Paper: arXiv 2302.08911

Authors: Raihan Tanvir, Md Tanvir Rouf Shawon, Md. Golam Rabiul Alam

Abstract

Stock market forecasting is a classic problem that has been thoroughly investigated using machine learning and artificial neural network based tools and techniques. Interesting aspects of this problem include its time reliance as well as its volatility and other complex relationships. To combine them, hidden markov models (HMMs) have been utilized to anticipate the price of stocks. We demonstrated the Maximum A Posteriori (MAP) HMM method for predicting stock prices for the next day based on previous data. An HMM is trained by analyzing the fractional change in the stock price as well as the intraday high and low values. It is then utilized to produce a MAP estimate across all possible stock prices for the next day. The approach demonstrated in our work is quite generalized and can be used to predict the stock price for any company, given that the HMM is trained on the dataset of that company’s stocks dataset. We evaluated the accuracy of our models using some extensively used accuracy metrics for regression problems and came up with a satisfactory outcome.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper applies a standard Maximum A Posteriori (MAP) Hidden Markov Model framework to stock price prediction on the Dhaka Stock Exchange. While mathematically sound regarding basic HMM formulations, the methodology is largely derivative of existing literature with modest empirical evaluation.

Research Flowchart

  flowchart TD
    A["Research Goal: Predict Next-Day Stock Price (DSE)"] --> B["Data Inputs: Historical Stock Data<br>(Close, High, Low Prices)"]
    B --> C["Feature Extraction: Compute Fractional Changes<br>(Price, Intraday High & Low)"]
    C --> D["Model Training: Train Hidden Markov Model (HMM)"]
    D --> E["Prediction: Maximum A Posteriori (MAP) Estimation"]
    E --> F["Outcomes: Accurate Next-Day Stock Predictions<br>& Evaluated Regression Metrics"]