ML in Finance: Portfolio Management via Side & Size Prediction on the Bonds Market

The rise in automation and utilization of algorithms in the last decades had been meaningful in several areas, finance, and portfolio management included. The present study combines two approaches to reach an integral optimized model. The first one is the traditional approach, which starting from...

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Autor principal: Sanchez, Nahuel Rodrigo
Otros Autores: Roccatagliata, Pablo
Formato: Tesis de maestría acceptedVersion
Lenguaje:Inglés
Publicado: Universidad Torcuato Di Tella 2023
Materias:
Predicción tecnológica
Algorithms
Riesgo del crédito
ARIMA regression
Out-of-sample backtests
Efficiency
Acceso en línea:https://repositorio.utdt.edu/handle/20.500.13098/11869
Aporte de:
Repositorio Digital Universidad Torcuato Di Tella de Universidad Torcuato Di Tella
id I57-R163-20.500.13098-11869
record_format dspace
spelling I57-R163-20.500.13098-118692023-06-07T07:33:50Z ML in Finance: Portfolio Management via Side & Size Prediction on the Bonds Market Sanchez, Nahuel Rodrigo Roccatagliata, Pablo Predicción tecnológica Algorithms Riesgo del crédito ARIMA regression Out-of-sample backtests Efficiency The rise in automation and utilization of algorithms in the last decades had been meaningful in several areas, finance, and portfolio management included. The present study combines two approaches to reach an integral optimized model. The first one is the traditional approach, which starting from forecasting the future bond yield curve, generates a decision to take: establish a long position (expecting a rise on the price) or a short one (expecting a fall on the price). Therefore, the output of this first model will be to determine the position side. The second approach is the application of the bet-sizing technique to optimize the resulting decisions from the traditional model by assigning them a probability of being correct: decisions with a low probability of generating profits will have a lower size, while decisions with a high probability of generating returns will have a bigger size. The algorithms used were the ARIMA regression for the traditional model and random forest for the bet-sizing model. Cross-validation and out-of-sample backtests were conducted to evaluate how the model would have performed and results show that employing the integrated optimized model exhibits higher Sharpe ratios than using only the traditional approach. The work demonstrates that the modern techniques used along with the traditional ones reach better efficiency on returns than when only traditional models are employed. Additionally, generalizations to other areas inside finance, both on asset management as well as on credit risk are discussed. 2023-06-06T17:47:41Z 2023-06-06T17:47:41Z 2020 info:eu-repo/semantics/masterThesis info:ar-repo/semantics/tesis de maestría info:eu-repo/semantics/acceptedVersion https://repositorio.utdt.edu/handle/20.500.13098/11869 eng info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-sa/2.5/ar/ 39 p. application/pdf application/pdf Universidad Torcuato Di Tella
institution Universidad Torcuato Di Tella
institution_str I-57
repository_str R-163
collection Repositorio Digital Universidad Torcuato Di Tella
language Inglés
orig_language_str_mv eng
topic Predicción tecnológica
Algorithms
Riesgo del crédito
ARIMA regression
Out-of-sample backtests
Efficiency
spellingShingle Predicción tecnológica
Algorithms
Riesgo del crédito
ARIMA regression
Out-of-sample backtests
Efficiency
Sanchez, Nahuel Rodrigo
ML in Finance: Portfolio Management via Side & Size Prediction on the Bonds Market
topic_facet Predicción tecnológica
Algorithms
Riesgo del crédito
ARIMA regression
Out-of-sample backtests
Efficiency
description The rise in automation and utilization of algorithms in the last decades had been meaningful in several areas, finance, and portfolio management included. The present study combines two approaches to reach an integral optimized model. The first one is the traditional approach, which starting from forecasting the future bond yield curve, generates a decision to take: establish a long position (expecting a rise on the price) or a short one (expecting a fall on the price). Therefore, the output of this first model will be to determine the position side. The second approach is the application of the bet-sizing technique to optimize the resulting decisions from the traditional model by assigning them a probability of being correct: decisions with a low probability of generating profits will have a lower size, while decisions with a high probability of generating returns will have a bigger size. The algorithms used were the ARIMA regression for the traditional model and random forest for the bet-sizing model. Cross-validation and out-of-sample backtests were conducted to evaluate how the model would have performed and results show that employing the integrated optimized model exhibits higher Sharpe ratios than using only the traditional approach. The work demonstrates that the modern techniques used along with the traditional ones reach better efficiency on returns than when only traditional models are employed. Additionally, generalizations to other areas inside finance, both on asset management as well as on credit risk are discussed.
author2 Roccatagliata, Pablo
author_facet Roccatagliata, Pablo
Sanchez, Nahuel Rodrigo
format Tesis de maestría
Tesis de maestría
acceptedVersion
author Sanchez, Nahuel Rodrigo
author_sort Sanchez, Nahuel Rodrigo
title ML in Finance: Portfolio Management via Side & Size Prediction on the Bonds Market
title_short ML in Finance: Portfolio Management via Side & Size Prediction on the Bonds Market
title_full ML in Finance: Portfolio Management via Side & Size Prediction on the Bonds Market
title_fullStr ML in Finance: Portfolio Management via Side & Size Prediction on the Bonds Market
title_full_unstemmed ML in Finance: Portfolio Management via Side & Size Prediction on the Bonds Market
title_sort ml in finance: portfolio management via side & size prediction on the bonds market
publisher Universidad Torcuato Di Tella
publishDate 2023
url https://repositorio.utdt.edu/handle/20.500.13098/11869
work_keys_str_mv AT sancheznahuelrodrigo mlinfinanceportfoliomanagementviasidesizepredictiononthebondsmarket
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