Towards autonomous investment analysts — Helping people to make good investment decisions

Since early days of computer science, researchers ask themselves where is the line that separates tasks machine can do from those only human beings can really accomplish. Several tasks were pointed as impossible to machines and later conquered by new advances in Artificial Intelligence. Nowadays, it seems we are not far from the day when driving cars will be included among the tasks machines can do in an efficient way. Certainly, even more complex activities will be dominated by machines in the future. In this paper, we argue that investment analysis, the process of assessment and selection of investments in terms of risk and return, should and can be among the tasks performed efficiently by machines in the (maybe not so far) future. Investment decisions have to be faced not only by financial professionals but by all people. Naturally, these professionals have more complex and often decisions to make, but everybody needs to invest to warrant good standard of living in the old age. In fact, there is significant research effort to create algorithms and/or quantitative methods to analyze investments. We present a brief review of them. Through this review, we may realize that there are many interconnected challenges in the quest for autonomous investment analysis. In this paper, we propose an adaptive multiagent architecture that deals with these three dimensions of complexity (nature of assets, multiple analysis algorithms per asset and horizon of investment) and keeps an explicit model of investor's preferences. This architecture breaks down the complexity faced by AIA in problems that can be addressed by a group of agents that work together to provide intelligent and customized investment advices for individuals. We believe that such architecture may contribute to development of AIA that deals with the complexity of the problem in a tractable way. Furthermore, this architecture allows the incorporation of known algorithms and techniques that may help to solve part of the issue.