Investments in random environments

Navarro-Barrientos, Jesús Emeterio; Cantero-Álvarez, Rubén; Rodrigues, João F. Matias; Schweitzer, Frank

doi:10.1016/j.physa.2007.11.029

Cited by 8 publications

(7 citation statements)

References 41 publications

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“…1 (left) shows the evolution of the budget distribution over time for q = 0.5. Note that the probability distribution of the budget converges to a stationary distribution with a power law in the tail, a property of investment models based on multiplicative processes repelled from zero [10,7].…”

Section: Results Of Computer Simulationsmentioning

confidence: 99%

“…In this model, agent k invests a portion q k (t)x k (t) of its total budget at every time step t yielding a gain or loss in the market m, expressed by r mk (t). Similar wealth models have been presented in [9,4,7] where the dynamics of the investment model are investigated using some results from the theory of multiplicative stochastic additive processes [2,10].…”

Section: Wealth Dynamicsmentioning

confidence: 99%

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Formation of Common Investment Networks by Project Establishment between Agents

Navarro-Barrientos

2011

Social Computing, Behavioral-Cultural Modeling and Prediction

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We present an investment model integrated with trust-reputation mechanisms where agents interact with each other to establish investment projects. We investigate the establishment of investment projects, the influence of the interaction between agents in the evolution of the distribution of wealth, as well as the formation of common investment networks and some of their properties. Simulation results show that the wealth distribution presents a power law in its tail. Also, it is shown that the trust and reputation mechanism presented leads to the establishment of networks among agents, which present some of the typical characteristics of real-life networks like a high clustering coefficient and short average path length. 1

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Section: Results Of Computer Simulationsmentioning

confidence: 99%

Section: Wealth Dynamicsmentioning

confidence: 99%

Formation of Common Investment Networks by Project Establishment between Agents

Navarro-Barrientos

2011

Social Computing, Behavioral-Cultural Modeling and Prediction

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“…In order to avoid bankruptcy, the agent may have an income, [21,34,38], or a budget-barrier may be assumed [23]. Some researchers have investigated different strategies to control the proportion of investment in this type of models for different scenarios [28,32]. On the other hand, some other authors use different artificial market models to compare the performance of agents with zero-intelligence and rational agents [9,12].…”

Section: Paper Submitted To Advances In Complex Systemsmentioning

confidence: 99%

“…We consider an investment model [31,32] where an agent is characterized by two individual variables: (i) its budget x(t), i.e. its wealth and (ii) its investment proportion q(t), i.e.…”

Section: Investment Modelmentioning

confidence: 99%

Adaptive Investment Strategies for Periodic Environments

Navarro-Barrientos

2008

Advs. Complex Syst.

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In this paper, we present an adaptive investment strategy for environments with periodic returns on investment. In our approach, we consider an investment model where the agent decides at every time step the proportion of wealth to invest in a risky asset, keeping the rest of the budget in a risk-free asset. Every investment is evaluated in the market via a stylized return on investment function (RoI), which is modeled by a stochastic process with unknown periodicities and levels of noise. For comparison reasons, we present two reference strategies which represent the case of agents with zero-knowledge and complete-knowledge of the dynamics of the returns. We consider also an investment strategy based on technical analysis to forecast the next return by fitting a trend line to previous received returns. To account for the performance of the different strategies, we perform some computer experiments to calculate the average budget that can be obtained with them over a certain number of time steps. To assure for fair comparisons, we first tune the parameters of each strategy. Afterwards, we compare the performance of these strategies for RoIs with different periodicities and levels of noise.

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“…And this investment yields a gain or loss on the market, expressed by r(t), the return on investment, RoI. Some authors assume that returns are obtained by means of continuous double auction mechanisms (LeBaron, 2001), however, in this paper we consider that the returns are not being influenced by agent's actions, this approach plays a role in more physics-inspired investment models, (Richmond, 2001;Navarro-Barrientos et al, 2008). Since q(t) always represents a portion of the total budget x(t), and it is bound to q(t) ∈ [0, 1].…”

Section: Introductionmentioning

confidence: 99%

Controlling Investment Proportion in Cyclic Changing Environments

Navarro-Barrientos¹

2008

Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics Service

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In this paper, we present an investment strategy to control investment proportions for environments with cyclic changing returns on investment. For this, we consider an investment model where the agent decides at every time step the proportion of wealth to invest in a risky asset, keeping the rest of the budget in a risk-free asset. Every investment is evaluated in the market modeled by stylized returns on investment (RoI). For comparison reasons, we present two reference strategies which represent agents with zero-knowledge and complete-knowledge of the dynamics of the RoI, and we consider an investment strategy based on technical analysis. To account for the performance of the strategies, we perform some computer experiments to calculate the average budget that can be obtained over a certain number of time steps. To assure for fair comparisons, we first tune the parameters of each strategy. Afterwards, we compare their performance for RoIs with fixed periodicity (stationary scenario) and for RoIs with changing periodicities (non-stationary scenario). INVESTMENT MODELWe consider an investment model (Navarro and Schweitzer, 2003) where an agent is characterized by

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Investments in random environments

Cited by 8 publications

References 41 publications

Formation of Common Investment Networks by Project Establishment between Agents

Formation of Common Investment Networks by Project Establishment between Agents

Adaptive Investment Strategies for Periodic Environments

Controlling Investment Proportion in Cyclic Changing Environments

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