Optimal Data Construction in Supervised Machine Learning for Financial Predictionopen access
- Authors
- Kim, Hongjoong; Moon, Kyoung-Sook
- Issue Date
- Mar-2024
- Publisher
- EDITURA ASE
- Keywords
- prediction offinancial market; deep learning; smoothing offinancial time series; data construction
- Citation
- ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, v.58, no.1, pp 5 - 20
- Pages
- 16
- Journal Title
- ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH
- Volume
- 58
- Number
- 1
- Start Page
- 5
- End Page
- 20
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/91240
- DOI
- 10.24818/18423264/58.1.24.01
- ISSN
- 0424-267X
1842-3264
- Abstract
- Despite the widespread adoption of machine learning and deep learning in financial market forecasting, achieving satisfactory results remains a persistent challenge due to the inherent complexities, nonlinearity, and uncertainties in financial data. This study addresses this challenge by introducing two innovative methods in supervised machine learning for feature and label construction. Features are derived from the graphical representation of price data to capture inherent patterns, and the target variable definition is grounded in data momentum, enabling predictions beyond the confines of the training data. These methodological advancements not only enhance regression accuracy, but also expedite model training by facilitating predictions of unobserved values during training. Empirical analysis, employing various financial market datasets and neural network models, demonstrates a substantial improvement in prediction accuracy and efficiency.
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