@misc{Świercz_Wojciech_Comparative_2024,
 author={Świercz, Wojciech and Szostak, Radosław},
 contributor={Pauka, Marek. Redakcja and Słoński, Tomasz. Redakcja},
 identifier={DOI: 10.15611/2024.90.1.05},
 year={2024},
 rights={Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy},
 description={Cytuj za: Świercz, W., Szostak, R. (2024). Comparative Analysis of Predictive Models in Stock Market Price Forecasting. In M. Pauka, T. Słoński (Eds.), Finanse (pp. 60-78). Publishing House of Wroclaw University of Economics and Business.},
 publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu},
 language={eng},
 abstract={In this paper the authors test ARMA, ARIMA and LSTM neural network's model performance on one minute stock market data. Simulation of a random walk is also performed. Models are adjusted and/or trained on S&P500 data split 80:20. Test is performed on last 20% of S&P500 data and stocks: AAPL, 3M, GM. Correlations were checked to make correct conclusions. Out of all models ARIMA model performed best, achieving in some instances R2 score as high as 0.99996. All models performed well, with Random Walk simulation performing the worst.},
 title={Comparative Analysis of Predictive Models in Stock Market Price Forecasting},
 type={rozdział},
 keywords={ARMA, ARIMA, neural networks, stock market forecasting, sieci neuronowe, predykcja rynku akcji},
}