Machine learning algorithms applied to the estimation of liquidity: the 10-year United States treasury bond
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Ignacio Manuel Luque Raya [1] ; Pablo Luque Raya [2]
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[1] Universidad de Granada
Universidad de Granada
Granada, España
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[2] Credit Suisse Group AG
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- Localización: European journal of management and business economics, ISSN-e 2444-8494, ISSN 2444-8451, Vol. 33, Nº. 3, 2024, págs. 341-365
- Idioma: inglés
- Enlaces
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Resumen
Purpose–Havingdefinedliquidity,the aimistoassessthepredictivecapacity ofits representative variables, so that economic fluctuations may be better understood. Design/methodology/approach– Conceptual variables that are representative of liquidity will be used to formulate the predictions. The results of various machine learning models will be compared, leading to some reflections on the predictive value of the liquidity variables, with a view to defining their selection. Findings– The predictive capacity of the model was also found to vary depending on the source of the liquidity, in so far as the data on liquidity within the private sector contributed more than the data on public sector liquidity to the prediction of economic fluctuations. International liquidity was seen as a more diffuse concept, and the standardization of its definition could be the focus of future studies. A benchmarking process was also performed when applying the state-of-the-art machine learning models. Originality/value– Better understanding of these variables might help us towarda deeperunderstanding of the operation of financial markets. Liquidity, one of the key financial market variables, is neither well-defined nor standardized in the existing literature, which calls for further study. Hence, the novelty of an applied study employing modern data science techniques can provide a fresh perspective on financial markets.
