Multi-Layer Perceptron-Based Classification with Application to Outlier Detection in Saudi Arabia Stock Returns.

Autor:	Rashedi, Khudhayr A., Ismail, Mohd Tahir, Al Wadi, Sadam, Serroukh, Abdeslam, Alshammari, Tariq S., Jaber, Jamil J.
Předmět:	RATE of return on stocks OUTLIER detection STOCK price indexes RECEIVER operating characteristic curves PRICE inflation
Zdroj:	Journal of Risk & Financial Management; Feb2024, Vol. 17 Issue 2, p69, 13p
Abstrakt:	We aim to detect outliers in the daily stock price indices from the Saudi Arabia stock exchange (Tadawul) with 2026 observations from October 2011 to December 2019 provided by the Saudi Authority for Statistics and the Saudi Central Bank. We apply the Multi-Layer Perceptron (MLP) algorithm for detecting outliers in stock returns. We select the inflation rate (Inflation), oil price (Loil), and repo rate (Repo) as input variables to the MLP architecture. The performance of the MLP is evaluated using standard metrics for binary classification, namely the false positive rate (FP rate), false negative rate (FN rate), F-measure, Matthews correlation coefficient (MCC), accuracy (ACC), and area under the ROC curve (AUC). The results demonstrate the efficiency and good performance of the MLP algorithm based on different criteria tests. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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