Sirvi Märksõna "autoregressive moving average" järgi

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Predicting stock return and volatility with machine learning and econometric models— a comparative case study of the Baltic stock market
(Tartu Ülikool, 2021) Nõu, Anders; Sharma, Rajesh, juhendaja; Eratalay, Mustafa Hakan, juhendaja; Lapitskaya, Darya, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Predicting the stock market is a widely researched area of study that is a challenging task. The nature of the problem lies in correctly forecasting the direction and the magnitude of the stock market movement. The severity of the problem exists due to the stock market being impacted by a multitude of factors. There are numerous ways to analyse the stock market and make appropriate investment decisions, but it is challenging to decide the best approach. Here we show which approach is more effective in predicting the returns and volatility of the Baltic stock market: the machine learning or econometric approach. There is a low amount of research on using machine learning or econometric models to predict the Baltic stock market. However, there are no comparative researches that offer a fair comparison between the different approaches for the Baltic stock market. Regarding results, the lowest symmetric mean absolute percentage error for the support vector regression model is 61.90%, and for the autoregressive moving average model, it is 165.43%. The lowest symmetric mean absolute percentage error for GARCH is 51.05%, and for the GARCH-ANN model, it is 61.65%. Overall, the machine learning models outperform the econometric models in most of the evaluated metrics. However, the econometric models’ results are comparable to the machine learning models’ results in most cases.
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Predicting stock return and volatility with machine learning and econometric models: A comparative case study of the Baltic stock market
(2021) Nõu, Anders; Lapitskaya, Darya; Eratalay, Mustafa Hakan; Sharma, Rajesh
For stock market predictions, the essence of the problem is usually predicting the magnitude and direction of the stock price movement as accurately as possible. There are different approaches (e.g., econometrics and machine learning) for predicting stock returns. However, it is non-trivial to find an approach which works the best. In this paper, we make a thorough analysis of the predictive accuracy of different machine learning and econometric approaches for predicting the returns and volatilities on the OMX Baltic Benchmark price index, which is a relatively less researched stock market. Our results show that the machine learning methods, namely the support vector regression and k-nearest neighbours, predict the returns better than autoregressive moving average models for most of the metrics, while for the other approaches, the results were not conclusive. Our analysis also highlighted that training and testing sample size plays an important role on the outcome of machine learning approaches.