Discussing the article: "Neural Networks in Trading: An Ensemble of Agents with Attention Mechanisms (MASAAT)"
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Check out the new article: Neural Networks in Trading: An Ensemble of Agents with Attention Mechanisms (MASAAT).
Portfolio management of financial instruments is a key component of investment decision-making, aimed at increasing returns while minimizing risks through the dynamic allocation of capital across assets. The high volatility of financial markets, where asset prices depend on a multitude of factors, complicates the construction of an optimal portfolio that simultaneously addresses two conflicting objectives: maximizing profits and minimizing risks. Traditional financial models, built on various investment principles, often prove effective in a single market but may fail under the complex and dynamic conditions of modern markets.
In recent years, growing attention has been given to machine learning methods for analyzing non-stationary price series. Among these, deep learning and reinforcement learning strategies have demonstrated notable success in computational finance. However, price data in financial markets are typically noisy time series, where extracting signals indicative of future trends is challenging.
One promising approach is presented in the paper "Developing an attention-based ensemble learning framework for financial portfolio optimisation". The authors introduce an innovative adaptive trading framework integrating attention mechanisms and time-series analysis (Multi-Agent and Self-Adaptive portfolio optimisation framework integrated with Attention mechanisms and Time series — MASAAT). Within this framework, multiple agents are deployed to observe and analyze directional changes in asset prices at varying levels of granularity. The goal is to enable thorough portfolio rebalancing to balance returns and risks in highly volatile markets.
Author: Dmitriy Gizlyk