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Utilizing Machine Learning for Trading Algorithms Exploiting the Time Series Momentum Anomaly

Odenbrand, Martin LU and Svensson Bromert, Sebastian LU (2019) In LUTFMS—3368—2019 FMSM01 20191
Mathematical Statistics
Abstract
Momentum or trend following investing refers to trading strategies constructed around the idea that in financial markets, the current trend will, more often then not, prevail. In the context of asset prices, this
means that previous returns or the price development of an asset is indicative of similar future returns and price development. While at odds with established theory such as the weaker form of market efficiency, as defined by the efficient market hypothesises, the pricing anomalies have proven robust enough for an industry of funds, using systematic trading, to rely heavily upon them. This thesis aims at building a profitable trading strategy around the momentum anomaly by using machine learning and common momentum indicators.
... (More)
Momentum or trend following investing refers to trading strategies constructed around the idea that in financial markets, the current trend will, more often then not, prevail. In the context of asset prices, this
means that previous returns or the price development of an asset is indicative of similar future returns and price development. While at odds with established theory such as the weaker form of market efficiency, as defined by the efficient market hypothesises, the pricing anomalies have proven robust enough for an industry of funds, using systematic trading, to rely heavily upon them. This thesis aims at building a profitable trading strategy around the momentum anomaly by using machine learning and common momentum indicators.
The underlying assets will be futures contracts due to their frequent use in the industry and generally high liquidity. Following the exploration of different machine learning algorithms, Random forest was chosen and subsequently optimised on training data by cross-validation using the model evaluation metric Matthews Correlation coefficient. These fitted models were backtested in three ways and benchmarked against simple trading strategies as well as buy-and-hold strategies using several performance metrics. The final result indicates great performance during strong negative trends, such as the financial crises, and an ability to lessen drawdowns. However, the models ultimately fail to act as robust and profitable strategies over a longer time horizon. (Less)
Please use this url to cite or link to this publication:
author
Odenbrand, Martin LU and Svensson Bromert, Sebastian LU
supervisor
organization
course
FMSM01 20191
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Machine learning, time series momentum, moving average crossover, MACD, Hodrick-Prescott filter, random forest, pricing anomaly, computational finance
publication/series
LUTFMS—3368—2019
report number
2019:E31
ISSN
1404-6342
language
English
id
8982865
date added to LUP
2020-03-09 10:37:08
date last changed
2020-03-09 10:37:08
@misc{8982865,
  abstract     = {{Momentum or trend following investing refers to trading strategies constructed around the idea that in financial markets, the current trend will, more often then not, prevail. In the context of asset prices, this
means that previous returns or the price development of an asset is indicative of similar future returns and price development. While at odds with established theory such as the weaker form of market efficiency, as defined by the efficient market hypothesises, the pricing anomalies have proven robust enough for an industry of funds, using systematic trading, to rely heavily upon them. This thesis aims at building a profitable trading strategy around the momentum anomaly by using machine learning and common momentum indicators.
The underlying assets will be futures contracts due to their frequent use in the industry and generally high liquidity. Following the exploration of different machine learning algorithms, Random forest was chosen and subsequently optimised on training data by cross-validation using the model evaluation metric Matthews Correlation coefficient. These fitted models were backtested in three ways and benchmarked against simple trading strategies as well as buy-and-hold strategies using several performance metrics. The final result indicates great performance during strong negative trends, such as the financial crises, and an ability to lessen drawdowns. However, the models ultimately fail to act as robust and profitable strategies over a longer time horizon.}},
  author       = {{Odenbrand, Martin and Svensson Bromert, Sebastian}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{LUTFMS—3368—2019}},
  title        = {{Utilizing Machine Learning for Trading Algorithms Exploiting the Time Series Momentum Anomaly}},
  year         = {{2019}},
}