A Study on Stock Forecasting Using Deep Learning and Statistical Models

ArXiv ID: 2402.06689 “View on arXiv”

Authors: Unknown

Abstract

Predicting a fast and accurate model for stock price forecasting is been a challenging task and this is an active area of research where it is yet to be found which is the best way to forecast the stock price. Machine learning, deep learning and statistical analysis techniques are used here to get the accurate result so the investors can see the future trend and maximize the return of investment in stock trading. This paper will review many deep learning algorithms for stock price forecasting. We use a record of s&p 500 index data for training and testing. The survey motive is to check various deep learning and statistical model techniques for stock price forecasting that are Moving Averages, ARIMA which are statistical techniques and LSTM, RNN, CNN, and FULL CNN which are deep learning models. It will discuss various models, including the Auto regression integration moving average model, the Recurrent neural network model, the long short-term model which is the type of RNN used for long dependency for data, the convolutional neural network model, and the full convolutional neural network model, in terms of error calculation or percentage of accuracy that how much it is accurate which measures by the function like Root mean square error, mean absolute error, mean squared error. The model can be used to predict the stock price by checking the low MAE value as lower the MAE value the difference between the predicting and the actual value will be less and this model will predict the price more accurately than other models.

Keywords: Deep Learning, Time Series Forecasting, LSTM, ARIMA, Stock Price Prediction, Equities (Stock Indices)

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Philosophers
• Why: The paper primarily reviews and applies standard deep learning and statistical models (ARIMA, LSTM, RNN, CNN) with a focus on common error metrics (RMSE, MAE), showing limited advanced mathematical derivations or novel statistical theory. While it uses a specific S&P 500 dataset and describes an experimental setup, the methodology is introductory, lacks detailed backtesting protocols, code, or robust implementation specifics, and serves more as a conceptual review than a backtest-ready empirical study.

  flowchart TD
    A["Research Goal: Determine the best model<br>for accurate stock price forecasting"] --> B["Input Data: S&P 500 Index Time Series"]
    B --> C["Methodology: Compare Models"]
    C --> D["Statistical Models<br>ARIMA, Moving Averages"]
    C --> E["Deep Learning Models<br>LSTM, RNN, CNN, Full CNN"]
    D --> F["Model Evaluation<br>Metrics: RMSE, MAE, MSE"]
    E --> F
    F --> G{"Outcome: Model Selection"}
    G --> H["Best Model: Lowest MAE<br>indicates highest prediction accuracy"]