Predicting stock market fluctuations is notoriously challenging, even for experienced experts. Despite substantial research efforts to identify the best datasets and methods for forecasting future trends, predicting stock market fluctuations remains daunting. Investors with access to options chain data have a unique advantage in predicting future stock movements. Using a novel approach, this study leverages historical prices and options chain data to predict the stock market’s closing price movement. We employ a piece-wise linear encoding technique to convert historical prices and options chain data into a high-dimensional vector embedding. This numerical embedding serves as input for various statistical and neural network classifiers evaluated based on their accuracy and Matthew correlation coefficient (MCC). Furthermore, we introduce a cosine similarity-based strategy that does not require training, unlike conventional machine learning and deep learning models. This approach relies on finding comparable vectors in past data to forecast stock movements accurately. The proposed model’s results demonstrate a substantial improvement in both accuracy and MCC scores over the best-performing baseline methods, highlighting the effective utilisation of numerical embedding for precise predictions of closing price movements. Furthermore, the model exhibits consistent performance gains with increasing data volume and significantly outperforms state-of-the-art methods in terms of performance, training and inference speed, making it both scalable and computationally efficient.