However, it is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting, where the model can adapt itself to match the current market regime identified based on only point-in-time market information. We then apply this to two settings, single-parameter agents and mechanisms for two agents in which one has a two-value domain, and show that under these models the revelation principle holds: direct mechanisms are just as powerful as indirect ones.įactor model is a fundamental investment tool in quantitative investment, which can be empowered by deep learning to become more flexible and efficient in practical complicated investing situations. In this work we first extend the cycle monotonicity framework for direct-revelation NOM mechanism design to indirect mechanisms. Specifically a mechanism is not obviously manipulable (NOM) if the best and worst outcomes when acting truthfully are no worse than the best and worst outcomes when acting dishonestly. Studying agent strategies in real-life mechanisms, Troyan and Morrill introduce a relaxation of strategyproofness known as non-obvious manipulability, which only requires comparing certain extrema of the agents’ utility functions in order for a mechanism to be incentive-compatible.

A celebrated result for dominant-strategy incentive-compatible mechanisms that allows us to restrict attention to direct mechanisms, known as the revelation principle, does not hold for OSP: the implementation details matter for the obvious incentive properties of the mechanism. Starting with Li’s strengthening of strategyproofness, obvious strategyproofness (OSP) requires truthtelling to be 'obvious' over dishonesty, roughly meaning that the worst outcome from truthful actions must be no worse than the best outcome for dishonest ones.

Recent work in algorithmic mechanism design focuses on designing mechanisms for agents with bounded rationality, modifying the constraints that must be satisfied in order to achieve incentive compatibility. StockFormer significantly outperforms existing approaches across three publicly available financial datasets in terms of portfolio returns and Sharpe ratios. The entire model is jointly trained by propagating the critic’s gradients back to the predictive coding module. The RL agent adaptively fuses these states and then executes an actor-critic algorithm in the unified state space. The predictive coding part consists of three Transformer branches with modified structures, which respectively extract effective latent states of long-/short-term future dynamics and asset relations. In this paper, we present StockFormer, a hybrid trading machine that integrates the forward modeling capabilities of predictive coding with the advantages of RL agents in policy flexibility. Typical RL-for-finance solutions directly optimize trading policies over the noisy market data, such as stock prices and trading volumes, without explicitly considering the future trends and correlations of different investment assets as we humans do. Special Track on AI, the Arts and Creativity.Main Tarck Program Committee Members Expand.

IJCAI-AIJ 2023 Travel and Accessibility Grant Program.

Joost kap medical examining board