VolTS: A Volatility-based Trading System to forecast Stock Markets Trend using Statistics and Machine Learning

ArXiv ID: 2307.13422 “View on arXiv”

Authors: Unknown

Abstract

Volatility-based trading strategies have attracted a lot of attention in financial markets due to their ability to capture opportunities for profit from market dynamics. In this article, we propose a new volatility-based trading strategy that combines statistical analysis with machine learning techniques to forecast stock markets trend. The method consists of several steps including, data exploration, correlation and autocorrelation analysis, technical indicator use, application of hypothesis tests and statistical models, and use of variable selection algorithms. In particular, we use the k-means++ clustering algorithm to group the mean volatility of the nine largest stocks in the NYSE and NasdaqGS markets. The resulting clusters are the basis for identifying relationships between stocks based on their volatility behaviour. Next, we use the Granger Causality Test on the clustered dataset with mid-volatility to determine the predictive power of a stock over another stock. By identifying stocks with strong predictive relationships, we establish a trading strategy in which the stock acting as a reliable predictor becomes a trend indicator to determine the buy, sell, and hold of target stock trades. Through extensive backtesting and performance evaluation, we find the reliability and robustness of our volatility-based trading strategy. The results suggest that our approach effectively captures profitable trading opportunities by leveraging the predictive power of volatility clusters, and Granger causality relationships between stocks. The proposed strategy offers valuable insights and practical implications to investors and market participants who seek to improve their trading decisions and capitalize on market trends. It provides valuable insights and practical implications for market participants looking to.

Keywords: Volatility-based Trading, K-means++ Clustering, Granger Causality Test, Predictive Analytics, Backtesting, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts including volatility estimators (Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang), Granger Causality tests, and k-means clustering, which require a solid mathematical foundation to understand and implement. Empirically, it is highly rigorous, featuring a detailed backtesting framework with specific metrics (Maximum Drawdown, Sharpe, Sortino, Calmar ratios), uses real market data from NYSE and NasdaqGS, and includes a dedicated backtesting module (AitaBT), making it highly actionable for implementation.

  flowchart TD
    A["Research Goal<br>Forecast Stock Trend via<br>Volatility-based Trading Strategy"] --> B
    
    subgraph B ["Methodology"]
        direction TB
        B1["Data Collection<br>NYSE/NasdaqGS Stocks"] --> B2["Clustering<br>K-means++ on Volatility"]
        B2 --> B3["Granger Causality Test<br>Identify Predictive Pairs"]
        B3 --> B4["Trading Signal Logic<br>Buy/Sell based on Predictor"]
    end

    B --> C["Backtesting & Evaluation<br>Performance Metrics"]

    C --> D{"Key Findings"}
    D --> D1["Strategy Reliability & Robustness"]
    D --> D2["Profitable Opportunities Captured"]
    D --> D3["Volatility Clusters are Effective Predictors"]