We introduce a method to estimate the failure time of a class of weighted k-out-of-n systems using the idea of rational expectations, which to the best of our knowledge is a new approach, not found elsewhere in the existing literature. This paper explores the predictive power of several statistical indicators (variance, skewness, kurtosis, Gini coefficient, entropy) and shows how they perform differently as the system approaches global failure. The proposed method is shown to outperform a benchmark prediction model obtained without rational expectations, and our results offer a panoramic view of the predictive power of the statistical indicators under different assumptions about the initial weight distributions.

This study aimed to connect the behavioral corporate finance perspective (micro level) with complexity theory via agent-based modeling to analyze the impact of selected psychological factors of chief executive officers (CEOs) on stock market volatility (macro level). Specifically, we wanted to explore whether Polish CEOs’ subjective well-being (SWB) influenced their managerial decisions during the COVID-19 pandemic and how it might be related to the volatility of stock prices during this critical period in Poland. Our study was based on a survey of Polish CEOs who managed companies listed on the Warsaw Stock Exchange. In particular, 255 CEOs completed the Satisfaction with Life Scale, the Positive and Negative Affect Scale, and a business survey on the impact of the COVID-19 pandemic on company management. Using the results of this survey, we built an agent-based model to investigate how CEOs’ decision-making, stemming from their SWB levels, influences the perception of prices by individual traders and, in turn, how it is translated into aggregate stock market volatility. The results indicate the pathways through which the microscopic-level SWB of CEOs influences market price formation at a macroscopic level. The findings obtained from our model may shed new light on the rational expectations theory applied to stock market volatility during the financial crisis.

This paper develops a model for predicting the failure time of a wide class of weighted k-out-of-n reliability systems. To this aim, we adopt a rational expectation-type approach by artificially creating an information set based on the observation of a collection of systems of the same class–the catalog. Specifically, we state the connection between a synthetic statistical measure of the survived components’ weights and the failure time of the systems. In detail, we follow the evolution of the systems in the catalog from the starting point to their failure–obtained after the failure of some of their components. Then, we store the couples given by the measure of the survived components and the failure time. Finally, we employ such couples for having a prediction of the failure times of a set of new systems–the in-vivo systems–conditioned on the specific values of the considered statistical measure. We test different statistical measures for predicting the failure time of the in-vivo systems. As a result, we give insights on the statistical measure which is more effective in contributing to providing a reliable estimation of the systems’ failure time. A discussion on the initial distribution of the weights is also carried out.

Here we propose and study a new model of innovation adoption, IA. Our hypothesis is that individuals’ decisions regarding the purchase of a new product, are driven by the perceived type of adoption trend. Our assumptions split adopters into four groups, Innovators, Early Adopters, Majority, and Laggards, based on their innovativeness, and assign particular preferences for various adoption trends based on their psychological profile. We have built several mathematical models to test our hypothesis and generated forecasts for retail sales of products sold in a supermarket chain in Poland. The performance in sales forecasting of our IA model, points to evidence of customers’ behavior as described by our hypotheses, and the usefulness in quantifying psychological behavior in a general social context of innovation.

It is hard to overstate the importance that the concept of symmetry has had in every field of physics, a fact alluded to by the Nobel Prize winner P.W. Anderson, who once wrote that “physics is the study of symmetry”. Whereas the idea of symmetry is widely used in science in general, very few (if not almost no) applications has found its way into the field of finance. Still, the phenomenon appears relevant in terms of for example the symmetry of strategies that can happen in the decision making to buy or sell financial shares. Game theory is therefore one obvious avenue where to look for symmetry, but as will be shown, also technical analysis and long term economic growth could be phenomena which show the hallmark of a symmetry.

Just like soldiers crossing a bridge in sync can lead to a catastrophic failure, we show via experiments, theory, and simulations, how synchronization in human decision making can lead to extreme outcomes. Individual decision making and risk taking are well known to be gender dependent. Much less is however understood about gender's impact on the creation of collective risk through aggregate decision making, where the decision of one individual can affect the decision making of other individuals, eventually leading to synchronization in behavior. To study the formation of collective risk created due to synchronization in human decision making, we have devised a series of experiments that can be analyzed and understood within a game theoretical framework. In the experiments each individual in groups of either men or women decide to buy or sell a financial asset based on an information set containing past price behavior. Risk can be generated collectively through coordination in the aggregate decision making, which leads to a price formation far from the fundamental value of the asset. Here we show how collective risks can be generated in groups of both genders, but the pathway to formation of collective risks happens through an individual risk taking which are different for groups composed of men respectively women. A priori we find that it is impossible to know whether a given group will engage in the formation of collective risk, but via a fluctuation based game theoretical framework we are able to estimate the likelihood that it will happen. Our results highlight some of the foundations for creation of excessive collective risks relevant for example in the understanding of financial systemic risks.

We introduce a new methodology that enables detection of the onset of convergence towards Nash equilibria in simple repeated games with infinitely large strategy spaces, thereby revealing the heuristics used in decision-making. The method works by constraining on a special finite subset of strategies, called decoupled strategies. We show how the technique can be applied to understand price formation in financial market experiments by introducing a predictive measure ΔD: the different between positive decoupled strategies (recommending to buy) and negative decoupled strategies (recommending to sell). Using ΔD we illustrate how the method can predict (at certain special times) participants' actions with a high success rate in a series of experiments.

We introduce a non-linear pricing model of individual stock returns that defines a ”stickiness” parameter of the returns. The pricing model resembles the capital asset pricing model (CAPM) used in finance but has a non-linear component inspired from models of earth quake tectonic plate movements. The link to tectonic plate movements happens, since price movements of a given stock index is seen adding ”stress” to its components of individual stock returns, in order to follow the index. How closely individual stocks follow the index’s price movements, can then be used to define their ”stickiness”.

Over the past decades, complexity has emerged as an alternative way to understand and model complex dynamical phenomena in a number of fields. Instead of focusing on local and independent behaviors, complexity suggests that observed phenomena are the results of complex interactions between components of a system. The DySES conference, which took place at the Sorbonne in 2018, gave the unique opportunity to present the state of the art in the complexity field and in many areas including the socioeconomic ones. This special issue of Soft Computing presents papers in finance both in the field of complexity and beyond it. Many topics are covered. Jay et al. focus on portfolio management and introduce robust covariance matrices using recent developments in the random matrix theory (RMT). Levantesi et al. propose a novel approach, combining the random forest and the 2D P-spline in order to provide a more accurate mortality rate forecasting, therefore extending the Lee–Carter model. Chorro et al. focus on option pricing. They start from the inverse Gaussian GARCH model and build a new pricing kernel. Cerqueti et al. work on complex networks and introduce new influence measures based on vertex centrality. They apply their measure to the SP100 universe. Still within the complexity field, Gatfaoui et al. introduce a test of non-chaoticity when data are noisy. They show that financial data do not have a chaotic behavior. Fiori et al. build a model to capture systemic risk. In particular, they set the focus on the inequalities of wealth and income, using the well-known Gini coefficient. Di Paolo tackles the problem of the increase in life expectancy with regard to pension funds and annuity providers. The concept of observed survival probabilities is extensively used. Within a related area, D’Amato et al. propose a de-risking strategy model for LTC insurers that face demographic changes implying disability risks. They suggest new methodologies. Baione et al. consider non-life risk premium and study the diversification effect. They use quantile regressions to estimate the individual conditional loss. Kaucic et al. study investment strategies known as enhanced indexing. They introduce an improved version of the particle swarm optimization algorithm (PSO) and present an example using the Euro Stoxx. At last, Kudlak et al. question the importance of dedicated local budgets concerning crisis management. As guest editors, we would like to thank Soft Computing for giving us the opportunity to have this special issue done on all these topics.

This paper investigates the relations between multiple measures of investor sentiment and the returns, volatility, trading volume, and liquidity. Using both data outside and inside market, we find that the Bullishness from socio-finance model are significant related to future realized volatility and trading volume, similar to Tweet, which is thought to capture information of well-informed investors in Bitcoin market.

This paper proposes a stochastic model for describing rational expectations. The context is systemic risk, with interconnected components of a unified system. The evolution dynamics leading to the failure of the system is explored either under a theoretical point of view as well as through an extensive scenario analysis.

This chapter argues for the use of game theory or agent-based modeling to go beyond the standard methods used in traditional approaches to finance. The theory of rational expectations is at the core of most theories of finance in use since the 1970s, but it is also very unrealistic. This chapter first introduces some very general thoughts about elements needed in a new framework for finance. Then a few concrete examples of heterogeneous agent-based models will be introduced, and several of their main results will be discussed. Finally, applications and methods to real-market data will be introduced, notably the idea of “decoupling” to explain the short-lived synchronization of investors.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Dynamics of Socio-Economic systems: attractors, rationality and meaning Andrzej Nowak, Jørgen Vitting Andersen, Wojciech Borkowski

We introduce a new methodology that enables the detection of onset of convergence towards Nash equilibria, in simple repeated-games with infinite large strategy spaces. The method works by constraining on a special and finite subset of strategies. We illustrate how the method can predict (in special time periods) with a high success rate the action of participants in a series of experiments.

We introduce tools to capture the dynamics of three different pathways, in which the synchronization of human decision-making could lead to turbulent periods and contagion phenomena in financial markets. The first pathway is caused when stock market indices, seen as a set of coupled integrate-and-fire oscillators, synchronize in frequency. The integrate-and-fire dynamics happens due to change blindness, a trait in human decision-making where people have the tendency to ignore small changes, but take action when a large change happens. The second pathway happens due to feedback mechanisms between market performance and the use of certain (decoupled) trading strategies. The third pathway occurs through the effects of communication and its impact on human decision-making. A model is introduced in which financial market performance has an impact on decision-making through communication between people. Conversely, the sentiment created via communication has an impact on financial market performance. The methodologies used are: agent based modeling, models of integrate-and-fire oscillators, and communication models of human decision-making

We introduce a new methodology that enables the detection onset of convergence towards Nash equilibria, in simple market games with infinite larges strategy spaces. The method works by constraining on a special and finite subset of strategies. We illustrate how the method can be used to … in a series of experiments.

The complexity of scenarios which could lead to the onset of financial market instability seems to demand new tools, in particular concerning the role of human decision-making during crises. Here, we present agent-based models that could provide new insights into the way periods of market turmoil unfold. We illustrate the method through a well-controlled set up in a series of experiments. We are thereby able to (1) validate the impact of model parameters and test their relevance by predicting the average outcome of an experiment and (2) consider each individual experiment and predict outcomes through a scenario analysis. These illustrations should show the appeal of the method in applications to real market situations.