Personalized Stock Market Prediction using Dynamic Risk Profiling

In today’s time, they say that retail investors have difficulty predicting the fluctuations of the stock market because of the lack of a personalized touch in prediction models. This refers to traditional trading and its predictions that tend to completely disregard the individuality of a person. The platform, a new concept hybrid stock prediction model offers a solution that is much needed. It uses machine learning techniques to provide suggestions for investment. Some of them include Long Short-Term Memory networks (LSTM), Hidden Markov Models (HMM) and even Transformers. By integrating with Edge’s contextual comprehension with LSTM’s sequence forecasting, our new model managed to decrease RMSE to 1.87 which is a 22% improvement in comparison to when individual models were used. Our approach also succeeds to create a Sharpe Ratio of 1.38 because of the increased returns and assures to lower portfolio volatility by 18%. In other words, the system was able to reduce the volatility of portfolio returns and increase the risk-adjusted returns. In conclusion, the platform enables its users to expand into multiple international markets as well as allowing them to make sound financial decisions because it helps solve crucial issues that traditional systems face.