Abstract
We present the systematic method of Multitask Learning for incorporating prior knowledge (hints) into the inductive learning system of neural networks. Multitask Learning is an inductive transfer method which uses domain information about related tasks as inductive bias to guide the learning process towards better solutions of the main problem. These tasks are presented to the learning system in a shared representation. This paper argues that there exist many opportunities for Multitask Learning especially in the world of financial modeling: It has been shown, that many interdependencies exist between international financial markets, different market sectors and financial products. Models with an isolated view on a single market or a single product therefore ignore this important source of information. An empirical example of Multitask Learning is presented where learning additional tasks improves the forecasting accuracy of a neural network used to forecast the changes of five major German stocks.
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References
Abu-Mostafa, Y.S.: Hints, Neural Computation, 7: 639–671, 1995
Caruana R.: Learning many related tasks at the same time with backpropagation, Advances in Neural Information Processing Systems, 7, 656–664, 1995
Caruana R.: Algorithms and Applications for Multitask Learning, The 13th International Conference on Machine Learning, Bari, Italy, 87–95, 1996
Caruana R., Baluja S., Mitchell T., Using the Future to “Sort Out” the Present: Rankprop and Multitask Learning for Medical Risk Evaluation, Advances in Neural Information Processing Systems, 8, 1996
Gjerde O., Saetten F.: Linkages among European and world markets, European Journal of Finance, 2:165–179, 1996
MacKay D.J.C.: A practical Bayesian framework for backpropaga-tion networks, Neural Computation, 4(3): 448–472, 1992
Riedmiller M. and Braun H.: A direct adaptive method for faster backpropagation learning: The RPROP algorithm, Proceedings of the IEEE International Conference on neural networks, 1993
Steiner M., Bruns C: Wertpapiermanagement, Schäffer-Poeschel, 1993
Towell G.G., Shavlik J.W.: Knowledge-Based Artificial Neural Networks, Artificial Intelligence, 70: 119–165, 1994
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© 1998 Springer Science+Business Media Dordrecht
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Bartlmae, K., Gutjahr, S., Nakhaeizadeh, G. (1998). Incorporating Prior Knowledge About Financial Markets Through Neural Multitask Learning. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_34
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DOI: https://doi.org/10.1007/978-1-4615-5625-1_34
Publisher Name: Springer, Boston, MA
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