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    Conformity of Earthquake Magnitudes to Benford's Law: the Case of Kahramanmaras Earthquakes
    (Afet ve Acil Durum Yonetimi Baskanligi (AFAD), 2023) Ayyildiz, Nazif; Karadeniz, Erdinc; Iskenderoglu, Omer
    The aim of this study is to determine whether the earthquake magnitude data comply with Benford's Law. To this end, magnitude data of 14.565 earthquakes that occurred in Turkey between January 1, 2023, and February 27, 2023, were analyzed by comparing them with the numerical distribution of Benford's Law. According to the results obtained from the analysis, it has been determined that the earthquake magnitude digits conform to Benford's Law and closely follow Benford's Law with very small deviations. These small deviations are thought to arise from the rounding of magnitude data to a single decimal place, the inability to detect very small magnitude earthquakes occurring deeper than a certain depth, or very small measurement errors in the existing data. Therefore, it can be said that earthquake occurrences occur as a result of natural processes and earthquake magnitudes are determined correctly. © 2023 The Author(s).
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    How effective is machine learning in stock market predictions?
    (Cell Press, 2024) Ayyildiz, Nazif; Iskenderoglu, Omer
    In this study, it is aimed to compare the performances of the algorithms by predicting the movement directions of stock market indexes in developed countries by employing machine learning algorithms (MLMs) and determining the best estimation algorithm. For this purpose, the movement directions of indexes such as the NYSE 100 (the USA), NIKKEI 225 (Japan), FTSE 100 (the UK), CAC 40 (France), DAX 30 (Germany), FTSE MIB (Italy), and TSX (Canada) were estimated by employing the decision tree, random forest k -nearest neighbor, naive Bayes, logistic regression, support vector machines and artificial neural network algorithms. According to the results obtained, artificial neural networks were found to be the best algorithm for NYSE 100, FTSE 100, DAX 30 and FTSE MIB indices, while logistic regression was determined to be the best algorithm for the NIKKEI 225, CAC 40, and TSX indices. The artificial neural networks, which exhibited the highest average prediction performance, have been determined as the best prediction algorithm for the stock market indices of developed countries. It was also noted that artificial neural networks, logistic regression, and support vector machines algorithms were capable of predicting the directional movements of all indices with an accuracy rate of over 70 %.