International Journal of Social Science & Economic Research
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Title:
Investigating Performance of ESN’s in Forecasting Financial Metrics When Compared To Traditional RNN Types

Authors:
Barin, Batu

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Barin, Batu
Uskudar American Academy, Istanbul, Turkiye

MLA 8
Batu, Barin. "Investigating Performance of ESN’s in Forecasting Financial Metrics When Compared To Traditional RNN Types." Int. j. of Social Science and Economic Research, vol. 9, no. 6, June 2024, pp. 1950-1982, doi.org/10.46609/IJSSER.2024.v09i06.023. Accessed June 2024.
APA 6
Batu, B. (2024, June). Investigating Performance of ESN’s in Forecasting Financial Metrics When Compared To Traditional RNN Types. Int. j. of Social Science and Economic Research, 9(6), 1950-1982. Retrieved from https://doi.org/10.46609/IJSSER.2024.v09i06.023
Chicago
Batu, Barin. "Investigating Performance of ESN’s in Forecasting Financial Metrics When Compared To Traditional RNN Types." Int. j. of Social Science and Economic Research 9, no. 6 (June 2024), 1950-1982. Accessed June, 2024. https://doi.org/10.46609/IJSSER.2024.v09i06.023.

References

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ABSTRACT:
This research investigates the performance of Echo State Networks (ESN) in forecasting financial metrics and compares their effectiveness against traditional recurrent neural network (RNN) architectures like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU), as well as Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) models. By analyzing datasets sourced from Yahoo Finance for various financial indices, exchange-traded funds and stocks over five years, this study examines the accuracy, and structural simplicity of ESNs in predicting close prices, daily volatility, and log returns. Results indicate that ESNs, with their reservoir computing capabilities, outperform traditional RNNs by achieving lower mean absolute error (MAE) and mean squared error (MSE) overall, highlighting their potential as efficient and robust tools for financial time-series forecasting.

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