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This paper models the benefits of Islamic banking on the efficiency of the banking sector and on societal happiness. This paper aims to examine how the adoption of Islamic banking to various degrees affects economics outcomes.

This study uses machine-learning tools to build a happiness function and integrate it in an agent-based model to test for the direct and indirect welfare effects of implementing Islamic banking principles.

This study shows that even though Islamic banking systems tend to reduce economic activity, financial stability and societal happiness is improved. Additionally, a banking sector using Islamic principles across all its members is better equipped to handle banking crises because contagion to both economic activity and societal welfare is greatly reduced. At the same time, adoption of the profit-and-loss sharing (PLS) paradigm by banks may also slow down economic growth.

The findings extend existing literature on the advantages of Islamic banking, by quantifying the welfare benefits of the PLS paradigm on happiness and financial stability.

To the best of the authors’ knowledge, this paper is the first to combine agent-based modelling with machine learning tools to examine the benefits of the Islamic banking model on financial stability, social welfare and unemployment.

The term “agent-based modelling” (ABM) is a buzzword which is widely used in the scientific literature even though it refers to a variety of methodologies implemented in different disciplinary contexts. The numerous works dealing with ABM require a clarification to better understand the lines of thinking paved by this approach in economics. All modelling tasks are a means and a source of knowledge, and this epistemic function can vary depending on the methodology. this paper is to present four major ways (deductive, abductive, metaphorical and phenomenological) of implementing an agent-based framework to describe economic systems. ABM generates numerous debates in economics and opens the room for epistemological questions about the micro-foundations of macroeconomics; before dealing with this issue, the purpose of this paper is to identify the kind of ABM the author can find in economics.

The profusion of works dealing with ABM requires a clarification to understand better the lines of thinking paved by this approach in economics. This paper offers a conceptual classification outlining the major trends of ABM in economics.

There are four categories of ABM in economics.

This paper suggests a methodological categorization of ABM works in economics.

The purpose of this paper is to examine the link between banking crises and the subjective well-being of individuals. In addition, the authors examine the transmission of crises from the banking sector to well-being and show that negative financial shocks have significant adverse effects.

The authors employ agent-based modeling to test for the direct and indirect welfare effects of banking crises. The model includes a support vector machine (SVM) optimized subjective well-being function. The existing literature suggests that this is influenced by both the negative psychological effects of recessions and the adverse economic effects of income loss and increased unemployment.

The authors show that the different choices of policy response to a banking crisis carry different opportunity costs in terms of welfare and that societal preferences should be taken into account. The authors demonstrate that these effects influence different population classes in an asymmetric manner. Finally, the results demonstrate that the welfare loss of a bank failure is much higher than the cost of a bailout.

The authors are able to propose to the authorities the best policy mix in order to handle banking crises in the most adequate manner, according to society's preferences between financial stability and public goods.

The findings extend the existing literature on subjective well-being, by quantifying the welfare cost of banking crises and showing that authorities should reconsider bank bailouts as a policy solution to bank distress.

The originality of this article lies in the use of an agent-based model to model the relationship between societal well-being and financial stability. Also, the authors extend existing agent-based methodologies to include machine learning optimization techniques.

This study aims to critically review recent contributions to the methodology of financial economics and discuss how they relate to one another and directions for further research.

A critical review of recent literature on new methodologies for financial economics.

Recent books have made important contributions to the study of financial economics. They suggest new approaches that include an emphasis on radical uncertainty, adaptive markets, agent-based modeling and narrative economics, as well as extensions of behavioral finance to include concepts such as diagnostic expectations. Many of these contributions can be seen more as complements than substitutes and provide fruitful directions for further research. Efficient markets can be seen as holding under particular circumstances. A major them of most of these contributions is that the study of financial crises and other aspects of financial economics requires the use of multiple theories and approaches. No one approach will be sufficient.

There are great opportunities for further research in financial economics making use of these new approaches.

These recent contributions can be quite useful for improved analysis by researchers, private participants in the financial sector and macroeconomic and regulatory officials.

Provides an introduction to these new approaches and highlights fruitful areas for their extensions and applications.

The purpose of this paper is to provide market risk calculation for an equity-based trading portfolio. Instead of relying on the purely stochastic internal model method which banks currently apply in line with the Basel regulatory requirements, the author also propose including alternative price mechanisms from the financial literature in the regulatory framework.

For this purpose, a financial market model with heterogeneous agents is developed, capturing the realistic feature that parts of the investors do not follow the assumption of no arbitrage, but are motivated by behavioral heuristics instead.

Although both the standard stochastic and the behavioral model are restricted to a calibration including the last 250 trading days, the latter is able to capitalize possible turbulence on financial markets and likewise the well-known phenomenon of excess volatility – even if the last 250 days reflect a non-turbulent market.

Thus, including agent-based models in the regulatory framework could create better capital requirements with respect to their level and counter-cyclicality.

This in turn could reduce the extent to which bubbles arise, since market participants would have to anticipate comprehensively the costs of such bubbles bursting. Furthermore, a key ratio is deduced from the agent-based construction to lower the influence of speculative derivatives.

The purpose of this paper is to examine the debate on whether psychology affects asset prices using agent-based modeling.

The authors set up three simulation regimes where the first regime contains fundamental investors who invest based on the mean-variance framework. The second regime includes purely irrational investors who invest based on behavioral biases. The third regime combines the two types of investors. The authors test whether the return properties from regime 3 converge to that of regime 1 or 2.

Results suggest that the type of irrationality affects return properties in different ways. Irrational investors who are introspective in their irrationality, only examining their performance and deficiencies, do not have much of a systematic effect on stock returns when combined with rational investors. However, irrational investors that aggregate information in an irrational manner have a systematic effect when combined with rational investors.

Research implication of using simulation analysis is that the results need to be verified via other methods such as empirical and/or experimental analysis.

Practical implications of the research is that policy makers can look for factors that investors use to aggregate to better understand the movement of financial prices and ignore other factors.

Social implication is that mass psychology impacts financial prices.

No other paper has used agent-based/behavioral analysis to better understand how different types of behavior may impact financial prices in different ways.

An agent-based market simulation is utilized to examine the impact of high frequency trading (HFT) on various aspects of the stock market. This study aims to provide a baseline understanding of the effect of HFT on markets by using a paradigm of zero-intelligence traders and examining the resulting structural changes.

A continuous double auction setting with zero-intelligence traders is used by adapting the model of Gode and Sunder (1993) to include algorithmic high frequency (HF) traders who retrade by marking up their shares by a fixed percentage. The simulation examines the effects of two independent factors, the number of HF traders and their markup percentage, on several dependent variables, principally volume, market efficiency, trader surplus and volatility. Results of the simulations are tested with two-way ANOVA and Tukey’s post hoc tests.

In the simulation results, trading volume, efficiency and total surplus vary directly with the number of traders employing HFT. Results also reveal that market volatility increased with the number of HF traders.

Increases in volume, efficiency and total surplus represent market improvements due to the trading activities of HF traders. However, the increase in volatility is worrisome, and some of the surplus increase appears to come at the expense of long-term-oriented investors. However, the relatively recent development of HFT and dearth of appropriate data make direct calibration of any model difficult.

The simulation study focuses on the structural impact of HF traders on several aspects of the simulated market, with the effects isolated from other noise and problems with empirical data. A baseline for comparison and suggestions for future research are established.

How a micro-founded discipline such as economics could deal with the increasing global economic reality? This question has been asked frequently since the last economic crisis that appeared in 2008. In this challenging context, some commentators have turned their attention to a new area of knowledge coming from physics: econophysics which mainly focuses on a macro-analysis of economic systems. By showing that concepts used by econophysicists are consistent with an existing economic knowledge (developed by J.S. Mill), the purpose of this paper is to claim that an interdisciplinary perspective is possible between these two communities.

The authors propose a historical and conceptual analysis of the key concept of emergence to emphasize the potential bridge between econophysics and economics.

Six methodological arguments will be developed in order to show the existence of conceptual bridges as a necessary condition for the elaboration of a common language between economists and econophysics which would not be superfluous, in this challenging context, to clarify the growing complexity of economic phenomena.

Although the economics and econophysics study same the complex economic phenomena, very few collaborations exist between them. This paper paves a conceptual/methodological path for more collaboration between the two fields.

The securities transaction tax (STT) has been theoretically considered as an important regulation device for decades. However, its role and effectiveness in financial markets is still not well understood both theoretically and empirically. By use of agent-based modeling method, the purpose of this paper is to present a new artificial stock market model with self-adaptive agents, which allows the assessment of the impacts from various levels of STTs in distinctive market environments and thus a comprehensive understanding of the effects of STTs is achieved.

In the model, agents are allowed to employ the strategies used by the following five types of investors: contrarians, random traders, momentum traders, fundamentalists and exit strategy holders. Specifically, the authors start with the investigation of the dynamics of a tax free benchmark market; then the patterns of market behaviors and the behaviors of various types of investors are discussed with different levels of STTs in markets with mild and high fluctuations.

The simulation results consistently show that a moderate transaction tax does contribute to market stabilization in terms of reducing market volatility while with a price of mild decrease of market efficiency and liquidity. The findings suggest that a balance between market stability and efficiency could be reached if regulatory authorities introduce STTs to markets discreetly.

This paper enriches the comprehensive understanding of the effects of STT, and gives good explanation about the controversy between Tobin’s proponents and anti-Tobin group.

We examine the stability of market prices for 35 technology and 35 industrial stocks for the period December 31, 1993 to October 31, 2002. A phase portrait plot of the detrended log prices and de‐meaned returns of the two sectors shows a chaotic pattern in the stock prices indicating the presence of nonlinearity. However, when we compute the Lyapunov exponents, negative values are obtained. This shows that the price fluctuations for the 70 stocks result primarily from diffusion processes rather than from nonlinear dynamics. We evaluate forecast errors from a naïve model, a neural network, and ARMA models and find that the forecast errors are correlated with average changes in closed‐end fund discounts and other sentiment indexes. These results support an investor sentiment explanation for the closed‐end fund puzzle and behavioral theories of investor overreaction.