Artificial intelligence-driven financial innovation: A robo-advisor system for robust returns across diversified markets

With the advancement in artificial intelligence, robo-advisor systems have emerged as powerful tools for formulating financial product trading strategies and assisting investors in making rational investment decisions. Consequently, to reduce risk and provide investors greater returns in volatile markets, improving the performance of these systems has become a key research focus. This paper proposes an enhanced robo-advisor system that employs deep mathematical feature engineering to embed a hybrid mechanism for robust feature extraction. The system implements a novel integrated algorithm, where technical indicators are first decomposed using variational mode decomposition technology, followed by feature extraction through a deep convolutional neural network with an attention mechanism. The high-level features are then fed into a bidirectional gated recurrent unit network to predict returns on short-term time-scale financial products. The experimental results indicate that the proposed robo-advisor system achieves robust, remarkable return performance on several types of assets under different market conditions, and provides decision support for investors in managing asset risks and seeking cross-market investment opportunities.