Portfoolioids

Portfoolioids are a class of synthetic, behaviorally biased investment agents used in portfolio simulation, education, and research. The name fuses portfolio with fool, signaling that these agents embody common investment biases and heuristics. Portfoolioids are not real investors; they are computational profiles that act under rules and parameters within a market model.

Operation and design: Each Portfoolioid is characterized by a trait vector: risk tolerance, time horizon, bias tendencies (overconfidence, loss aversion, recency bias), learning rate, and asset class preferences. They observe market signals, news, and past returns, and decide to buy, hold, or sell. In simulations, multiple Portfoolioids interact within an order-driven or price-formation market, producing price paths and performance statistics. They can be static or adaptive via reinforcement learning or evolutionary algorithms.

Applications: Portfoolioids are used to study how behavioral biases affect portfolio outcomes, assess robustness of strategies under noise, and teach students about diversification and risk management. They can also serve as a benchmarking tool against traditional optimization approaches.

Limitations: As abstractions they oversimplify real-world constraints (transaction costs, taxes, liquidity). The results depend on the chosen trait distributions and market models; findings may not generalize to actual markets. There are ethical considerations around simulating irrational behavior.

See also: Behavioral finance, agent-based modeling, reinforcement learning in finance, portfolio theory.