Data-Driven Stop-Loss Hedging Strategies for NSE Using Machine Learning Techniques

The financial markets are inherently volatile, and investors constantly seek ways to mitigate risks while maximizing returns. This work aims to develop a stop-loss hedging strategy for a portfolio of stocks listed on the National Stock Exchange (Nse) to minimize losses and maximize returns. This strategy utilizes real-time market data to adjust stop-load levels dynamically by predicting future stock. This will protect against market downwards. We used long short-term memory (LSTM) neural networks to predict the next stock value. This AI-driven model allows the system to predict the next stock by using real-time market data and adjust the stoploss level accordingly. In addition, this system integrates a hedging strategy. It determines whether to hedge based on the predicted stock price compared to the current price. If the predicted price is less than the current price a hedging strategy like short selling or buying put option is suggested. This automated solution is useful for retail investors. This strategy will protect against losses and returns maximum profits. When the market is upside the stoploss position also increases and will make maximum profits. If the market is downward it will protest from losses by putting a stoploss level and it will return minimum losses. Here we use different algorithms in the work like decision tree, random forest, LSTM, and SVM. Based on the all algorithms we got the highest accuracy for the decision tree and the accuracy is 99.54%.