AI Prediction for Stock Trading

Abstract

The key element of stock trading is accurate stock price prediction, and reliable and stable decisions can significantly affect profitability. Traditional prediction models have limitations due to intricacies and instantaneous dynamics within financial environments. This study explores an Artificial Intelligence (AI)-driven stock price prediction method tailored for stocks trading, using advanced machine-learning and deep-learning models to improve forecast accuracy and trading efficiency.

An initial focus was placed on the key role of feature engineering and selection in optimizing model performance. It integrates a variety of technical indicators such as moving averages (SMA/EMA), relative strength index (RSI), MACD and Bollinger bands, as well as principal analysis (PCA) and quantum-inspired feature extraction, and scaling methods such as MinMaxScaler are used to standardized input data. These are combined with advanced feature filtering techniques to enhance overall model performance.

The research integrates different AI methods, feature engineering with historical price trends, market indicators, and sentiment insights drawn from financial news and social platforms, Natural Language Processing (NLP) technology is included to enhance the prediction model by capturing market sentiment and investor behavior. These include Long Short-Term Memory (LSTM) networks, transformer models, LightGBM, Reinforcement Learning (RL), and Hybrid LSTM + Transformer with Quantum-Enhanced Feature Selection.

To evaluate the true effectiveness of the AI models, the study conducted rigorous back testing using real-time stock price data and annualized returns using actual simulated investments. The performance of AI-driven models was compared with traditional benchmarks, including ARIMA, GARCH, and classic machine learning algorithms such as support vector machines (SVM) and random forests. The main technical indicators used to evaluate the model are R², mean absolute error (MAE), and root mean square error (RMSE).

The experimental results demonstrate that the AI hybrid model achieves R² = 0.97 (MAE = 0.0186, RMSE = 0.0278) on NVDA 15-minute intraday data in predicting short-term stock price movements, which is essential for ultra-short-term and ultra-high frequency trading strategies. Model interpretability was further explored using SHAP (which measures the average marginal contribution of each feature) and LIME (which provides instance-level explanations), helping ensure transparency in the AI-driven decision-making process.

In addition, a hybrid data merging strategy and multiple feature engineering data are proposed to increase robustness by integrating a hybrid AI structure of deep learning and traditional statistical models. This study sheds light on how AI can help lower risks associated with stock market investments and optimizing options trading decisions. However, challenges such as market emergencies events, data noise, and overfitting require further exploration.

Overall, this research demonstrates the power of AI models to predict the financial market and provides help for investors and researchers who use AI to make advanced financial forecasts.