitation will make the barriers to the movement to fall by 1.133 points. Hence, calling for all border crossing agencies and stakeholders to work in synergy to continue streamlining the level of trade facilitation.

Heavy-tailed distributions of volume and price-change resulting from strategy coordination and decision noise

An agent-based model is created to determine the distribution of volume and price change resulting from orders to purchase a stock from high-frequency/short-term, boundedly rational trading agents. The agents who place the orders follow different strategies. Agents who follow the same strategy have coordinated behavior, even if they do not share information—this is tacit collusion (explicit collusion happened during the GameStop and other short squeezes). We look at incoming orders that are composed of a given number of agents that can range from one to ten thousand. It is assumed that each incoming order is a result of a trigger from a particular strategy. A square root price impact function is used to determine the effect on price as a result of the order. The profit of an agent is determined from this price change since they are high-frequency traders. The model produces power-law distributions for individual quantity, aggregate quantity, and price-change, and we fit three stocks to the model by adjusting the temperature. When the market reflects reversion-to-the-mean behavior, the distributions appear to have lighter tails than when the market reflects the hot-hand behavior. The higher risk of heavier tails is seen to be a result of hot-hand trading behavior for this model. The model also has a phase transition in the hot-hand component, in the sense of a nonanalyticity, where the order parameter is the exponent of the power-law tail of the c.d.f. function for aggregate quantity Q. This order parameter is a function of the temperature, varies linearly with inverse temperature in a high-temperature region, and varies nonlinearly in a low-temperature region. The relationship predicted between temperature and aggregate volume has been validated by data from the GameStop short squeeze.

Portfolio return prediction model based on gold and Bitcoin

Maximizing returns has always been people's investment goal. Gold and bitcoin are popular with investors because of their hedges and volatility. However, markets are risky and can be influenced by different economic, political and environmental factors. As a result, bitcoin and gold prices fluctuate wildly, leading to uncertain investment and uncertain returns. In order to maximize the profit, this paper completes the data processing and model construction to make decisions. Based on the Markov decision process of avoiding risk avoidance, reducing transaction cost and maintaining liquidity, and assuming that the stock market is not affected by enhanced trading agent, deep reinforcement learning (DRL) is used to simulate stock trading. The application of the model is helpful to forecast the return of investment portfolio and brings strong application value to the relevant practitioners.

To learn or not to learn? Evaluating autonomous, adaptive, automated traders in cryptocurrencies financial bubbles

Financial bubbles represent a severe problem for investors. In particular, the cryptocurrency market has witnessed the bursting of different bubbles in the last decade, which in turn have had spillovers on all the markets and real economies of countries. These kinds of markets and their unique characteristics are of great interest to researchers. Generally, investors and financial operators study market trends to understand when bubbles might occur using technical analysis tools. Such tools, which have been historically used, resulted in being precious allies at the basis of more advanced systems. In this regard, different autonomous, adaptive and automated trading agents have been introduced in the literature to study several kinds of markets. Among these, we can distinguish between agents with Zero/Minimal Intelligence (ZI/MI) and Computational Intelligence (CI)-based agents. The first ones typically trade on the market without resorting to complex learning strategies; the second ones usually use (deep) reinforcement learning mechanisms. However, these trading agents have never been tested on the cryptocurrencies market and related financial bubbles, which are still mostly overlooked in the literature. It is unclear how these agents can make profits/losses before, during, and after a bubble to adjust their strategy and avoid critical situations. This paper compares a broad set of trading agents (between ZI/MI and CI ones) and evaluates them with well-known financial indicators (e.g., volatility, returns Sharpe ratio, drawdown, Sortino and Omega ratio). Among the experiment’s outcomes, ZI/MI agents were more explainable than CI ones. Based on the results obtained above, we introduce GGSMZ, a trading agent relying on a neuro-fuzzy mechanism. The neuro-fuzzy system is able to learn from the trades performed by the agents adopted in the previous stage. GGSMZ’s performances overcome those of other tested agents. We argue that GGSMZ could be used by investors as a decision support tool.

Active distribution network operation management integrated with P2P trading

With the steady development of electricity trading market, active distribution networks (ADNs) need to face new challenges in operation management. Meanwhile, the traditional operation management with peer-to-peer (P2P) trading leads to a heavy computational burden due to a great number of prosumers. An operation management model for large-scale distribution networks integrated with P2P trading is proposed in this paper, where network usage fees are specially addressed. To simplify the management of the P2P trading market, the trading agent is introduced into the P2P trading platform. In addition, this model is adaptive to the reconfiguration of ADN, so the operation management system and the corresponding market structure can be automatically updated according to the new network topology. As shown in case studies, the total trading amount in the P2P trading market can increase by about 62MWh by adjusting the unit price of network usage fees, and the computational time can decline by about 77% in a practical-scale 152-bus system. Also, the total trading amount can be doubled approximately, and the computational time can be reduced by about 85% in the IEEE 8500-bus system.

Fair Trade – Possibilidades para o Etno-Desenvolvimento Quilombola

A integração de projetos de etno-economia ao movimento do fair trade permite construir rede de estruturas e experiências locais, assegurando a diversidade e a preservação cultural e o consequente empoderamento de comunidades quilombolas. Neste contexto, o estudo teve por objetivos, investigar à partir do direito possibilidades para o comércio justo e solidário como elementos integradores de mercados e facilitadores das propostas de etno-desenvolvimento para comunidades quilombolas no Brasil. Da perspectiva metodológica, propôs-se a crítica da realidade com uso do raciocínio dialético, regido por transformações originadas em contradições e lutas, realizado a partir do levantamento de dados e documentos em organizações públicas e privadas e bibliográfico. Parte-se da perspectiva teórica do etno-desenvolvimento de Bonfil Batalla, dos estudos de etno-economia de Cavalcanti, economia solidária de Paul Singer e reflexões sobre economia de Fernand Braudel. Tem-se por hipótese a função prospectiva do direito como instrumento de construção da realidade social, como uma variável dependente e propulsora da organização econômica na sociedade. O estudo resultou a revisão crítica dos institutos para fins coletivistas e de cooperação entre os agentes das relações etno-econômicas e de comércio justo e inserção em redes de cooperação.