### Abstract

Any investment strategy requires some form of asset evaluation, that is, determination of the price or fundamental value, and the prediction of likely future price movements using financial, technical, or fundamental indicators. The decision to adopt a long or short position in an asset requires a view on its immediate future price movements. Financial forecasting involves a huge data processing exercise, which may be noisy, nonstationary, and unstructured in nature, for which customary statistical methods, such as linear logistic regression and discriminant analysis are frequently applied. The development of more flexible methods, such as support vector machine classification, offers practitioners potentially better and more powerful solutions. This chapter applies support vector machines (SVM) to predict the direction of price changes for a small set of Dow Jones Industrial Average stocks and tests them against the predictions obtained from logistic regression analysis. SVM is a machine learning algorithm, which is characterized by its particular decision functions and ability to apply linear and non-linear transformations using different kernel functions. The results show that SVM improves on simple logistic regression and provides more accuracy in predicting price changes. As SVM is established on structural risk minimization, it is more resistive to overfitting than other learning methods used for empirical risk minimization and may perform better. It also performs well in comparison to other commonly used forecasting methods such as ARIMA and Artificial Neural Networks.

Language | English |
---|---|

Title of host publication | Handbook of short selling |

Editors | Greg N. Gregoriou |

Place of Publication | Waltham |

Publisher | Elsevier |

Chapter | 32 |

Pages | 467-478 |

Number of pages | 12 |

Edition | 1st |

ISBN (Electronic) | 9780123877253 |

ISBN (Print) | 9780123877246 |

DOIs | |

Publication status | Published - 2012 |

Externally published | Yes |

### Fingerprint

### Keywords

- book-to-market ratio
- dividend yield
- hyperplane
- investment returns
- logistic regression
- price-to-earnings ratio
- radial basis function
- Sharpe ratios
- support vector machines
- traded volume factor

### Cite this

*Handbook of short selling*(1st ed., pp. 467-478). Waltham: Elsevier. https://doi.org/10.1016/B978-0-12-387724-6.00032-5

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*Handbook of short selling.*1st edn, Elsevier, Waltham, pp. 467-478. https://doi.org/10.1016/B978-0-12-387724-6.00032-5

**Machine learning and short positions in stock trading strategies.** / Allen, David E.; Powell, Robert J.; Singh, Abhay K.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review

TY - CHAP

T1 - Machine learning and short positions in stock trading strategies

AU - Allen, David E.

AU - Powell, Robert J.

AU - Singh, Abhay K.

PY - 2012

Y1 - 2012

N2 - Any investment strategy requires some form of asset evaluation, that is, determination of the price or fundamental value, and the prediction of likely future price movements using financial, technical, or fundamental indicators. The decision to adopt a long or short position in an asset requires a view on its immediate future price movements. Financial forecasting involves a huge data processing exercise, which may be noisy, nonstationary, and unstructured in nature, for which customary statistical methods, such as linear logistic regression and discriminant analysis are frequently applied. The development of more flexible methods, such as support vector machine classification, offers practitioners potentially better and more powerful solutions. This chapter applies support vector machines (SVM) to predict the direction of price changes for a small set of Dow Jones Industrial Average stocks and tests them against the predictions obtained from logistic regression analysis. SVM is a machine learning algorithm, which is characterized by its particular decision functions and ability to apply linear and non-linear transformations using different kernel functions. The results show that SVM improves on simple logistic regression and provides more accuracy in predicting price changes. As SVM is established on structural risk minimization, it is more resistive to overfitting than other learning methods used for empirical risk minimization and may perform better. It also performs well in comparison to other commonly used forecasting methods such as ARIMA and Artificial Neural Networks.

AB - Any investment strategy requires some form of asset evaluation, that is, determination of the price or fundamental value, and the prediction of likely future price movements using financial, technical, or fundamental indicators. The decision to adopt a long or short position in an asset requires a view on its immediate future price movements. Financial forecasting involves a huge data processing exercise, which may be noisy, nonstationary, and unstructured in nature, for which customary statistical methods, such as linear logistic regression and discriminant analysis are frequently applied. The development of more flexible methods, such as support vector machine classification, offers practitioners potentially better and more powerful solutions. This chapter applies support vector machines (SVM) to predict the direction of price changes for a small set of Dow Jones Industrial Average stocks and tests them against the predictions obtained from logistic regression analysis. SVM is a machine learning algorithm, which is characterized by its particular decision functions and ability to apply linear and non-linear transformations using different kernel functions. The results show that SVM improves on simple logistic regression and provides more accuracy in predicting price changes. As SVM is established on structural risk minimization, it is more resistive to overfitting than other learning methods used for empirical risk minimization and may perform better. It also performs well in comparison to other commonly used forecasting methods such as ARIMA and Artificial Neural Networks.

KW - book-to-market ratio

KW - dividend yield

KW - hyperplane

KW - investment returns

KW - logistic regression

KW - price-to-earnings ratio

KW - radial basis function

KW - Sharpe ratios

KW - support vector machines

KW - traded volume factor

UR - http://www.scopus.com/inward/record.url?scp=84884432681&partnerID=8YFLogxK

U2 - 10.1016/B978-0-12-387724-6.00032-5

DO - 10.1016/B978-0-12-387724-6.00032-5

M3 - Chapter

SN - 9780123877246

SP - 467

EP - 478

BT - Handbook of short selling

A2 - Gregoriou, Greg N.

PB - Elsevier

CY - Waltham

ER -