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
Daily Log returnsr (t,τ) = log (price (t+τ)-log(price(t))
Rank Frequencies or Complementary Cumulative Distributions
Alpha Values by group
Moving Averages and Bollinger Bands
Expanded View of Moving Average and Bollinger Bands
The Qualitative ApproachHannah Irons
The CorrelationsHelen Duncan
Correlations
•Correlation of log returns calculated
•Similar companies respond to global events in similar ways
Correlations
Schlumberger and Baker Hughes
Correlations
Correlations
Correlations
Correlations
Correlations
Correlations
Correlations
Correlations
Correlations
Correlations
The ModelAndrew Payne
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
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
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
Random and Preferential attachment
Small world networks
The END