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Constructing smart portfolios from data driven quantitative investment models

Constructing smart portfolios from data driven quantitative investment models
Constructing smart portfolios from data driven quantitative investment models
In this paper we present a smart portfolio management methodology, which advances existing portfolio management techniques at two distinct levels. First, we develop a set of investment models that target regimes found in the data over different time horizons. We then build a meta-model which uses the Kelly criterion to determine an optimal allocation over these investment strategies, thus simultaneously capturing regimes operating in the data over different time horizons. Finally, in order to detect changes in the relevant data regime, and hence investment allocations, we use a forecasting algorithm which relies on a Kalman filter. We call our combined method, that uses both the Kelly criterion and the Kalman filter, the K2 algorithm. Using a large-scale historical dataset of both stocks and indices, we show that our K2 algorithm gives better risk adjusted returns in terms of the Sharpe ratio, better average gain to average loss ratio and higher probability of success compared to existing benchmarks, when measured in out-of-sample test.
1-8
Mehra, Chetan
dfd50ace-2d70-4846-a4a9-2b36dd352bfa
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Robu, Valentin
36b30550-208e-48d4-8f0e-8ff6976cf566
Mehra, Chetan
dfd50ace-2d70-4846-a4a9-2b36dd352bfa
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Robu, Valentin
36b30550-208e-48d4-8f0e-8ff6976cf566

Mehra, Chetan, Prugel-Bennett, Adam, Gerding, Enrico and Robu, Valentin (2014) Constructing smart portfolios from data driven quantitative investment models. Computational Intelligence for Financial Engineering and Economics (CIFEr), London, GB, 27 - 28 Mar 2014. IEEE, 1-8.. pp. 1-8 .

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper we present a smart portfolio management methodology, which advances existing portfolio management techniques at two distinct levels. First, we develop a set of investment models that target regimes found in the data over different time horizons. We then build a meta-model which uses the Kelly criterion to determine an optimal allocation over these investment strategies, thus simultaneously capturing regimes operating in the data over different time horizons. Finally, in order to detect changes in the relevant data regime, and hence investment allocations, we use a forecasting algorithm which relies on a Kalman filter. We call our combined method, that uses both the Kelly criterion and the Kalman filter, the K2 algorithm. Using a large-scale historical dataset of both stocks and indices, we show that our K2 algorithm gives better risk adjusted returns in terms of the Sharpe ratio, better average gain to average loss ratio and higher probability of success compared to existing benchmarks, when measured in out-of-sample test.

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More information

e-pub ahead of print date: 27 March 2014
Venue - Dates: Computational Intelligence for Financial Engineering and Economics (CIFEr), London, GB, 27 - 28 Mar 2014. IEEE, 1-8., 2014-03-27
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 362984
URI: http://eprints.soton.ac.uk/id/eprint/362984
PURE UUID: e8c04edb-ade0-4d88-b469-f767649fdadc
ORCID for Enrico Gerding: ORCID iD orcid.org/0000-0001-7200-552X

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Date deposited: 18 Mar 2014 16:22
Last modified: 20 Jul 2019 00:56

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Contributors

Author: Chetan Mehra
Author: Adam Prugel-Bennett
Author: Enrico Gerding ORCID iD
Author: Valentin Robu

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