Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
Volatility assessment remains a central task in investment risk management and stock market strategy development. It measures the degree of asset price fluctuation, indicating how much a price can vary during a trading day. Errors in estimating future volatility directly affect option pricing, portfolio risk management, and the overall effectiveness of investment strategies.
There are traditional methods for estimating volatility, such as the so-called HAR model. It evaluates three time horizons based on specified parameters and produces a linear forecast of next-day volatility. However, this model does not perform well when assessing nonlinear risks. The author therefore set out to determine whether machine learning methods could deliver better results than the traditional econometric approach.
The study is based on high-frequency data for the ten most liquid stocks traded on the Moscow Stock Exchange from 2014 to 2025. Price changes at 10-minute intervals were analysed, making it possible to calculate realised volatility and its variations for subsequent forecasting. In addition, variables reflecting return characteristics and market structure were incorporated to capture the specific features of trading on the Russian stock exchange. The results of the classical HAR model were then compared with those of machine learning methods, including Random Forest, XGBoost, and LightGBM, which can account for non-linearities and complex interactions among variables. To evaluate the forecast quality, the author not only applied standard statistical metrics but also tested the predicted volatility in a simulation of actual stock trading.
Nikita Lysenok
The results demonstrated a consistent advantage of the machine learning models over the classical HAR model. The most significant improvements were observed in short- and medium-term forecasts. Average forecast errors decreased by approximately 15%, while prediction accuracy increased by up to 23%, depending on the method, with LightGBM delivering the best performance. 'Machine learning models demonstrate superior performance during both stable market conditions and periods of heightened volatility. While they do not possess the gift of foresight, they can more accurately signal rising risks, providing investors with time to adjust and rebalance their portfolios,' explains Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE FES.
A simulation of stock trading using enhanced volatility estimates from machine learning showed an increase in annual profitability of 7 percentage points, rising from 6.48% to 13.68%. 'This is where the cumulative effect and risk asymmetry come into play. More accurate volatility estimates allow for optimal position sizing. The difference may be small for a single trade, but over hundreds of trades, it translates into substantial gains in annual returns,' explains Lysenok.
At the same time, the author emphasised that machine learning methods for predicting volatility require careful parameter tuning and are highly dependent on the quality of the training data. Higher accuracy in forecasting volatility does not automatically translate into economic gains, and trading still demands considerable skill and involves significant risk. 'A key factor in sustainable profitability is risk management: accurate volatility forecasts allow for timely position adjustments and help avoid severe drawdowns,' the author notes.
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