scholarly journals Behavioral agent-based framework for interacting financial markets

2020 ◽  
Vol 5 (2) ◽  
pp. 94-115
Author(s):  
Heba M. Ezzat
Keyword(s):  
Financial Markets ◽  
Trading Strategy ◽  
Past Performance ◽  
Time Step ◽  
Monetary Policies ◽  
Content Type ◽  
Agent Based ◽  
Market Makers ◽  
Novel Approach ◽  
Excess Volatility

Purpose This paper aims at developing a behavioral agent-based model for interacting financial markets. Additionally, the effect of imposing Tobin taxes on market dynamics is explored. Design/methodology/approach The agent-based approach is followed to capture the highly complex, dynamic nature of financial markets. The model represents the interaction between two different financial markets located in two countries. The artificial markets are populated with heterogeneous, boundedly rational agents. There are two types of agents populating the markets; market makers and traders. Each time step, traders decide on which market to participate in and which trading strategy to follow. Traders can follow technical trading strategy, fundamental trading strategy or abstain from trading. The time-varying weight of each trading strategy depends on the current and past performance of this strategy. However, technical traders are loss-averse, where losses are perceived twice the equivalent gains. Market makers settle asset prices according to the net submitted orders. Findings The proposed framework can replicate important stylized facts observed empirically such as bubbles and crashes, excess volatility, clustered volatility, power-law tails, persistent autocorrelation in absolute returns and fractal structure. Practical implications Artificial models linking micro to macro behavior facilitate exploring the effect of different fiscal and monetary policies. The results of imposing Tobin taxes indicate that a small levy may raise government revenues without causing market distortion or instability. Originality/value This paper proposes a novel approach to explore the effect of loss aversion on the decision-making process in interacting financial markets framework.

PiS policy shift puts Polish markets under strain

2016 ◽  
Keyword(s):  
Financial Markets ◽  
Foreign Currency ◽  
Monetary Policies ◽  
Government Bonds ◽  
Content Type ◽  
Local Currency ◽  
Policy Shift ◽  
Law And Justice ◽  
Rating Downgrades ◽  
High Share

Significance However, the unexpected downgrade of Poland by Standard & Poor's (S&P) on January 15 has focused attention on the financial and economic policy stance of the Law and Justice (PiS) government, in particular, the party's plans for a Hungarian-style forced conversion of foreign currency (FX)-denominated mortgages in local currency contracts. Poland's equity markets have fallen sharply, although the zloty and local government bonds are proving more resilient, despite coming under increasing pressure. Impacts The threat is looming over Poland of further rating downgrades if the credibility of its fiscal and monetary policies is undermined. Emerging Europe's high share of FX-denominated debt, particularly in the south-east, might be a source of financial vulnerability. Non-resident investors are still purchasing Poland's domestic bonds and may even be attracted by the recent rise in yields. CEE's negligible trade linkages with China and favourable status as an oil importer put its financial markets among the most resilient EMs.

Near-zero growth is expected in Kazakhstan in 2016

2016 ◽  
Keyword(s):  
Financial Markets ◽  
Banking System ◽  
Oil Prices ◽  
Domestic Demand ◽  
Living Standards ◽  
External Shocks ◽  
Monetary Policies ◽  
Content Type ◽  
Trading Partners ◽  
Zero Growth

Significance Low global oil prices and GDP declines in Russia and other trading partners caused a slowdown in growth in Kazakhstan in 2015 and early 2016. External shocks led to a large devaluation of the currency, hikes in inflation, and low domestic demand and industrial activity. Savers switched from tenge to dollars, and devaluation brought reduced liquidity and increased volatility in the financial markets, undermining the banking system. Impacts Falling living standards are a key political risk for President Nursultan Nazarbayev. Higher oil prices and a modest Russian recovery may offer Kazakhstan some respite. Tenge depreciation against trading partners' currencies will boost non-commodity exports. 'Dollarisation' of the economy will reduce the central bank's ability to implement monetary policies.

scholarly journals MARKET FLUCTUATIONS EXPLAINED BY DIVIDENDS AND INVESTOR NETWORKS

2017 ◽  
Vol 20 (08) ◽  
pp. 1750007 ◽  
Author(s):  
MATTHEW OLDHAM
Keyword(s):  
Stock Market ◽  
Complex Systems ◽  
Financial Markets ◽  
Network Topology ◽  
Network Science ◽  
Agent Based ◽  
Excess Volatility ◽  
Artificial Stock Market ◽  
Analytical Frameworks ◽  
Insight Into

The inability of investors and academics to consistently predict, and understand the behavior of financial markets has forced the search for alternative analytical frameworks. Analyzing financial markets as complex systems is a framework that has demonstrated great promises, with the use of agent-based models (ABMs) and the inclusion of network science playing an important role in increasing the relevance of the framework. Using an artificial stock market created via an ABM, this paper provides a significant insight into the mechanisms that drive the returns in financial markets, including periods of elevated prices and excess volatility. The paper demonstrates that the network topology that investors form and the dividend policy of firms significantly affect the behavior of the market. However, if investors have a bias to following their neighbors then the topology becomes redundant. By successfully addressing these issues this paper helps refine and shape a variety of additional research tasks for the use of ABMs in uncovering the dynamics of financial markets.

Disposition effect and multi-asset market dynamics

2019 ◽  
Vol 11 (2) ◽  
pp. 144-164
Author(s):  
Heba M. Ezzat
Keyword(s):  
Asset Pricing ◽  
Financial Markets ◽  
Switching Behavior ◽  
Asset Markets ◽  
Disposition Effect ◽  
Stylized Facts ◽  
Content Type ◽  
Agent Based ◽  
Multiple Assets ◽  
Pricing Dynamics

Purpose Asset pricing dynamics in a multi-asset framework when investors’ trading exhibits the disposition effect is studied. The purpose of this paper is to explore asset pricing dynamics and the switching behavior among multiple assets. Design/methodology/approach The dynamics of complex financial markets can be best explored by following agent-based modeling approach. The artificial financial market is populated with traders following two heterogeneous trading strategies: the technical and the fundamental trading rules. By simulation, the switching behavior among multiple assets is investigated. Findings The proposed framework can explain important stylized facts in financial time series, such as random walk price dynamics, bubbles and crashes, fat-tailed return distributions, absence of autocorrelation in raw returns, persistent long memory of volatility, excess volatility, volatility clustering and power-law tails. In addition, asset returns possess fractal structure and self-similarity features; though the switching behavior is only allowed among the asset markets. Practical implications The model demonstrates stylized facts of most real financial markets. Thereafter, the proposed model can serve as a testbed for policy makers, scholars and investors. Originality/value To the best of knowledge, no research has been conducted to introduce the disposition effect to a multi-asset agent-based model.

Fast growth will support Central European asset prices

2018 ◽  
Keyword(s):  
Emerging Markets ◽  
Financial Markets ◽  
Euro Area ◽  
Asset Prices ◽  
Bond Market ◽  
Bond Markets ◽  
Monetary Policies ◽  
Content Type ◽  
Central European

Subject The muted impact on Central Europe’s financial markets of this month’s sharp declines in asset prices in Russia and Turkey. Significance Much harsher US sanctions on Russia, together with Turkey’s continued loss of policy credibility, have led Russian and Turkish stocks to plunge by nearly 12.0% and 7.5%, respectively, in dollar terms since the start of April. This contrasts with rises in Polish (5.5%), Czech (3.3%) and Hungarian (2.0%) equities, and a slight decline for the MSCI Emerging Markets (EM) index. Central Europe’s vulnerability is rather to the recent slowdown in growth in the euro-area; the ECB’s ultra-loose monetary policies are providing support to the region’s bond markets. Impacts The VIX Index ‘fear gauge’ is back below its long-term average and is at its lowest level since the outbreak of volatility in late January. The latest reading from Germany’s ZEW Index shows a majority of investors now expecting the country’s economic prospects to deteriorate. Turkey’s high-yielding local bond market has managed to attract nearly 900 million dollars of foreign inflows so far this year.

The Role of Risk Aversion and Technical Trading in the Behaviour of Financial Markets

2008 ◽  
pp. 169-179
Author(s):  
José Antonio Pascual
Keyword(s):  
Financial Markets ◽  
Financial Economics ◽  
Stock Markets ◽  
Financial Time Series ◽  
Unit Roots ◽  
Social Simulation ◽  
Statistical Features ◽  
Volatility Clustering ◽  
Agent Based ◽  
Excess Volatility

In this paper we show how agent based social simulation helps us to improve some of the traditional models and theories in financial economics. In particular, we explore the links between the micro-behaviour of investors and the aggregated behaviour of Stock Markets. First, we build an agent based model of an artificial financial market, populated only with rational investors. We observe that the statistical features of this market are in agreement with the theoretical markets suggested by mainstream financial economics, but far away from the features shown by real financial markets, like the Spanish Ibex-35, the Spanish Stock Market main Index. In order to fill the gap, we introduce heterogeneity in the model. We add psychological investors, as suggested by Kahnemen and Tversky (1979), and we are able to reproduce non-normality, excess kurtosis, excess volatility, and volatility clustering. Then, we introduce technical traders, and we also get from the model higher levels of excess volatility and unit roots. In other words, psychological dealers seem to be responsible for volatility clustering, whereas technical traders trend to introduce unit roots into the process. All these “financial patterns” are a common feature not only for Spanish Ibex-35, also the most important stock markets. We conclude that agent based social simulation helps us to fill the gap between economic theory and real markets, as we explain the statistical features of financial time series from the bottom-up.

New Bundesbank chief likely to be less hawkish

2022 ◽  
Keyword(s):  
Monetary Policy ◽  
Financial Markets ◽  
Economic Recovery ◽  
Governing Council ◽  
Monetary Policies ◽  
Content Type ◽  
Political Tension

Significance Weidmann decided to quit early as his efforts to oppose ultra-loose monetary policies were continuously resisted in the ECB. Unlike his predecessors, Nagel does not appear to possess strongly held convictions regarding monetary policy, suggesting he will be more pragmatic in relations with the ECB. Impacts Nagel will make digital modernisation a key objective during his time as Bundesbank president. Nagel’s support for stronger German connections with Chinese financial markets may weaken amid political tension. A strong economic recovery in 2022 would embolden those in the ECB governing council supporting the phasing out of asset-buying programmes.

scholarly journals Agent-based risk management – a regulatory approach to financial markets

2015 ◽  
Vol 42 (5) ◽  
pp. 780-820 ◽  
Author(s):  
Thomas Theobald
Keyword(s):  
Financial Markets ◽  
Behavioral Model ◽  
Capital Requirements ◽  
Regulatory Framework ◽  
Market Model ◽  
Content Type ◽  
Agent Based ◽  
No Arbitrage ◽  
Market Participants ◽  
Regulatory Approach

Purpose – 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. Design/methodology/approach – 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. Findings – 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. Practical implications – Thus, including agent-based models in the regulatory framework could create better capital requirements with respect to their level and counter-cyclicality. Originality/value – 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.

Teaching AI Agents Ethical Values Using Reinforcement Learning and Policy Orchestration

2019 ◽  
Author(s):  
Ritesh Noothigattu ◽  
Djallel Bouneffouf ◽  
Nicholas Mattei ◽  
Rachita Chandra ◽  
Piyush Madan ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Ethical Values ◽  
Large Role ◽  
Learning To Learn ◽  
Inverse Reinforcement Learning ◽  
Time Step ◽  
Novel Approach

Autonomous cyber-physical agents play an increasingly large role in our lives. To ensure that they behave in ways aligned with the values of society, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society. We detail a novel approach that uses inverse reinforcement learning to learn a set of unspecified constraints from demonstrations and reinforcement learning to learn to maximize environmental rewards. A contextual bandit-based orchestrator then picks between the two policies: constraint-based and environment reward-based. The contextual bandit orchestrator allows the agent to mix policies in novel ways, taking the best actions from either a reward-maximizing or constrained policy. In addition, the orchestrator is transparent on which policy is being employed at each time step. We test our algorithms using Pac-Man and show that the agent is able to learn to act optimally, act within the demonstrated constraints, and mix these two functions in complex ways.

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