Title：Evolutionary Finance
Author：Bartholomew Dowling
Edition：10 Aug 2005
Format：High Quality pdf Non-scanned Version
Pages：297 pages
Publisher：Palgrave Macmillan
Contents
List of Figures and Tables ix
Acknowledgments xii
Preface xiv
1 Introduction 1
2 The “Old” View of Finance 10
2.1 The efficient markets hypothesis: The traditional (albeit
incomplete) standard-bearer for information assessment 10
2.2 A little more on the link between the theory and the
applied 13
2.3 Cost, ability and speed: Important information
determinants 17
2.4 Do empirical studies of the EMH shed any light on the
actual speed of information transferal? 22
2.5 Is “Strong EMH” all there is to the “Traditional” view of
markets and information? 27
3 The “New” View of Finance 32
3.1 “New” view challenge no. 1: Determinism, complexity
theory and the nonlinear dynamics school 33
3.2 “New” view challenge no. 2: Bounded rationality,
heterogeneous agents and the Behavioral Finance school 46
3.3 “New” view challenge no. 3: Trading rules, evolutionary
games and artificial markets 55
3.4 So where does Evolutionary Finance fit-in to the “new”
view genre? 71
4 The Mechanics of Modeling Information as an
Evolutionary Process 73
4.1 Evolutionary information basics: Memetics and the
contribution of Richard Dawkins 75
4.2 Moving past the elementary: Taking the evolutionary
information concept further into the field of finance 78
4.3 The building blocks of our evolutionary approach toward
information in finance 82
4.4 Some consequences of our evolutionary approach toward
modeling information 90
4.5 How investors interpret Evolutionary Information 103
vii
viii Contents
5 Putting it Altogether – An Evolutionary Model of the
Marketplace 118
5.1 Stage I: Developing an intertemporal optimization
model of information production/consumption and
solving for general equilibrium conditions 121
5.2 Stage II: Linking analyst research output to asset price
dynamics 131
5.3 Stage III: Highlighting our preferred evolutionary model
of the market – constructing the informational genome
of asset prices 143
6 The Implications of Our Evolutionary Perspective for
Distributional Form 161
6.1 Foundations for an evolutionary approach toward
distributional form 163
6.2 Analyst/investor strategies and the ecology of the market 180
6.3 Some implications of our results 185
7 Evolutionary Finance – an Applied Perspective 202
7.1 A primer on Evolutionary Algorithms 206
7.2 Evolutionary asset selection 220
7.3 Evolutionary Portfolio construction 227
7.4 Does it work? the results of ten years of out-of-sample
backtesting for the investment recommendations from
Natural Selection™ 229
8 Future Directions – The Path Ahead for
Evolutionary Finance 235
8.1 Future directions for Evolutionary Finance – the theory 237
8.2 Future directions for Evolutionary Finance – the practice 238
Appendix 1: A Glossary of Investment Terms 239
Appendix 2: An OLG Form Evolutionary Model of the
Marketplace 267
A2.1 An introduction to our OLG framework 268
A2.2 Equilibrium for the consumer 269
A2.3 Equilibrium for the producer 269
A2.4 Factor market equilibrium 270
A2.5 General equilibrium 270
A2.6 Introducing money and prices 272
A2.7 Adding analyst driven expectations 274
Appendix 3: Some Background on Evolutionary Finance™ Ltd 278
References 280
Index 295
List of Figures and Tables
Figures
1.1 Their view versus our view on information within financial
markets 3
2.1 The strong EMH/traditionalist financial engineering edifice 14
2.2 The market efficiency spectrum and optimal investor behavior 16
2.3 The link between ability and cost in determining market
efficiency 21
2.4 The “traditional finance” model of risk/return distribution –
the Gaussian form 29
3.1 Stochastic or deterministic? Can you spot the difference? 35
3.2 Some useful tests for normalcy 36
3.3 Our logistic equation calibrated at φ = 2.9 39
3.4 Our logistic equation calibrated at φ = 3.5 40
3.5 Our logistic equation calibrated at φ = 4.0 40
3.6 An example of a typical two-person game 58
3.7 An example of an extensive form game 63
3.8 The prisoners dilemma conundrum 66
4.1 The ever-expanding stock of knowledge 98
4.2 An information cascade for new theme contagion between
analysts 100
4.3 The memetic-based diffusion of a given theme 102
4.4 The evolution of interpretations of investor behavior 112
4.5 The pace of information absorption by the human mind 116
5.1 Calibration from our market sentiment oscillator –
(1/α) = 6,ψ = 0.1, L = 2, η = 1 133
5.2 Calibration from the bull/bear analyst cycle –
B0 = 1, B1 = 1, ξ = 0.04, ζ = 0.04 137
5.3 Calibration of “research pulses” –
δϑ = 6, δκ = 3, βϑ = 0.05, βκ = 0.1, ϑ, κ = 1, L = 1 140
5.4 Calibration of contrarian thought contagion –
υ, = 1, ι = 0.09, λ = 0.03, χ = 0.08 142
5.5 Calibration of increasing analyst uncertainty – = 0.01,
= 0.007, = 1, ω = 0, ς0 = 0.01, θ0 = 0, γ0 = 0.01,
ς1 = 0.02, θ1 = 0.01, γ1 = 0 146
5.6 Calibration of equilibrium price convergence – = 0.04,
= 0.04, = 1, ω = 0, ς0 = 0.1, θ0 = 0.01, γ0 = 0, ς1 = 0.1,
θ1 = 0, γ1 = 0.02 147
ix
x List of Figures and Tables
5.7 Calibration of jump diffusion – = 0.06, = 0.02, = 1,
ω = 0, ς0 = 0.9, θ0 = 0.001, γ0 = 0, ς1 = 0.06, θ1 = 0, γ1 = 0.01 148
5.8 Calibration of overwhelming sellers – = 0.01,
= 0.01, = 1, ω = 0, ς0 = 0.003, θ0 = 0, γ0 = 0.01,
ς1 = 0.039, θ1 = 0.001, γ1 = 0 149
5.9 Calibration of associative asset prices – representative asset (i)
= 0.01, = 0.01, = 1, ω = 0.01, ς0 = 0.045, θ0 = 0.01,
γ0 = 0.01, ς1 = 0.01, θ1 = 0.01, γ1 = 0.01; associative asset (a)
= 0.01, = 0.01, = 1, ω = 0, ς0 = 0.01, θ0 = 0.01,
γ0 = 0.01, ς1 = 0.063, θ1 = 0.01, γ1 = 0.01 151
5.10 The final 500 signals in the information genome of the market
index for calibration 5 152
5.11 The informational impact of memetic information contained
within the information genome for calibration 5 153
5.12 The inner core of the informational genome for calibration 5 153
5.13 The informational radix for calibration 5 157
5.14 A cross-sectional snapshot of the informational radix for
calibration 5 157
5.15 Alternate calibration of associative asset prices – representative
asset (i) = 0.01, = 0.0107, = 2, ω = 0.01, ς0 = 0.045,
θ0 = 0.01, γ0 = 0.01, ς1 = 0.01, θ1 = 0.01, γ1 = 0.01; associative
asset (a) = 0.01, = 0.01, = 1, ω = 0, ς0 = 0.01, θ0 = 0.01,
γ0 = 0.01, ς1 = 0.063, θ1 = 0.01, γ1 = 0.01 159
6.1 An extensive form game of analyst information producing
behavior – independent or interdependent? 165
6.2 The cumulative distribution of pure strategy following clusters
for varying levels of OE – exhibiting alternate degrees of analyst
ecology within the marketplace 181
6.3 The extreme skew in the distributional form of asset prices
stemming from the emergence of a dominant player within the
marketplace 182
6.4 Swings in the distributional form of asset prices as cooperative
analyst clusters form, become successful and then their
influence wanes 185
6.5 Violent swings in the distributional form of asset prices as
enterprising analysts embark upon a “Big Call” information
producing strategy 185
6.6 The index values and distributional form outcome of an
independent information game between two competing
analysts 189
6.7 A trinomial tree extension to our interdependent extensive form
game and the linkage that this has with our Evolutionary
Distributional Form 191
6.8 VaR at its simplest 194
List of Figures and Tables xi
6.9 The hypothetical efficient frontier for various gradings
of Corr10 199
7.1 Information processing by the human brain and by a Neural
Network Algorithm 218
7.2 The price-based informational radix for the S&P500 futures
contract 222
7.3 The information radix for a seasonality strategy approach
to the S&P500 futures contract – both a complementary and
contrarian viewpoint 225
7.4 A 5 percent drawdown probability assessment for the
S&P500 futures contract – using our proprietary EVaR
principles as a foundation 228
7.5 Rolling 12-month performance 230
7.6 Drawdown distribution 230
7.7 Asset class preference 231
7.8 Instrument preference 231
7.9 Geographic diversification 232
A.2.1 Steady-state equilibrium for intertemporal
saving/investment 271
Tables
2.1 The various categorizations of empirical EMH analysis 23
4.1 The definitional foundations of Evolutionary Finance
vis-à-vis evolutionary biology 83
7.1 Key performance indicator summary 232
7.2 Monthly performance profile since 2000 (%) 233 |
