A case study into financial market

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description

Presentation of a project done by a group students of the "Science of Complex Systems 2012" course at University of Kent, UK. http://blogs.kent.ac.uk/complex/lectures-ph724/

Transcript of A case study into financial market

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Probability, Prediction, and TradingHoward Barty

Prices, Log Returns, and Rank Frequencies

Analysing the tails Calculating Alpha Values Generating Moving Averages

and Bollinger Bands

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Daily Log returnsr (t,τ) = log (price (t+τ)-log(price(t))

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Rank Frequencies or Complementary Cumulative Distributions

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Alpha Values by group

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Moving Averages and Bollinger Bands

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Expanded View of Moving Average and Bollinger Bands

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The Qualitative ApproachHannah Irons

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The CorrelationsHelen Duncan

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Correlations

•Correlation of log returns calculated

•Similar companies respond to global events in similar ways

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Correlations

Schlumberger and Baker Hughes

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Correlations

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Correlations

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Correlations

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Correlations

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Correlations

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Correlations

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Correlations

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Correlations

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Correlations

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Correlations

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The ModelAndrew Payne

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Transmission of information and herd behaviour: An application to financial markets

V.M.Eguiluz & M.G.Zimermann

N agents initially unconnected

loop1. Randomly choose agent2. With probability a trade (generate event, size si )3. With probability 1-a expand network

repeat

Price changes Herding parameter

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Checking the integrity of the model

Graph taken from Eguiluz and Zimmermann’s paper

N =10,000Iter=10,000,000

Graph produced by model created from description

N =5,000Iter=100,000

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Changes to the model

Define two networks

1. StructuralFixed, non-evolvingPredetermined (3 types) Representative of agents business connections

2. InformationConstantly evolving Representative of ‘hot tips’

Types of structural network used

1.Preferential attachment2.Small world3.Random

Three of each type with average connectivity of 2,4 & 6

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Random and Preferential attachment

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Small world networks

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The END