Forecasting Stock Trends with Volatility Clusters and Granger Causality
Summary
This proposed system uses volatility relationships among large stocks to generate trend signals. It first explores price data with correlation and autocorrelation analyses, technical indicators, hypothesis tests, statistical models, and variable selection. It then applies k-means++ to group the mean volatility of nine large stocks from the NYSE and NasdaqGS, focusing on the resulting mid-volatility cluster.
Granger causality tests are used within that cluster to identify stocks whose volatility may help predict another stock. A stock with a strong predictive relationship serves as a trend indicator for buy, sell, or hold decisions in the target stock. The document reports extensive backtesting and performance evaluation, and claims profitable opportunities and robustness, but supplies no numerical results, benchmark comparisons, or test design details in the provided text. Granger causality can indicate predictive value within a dataset; the description alone does not establish that the relationships will persist or support live trading after costs.
Key ideas
- The system combines statistical analysis, machine learning, and technical indicators to study stock trends.
- K-means++ groups stocks according to mean volatility, with analysis focused on the mid-volatility cluster.
- Granger causality tests identify candidate volatility-based predictors among stocks in that cluster.
- A predictor stock supplies buy, sell, or hold trend signals for a target stock.
- The authors report backtesting but provide no quantitative results or validation details in the supplied text.
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Full text
# VolTS: A Volatility-based Trading System to forecast Stock Markets Trend using Statistics and Machine Learning # VolTS: A Volatility-based Trading System to forecast Stock Markets Trend using Statistics and Machine Learning 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.
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