Initial Public Offering (IPO) Pricing Using a Multi-agent System

The study of IPO as a source of financial resources meets the current needs of the corporate sector in conditions of disintermediation and securitization trends of the financial market. In developed economies, there is a process of shifting the emphasis in the direction of capital attraction precisely on the stock market segment of the financial market, which has a number of advantages over the banking system that are also connected with the innovation and investment development of the national economy. The formation of the proper basis for the development of IPO market determines the timeliness of the consideration of this issue because it didn’t obtain a sufficient spread. The emergence of new instruments is the feature that characterizes the modern stage of the development of the Ukrainian and world economy. Their application is a condition for the further development and use of the potential opportunities for the improvement of economic systems. Enterprises are able to meet current market needs and challenges due to orientation to modern trends and timely reaction to the latest achievements. One of them is the IPO (initial public offering), which is the primary public offering of shares. Participants of the Ukrainian market face a number of obstacles during IPO on the domestic and foreign stock exchanges. It necessitates a comprehensive review of this issue for a further developing of this method of accumulating financial resources. The purpose of this article is to identify the IPO market as a system of interrelated components that form its essence, and on the basis of a generalization of the constraining factors of development – providing recommendations on the perspective lines of improving the IPO market, taking into consideration the peculiarities of the Ukrainian and world stock market. Methodology. Analytical market reviews, materials of periodicals, resources of the Internet are the informational and methodological basis of the investigation. To achieve the goal set, the following general scientific and special methods were used: economic and mathematical methods, structural-logical analysis, analogy, synthesis, comparison and integration, factor analysis. Results. The factors that should be taken into account by the issuer for the success of the IPO procedure are justified. Problematic aspects and directions of their elimination are determined for every concrete stage of the IPO. The model for the domestic companies to enter the IPO market is proposed that has a special significance in the context of increasing the volume of financing innovative activity. Directions for reforming the regulatory and legislative framework are considered. Practical implications. Directions for improving the process of passing through the IPO stages can be applied by issuers, taking into account the peculiarities of the domestic and world stock market. Value/originality. The biggest part of the scientific works is devoted to the theoretical aspects of IPO, clarifying the essence of this process, structuring its participants, the advantages of carrying out. At the same time, a part of the works is devoted to the consideration of IPO as an instrument for attracting financial resources by corporations. The authors reviewed IPO in a system that combines the above aspects with its procedure stages and propositions to cancel out constraining factors that have a place on each step of IPO. The presented integrated approach will allow expanding the use of the IPO in the financial market as a source of financial resources for the corporative sector. It is also important to consider the impact of the planned legislative reforms on the development of the IPO market in Ukraine. Taking into account all the components of the IPO market and existing features of the IPO within the framework of the national financial market and world stock exchanges will highlight directions of the IPO market spread.

A Study of Underpricing, Ownership and Liquidity of Initial Public Offers (IPO) and their Impact on Performance of IPO Stocks in Indian Financial Markets

Financial markets are complex systems consisting of entities interacting and evolving in an uncertain environment. Their modelling and simulation requires the use on the one hand of a suited technology that is multi-agent systems (MASs) to model the various actors of a market, and on the other hand the evolutionary game theory to formalise interactions and heterogeneous investment strategies. The goal of this paper is to model, simulate and analyse financial markets dynamics. For this purpose, we propose three market models (fundamentalist, strategic, conventionalist) summarising various facets of real market speculation depending on the information held and the price formation process chosen by the investors. Each model is built using a multi-agent system. Moreover, investors’ agents are modelled by classifier systems that are advanced structures to study their evolutionary and adaptive aspects.

An initial public offering (IPO) is basically a market funding event, with young firms signifying their rite of passage to the public capital market. The general public may have treated IPO investing like a lottery simply because they have a minute chance of explosive payoffs; yet historically, IPOs have underperformed the market in their early years. In this study, the authors find that over the last 90 years of U.S. stock market history, there is at best some weak evidence that significant IPO underperformance has existed only in the first two years after issuance. However, the authors show, for the first time, strong evidence that IPOs actually outperformed from the third year on. They postulate that the “reversal of IPOs’ fortune” is a result of private firms exercising a “going public” option while simultaneously creating a “going private” option.

One of the major power of a national economic activity is business activity. Any kind of business needs strong entrepreneur. In order to enlarge or expand its strong business, an entrepreneur shall be supported by a health financial asset. The financial necessity of a business can be provided from several sources, namely, conventional financing (banking or credit) and other sources including capital market financing. The capital market financing recently can be accessed only by big scale entrepreneur, i.e. through initial public offering. Prevailing legal and financial requirements (through Initial Public Offering) has made the access to obtain capital market financing is restricted for micro, small and middle scale entrepreneur in Indonesia. Accordingly, Otoritas Jasa Keuangan (Financial Services Authority) has created some programs to encourage such micro, small, and middle scale business to access the capital market financing. This article based on the research which used normative juridical approach that focuses on secondary data, such as regulations and literature. The purpose of this article is to describe the major legal issues of Initial Public Offering for micro, small, and middle scale business in Indonesia. Further, it will also describe the current legal barrier for such entrepreneur to conduct the Initial Public Offering.

Finance is concerned with how the savings of investors are allocated through financial markets and intermediaries to firms, which use them to fund their activities. Finance can be broadly divided into two fields. The first is asset pricing, which is concerned with the decisions of investors. The second is corporate finance, which is concerned with the decisions of firms. Game theory is an essential tool for describing the behavior of investors, financial intermediaries, and corporate managers.This chapter provides a broad survey of game-theoretic research bearing on financial decision making, beginning with an assessment of pre-game-theoretic financial models and results – including asset pricing models, market efficiency, and classic results in corporate finance. It goes on to consider game-theoretic models of corporate financial decisions under asymmetric information – signaling models, agency costs, intermediation, the market for corporate control, and initial public offerings. Taking a game-theoretic lens to asset pricing, market microstructure models are also reviewed. A final section assesses recent research concerning higher-order beliefs, informational cascades, and differences in beliefs not explained by differences in information.KeywordsAsset PriceAsymmetric InformationCapital StructureInitial Public OfferingCapital Asset Price ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The key technology concerning the dispatch and control of the cyber-physical-social systems (CPSS) integration and group machine learning (ML) based Web-of-Cells (WoC) is systematically investigated, aiming to develop an intelligent dispatching system that has a high penetration of distributed generations (DG). Based on practical engineering demands and the weakly-centralized WoC, which is characterized by self-organized co-evolution, high independence, high-efficiency synergy, and autonomous learning, a variety of advanced theoretical tools such as complex network theory, group ML, evolutionary game theory, and CPSS-based parallel system theory have been adopted to address the following key issue: How can we achieve overall optimal dispatching and control decision-making in a class of complex systems relying on a large number of group cells with characteristics of limited information, weak controllability, small capacity, and wide distribution? Starting from this, four basic scientific issues are discussed: 1) a modeling method for a self-organized coupled network of the CPSS integration-based WoC; 2) a stability analysis and stability control system of the self-organized evolution of the WoC; 3) a highly autonomous group intelligent decision (GID) method of an independent cell; 4) multi-cell synergetic evolutionary game and GID theory. Hence, an innovative breakthrough on the intersection of complex network game theory and group ML is expected to be obtained, contributing to the emergence of group knowledge in complex circumstances and a significant improvement in the level of GID. Lastly, based on the theoretical investigation in this paper, explorations on the development of the CPSS platform for the WoC as well as its engineering practice in application are conducted. Furthermore, the in-depth development of the WoC, as well as its anticipated technical challenges, are prospected and analyzed with the hope of applying it on practical smart distribution grid demonstration projects in the future.

Normative systems are a widely used framework to coordinate interdependent activities in multi-agent systems. Most research in this area has focused on how to compute normative systems that effectively accomplish a coordination task, as well as additional criteria such as synthesising norms that do not over-regulate a system, and the emergence of norms that remain stable over time. We introduce a framework for the synthesis of stable normative systems that are sufficient and necessary for coordination. Our approach is based on ideas from evolutionary game theory. We simulate multi-agent systems in which useful norms are more likely to prosper than useless norms. We empirically show the effectiveness of our approach in a simulated traffic domain.

Product Market Advertising and Initial Public Offerings: Theory and Empirical Evidence

The Long-Run Performance of REIT IPOs

PurposeThe purpose of this paper is to study a novel and direct measurement of investor sentiment index in the Tunisian stock market that overcomes the weaknesses of a well-known investor sentiment index by Baker and Wurgler (2006, 2007).Design/methodology/approachBased on the data of 43 firms of the Tunisian stock market index (Tunindex) over the period 2004–2016, the author constructs a monthly investor sentiment that reflects both the economic fundamentals and the investor sentiment components. Seven indirect indicators collected from investor sentiment literature and Tunisian stock exchange were analyzed. Specifically, after accounting to remove the sentiment component for macroeconomic factors, the author estimates each sentiment proxy with a number of controlling variables. The residual from the estimation is used to define the author’s measure of excessive investor sentiment. To determine the best timing of sentiment indicators, the author employs a factor sentiment series as the first principal component of these total seven sentiment proxies and their lags of a month. Furthermore, by capturing the highest saturations with the first factor analysis, the author regressed each selected indicator’s lead or one-month lag in a second linear principal component analysis to reach the author’s Tunisian market’s total sentiment index.FindingsThe results show that all employed indicators may reflect the investor sentiment on the Tunisian stock market. The findings also indicate significant evidence that the author’s sentiment index takes into consideration the political and economic events such as the Jasmine Revolution experienced by Tunisia during the period from January 2, 2004 to December 30, 2016. Moreover, investor sentiment index flow appears to be one leading mechanism for the performance of Tunindex.Originality/valueResults found have clearly shown that the author’s seven indirect indicators can reflect investor sentiment in the Tunisian context. The various sentiment proxies are bullish indicators of investor sentiment. Brown and Cliff (2004) argue that the higher bull/bear ratio, the more investor sentiment is bullish. An important value of price–earnings ratio implies that the level of investor confidence as for change in market is also important. Liquidity measured by trading volume, market turnover ratio and liquidity ratio reflects individual investor sentiment. Otherwise, it seems that investors only invest when they are optimistic and reduce market liquidity once they became pessimistic. The monthly response rate to initial public offerings (IPOs) represents a bullish sentiment indicator. Indeed, the more optimistic investors are, the higher the response rate to IPOs. Investor satisfaction also reflects investor sentiment. In other words, a high level of satisfaction translates an important level of optimism. In addition, the author also recognizes that the authors’ Tunisian sentiment index follow general trend of stock market prices and appears to be an important determinant of Tunindex returns during the period of study, from January, 2004 to December, 2016. The author suggests investor sentiment can help predict Tunindex returns, distinguishing between turbulent and tranquil periods in the financial market. The graphical illustration of monthly investor sentiment index shows that it captures extreme events such as the Tunisian revolution of January, 2011, also known as the Jasmine revolution which marked the start of the Arab Spring and the consequences of economic and political turmoil in Tunisia that have disrupted economic activity in the next few years. Like all research work, the current research paper has certain limitations. The choice of control variables allowing the author to separate sentiment component of that fundamental might be criticized. Moreover, there is no unanimous number of control variables but they are chosen according to data availability. The author also believes that one of the study’s weaknesses is that the author has not examined the impact of investor sentiment on the Tunisian stock market. For future interesting avenues of research, the author proposes, first, to study the effect of investor sentiment on financial asset returns and check, second, if sentiment factor constitutes an additional source of business risk valued by the marketplace.

Multi-agent learning is a crucial method to control or find solutions for systems, in which more than one entity needs to be adaptive. In today's interconnected world, such systems are ubiquitous in many domains, including auctions in economics, swarm robotics in computer science, and politics in social sciences. Multi-agent learning is inherently more complex than single-agent learning and has a relatively thin theoretical framework supporting it. Recently, multi-agent learning dynamics have been linked to evolutionary game theory, allowing the interpretation of learning as an evolution of competing policies in the mind of the learning agents. The dynamical system from evolutionary game theory that has been linked to Q-learning predicts the expected behavior of the learning agents. Closer analysis however allows for two interesting observations: the predicted behavior is not always the same as the actual behavior, and in case of deviation, the predicted behavior is more desirable. This discrepancy is elucidated in this article, and based on these new insights Frequency Adjusted Q- (FAQ-) learning is proposed. This variation of Q-learning perfectly adheres to the predictions of the evolutionary model for an arbitrarily large part of the policy space. In addition to the theoretical discussion, experiments in the three classes of two-agent two-action games illustrate the superiority of FAQ-learning.

Optimizing environmental regulation policies for digital transformation in energy enterprises in China: An evolutionary game theory approach

In order to research how to promote online shopping consumers’ application of after service, build an evolutionary game model of both consumers and e-stores. This paper introduces the variables of supervision and punishment, tries to introduce the smart contract as a powerful service guarantee, and analyzes the influence relationship of variables between the two players and their strategic choices. This paper analyzes the ESS of the system when the relationship among smart contract, revenue, supervision and punishment meets 8 different conditions. Finally, giving suggestions to optimize the after service in online shopping according to the results.

In the big data era, global business competition focuses on industrial chain coordination. The whole rural e-commerce industry chain, as an advanced system characterized by digital transformation, is experiencing rapid growth. This paper aims to explore the evolutionary mechanism of collaborative behavior in the digital transformation of platform enterprises and participating enterprises across the whole rural e-commerce industry chain. To achieve this, this paper combines prospect theory and evolutionary game theory, introduces the value function and decision weight of prospect theory, and constructs a two-party game model between platform enterprises and participating enterprises. Based on the demonstration of the impact of individual changes in major objective factors, such as the cooperative innovation benefit coefficient, as well as major behavioral characteristic factors, such as decision-makers’ risk attitude coefficients, on enterprises’ strategic choices, we further reveal the influence of the interaction of key factors on the evolutionary results through case simulations. The findings indicate that when the behavior characteristics of the players are introduced, the threshold interval of the cost–benefit ratio of the two sides to reach the optimal state of decision-making is obviously reduced. Under moderate risk attitudes and degrees of loss sensitivity, enhancing the resource absorption capacity of enterprises in the chain and reducing the potential risk loss of platform enterprises to alleviate the influence of subjective behavior characteristics on cooperation willingness are effective measures. Improving innovation ability is the key factor in alleviating the negative impact of uncertainty on the decision-making of both parties. This paper is one of the few studies to integrate prospect theory with evolutionary game analysis in examining the collaborative behaviors between platform enterprises and participating enterprises. Effective strategies are proposed to promote enterprises achieving synergy.