Deep learning for Stock Market Prediction (2004.01497v1)

Abstract: Prediction of stock groups' values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all the algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.

• The paper demonstrates that LSTM models achieve minimal prediction errors and near-perfect R2 values across multiple forecasting horizons.
• It evaluates a range of techniques, including tree-based methods like XGBoost and various ensemble algorithms, using ten key technical indicators.
• The study underscores the practical impact of AI-driven forecasts in aiding investor decisions and enhancing risk management in financial markets.

Analysis of "Deep Learning for Stock Market Prediction"

"Deep Learning for Stock Market Prediction" by Nabipour et al. addresses the ever-complex problem of forecasting stock values utilizing advanced machine learning techniques. The research specifically focuses on four sectors from the Tehran Stock Exchange over a ten-year period, applying both classical and novel ensemble-based and deep learning models. The authors aim to predict stock market values for various time horizons and evaluate the efficacy of diverse algorithms.

Notably, the paper applies a comprehensive suite of machine learning algorithms, including Decision Trees, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting, XGBoost, Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM). Among these, the LSTM model displayed superior performance over other methodologies due to its capacity to capture dependencies in time-series data, achieving minimal prediction errors with formidable R2 values close to 1 across multiple prediction windows. Tree-based models also demonstrated competitive predictive capabilities, especially XGBoost, which balanced accuracy and computational efficiency.

The paper uses ten technical indicators drawn from historical stock data to train these models. This approach highlights the necessity of incorporating rich feature sets that capture market dynamics comprehensively. The presentation of results via various error metrics, including MAPE, MAE, and R2, enables a rigorous assessment of each model's accuracy and fit. It is noteworthy that the LSTM, despite having increased computational overhead, consistently achieved the lowest error rates, indicating its robustness in handling nonlinear and intricate stock market data.

Practically, the implications of this research are substantial. Accurate predictions from machine learning models hold potential to aid investors and mitigate risks by providing reliable forecasts across different sectors. Theoretically, the work contributes to existing literature by evidencing the efficacy of deep learning models in a challenging domain like stock market prediction, signifying a step forward in utilizing AI for complex financial analyses.

Future research could aim at enhancing the computational efficiency of LSTM models, perhaps by exploring hybrid models that incorporate other machine learning techniques to offset LSTM's intensive resource requirements. Additionally, expanding the dataset to incorporate diverse international markets may yield insights into the adaptability of these algorithms across different contexts. Furthermore, integrating sentiment analysis from news and social media could refine prediction models by factoring in qualitative market shifts, offering a holistic approach to stock market forecasting using AI.

Overall, this paper effectively demonstrates the potential of machine learning, particularly deep learning, in the domain of financial predictions, urging a paradigm shift from traditional statistical methods toward robust AI-driven forecasting tools.