Network theory and behavioral finance in a heterogeneous market environment

This article addresses the stock market as a complex system. The complexity of the stock market arises from the structure of the environment, agent heterogeneity, interactions among agents, and interactions with market regulators. We develop the idea of a meta‐model, which is a model of models represented in an agent‐based model that allows us to investigate this type of market complexity. The novelty of this article is the incorporation of various complexities captured by network theoretical models or induced by investment behavior. The model considers agents heterogeneous in terms of their strategies and investment behavior. Four investment strategies are included in the model: zero‐intelligence, fundamental strategy, momentum (trend followers), and adaptive trading strategy using the artificial neural network algorithm. In terms of behavior, the agents can be risk averse or loss occupied with overconfidence or conservative biases. The agents may interact with each other by sharing market sentiments through a structured scale‐free network. The market regulator controls the market through various control tools such as the risk‐free rate and taxation. Parameters are calibrated to the S&P500. The calibration is implemented using a scatter search heuristic approach. The model is validated using various stylized facts of stock return patterns such as excess kurtosis, auto‐correlation, and ARCH effect phenomena. Analysis at the macro and micro level of the market was performed by measuring the sensitivity of volatility and market capital and investigating the wealth distributions of the agents. We found that volatility is more sensitive to the model parameters than to market capital, and thus, the level of volatility does not affect market capital. In addition, the findings suggest that the efficient market hypothesis holds at the macro level but not at the micro level. © 2016 Wiley Periodicals, Inc. Complexity 21: 530–554, 2016     

Free trial or no free trial: Optimal software product design with network effects

A common business strategy to promote product adoption in software industry is to provide a free trial version with limited functionalities of the commercial product to increase the installed user base. The increase of user base will lead to higher value of the software because of positive network effects. However, offering a free trial version may cannibalize some demand of the commercial software. This paper examines the tradeoff between network effects and the cannibalization effect, and aims to uncover the conditions under which firms should introduce the free trial product. We find that when network intensity is strong, it is more profitable for a software monopoly to offer free trial than to segment the market with two versions of different qualities. In addition, this paper solves the joint decision problem of finding the optimal quality for the firm’s free trial software and the optimal price of its commercial product.     

The Dynamic Interaction of Speculation and Diversification

A discrete time model of a financial market is developed, in which heterogeneous interacting groups of agents allocate their wealth between two risky assets and a riskless asset. In each period each group formulates its demand for the risky assets and the risk‐free asset according to myopic mean‐variance maximizazion. The market consists of two types of agents: fundamentalists, who hold an estimate of the fundamental values of the risky assets and whose demand for each asset is a function of the deviation of the current price from the fundamental, and chartists, a group basing their trading decisions on an analysis of past returns. The time evolution of the prices is modelled by assuming the existence of a market maker, who sets excess demand of each asset to zero at the end of each trading period by taking an offsetting long or short position, and who announces the next period prices as functions of the excess demand for each asset and with a view to long‐run market stability. The model is reduced to a seven‐dimensional nonlinear discrete‐time dynamical system, that describes the time evolution of prices and agents' beliefs about expected returns, variances and correlation. The unique steady state of the model is determined and the local asymptotic stability of the equilibrium is analysed, as a function of the key parameters that characterize agents' behaviour. In particular it is shown that when chartists update their expectations sufficiently fast, then the stability of the equilibrium is lost through a supercritical Neimark–Hopf bifurcation, and self‐sustained price fluctuations along an attracting limit cycle appear in one or both markets. Global analysis is also performed, by using numerical techniques, in order to understand the role played by the chartists' behaviour in the transition to a regime characterized by irregular oscillatory motion and coexistence of attractors. It is also shown how changes occurring in one market may affect the price dynamics of the alternative risky asset, as a consequence of the dynamic updating of agents' portfolios.     

A Hybrid Variational Model for the Analysis of Free‐Corner Effects in Layered Plates

A variational model for the assessment of free‐edge and free‐corner effects [1‐3] in thermally loaded rectangular cross‐ply laminate plates is presented. The physical layers of the plate are discretized by an arbitrary number of mathematical layers through the laminate thickness. A layerwise displacement field with unknown interface functions which depend on the inplane coordinates is formulated wherein an a priori assumed layerwise linear thickness interpolation scheme is employed. The application of the principle of minimum elastic potential yields a set of governing Euler‐Lagrange differential equations for the unknown inplane functions which for the special free‐corner problem in rectangular cross‐ply plates can be solved in a closed‐form analytical manner. Boundary conditions of traction free laminate edges are fulfilled in an average sense. Since the approach utilizes a discretization through the plate thickness yet allows for closed‐form solutions within the layer planes for all state variables it is appropriate to speak of a hybrid analysis approach. The presented method allows easy application, can be run on every standard personal computer and is in favourable agreement with comparative finite element calculations. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

A market approach to decentralized control of a manufacturing cell

Based on a fictitious market model, a decentralized approach is presented for the workstation scheduling in a CNC workshop. A multi-agent framework is proposed, where job agents and resource agents act as buyers and sellers of resource in the virtual market. With cost and benefit calculation of these agent activities, which reflects the state of the production environment, various, and often conflicting goals and interests influencing the scheduling process in practice can be balanced through a unified instrument offered by the markets. The paper first introduces a heuristic procedure that makes scheduling reservations in a periodic manner. A multi-agent framework is then introduced, in which job agents and resource agents seek appropriate job–workstation matches through bidding in the construction of the above periodic “micro-schedules”. A pricing policy is proposed for the price-directed coordination of agent activities in this. Simulation results demonstrate the feasibility of the proposed approach and give some insights on the effects of some decision making parameters. Future work will be focused on the designing of some more sophisticated coordination mechanism and its deployment.     

A discrete time model for software reliability with application to a flight control software

This work is motivated by a particular software reliability problem in a unit of flight control software developed by the Indian Space Research Organization (ISRO), in which the testing of the software is carried out in multiple batches, each consisting of several runs. As the errors are found during the runs within a batch, they are noted, but not debugged immediately; they are debugged only at the end of that particular batch of runs. In this work, we introduce a discrete time model suitable for this type of periodic debugging schedule and describe maximum likelihood estimation for the model parameters. This model is used to estimate the reliability of the software. We also develop a method to determine the additional number of error‐free test runs required for the estimated reliability to achieve a specific target with some high probability. We analyze the test data on the flight control software of ISRO. Copyright © 2011 John Wiley & Sons, Ltd.     

Mean‐Reverting Market Model: Speculative Opportunities and Non‐Arbitrage

The paper studies arbitrage opportunities and possible speculative opportunities for diffusion mean‐reverting market models. It is shown that the Novikov condition is satisfied for any time interval and for any set of parameters. It is non‐trivial because the appreciation rate has Gaussian distribution converging to a stationary limit. It follows that the mean‐reverting model is arbitrage‐free for any finite time interval. Further, it is shown that this model still allows some speculative opportunities: a gain for a wide enough set of expected utilities can be achieved for a strategy that does not require any hypothesis on market parameters and does not use estimation of these parameters.     

A multi‐stage ‘infection’ model of stock investors' reaction to new product announcement signal

Drawing on viral dynamics theory, this paper presents a differential equations model with time delay to investigate the stock investor behavior driven by new product announcement (NPA) signal. Visually, we look upon investors in stock market as cells in vivo and the NPA signals as free virus. The potential investors will be ‘infected’ by the dissociative NPA signal and then make investment decisions. In order to better understand the ‘infection’ process, we extract and establish a multi‐stage process during which NPA signal is delivered and ‘infects’ the potential investors. A time‐delay effect is employed to reflect the evaluation stage at which potential investors comprehensively evaluate and decide whether to invest or not. In addition, we introduce a set of external and internal factors into the model, including information sensitivity and investor sentiment, and so on, which are pivotal for examining investor behavior. Equilibrium analysis and numerical simulations are employed to check out the properties of the model and highlight the practical application values of the model. Copyright © 2016 John Wiley & Sons, Ltd.     

Pricing Volatility Swaps Under Heston's Stochastic Volatility Model with Regime Switching

A model is developed for pricing volatility derivatives, such as variance swaps and volatility swaps under a continuous‐time Markov‐modulated version of the stochastic volatility (SV) model developed by Heston. In particular, it is supposed that the parameters of this version of Heston's SV model depend on the states of a continuous‐time observable Markov chain process, which can be interpreted as the states of an observable macroeconomic factor. The market considered is incomplete in general, and hence, there is more than one equivalent martingale pricing measure. The regime switching Esscher transform used by Elliott et al. is adopted to determine a martingale pricing measure for the valuation of variance and volatility swaps in this incomplete market. Both probabilistic and partial differential equation (PDE) approaches are considered for the valuation of volatility derivatives.     

AN AGENT‐BASED EVENT DRIVEN FORAGING MODEL

ABSTRACT. A game‐theoretic foraging model is developed in an agent‐based framework. The animal's environment is described in terms of agents representing prey, predators and the habitat. The animal has two modes of behavior for each agent: tactical mode behaviors alter the outcomes of the events and strategic mode behaviors alter the probability of future events. Possible event outcomes are represented as utilities which have both benefit and cost components. The probabilities of successful behaviors are tracked via short‐term (tactical), intermediate‐term (strategic) and long‐term (reference) memories. Probabilities change continually through the moment‐to‐moment events, and switches in behavior occur when the expected utility of one behavior exceeds that of another. At the switch point, the expected utilities of the behaviors are equivalent; through this property the model parameters can be calibrated from behavioral experiments. A bluegill foraging study on giving‐up time is used to calibrate the model and then habitat foraging is simulated. Problems with interpreting behavioral studies without fully characterizing the multiple scales of interactions with agents are discussed.     

A BRIEF INTRODUCTION TO SCALE‐FREE NETWORKS

ABSTRACT. This article provides a brief introduction to scale‐free networks. The notion of a scale‐free network is defined and some examples given. Properties frequently exhibited by scale‐free networks are discussed. The importance of the phenomenon of preferential attachment in generating scale‐free networks is illustrated with two examples for the spread of a persistent disease. The models are similar in that they both yield a total infected population (1) which is geometrically distributed, and growing exponentially in expectation; and (2) in which the average distance from the original source of infection grows in a similar way over time. However one model, which has preferential attachment (infection), yields a scale‐free network, while the other which has homogeneous infectivity does not. The possible application of the theory of scale‐free networks to resource management is briefly discussed.     

Cultural evolution as a nonstationary stochastic process

We present an individual based model of cultural evolution, where interacting agents are coded by binary strings standing for strategies for action, blueprints for products or attitudes and beliefs. The model is patterned on an established model of biological evolution, the Tangled Nature Model (TNM), where a “tangle” of interactions between agents determines their reproductive success. In addition, our agents also have the ability to copy part of each other's strategy, a feature inspired by the Axelrod model of cultural diversity. Unlike the latter, but similarly to the TNM, the model dynamics goes through a series of metastable stages of increasing length, each characterized by mutually enforcing cultural patterns. These patterns are abruptly replaced by other patterns characteristic of the next metastable period. We analyze the time dependence of the population and diversity in the system, show how different cultures are formed and merge, and how their survival probability lacks, in the model, a finite average life‐time. Finally, we use historical data on the number of car manufacturers after the introduction of the automobile to the market, to argue that our model can qualitatively reproduce the flurry of cultural activity which follows a disruptive innovation. © 2015 Wiley Periodicals, Inc. Complexity 21: 214–223, 2016     

Interacting neural networks and the emergence of social structure

The article describes a computational model for the simulation of the emergence of social structure or social order, respectively. The model is theoretically based on the theory of social typifying by Berger and Luckmann. It consists of interacting artificial actors (agents), which are represented by two neural networks, an action net, and a perception net. By mutually adjusting of their actions, the agents are able to constitute a self‐organized social order in dependency of their personal characteristics and certain features of their environment. A fictitious example demonstrates the applicability of the model to problems of extra‐terrestrial robotics. © 2007 Wiley Periodicals, Inc. Complexity 12: 41–52, 2007     

A mathematical criterion for a stable government†

The paper presents a mathematical model of the problem of the politico‐economic stability of a welfare state. The variables characterizing the state of a government are identified. A sufficient criterion which ensures the damping out of the effects arising due to arbitrary perturbations in the variables, is obtained. The model assumes the prevalence of normal conditions, that is, times free from unforeseen calamities such as severe earthquake, outbreak of epidemic on a large scale, invasion by another country, acute famine, etc.

     

MULTISPECIES EXPLOITATION WITH EVOLUTIONARY SWITCHING OF HARVESTING STRATEGIES

In this paper, we propose a bioeconomic model which describes a fishery in which each of two noninteracting species is harvested by a given group of fishers during a defined time period. Then the Fishing Regulatory Authority allows each fisher to reconsider the harvesting decision at fixed (discrete) periods of time. The model derives from an Italian fisheries management experience in the Northern Adriatic Sea, where this kind of “self‐adjusting” fishing policy has been proposed to regulate harvesting of two shellfish species. The proposed dynamic model assumes the form of a hybrid system, as the natural growth functions of the two species (in continuous time) are coupled with a discrete time adaptive system that regulates how agents switch from one harvesting strategy to the other period by period according to an evolutionary mechanism based on profit comparison. In order to obtain some insights into the basic mechanisms of the system, some relevant benchmark cases are analyzed before tackling (mainly numerically) the complete hybrid model. Our results suggest that, for proper sets of parameters, this kind of myopic and adaptive self‐regulation may ensure a virtuous trade‐off between profit maximization and resource conservation, driven by cost externalities and market pressure.     

Statistical mechanics of nonlinear nonequilibrium financial markets

An approach to understanding the nature of markets is modelled using methods of modern nonlinear nonequilibrium statistical mechanics. This permits examination of the premise that markets can be described by nonlinear nonequilibrium Markovian distributions. Corrections to previous nonlinear continuous time models are explicitly presented. A quite general microscopic model is presented of individual agents operating on a market, and explicit relationships are derived between variables describing these agents and the macroscopic market.     

Asymmetric response and interaction of U.S. and local news in financial markets

This paper examines the extent to which financial returns on market indices exhibit mean and volatility asymmetries, as a response to past information from both the U.S. market and the local market itself. In particular, we wish to assess the asymmetric effect of a combination of local and U.S. market news on volatility. To the best of the authors knowledge, this joint effect has not been considered previously. We propose a double threshold non‐linear heteroscedastic model, combined with a GJR‐GARCH effect in the conditional volatility equation, to capture jointly both mean and volatility asymmetric behaviours and the interactive effect of U.S. and local market news. In an application to five major international market indices, clear evidence of threshold non‐linearity is discovered, supporting the hypothesis of an uneven mean‐reverting pattern and volatility asymmetry, both in reaction to U.S. market news and news from the local market itself. Significant, but somewhat different, interactive effects between local and U.S. news are observed in all markets. An asymmetric pattern in the exogenous relationship between the local market and the U.S. market is also found. Copyright © 2005 John Wiley & Sons, Ltd.