Behavior Of Market Participants Research Articles

Behind the bubble: Exploring the motivations of NFT buyers

Non-fungible tokens (NFTs) represent a multibillion-dollar global market. While considerable speculation exists about the future utility of NFTs, there has been limited research into the consumer behaviors of market participants. This research paper examines the motivations of NFT buyers through the lens of self-determination theory. Using a sample of 482 participants, the authors tested a conceptual framework to better understand both NFT buyers' intrinsic and extrinsic motivations. This study expands the literature on NFTs in three different ways: i) it is the first study, as far as we know, to focus exclusively on NFT buyers and their motivations in purchasing NFTs; ii) it explores a variety of potential motivations theorized in the literature; and iii) it tests the expected future value of NFTs as both a motivation and as a moderator for NFT buyers. The authors determined that intrinsic motivation had the most substantial effect on purchase intention, and the expectations of NFTs’ future value positively moderated the effect of amotivation on purchase intention. In contrast, high expectations of future value moderated the effect of external regulation on purchase intention. The results suggest that NFT buyers are not as impacted by potential social or monetary gain as often characterized in the academic literature but behave more like traditional buyers of luxury goods.

A NEW METHOD FOR DETERMINING THE COMPETITIVE POSITION OF A LIFE INSURANCE COMPANY IN THE INSURANCE SERVICES MARKET

A NEW METHOD FOR DETERMINING THE COMPETITIVE POSITION OF A LIFE INSURANCE COMPANY IN THE INSURANCE SERVICES MARKET

Effectiveness of deterministic option pricing models: new evidence from Nifty and Bank Nifty Index options

AbstractThis research delves into the empirical performance of deterministic option pricing models in the dynamic financial landscape of India. The primary focus is on uncovering pricing discrepancies and discerning whether these disparities arise from inherent limitations in the theoretical foundations of the models or are influenced by the trading behaviors of market participants. The investigation centers on the analysis of call and put option contracts for the Nifty Index and Bank Nifty Index, both extensively traded on the National Stock Exchange (NSE) of India. The study’s findings highlight that models developed to address the theoretical constraints of the benchmark Black–Scholes model demonstrate noteworthy performance. However, the complexity of these models does not consistently translate into enhanced pricing efficiency. Notably, the Black–Scholes and Practitioner Black–Scholes models exhibit superior performance across various moneyness-maturity categories. Furthermore, the research underscores the substantial impact of option contract liquidity on the efficiency of the pricing models. Specifically, highly traded at-the-money and out-of-the-money option contracts exhibit a higher level of pricing accuracy.

Поведінка фінансових ринків в експериментальному економічному аналізі

Behavioral aspects play an important role, and an experimental analysis of financial markets allows studying the psychological and behavioral factors that are affecting participants. The aim of the article is to analyze the interaction of financial market participants in a controlled experimental environment in order to identify and reveal the peculiarities of their behavior, strategies and mutual influence. The information base of the research is official information and scientific publications. Research was conducted using the following methods: monographic; analysis, synthesis, induction, deduction; system analysis. Systematic changes in consumer demand, production and other spheres of economy determine not only household habits, but also affect decision-making by participants in economic relations. The identified calendar anomalies indicate that economic entities react to time factors and seasonal changes, which is important for management and forecasting strategies. Price bubbles are an expression not only of objective market conditions, but also of psychological and socio-cultural influences on the behavior of market participants. Understanding and predicting price bubbles requires a comprehensive approach that combines economic, psychological and social aspects. This will allow more accurate analysis and management of risks associated with price fluctuations and their possible consequences for economic stability. As part of the experimental economic analysis, it is found that calendar and seasonal effects play a significant role in the formation of economic trends. The study of the phenomenon of price bubbles and adaptive expectations in experimental economic analysis reveals deep structures of market dynamics.

DECODING COLLUSION: AN ANALYSIS OF COOPERATIVE BEHAVIOR UNDER DIFFERENT PRICING SCHEMES

This research delves into the intricate dynamics of collusion behavior within markets, exploring cooperative interactions among entities under various pricing schemes. Titled "Decoding Collusion," the study employs game theory and econometric analyses to investigate how different pricing mechanisms influence collusion tendencies. By scrutinizing the behaviors of market participants across diverse pricing structures, the research aims to unravel the complexities of cooperative strategies and shed light on the factors influencing collusion in economic environments.

Thermodynamic Analysis of Financial Markets: Measuring Order Book Dynamics with Temperature and Entropy.

This study bridges finance and physics by applying thermodynamic concepts to model the limit order book (LOB) with high-frequency trading data on the Bitcoin spot. We derive the measures of Market Temperature and Market Entropy from the kinetic and potential energies in the LOB to provide a deeper understanding of order activities and market participant behavior. Market Temperature emerges as a robust indicator of market liquidity, correlating with liquidity measures such as Active Quote Volume, bid-ask spread and match volume. Market Entropy, on the other hand, quantifies the degree of disorder or randomness in the LOB, providing insights into the instantaneous volatility of price in the high-frequency trading market. Our empirical findings not only broaden the theoretical framework of econophysics but also enhance comprehensive understanding of the market microstructure and order book dynamics.

The 2008 Financial Crisis: Through the Lens of Phenomenology, Narratology, Metaphorology Methodology

Modern financial theories often fail to predict black swan events in the financial market because these financial theories are built on market participants’ typical behaviours in the last few decades. These theories have not captured the behaviour of market participants in black swan events. In the context of this study, qualitative research such as phenomenology, narratology and metaphorology can be deployed to study randomly from the perspective of uncertainty, risk and chaotic behaviour. These research methodologies remove the numerical assumptions used by the quantitative financial model but lighten the participants’ real experience, who witnessed the crisis very carefully. Thereby, the behaviour of market participants can be captured and examined. The employment of these qualitative research methodologies, along with the pure financial theories, will be more efficient to predict these events, which allows stakeholders to take necessary steps to avert this kind of crisis.

Finding individual strategies for storage units in electricity market models using deep reinforcement learning

Modeling energy storage units realistically is challenging as their decision-making is not governed by a marginal cost pricing strategy but relies on expected electricity prices. Existing electricity market models often use centralized rule-based bidding or global optimization approaches, which may not accurately capture the competitive behavior of market participants. To address this issue, we present a novel method using multi-agent deep reinforcement learning to model individual strategies in electricity market models. We demonstrate the practical applicability of our approach using a detailed model of the German wholesale electricity market with a complete fleet of pumped hydro energy storage units represented as learning agents. We compare the results to widely used modeling approaches and demonstrate that the proposed method performs well and can accurately represent the competitive behavior of market participants. To understand the benefits of using reinforcement learning, we analyze overall profits, aggregated dispatch, and individual behavior of energy storage units. The proposed method can improve the accuracy and realism of electricity market modeling and help policymakers make informed decisions for future market designs and policies.

Regulation Design in Insurance Markets

Regulators often impose rules that constrain the behavior of market participants. We study the design of regulatory policy in an insurance market as a delegation problem. A regulator restricts the menus of contracts an informed firm is permitted to offer, the firm offers a permitted menu to each consumer, and consumers choose contracts from offered menus. If consumer types and firm signals are ordered in a way that reflects coverage need, the regulator can leverage the firm’s information by forcing the firm to offer specified additional options on each menu. Several extensions illustrate the practical application of our results. (JEL D21, D43, D82, D86, G22, G28, L51)

An optimization-based partial marginal pricing method to reduce excessive consumer payment in electricity markets

Among the pricing schemes, locational marginal pricing is the most widely adopted scheme in current electricity markets. Despite the nice properties, theoretical study and practical experience have shown that considering the strategic bidding behaviors of market participants, locational marginal pricing may result in excessive consumer payment and prejudices market efficiency. In this paper, we propose an optimization-based partial marginal pricing method to reduce consumer payment under strategic bidding. The advantages of the locational marginal pricing method are reserved to the best extent while the remaining problems are delicately handled. To achieve this, we first analyze the characteristics and problems of the locational marginal pricing scheme and present a partial marginal pricing structure. Second, we establish an optimization-based partial marginal pricing model, in which discriminatory price components are introduced in price formulation and the pricing properties including competitive equilibrium and revenue adequacy are formulated as constraints. Third, to verify the performance of the proposed pricing scheme considering the strategic bidding, a bi-level strategic bidding problem of the generator under the proposed pricing method is established and transformed into a mixed integer linear programming problem. The numerical results indicate that under the conventional pricing methods, the consumer payments under strategic bidding can be up to 16.67 times higher than those under truthful bidding, while the proposed method can effectively reduce excessive consumer payments by 92.1% while maintaining the desired pricing properties.

The coupling effect of carbon emission trading and tradable green certificates under electricity marketization in China

To accelerate the low-carbon transition in the energy sector, China is gradually promoting the construction of the national electricity market and carbon market. Carbon emission trading (CET) and tradable green certificates (TGCs) are advocated as promising tools to control carbon emissions. However, the efficacy of these concurrent low carbon regulations remains an open question, especially considering that their implementation schemes are quite different. Therefore, herein, the coupling effect of carbon emission trading and tradable green certificates is examined under electricity marketization in China. An interactive system dynamics (SD) model is established to capture the internal linkages among CET, TGCs and electricity market. Different markets are connected through the investment behaviour of market participants and the price formation mechanism, and the dynamic equilibrium process of multiple markets is analysed based on the current electricity marketization in China. The simulation results based on real-world data in China show that the coordination of TGCs and CET alleviates the pressure of the fiscal deficit of renewable energy and promotes the low-carbon transition in the energy sector, and relevant policy recommendations are put forward. Hopefully, our work can provide a useful reference and insight for low-carbon policy design and energy market construction.

The Effect of Price Anchoring on the Housing Market Based on Studies of Local Markets in Poland

Abstract The article attempts to explain market levels of housing prices by supplementing the set of typical objective explanatory variables with variables of behavioral background. The proposed explanatory variables reflect the anchoring effect of prices, understood as the acceptance by market participants of such price levels that are justified not only in terms of socio-economic factors, but also in levels entrenched in their minds. The purpose of the study is to show that the anchoring effect identified through behavioral economics can be generalized and applied to the market behavior of many market participants, and thus explain the weak correspondence between listed housing prices and their objective factors. The study covers 17 local real estate markets in Poland and employs econometric models built under slightly modified procedures of backward stepwise regression.

Mean-Field Multi-Agent Reinforcement Learning for Peer-to-Peer Multi-Energy Trading

With increasing numbers of prosumers employed with multi-energy systems (MES) towards higher energy utilization efficiency, an advanced energy management scheme is becoming increasingly important. The incorporation of MES into the existential energy market holds promise for future power systems. The continuous double auction (CDA) market, in a decentralized manner, makes it ideal for enabling peer-to-peer (P2P) energy trading due to its high transparency and efficiency. However, the CDA market is difficult to model when considering the highly stochastic and dynamic behaviors of market participants. For this reason, we formulate this task as a Decentralized Partially Observed Markov Decision Process and propose a novel multi-agent reinforcement learning method that allows each prosumer agent to stabilize the training performance with mean-field approximation and also to maintain the scalability and privacy with market public information. Case studies constructed on a real-world scenario of 100 prosumers show that our method captures the economic benefits of the P2P energy trading paradigm without violating the prosumers' privacy, and outperforms the state-of-the-art methods in terms of policy performance, scalability, and computational performance.

Правовое обеспечение рационального использования энергоресурсов

The applicable laws place the highest value on the rational use of energy resources in the sphere of energy saving and energy efficiency in the Russian economic development. However, there remains legal uncertainty in terms of direct levers of the promising and rational use of energy resources. The legislator maintains “qualified silence” in this matter, the judicial practice is contradictory and ambiguous in the issues of acknowledgment of irrational behavior of energy market participants, there is no standard of proof. Along with these stagnation processes in legal support of the rational use of energy resources, the legislator piles up new constituent elements of administrative offenses and criminally punishable acts in the energy security sphere. Such instruments fail to deliver the desired results in form of the establishment of the rational use of non-renewable energy resources. Renewable energy resources in the context of legal support of the rational use are out of the question as there is no respective framework law. That’s why the author concludes that it is needed to establish a legal model of the rational and safe use of energy resources (legal compliance).

Supervision of the Bank of Russia over unfair practices in the field of consumer protection of financial services

The article is an analysis of the work of the Bank of Russia in the field of determining unfair behavior patterns in the financial market. The author examines the positions of the Bank of Russia and researchers on the identified signs of unfair actions in the behavior of financial market entities, and analyzes them. The article also examines the existing problems in this area and suggests ways to solve them. The research is relevant and can be useful for professionals in the field of financial regulation and for anyone interested in the security and stability of the financial market. In the article "Definition of unfair practices in the framework of the supervision of the Bank of Russia over the behavior of financial market entities." an analysis of opinions on the establishment of signs of unfair activity in the financial market was also carried out. The scientific novelty of the article lies in the fact that the authors conduct research and analysis of existing practices and scientific opinions on the signs of unfair practices that violate the rights of consumers of financial services. They pay attention to the problems associated with unfair practices. The authors propose a definition of unfair practices in the financial market. The authors analyze the signs of unfair behavior of financial market participants and offer new ideas and recommendations for effective detection and prevention of unfair practices. Their research contributes to the development of the field of consumer protection of financial services and can become the basis for further research and practical measures in this area.

The response of money market funds to the COVID-19 pandemic

The Covid-19 Pandemic has increased the attention paid to money market funds. Using Covid-19 cases and a measure of lockdowns, shutdowns, etc., we analyze if money market fund investors and managers responded to the intensity of the pandemic. We ask whether or not the Federal Reserve implementation of the Money Market Mutual Fund Liquidity Facility (MMLF) had an effect on market participant behavior. We find that institutional prime investors responded significantly to the MMLF. Fund managers responded to the intensity of the pandemic but largely ignored the reduction in uncertainty created by the implementation of the MMLF.

Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems

This paper explores the application of statistical inference using entropy to characterize the transfer of data across financial systems. The transfer of data between financial systems is a critical process that affects the accuracy and reliability of financial information. In this study, we propose a new approach to quantify the amount of information transferred between two financial systems based on entropy measures. Specifically, we use Transfer entropy to measure the information content of data transferred between financial systems. The Kullback-Leibler separation of conditional probabilities is a design statistic known as Transfer entropy. This method enables the determination, measurement, and testing of information transmission without being limited to linear movements. We demonstrate the effectiveness of our approach by analyzing using this empirical technique, the importance of the borrowing exchange industry in comparison to the exchanges for debt securities. In addition to this, this paper investigates the greater connectivity that exists among operational risks, as represented, respectively, by iTraxx and VIX Europe. We show that our entropy-based approach can detect changes in the information content of data transferred between the two exchanges, which can be indicative of changes in market conditions or the behavior of market participants. Overall, this study highlights the potential of entropy-based statistical inference methods for characterizing the transfer of data across financial systems. This approach has the potential to provide valuable insights into the behavior of financial markets and the efficiency of financial systems, which can inform policy decisions and improve the accuracy and reliability of financial information.

Improving energy system design with optimization models by quantifying the economic granularity gap: The case of prosumer self-consumption in Germany

Energy system models are widely used to inform the political decisions required to successfully mitigate climate change in the energy sector. The energy system optimization models (ESOMs) used to identify cost-minimal transformation pathways assume the perfect behavior of market participants from a central planner’s perspective. Neglecting the decision-making under uncertainties or biased perceptions and attitudes leads to inaccurate assumptions regarding the requirements of a successful energy transition. In particular, ESOMs underestimate the required capacities for power generation, storage, and transmission compared with real-world energy systems, a phenomenon known as the “economic granularity gap”. Agent-based models (ABMs) are helpful tools for capturing the behavior of market actors. Hence, attempts have been made to identify and alleviate this phenomenon through the coupling of ESOMs and ABMs. In this paper, we propose an automated workflow for such model coupling and quantify the economic granularity gap for the case of photovoltaic-prosumer self-consumption. Our results show that the current business models and regulatory frameworks affecting prosumer self-consumption patterns require the adaptation of cost-minimal energy system designs. However, if correctly implemented, instruments such as dynamic tariffs could narrow the economic granularity gap, shifting real-world energy systems closer to their ideal counterparts.

КОНЦЕПЦІЇ КОНКУРЕНЦІЇ ЯК КЛЮЧОВА СКЛАДОВА НАУКОВОГО ЗАБЕЗПЕЧЕННЯ ФОРМУВАННЯ КОНКУРЕНТОСПРОМОЖНОСТІ ПРОДУКЦІЇ ТВАРИННИЦТВА

The scientific article has proved the importance of researching the concepts of competition within the framework of ensuring the formation of the competitiveness of Ukrainian livestock and Ukrainian livestock products. Five basic concepts of competition have been defined: “Contest as a static state,” “Competition as a process of rivalry,” “Porter’s diamond,” RAM, and IIT. The following basic concepts of competition have been theoretically characterized by a set of features, such as levels of using the concept (world, international, national, industry/regional, and individual enterprise/individual household); enterprises that can use the concept appropriately; behavior and activities of competitors; consumer behavior; the behavior of other market participants; key terms of using the concept (critical terms of the competition); a key criterion for the formation of competitiveness; possibilities of implementation into animal husbandry (macro – national level; meso – branch and regional level; micro – individual enterprises and households). In addition, the positive and negative aspects of using each of the five defined concepts of competition to form the competitiveness of Ukrainian livestock and Ukrainian livestock products have been determined. It has been proved that the concept of competition, “Competition as a static state,” can be used, with certain limitations, in animal husbandry at the macro-, meso-, and micro levels; “Competition as a process of rivalry” can be used in animal husbandry at the macro, meso, and micro levels; “Porter’s diamond” can be used in animal husbandry mainly at the meso level; RAM can be used in animal husbandry mainly at the micro level; and IIT can be used in animal husbandry mainly at the macro level. The limitation of the use of each of the researched concepts of competition in terms of the formation of the competitiveness of Ukrainian livestock farming and products of Ukrainian livestock breeding has also been noted. It requires a balanced, appropriate, and thorough use of concepts at the macro-, meso-, and micro-levels of the modern economic systems functioning.

Cooperative Stock Market Game

This study explores the game-theoretic model of the stock market, analyzing the behavior and strategic choices of market participants, and constructing a cooperative game model. The basic concepts of game theory are introduced, and the importance and practical application of game theory in the stock market are explained. A cooperative game-theoretic model is constructed, the characteristic function is established, and its superadditivity is proven. The model is analyzed, the Shapley value is derived. It is demonstrated that the Shapley value belongs to the core.