Stock price prediction using support vector regression on daily and up to the minute prices

TLDR

Predictive stock price systems aim to generate abnormal returns and support risk management, yet the Efficient Market Hypothesis suggests consistent anticipation is impossible; nevertheless, machine learning approaches are increasingly used, and prior studies have trained models on fixed periods without updating. The study applies Support Vector Regression to forecast prices for large and small capitalisations across three markets using both daily and minute‑level data. The SVR model’s prediction errors are evaluated and compared against the random walk benchmark proposed by the EMH. Results show that SVR has predictive power, particularly when the model is updated periodically, and that prediction precision improves during low‑volatility periods.

Abstract

The purpose of predictive stock price systems is to provide abnormal returns for financial market operators and serve as a basis for risk management tools. Although the Efficient Market Hypothesis (EMH) states that it is not possible to anticipate market movements consistently, the use of computationally intensive systems that employ machine learning algorithms is increasingly common in the development of stock trading mechanisms. Several studies, using daily stock prices, have presented predictive system applications trained on fixed periods without considering new model updates. In this context, this study uses a machine learning technique called Support Vector Regression (SVR) to predict stock prices for large and small capitalisations and in three different markets, employing prices with both daily and up-to-the-minute frequencies. Prediction errors are measured, and the model is compared to the random walk model proposed by the EMH. The results suggest that the SVR has predictive power, especially when using a strategy of updating the model periodically. There are also indicative results of increased predictions precision during lower volatility periods.