Cooperative Multiagent Search for Portfolio Selection

We present a new multiagent model for the multiperiod portfolio selection problem. Individual agents receive a share of initial wealth, and follow an investment strategy that adjusts their portfolio as they observe movements of the market over time. The agents share their wealth at the end of the final investment period. We show that a multiagent system can outperform a single agent that invests all the wealth in a simple stochastic market environment. Furthermore, a cooperative multiagent system, with a simple communication mechanism of explicit hint exchange, achieves a further increase in performance. Finally we show that communication is redundant in a more realistic market that satisfies the constraints between volatility and return implied by the Capital Asset Pricing Model.Engineering and Applied Science

Multiagent Cooperative Search for Portfolio Selection

We present a new multiagent model for the multiperiod portfolio selection problem. A system of cooperative agents divide initial wealth and follow individual worst-case optimal investment strategies from random portfolios, sharing their final profits and losses. The multiagent system achieves better average-case performance than a single agent with the same initial wealth in a simple stochastic market. A further increase in performance is achieved through communication of hints between agents and probabilistic strategy-switching. However, this explicit cooperation is redundant in a market that approximates the Capital Asset Pricing Model, a model of equilibrium stock price dynamics. Journal of Economic Literature Classification Numbers: C63, C73, D83, G11.Engineering and Applied Science

Improved Memory-Bounded Dynamic Programming for Decentralized POMDPs

Memory-Bounded Dynamic Programming (MBDP) has proved extremely effective in solving decentralized POMDPs with large horizons. We generalize the algorithm and improve its scalability by reducing the complexity with respect to the number of observations from exponential to polynomial. We derive error bounds on solution quality with respect to this new approximation and analyze the convergence behavior. To evaluate the effectiveness of the improvements, we introduce a new, larger benchmark problem. Experimental results show that despite the high complexity of decentralized POMDPs, scalable solution techniques such as MBDP perform surprisingly well.Comment: Appears in Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI2007

Adaptive Investment Strategies For Periodic Environments

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.Comment: Paper submitted to Advances in Complex Systems (November, 2007) 22 pages, 9 figure