Investment Advisory Robotics 2.0: Leveraging Deep Neural Networks for Personalized Financial Guidance

Abstract

The increasing complexity of financial markets and the growing need for personalisation present significant challenges to the traditional investment advisory model. To address this issue, this paper puts forth a proposal for an investment advisor robot 2.0 system based on deep neural networks. This system is designed to offer bespoke investment recommendations aligned with the risk appetite, financial objectives, and market dynamics of individual investors. The proposed method employs a long short-term memory network (LSTM) to generate personalised investment strategies in real time through the analysis of historical market data, investor behaviour data and real-time financial information. The innovation lies in the introduction of a multi-objective optimisation mechanism, which automatically adjusts the allocation of the portfolio through deep learning algorithms, optimises the balance between risk and return, and significantly improves the accuracy and practicality of investment recommendations. The experimental results demonstrate that, in comparison to the conventional investment advisory model, the system exhibits enhanced resilience in responding to market volatility across a range of simulated scenarios. It is also evident that the system is capable of formulating more forward-thinking investment strategies, while simultaneously demonstrating heightened stability and yield.