Graph centrality and deep learning for enhanced portfolio management

Financial market investors always try to manage their portfolios with regard to stock prices for maximizing returns with minimum risk in a constrained amount of time. Instead of selecting stocks at random, we present a novel method in this paper that creates a graph of the stock market. We determine the most significant stocks using this graph, centrality metrics, and graph representation techniques. Next, a hybrid deep learning model incorporating the Conv1D, GRU, and RNN layers predicts future prices and evaluates their risks. The S&P 500 index is used as a benchmark for these outcomes. These chosen stocks have been evaluated for their risk and return over three time periods — short, medium, and long-term — and contrasted with the S&P 500 benchmark. According to these findings, the suggested algorithms perform better than the S&P 500 over these time periods. Second, compared to selecting stocks at random, this method is more generalizable and can be used in a variety of markets and contexts. • A novel graph-based approach models S&P 500 data to capture risk dynamics. • We identify influential market stocks using graph centrality measures. • Node2Vec is applied to generate embeddings and select strategic stocks. • We use ensemble DL and Markowitz optimization to maximize the Sharpe ratio.