Neural Networks and the Financial Markets: Predicting, by John G. Taylor (auth.), Jimmy Shadbolt MSc, John G. Taylor
By John G. Taylor (auth.), Jimmy Shadbolt MSc, John G. Taylor BA, BSc, MA, PhD, FlnstP (eds.)
This quantity appears at monetary prediction from a wide variety of views. It covers:
- the industrial arguments
- the practicalities of the markets
- how predictions are used
- how predictions are made
- how predictions are changed into anything usable (asset destinations)
It combines a dialogue of normal conception with cutting-edge fabric on a variety of details processing ideas as utilized to state-of-the-art monetary difficulties. the entire ideas are verified with actual examples utilizing genuine industry info, and express that it truly is attainable to extract info from very noisy, sparse info units.
Aimed essentially at researchers in monetary prediction, time sequence research and knowledge processing, this ebook can also be of curiosity to quantitative fund managers and different pros focused on monetary prediction.
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Additional resources for Neural Networks and the Financial Markets: Predicting, Combining and Portfolio Optimisation
15) Both the duration and the convexity find important applications in analysing the risk inherent in holding bonds due to fluctuations in the interest rate structure of the market. 5 Summary In this chapter we have introduced the basic notions used in bond pricing: the time value of money, the coupon, the yield (the average return represented by cash flows in a given bond) and the yield curve (the set of values of the yield for different values of the maturity). The derived notions of duration and convexity were then defined from the yield curve.
It is considered that this has the effect of making financial markets relatively efficient, while the potential rewards associated with further innovation provide sufficient motivation for the development of new forms of competitive advantage. This suggests that some of the technological advances offered by sophisticated modelling techniques that are currently only available to specialist market participants have the potential to generate excess profits. Furthermore, this definition implies that the dynamics of financial markets will evolve through time as competitive advantages become more widely available to other market participants.
And so on . To map n-dimensional data without loss of information we need n principal components, but instead of the information being dependent on all the original n dimensions it is now mainly in the first few components. It turns out that the principal components are the same as the eigenvectors of the variance-covariance matrix and that they are ranked in order of the eigenvalues . For a detailed discussion of the theory behind the use of PCAs for data reduction see Bishop (1995) and Haykin (1999).