Stock volatility refers to the degree of variation in a stock’s price over time. It is a measure of the magnitude of price fluctuations, reflecting the market’s uncertainty and the potential for rapid changes in an asset’s value. High stock volatility signifies greater price swings, indicating a higher level of risk and uncertainty associated with the investment. Conversely, low stock volatility suggests relatively stable price movements and a lower level of market risk.
Understanding and analyzing stock volatility is crucial for investors, as it impacts investment decisions, risk management strategies, and portfolio diversification. Reference [1] proposed a trading approach that utilizes stock volatility as a filter,
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 midvolatility 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.
In our opinion, the proposed method appears relatively intricate, and the scope of the stock universe seems limited. Nonetheless, we concur with the author’s assertion that medium-volatility stocks are more favorable candidates for trading. The underlying principle of targeting medium-volatility stocks aligns with sound trading strategies that seek to balance risk and potential returns effectively.
In this study, our focus is on the mid-volatility to either close open positions or avoid entering a position when the expected volatility coefficient is high, thereby limiting the risk of losses. On the other hand, if the expected volatility is too low, it does not present any opportunities for gains.
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References
[1] Ivan Letteri, VolTS: A Volatility-based Trading System to forecast Stock Markets Trend using Statistics and Machine Learning, arXiv:2307.13422 [q-fin.TR]
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