Stock price prediction using support vector regression on daily and up to the minute prices
| Autor: | Herbert Kimura, Vinicius Amorim Sobreiro, Bruno Miranda Henrique |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
0209 industrial biotechnology Economics and Econometrics Computer science Applied Mathematics Financial market Risk management tools 02 engineering and technology Random walk lcsh:QA75.5-76.95 Computer Science Applications Efficient-market hypothesis Support vector machine 020901 industrial engineering & automation lcsh:Finance lcsh:HG1-9999 0202 electrical engineering electronic engineering information engineering Predictive power Econometrics Business Management and Accounting (miscellaneous) 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science Volatility (finance) Finance Stock (geology) |
| Zdroj: | Journal of Finance and Data Science, Vol 4, Iss 3, Pp 183-201 (2018) |
| ISSN: | 2405-9188 |
| Popis: | 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. Keywords: Prediction, Stock market, Machine learning, Support vector regression, High frequency trading |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|