@inproceedings{2b46b31b47384ad0b829d27769c1e960,
title = "“It looks all the same to me”: cross-index training for long-term financial series prediction",
abstract = "We investigate a number of Artificial Neural Network architectures (well-known and more “exotic”) in application to the long-term financial time-series forecasts of indexes on different global markets. The particular area of interest of this research is to examine the correlation of these indexes{\textquoteright} behaviour in terms of Machine Learning algorithms cross-training. Would training an algorithm on an index from one global market produce similar or even better accuracy when such a model is applied for predicting another index from a different market? The demonstrated predominately positive answer to this question is another argument in favour of the long-debated Efficient Market Hypothesis of Eugene Fama.",
keywords = "cross-training, Efficient Market Hypothesis, neural networks",
author = "Stanislav Selitskiy",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 9th International Conference on Machine Learning, Optimization, and Data Science, LOD 2023 ; Conference date: 22-09-2023 Through 26-09-2023",
year = "2024",
month = apr,
day = "16",
doi = "10.1007/978-3-031-53969-5\_26",
language = "English",
isbn = "9783031539688",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "348--363",
editor = "Giuseppe Nicosia and Varun Ojha and \{La Malfa\}, Emanuele and \{La Malfa\}, Gabriele and Pardalos, \{Panos M.\} and Renato Umeton",
booktitle = "Machine Learning, Optimization, and Data Science - 9th International Conference, LOD 2023",
address = "Germany",
}