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Realistic Simulation of Financial Markets: Analyzing Market Behaviors by the Third Mode of Science (Evolutionary Economics and Social Complexity Science)

Kita, Hajime, Taniguchi, Kazuhisa and Nakajima, Yoshihiro (eds.)
Springer-Verlag: Berlin, 2016
ISBN 9784431550563 (hb)

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Reviewed by Sandrine Jacob-Leal
Department Finance, Accounting, Control and Audit, ICN Business School Nancy- Metz

Cover of book I was very enthusiastic to read Realistic Simulation of Financial Markets – Analyzing Market Behaviors by the Third Mode of Science and eager to discover what the authors meant by the third mode of science. This book mainly focuses on the use of agent-based simulations (ABS) of financial markets especially based on the U-Mart system. The topics covered in this book are interesting and this volume stresses the need to adopt new approaches, in particular ABS, to better understand the constant evolution and complexity of financial markets. Furthermore, I like the complementarity of the two parts of the book. On the one hand, Part 1 provides an overview of the theoretical background and of open issues, of the specificities of ABS and of the main features of the U-Mart system. On the other hand, Part 2 describes various illustrations on how the U-Mart system has already been implemented, applied to specific contexts, and outlined what the potential findings associated with the artificial financial markets of the U-Mart system are. Accordingly, I believe that having both theoretical backgrounds to understand the need of alternative approaches and practical illustrations of artificial financial markets of the U-Mart system is particularly effective for researchers willing to engage in ABS. However, Part 1 has two main drawbacks. First, some chapters could have been addressed more specifically to the object of this book, i.e., financial markets, and others could have been further developed. Second, some chapters could have been better positioned in this part of the book. In this review, I provide comments on each chapter so that any interested reader could get an overview of the topics covered and can select the most relevant chapter(s).

As a strong believer in the power and relevance of ABS in understanding evolving and increasingly complex markets, I was glad to realise that, in Part 1, the authors aimed at (i) explaining why ABS is required to understand evolving and increasingly complex markets; and, (ii) providing the necessary background to realise why ABS can been seen as the third mode of scientific research. This presentation and knowledge is a necessary preliminary step for any researcher interested in this field. However, I am not completely convinced about the way it was demonstrated in this book.

In particular, given the specific focus of this volume, i.e., financial markets, I would have expected this overview to be more oriented towards financial markets. For instance, the first example of financial markets is only mentioned at the end of Chapter 1 (p.45). Furthermore, the overview of the history of (macro-)economic thought seems too broad to be covered in a single chapter of less than 50 pages. The author unavoidably made some shortcuts and simplifications, resulting in some superficiality in the way the history of (macro-)economics is presented in this chapter. Furthermore, this chapter presents some redundancies, repetitions and/or inconsistencies in the text (“starting point” the 1970s, p. 6, but then “over more than a century and a half”, p.34, and “1870s”, p.35) that may confuse some readers on this guided tour. Lastly, although Chapter 1 presents the evolution of the methods used in (macro-) economics, it does not present or discuss the most recent (over the last 15-20 years) developments and the numerous uses of ABS models in macroeconomics (see, for instance, Dosi et al., 2012; Gaffard and Napoletano, 2012; Dosi et al., 2015) and AB finance (e.g., LeBaron, 2000, 2001; Samanidou, 2007) as well as the more recent discussions and developments regarding alternative approaches of economic theory (see, for instance, Tesfatsion, 2006; Tesfatsion and Judd, 2006) that contribute to understanding the complexity and the dynamics in real and financial markets wherein (i) aggregate outcomes do not merely coincide with the sum of individual decisions at the micro-level (i.e., emergent nature of markets); and, (ii) behaviours evolve over time and aim at moving away from the representative, fully rational agent and the concept of equilibrium.

Chapter 2 provides an overview of social simulations, including ABS, but the presentation is rather brief (7 pages) and inevitably limited, especially given the extensive literature based on ABS models and the variety of existing models. Furthermore, the author does not really discuss the methods that were developed for computer simulation of social systems that would have been useful to grasp how ABS differs methodologically from research using other methods. How ABS can be applied to complex social interactions and agents’ behaviours should have been further explained as well. Nevertheless, this chapter can be useful to know the key steps in research using simulations, to clarify the underlying structure of ABM, its purpose and objectives and the key characteristics of the U-Mart system.

Chapter 3 presents the key design requirements of artificial market simulators for evaluating market institutions and trading strategies. It also nicely reminds researchers to the importance of the KISS principle that should underline any computer simulation models of social phenomena. The presentation of the U-Mart system and its technicalities are nicely written, detailed and clear. Although a discussion of the Itayose U-Mart system in terms of design requirements is provided, further discussion of the Zaraba-based U-Mart system was expected to fully understand the advantages and limits of both systems. Lastly, the example of the numerical experiment with the Zaraba-based U-Mart system is pretty effective and helps understanding how the Zaraba-based U-Mart system can be implemented and used.

Despite the fact that Chapter 4 is very short (6 pages), this overview is very useful to understand the requirements for ABS towards a new research scheme and how ABS can help further understanding complex social and economic systems. However, I think it would have been more effective to have positioned it before Chapter 3 or to have included the main ideas in Chapter 2.

The chapters in the second part of this volume address key aspects of real financial markets, namely: the evolution of trading strategies (Chapter 5); the role and the strategy of market makers (Chapter 6); the concept of resilience (Chapter 7); the rationale of arbitrary behaviour (Chapter 8) and how each aspect can be introduced and studied through ABS. Each of these chapters represents simple but practical examples of artificial markets. The authors wisely use graphs and figures to illustrate how each artificial financial market of the U-Mart system works. Their presentations and explanations are nicely written and clear. This part of the book is very useful for researchers engaging in ABS to understand how to handle these key aspects of financial markets through the U-Mart system and to have a nice overview of the variety of applications and findings of the U-Mart system.

* References

DOSI, G., Fagiolo, G., Napoletano, M. and A. Roventini (2012). Economic policies with endogenous innovation and Keynesian demand management. In: What’s Right With Macroeconomics?. Solow, R. M. and J.-P. Touffut (eds.), Edward Elgar Publishing, pp. 110-148.

DOSI, G., Napoletano, M., Roventini, A. and T. Treibich (2015). The short- and long-run damages of fiscal austerity: Keynes beyond Schumpeter. In: Contemporary issues in macroeconomics. Stiglitz, J. and M. Guzman (eds.), Palgrave Macmillan, pp. 79-97.

GAFFARD, J.-L. and M. Napoletano (eds.) (2012). Agent-based models and economic policy. In: Revue de l'OFCE - Analyse et prévisions.

LeBARON, B. (2000). Agent-based computational finance: Suggested readings and early research. In: Journal of Economic Dynamics and Control, 24(5), pp. 679-702.

LeBARON, B. (2001). A builder’s guide to agent-based financial markets. In: Quantitative Finance, 1(2), pp. 254-261.

SAMANIDOU, E., Zschischang, E., Stauffer, D. and T. Lux (2007). Agent-based models of financial markets. In: Reports on Progress in Physics, 70(3), p. 409.

TESFATSION, L. and K. L. Judd (eds.) (2006). Handbook of computational economics: agent-based computational economics (Vol. 2). Elsevier.

TESFATSION, L. (2006). Agent-based computational economics: A constructive approach to economic theory. In: Handbook of computational economics, Vol. 2, pp. 831-880.


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