Market Simulation Research Articles

Analysis of Optimal Market Making Strategies of CFD Market Using Artificial Market Simulation

Analysis of Optimal Market Making Strategies of CFD Market Using Artificial Market Simulation

Portfolio insurance, portfolio theory, market simulation, and risks of portfolio leverage

Portfolio insurance, portfolio theory, market simulation, and risks of portfolio leverage

Investigating the long-term benefits of EU Electricity Highways: The case of the Green Aegean Interconnector

This paper investigates the potential long-term benefits that could be arisen by the construction and operation of the envisioned “Green Aegean Interconnector”. This will be a pioneering 9-GW direct interconnection line connecting Greece with Germany to facilitate the transfer of massive amounts of RES generation from South-East to Central-North Europe. The ultimate goal of this analysis is to estimate the total electricity supply cost to be undertaken by the end-consumers in Greece with and without the said interconnector along with the cost/benefit of the Greek national economy associated with the foreseen cross-border electricity exchange. Detailed simulations of the Greek electricity market using a specialized electricity market simulation software on the basis of the main provisions of the revised Greek National Energy and Climate Plan are performed under realistic market evolution scenarios for a future 30-year study horizon (2026-2055). Simulation results and the associated cost-benefit analysis indicate that such a project would enable the operation of additional RES projects in Greece. This would, in turn, decrease the overall Greek end-consumers’ electricity cost and create a significant surplus for the Greek economy through the increased exporting activity, whereas at EU-level it would assist towards further achieving the European energy market integration.

Exploring the diffusion of low-carbon power generation and energy storage technologies under electricity market reform in China: An agent-based modeling framework for power sector

Promoting the widespread adoption of multi-complementary low carbon power generation technologies is a fundamental strategy for attaining carbon neutrality within the electricity sector. In the context of electricity market reform, this study develops an agent-based modeling framework integrated simulation with optimization. The model uses agent-based simulation to analyze annual market dynamics and low-carbon technology diffusion, with a two-stage optimization for energy storage and spot market simulation. The research findings indicate: (1) For different power generation technologies, the spot market can establish differentiated average prices, making the market price of carbon-captured coal power higher than that of conventional coal power, which are advantageous in reducing the subsidies required to promote the diffusion of carbon capture technology. (2) Expanding the spot market scale and implementing differentiated capacity compensation mechanisms both promote carbon capture technology diffusion. Nevertheless, with the marginal clearing mechanism, thermal power holds a average price advantage than renewable energy. Failing to control the growth of thermal power capacity will result in increased carbon emissions. (3) After 2030, energy storage's role in balancing supply and demand grows. Storage capacity should align with renewable energy scale and the regional characteristics of wind and solar resources to prevent overbuilding and stranded assets.

Trading electricity using tranched power delivery contracts

ABSTRACT This letter proposes a novel way to apportion real-time renewable generation outputs between diverse token holders, using a novel tranching mechanism. This novel tranching structure is proposed to better manage the unpredictability of renewable power delivery and also as a case study of the exotic new instruments that could be offered in decentralized electricity marketplaces. Under this framework, a wind farm directly sells claims on their future power output in the form of a digital token. The realized generation then offsets the token holders’ electricity consumption in near real-time. Two ways of structuring such forward delivery instruments are considered: a tranched system, where more senior token holders enjoy priority claims on available outputs, as compared against a pro-rata scheme, where the realized generation is equally apportioned between token holders. A notional market simulation is provided to explore how consumers may exploit the distinct power delivery profiles.

FuNVol: multi-asset implied volatility market simulator using functional principal components and neural SDEs

We introduce a new approach for generating sequences of implied volatility (IV) surfaces across multiple assets that are faithful to historical prices. We do so using a combination of functional data analysis and neural stochastic differential equations (SDEs) combined with a probability integral transform penalty to reduce model misspecification. We demonstrate that learning the joint dynamics of IV surfaces and prices produces market scenarios that are consistent with historical features and lie within the sub-manifold of surfaces that are essentially free of static arbitrage. Finally, we demonstrate that delta hedging using the simulated surfaces generates profit and loss (P&L) distributions that are consistent with realized P&Ls.

Portfolio Insurance, Portfolio Theory, Market Simulation, and Risks of Portfolio Leverage

Portfolio Insurance, Portfolio Theory, Market Simulation, and Risks of Portfolio Leverage

Limited or Biased: Modeling Subrational Human Investors in Financial Markets

Human decision-making in real-life deviates significantly from optimal decisions made by fully rational agents, primarily due to computational limitations or psychological biases. While existing studies in psychology and economics have discovered various types of human limitations and biases, there lacks a comprehensive framework to transfer these findings into models of subrational investors in financial markets. In this study, we introduce a unified framework that use reinforcement learning (RL) to incorporate five different aspects of human subrationality including bounded rationality, myopic behavior, prospect-biased behavior, optimistic and pessimistic behaviors. Unlike the data-driven approaches, our model is trained based on a high-fidelity multi-agent market simulator, which is not limited by the availability of subrational investor trading data. Our framework demonstrates investment behavior that is characteristic of each type of subrationality in hand-crafted market scenarios. We evaluate the Profit and Loss (PnL) of different types of subrational investors, and use SHAP value analysis to reveal the driving factors in their decision-making process. Finally, we explore the impact of subrationality on market quality regarding liquidity, volatility, and efficiency. Our experiments indicate that bounded-rational and prospect-biased human behaviors improve liquidity but diminish price efficiency, whereas human behavior influenced by myopia, optimism, and pessimism reduces market liquidity.

Cost-Effective Target Capacity Assessment in the Energy Transition: The Italian Methodology

Long-term transmission expansion planning has to face the energy transition in a restructured electricity market environment. Increased transmission capacity within and between Member States is likely to play an essential role in maintaining the secure and economic operation of the whole European power system and ensuring the integration of growing renewable generation. This paper proposes a novel iterative methodology aimed at assessing an optimal level of interconnection between relevant bidding zones, simultaneously investigating different potential alternatives. Starting from a reference grid, a multi-criteria analysis is adopted to select the additional transmission capacities to be tested in each iteration via network and market simulations in order to confirm that transmission expansion benefits outweigh the estimated realization costs. The proposed approach is applied to the Italian case in two contrasting energy scenarios for the mid-term 2030 and very-long-term 2040 horizons: different development strategies are derived, and the least regret criterion is applied to define the most cost-effective as the target development strategy for the Transmission System Operator (TSO). Furthermore, sensitivity analyses on relevant input data variation are performed to test the robustness of the results obtained.

A new energy-economy-environment modeling framework: Insights from decarbonization of the Turkish power Sector towards net-zero Emission targets

The power sector plays a crucial role towards decarbonization for many economies, especially in line with the net-zero targets to limit global warming to 1.5 °C. Technical constraints intrinsic to the sector, penetration of new technologies, investment and operational costs, and its connections with the rest of the economy make the power sector a complex system to analyze. Although there are numerous studies to integrate bottom-up power sector technology models with top-down macroeconomic models, this study is the first attempt to link the three separate and interrelated models within a single framework: an electricity market simulation model, a generation expansion planning model, and an applied general equilibrium model. The proposed framework is implemented to analyze a feasible decarbonization scenario for Türkiye, with a particular focus on the power sector. The results suggest that, given the existing capacity and potential for renewables, Türkiye can achieve a coal-phase out by early 2030s, alongside a trajectory towards a full-fledged fossil fuel phase-out in power generation. The results also indicate that while installed capacity and generation of coal-fired power plants are reduced, real GDP and electricity demand can be maintained and the carbon dioxide emissions from the power sector could be reduced by as much as 50% in 2030 compared to 2018 levels.

Viral decisions: unmasking the impact of COVID-19 info and behavioral quirks on investment choices

This study aims to investigate the impact of behavioral biases on investment decisions and the moderating role of COVID-19 pandemic information sharing. Furthermore, it highlights the significance of considering cognitive biases and sociodemographic factors in analyzing investor behavior and in designing agent-based models for market simulation. The findings reveal that these behavioral factors significantly positively affect investment decisions, aligning with prior research. The agent-based model’s outcomes indicate that younger, less experienced agents are more prone to herding behavior and perform worse in the simulation compared to their older, higher-income counterparts. In conclusion, the results offer valuable insights into the influence of behavioral biases and the moderating role of COVID-19 pandemic information sharing on investment decisions. Investors can leverage these insights to devise effective strategies that foster rational decision-making during crises, such as the COVID-19 pandemic.

Investigating the role of nuclear power and battery storage in Hungary's energy transition using hourly resolution electricity market simulations

Electricity supply in European countries faces a number of challenges, such as achieving carbon neutrality, tackling rising prices, reducing dependence on fossil fuels, including fossil fuel imports. To achieve these goals, the electricity systems of all European countries will have to undergo major changes, while taking into account technical, environmental, economic and social objectives. Our simulations provide essential data for this transition by analyzing different power plant portfolios and electricity consumption scenarios. The analyses focus on the cooperation of nuclear power and weather-dependent renewables, and on the possible role that battery-based electricity storage can play in the Hungarian electricity system.In this paper, we present the experience gained in setting up an electricity market model and the results of running the model on the electricity systems of Hungary and its six neighboring countries (Slovakia, Romania, Serbia, Croatia, Slovenia and Austria), taking into account the constraints of the cross-border capacities. The results of the sensitivity analysis for the 2030 power plant portfolios, battery capacities and renewables analyzed in this paper cover Hungary's import/export position, the energy source structure of its electricity generation, battery operation, CO2 emissions from electricity generation, expected prices in the system and the utilization parameters of nuclear power plants.

Forecasting the Mexican craft beer market: an integrated consumer perception conjoint analysis approach incorporating price and product attributes

PurposeThis paper aims to uncover patterns of Mexican craft beer consumers and guide companies’ decisions in the creation of new products, marketing strategies, advertising and promotion to increase craft beer sales and contribute to faster growth.Design/methodology/approachThis is a conjoint analysis with a selection of attributes for new or renewed products, marginal disposition to pay for particular characteristics through brand-specific choice-based design, and market simulation.FindingsThis paper clearly demonstrates consumers’ preferences and willingness to pay in Mexico, with a cutting-edge market research technique combining the prioritization of preferred craft beer characteristics, and the price consumers are willing to pay for such product characteristics.Research limitations/implicationsThe study's sample size of 501 responses is relatively small compared to the total number of craft beer consumers in Mexico. To enhance the validity and reliability of the findings, future studies should aim to obtain larger samples and compare their results with those of this study.Practical implicationsThis study has important implications for craft beer producers, allowing them to develop targeted craft beers with appealing attributes for Mexican consumers, such as color, aroma intensity, alcohol degree intensity, bitterness, foam level and price.Social implicationsThis study's market forecasting simulation technique is based on assumptions of consumer behavior and market dynamics. Although relevant variables were considered, unanticipated external factors or market changes could impact the forecasts' accuracy. This will allow for a more comprehensive understanding of craft beer consumer preferences in different markets and enhance the reliability of forecasting techniques.Originality/valueThis paper informs craft beer producers by providing valuable knowledge on customers’ preferences and willingness to pay to enhance craft beer companies’ product development processes.

Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study.

Recent advances in the field of machine learning have yielded novel research perspectives in behavioural economics and financial markets microstructure studies. In this paper we study the impact of individual trader leaning characteristics on markets using a stock market simulator designed with a multi-agent architecture. Each agent, representing an autonomous investor, trades stocks through reinforcement learning, using a centralized double-auction limit order book. This approach allows us to study the impact of individual trader traits on the whole stock market at the mesoscale in a bottom-up approach. We chose to test three trader trait aspects: agent learning rate increases, herding behaviour and random trading. As hypothesized, we find that larger learning rates significantly increase the number of crashes. We also find that herding behaviour undermines market stability, while random trading tends to preserve it.

모의 주식 투자 프로젝트의 학습 성과 분해

The disaggregation and detailed examination of the learning processes’ data that form the basis and the application of the learners’ knowledge allows to obtain additional valuableinformation that would, otherwise, have been overlooked when simply analyzing the aggregated data. This study analyzes the stock and the flow of the economic and financial knowledge among 481 college students who engaged in a stock market simulation project, and further disaggregates the multiple-choice data into four learning types, based on the pattern of pre-test and post-test responses: positive learning, retained learning, zero learning, and negative learning. The difference between pre- and post-test scores is mostly contributed by positive learning, but negative learning is also present. Likewise, the post-test score can be split into retained learning and positive learning components: the larger the contribution from retained learning, the smaller the contribution from positive learning. The regression results show that males, nonfreshmen, and students with experience in stock investing have more prior economic and financial understanding of the stock market. However, after engaging in the stock market simulation project, the differences in the scores mostly disappear due to knowledge catch-up. Gender, academic year, stock investment experience, reading economic articles, and the desire to engage in real stock investments are identified as factors influencing the stock, flow, and learning types of the economic and financial knowledge. Recognizing the categories that reflect zero learning and their content elements, and then incorporating those findings into the development and modification of the economic and financial education programs can further enhance the educational outcomes.

Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context

Financial simulators play an important role in enhancing forecasting accuracy, managing risks, and fostering strategic financial decision-making. Despite the development of financial market simulation methodologies, existing frameworks often struggle with adapting to specialized simulation context. We pinpoint the challenges as i) current financial datasets do not contain context labels; ii) current techniques are not designed to generate financial data with context as control, which demands greater precision compared to other modalities; iii) the inherent difficulties in generating context-aligned, high-fidelity data given the non-stationary, noisy nature of financial data. To address these challenges, our contributions are: i) we proposed the Contextual Market Dataset with market dynamics, stock ticker, and history state as context, leveraging a market dynamics modeling method that combines linear regression and clustering to extract market dynamics; ii) we present Market-GAN, a novel architecture incorporating a Generative Adversarial Networks (GAN) for the controllable generation with context, an autoencoder for learning low-dimension features, and supervisors for knowledge transfer; iii) we introduce a two-stage training scheme to ensure that Market-GAN captures the intrinsic market distribution with multiple objectives. In the pertaining stage, with the use of the autoencoder and supervisors, we prepare the generator with a better initialization for the adversarial training stage. We propose a set of holistic evaluation metrics that consider alignment, fidelity, data usability on downstream tasks, and market facts. We evaluate Market-GAN with the Dow Jones Industrial Average data from 2000 to 2023 and showcase superior performance in comparison to 4 state-of-the-art time-series generative models.

Enhancing Financial Literacy and Investment Skills through a Virtual Stock Trading Application

Currently, there are many real trading applications but there are not many simple stock market simulation applications where users can practice trading in the Indian stock market. We all know about the Stock Market and its losses. we also have heard stories about scams and huge losses in stocks from relatives, friends, and family, But not trying is not the solution to the problem, then what can we do about it? We can learn how it works, practice and then try it in the real market. Then our solution to this problem is to create an application where users can trade stocks with the demo (Virtual) money with real-time experience so that they can learn and afford to make mistakes and learn from those mistakes and make some good fortune in the real stock market and this initiative is focused on Indian audience for there are 58% of US citizens take part in trading activities while in India it is only 2% so by this initiative we think it can India to make more in the Sector of finance

Enhancing Financial Literacy and Investment Skills through a Virtual Stock Trading Application

Currently, there are many real trading applications but there are not many simple stock market simulation applications where users can practice trading in the Indian stock market. We all know about the Stock Market and its losses. we also have heard stories about scams and huge losses in stocks from relatives, friends, and family, But not trying is not the solution to the problem, then what can we do about it? We can learn how it works, practice and then try it in the real market. Then our solution to this problem is to create an application where users can trade stocks with the demo (Virtual) money with real-time experience so that they can learn and afford to make mistakes and learn from those mistakes and make some good fortune in the real stock market and this initiative is focused on Indian audience for there are 58% of US citizens take part in trading activities while in India it is only 2% so by this initiative we think it can India to make more in the Sector of finance.

Enhancing Financial Literacy and Investment Skills through a Virtual Stock Trading Application

Currently, there are many real trading applications but there are not many simple stock market simulation applications where users can practice trading in the Indian stock market. We all know about the Stock Market and its losses. we also have heard stories about scams and huge losses in stocks from relatives, friends, and family, But not trying is not the solution to the problem, then what can we do about it? We can learn how it works, practice and then try it in the real market. Then our solution to this problem is to create an application where users can trade stocks with the demo (Virtual) money with real-time experience so that they can learn and afford to make mistakes and learn from those mistakes and make some good fortune in the real stock market and this initiative is focused on Indian audience for there are 58% of US citizens take part in trading activities while in India it is only 2% so by this initiative we think it can India to make more in the Sector of finance. Key Words: Virtual stock trading, Stock market simulation, Financial literacy, Investment education, Online trading platforms, Stock market knowledge, Risk management, Investment skills, User experience, Financial education, Stock trading simulation, Virtual investing, Educational technology, Portfolio management.

Reduction of a Real Power System: Turkish Transmission System Case

This article introduces an enhanced Radial, Equivalent, Independent (REI) model for power system reduction, aiming to enhance computational efficiency while preserving accuracy. Unlike the conventional REI method, which divides the system into two regions, the proposed model partitions the grid into more suitable regions, maintaining regional control centers for a more effective reduction process. The model significantly decreases the number of busbars, transmission lines, generators, and load models while retaining the overall geographical structure of the grid. An 8-step application process is outlined for implementing this approach. Validation on a 48-bus power system and the IEEE 39-bus system demonstrates its efficiency. Application on the Turkish transmission system results in a 57% reduction in busbars (from 162 to 70) and a 49% reduction in transmission lines, simplifying the model while achieving an impressive average active load flow error of only 3.51%. The study concludes that the extended REI model offers improved computational efficiency without compromising accuracy, making it promising for applications like network planning, market simulations, and capacity management.