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