Multi-level Attention driven GAT for recommending top profitable stocks and sectors

Financial technology has drawn significant attention from investors and companies. Recently there is growing demand in use of graph networks for time series data. While traditional stock analysis primarily focuses on predicting stock prices, there is comparatively less emphasis on recommending profitable stocks. Neglecting relationships between stocks and sectors can miss crucial shared information. In this work, we aim to recommend the top profitable stocks and sectors, utilizing time series data of stock prices and sector information. We introduce a novel deep learning-based model, Multilevel-Attention based Graph Attention Network designed to incorporate relationship between both stocks and sectors and rank them. First, we created a dataset for Indian stock market of NIFTY50 companies. Second, we used LSTM to create embedding and constructed fully connected graphs for stocks and sectors, and then using graph attention networks to learn latent interactions among them. Lastly, we used Multi-level and Hierarchical attention networks to rank the stocks and sectors. Comparing our multi-level attention approach to an approach with the absence of multi-level attention, the mean F1 score increased from 0.256 to 0.309, showing an improvement of $20.84 \%$. Similarly, mean accuracy score increased from 0.251 to 0.388, showing an improvement of $54.40 \%$. The implication of our research is varied as it can be applied in numerous scenarios like forecasting, helping in managing portfolios and assessing risks by analyzing trends and patterns of stocks.