asymmetric return rates and wealth distributions ... - emge
TRANSCRIPT
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Asymmetric return rates and wealthdistributions induced by introduction of
technical analysis into a behavioralagent based model
Prof. Dr. FISCHER STEFAN1,2 Prof. Dr. ALLBENSATMAN2,3
1Escola de Engenharia de Minas Gerais - EMGE2Centro Federal de Educação Tecnológica de Minas Gerais - Departamento de Física
e Matemática - CEFET-MG3Institute of Science and Technology for Complex Systems (INCT-SC)
July 24, 2018
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
INTRODUCTION
AIMHYBRID FINANCE MODEL OF THE INVESTORSDecision Making
RESULTS
CONCLUSION
REFERENCE
ACKNOWLEDGEMENT
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Behavioral Finance - Agent-Based Model
1. Behavioral aspects of the investors have become an importantfield of study in Finance and Econophysics.
2. Complex Network.
3. State of the investors :Buying;Holding;Selling;
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Introduction - Agent-Based Model
4. Three psychological profiles:Imitation;Anti-Imitation;Random Trader;
5. A scenario named mixing which has these three psychologicalprofile working altogether in the system.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Introduction - Agent-Based Model
1 10 100 100099.8100.0100.2100.4100.6100.8101.0101.2101.4
selling
holding
buying
anti-imitation
indifferent
imitation
D
C
B
A
1 10 100 10001
10
100
1000 DFA Power Law Fit
2 4 6 8 10
2
4
6
8
10
2 4 6 8 10
2
4
6
8
10
A 85
2 4 6 8 10
2
4
6
8
10
B 95
2 4 6 8 10
2
4
6
8
10
C 160
2 4 6 8 10
2
4
6
8
10
D 1500
Figure: Detailed analysis of the model for the case of mixing scenario. We show the stateof the system at four different moments, as indicated in the main panel by the lettersA,B,C and D. For each point, we plot in the bottom panels the state of each investor ofthe network, with a color legend. We also show the behavioral profile of the investors inthe top right inset.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Introduction - Agent-Based Model
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIMHYBRID FINANCEMODEL OF THEINVESTORS
Decision Making
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
AIMThere are no certainties in this investment world, and where there areno certainties, you should begin by understanding yourself. —James L.
Fraser—
1. Applying two strategies at the same time: Neighborhood andTechnical Analysis.
2. Analyzing how the wealth distribution behaves under differentscenarios;
3. Testing the Hub behavior profile for the system;
Figure: Picture from http://www.thisismoney.co.uk/money/markets/article-2793943/bis-warns-violent-market-crash-investor-confidence
4. Extracting information, considering how the Hubs influence therichness distribution in a behavioral finance model;
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIMHYBRID FINANCEMODEL OF THEINVESTORS
Decision Making
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Complex Network - Free Scale Network (FSN)
We consider the Albert-Barabàsi algorithm 1 to build theFSN.
Figure: Free Scale Network: link distribution by node. The straight line corresponds tothe best curves obtained in each case.
1[1, 2]
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIMHYBRID FINANCEMODEL OF THEINVESTORS
Decision Making
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Complex Network - Free Scale Network (FSN)
We have set a matrix of 63 × 63 where the nodes represent theinvestors. The Figure below shows how the investors areconnected through the FSN
Figure: Right: Degree of connections which are seen from inside out. Left: It shows allthe connections.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIMHYBRID FINANCEMODEL OF THEINVESTORS
Decision Making
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
HUB OF THE SYSTEM
-50 0 50 100 150 200 250 300 350 400
1
10
100
1000Hub do sistema - 1 investidor com 351 links
Distribuição de links por investidoresNú
merio
de In
vesti
dore
s
Links
links x investidores
Figure: The graph shows that the most connected hub of the system has 351 connectionsfollowed by the one which has 116.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIMHYBRID FINANCEMODEL OF THEINVESTORS
Decision Making
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
HUB - Free Scale Network(FSN)
1 10 100
1
10
100
1000
Linear Fit for Data7_B on linearized scales.yscale(Y) = A + B * xscale(X)where scale() is the current axis scale function.
Parameter Value Error------------------------------------------------------------A 6.32252 0.33547B -3.32245 0.20679
Links x Investors Linear Fit
Hub
do
sist
ema
- 1
inve
stid
or c
om 3
51 li
nks
Distribuição de links por investidores
Log(
Núme
rio de
Inve
stido
res)
Log(Links)
Figure: Logarithm scale α ≈ −3.21.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIMHYBRID FINANCEMODEL OF THEINVESTORS
Decision Making
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Hybrid Finance Model of Investors
M3(t)
M2(t)
M1(t)
(Trend of the Index )
time step
index
0 1 2 3 4 5 6 7 8 9 10 11
1
2
3
4
5
6
7
8
M1(t)
M2(t)
M3(t)
(Computing M1(t),M2(t) and M3(t))
Figure: Left: Trend of the index M1(t) > M2(t) > M3(t) > 0; Right: Figure shows howthe values of M1(t),M2(t) and M3(t) are computed.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIMHYBRID FINANCEMODEL OF THEINVESTORS
Decision Making
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Hybrid Finance Model of InvestorsTendência do Índice
LINHA M1(t) > M5(t) M5(t) > M10(t) M1(t) > 0 M5(t) > 0 M10(t) > 0A 0 0 1 1 1B 1 1 1 1 1C 0 1 1 1 1D 1 0 1 1 1E 0 1 1 1 0F 1 0 1 0 1G 1 1 1 1 0H 1 1 0 0 0I 1 1 1 0 0J 0 1 0 1 0K 1 0 1 0 0L 0 0 0 0 1M 0 1 0 1 1N 1 0 0 0 0O 1 0 0 0 1P 0 0 0 0 0Q 0 0 0 1 1R 0 1 0 0 0
Table: The header of the table: M1(t), M5(t) and M10(t) stand for the momentumconsidering the difference for 1, time-lag, 5 time-lag and 10 time-lag, respectively. Therows are filled in with the tautology (1:true; 0:false)
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIMHYBRID FINANCEMODEL OF THEINVESTORS
Decision Making
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Hybrid Finance Model of Investors
Caso-1 Caso-2 Caso-3LINHA P(COMPRAR) P(MANTER) P(VENDER) P(COMPRAR) P(MANTER) P(VENDER) P(COMPRAR) P(MANTER) P(VENDER)
PA 0.8 0.1 0.1 0.1 0.1 0.8 0.6 0.3 0.1PB 1.0 0.0 0.0 0.0 0.0 1.0 0.7 0.3 0.0PC 0.8 0.1 0.1 0.1 0.1 0.8 0.6 0.3 0.1PD 1.0 0.0 0.0 0.0 0.0 1.0 0.7 0.3 0.0PE 0.6 0.2 0.2 0.2 0.2 0.6 0.4 0.4 0.2PF 0.6 0.2 0.2 0.2 0.2 0.6 0.4 0.4 0.2PG 0.6 0.2 0.2 0.2 0.2 0.6 0.4 0.4 0.2PH 0.1 0.1 0.8 0.8 0.1 0.1 0.1 0.3 0.6PI 1.0 0.0 0.0 0.0 0.0 1.0 0.7 0.3 0.0PJ 0.2 0.2 0.6 0.6 0.2 0.2 0.2 0.4 0.4PK 1.0 0.0 0.0 0.0 0.0 1.0 0.7 0.3 0.0PL 0.2 0.2 0.6 0.6 0.2 0.2 0.2 0.4 0.4PM 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.3 0.7PN 0.1 0.1 0.8 0.8 0.1 0.1 0.1 0.3 0.6PO 0.2 0.2 0.6 0.6 0.2 0.2 0.2 0.4 0.4PP 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.3 0.7PQ 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.3 0.7PR 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.3 0.7SUM Σ8.2 Σ1.6 Σ8.2 Σ8.2 Σ1.6 Σ8.2 Σ6.0 Σ6.0 Σ6.0
Table: The header of the table: Case-1 - We follow the tendency of the index of going upor down; Case-2 - We invert the tendence of the index of going up or down; Case-3 - Webring the system to the balance when the sum of those probabilities of buying, holdingand selling has the same result. P(Buy), P(Hold) and P(Sell) stand for the probabilitygiven for buying, holding and selling.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIMHYBRID FINANCEMODEL OF THEINVESTORS
Decision Making
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Probability to follow the TA
1.Wealthi (t) = Ai (t) + Qi (t) × I(t)
Ai (t) = amount of money of the investor i at time t;Qi (t) = quantity of stocks of the investor i at time t;I(t) = index updated of the stock at time t.
2. Applying a stochastic process to the model considering 1%,5%,30%,50%,70%,95% e 99% as the probabilities offollowing the TA strategy.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIMHYBRID FINANCEMODEL OF THEINVESTORS
Decision Making
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Process of Choice
Figure: Flow chart shows the Imitator investor and how his decision is taken based on twostrategies. For instance, the majority of his neighborhood is buying. TI means: trend ofthe index.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIMHYBRID FINANCEMODEL OF THEINVESTORS
Decision Making
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Probability to follow the Technical Analysis
Figure: Flow chart shows the Anti-Imitator investor and how his decision is taken basedon two strategies. For instance, the minority of his neighborhood is buying. TI means:trend of the index.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIMHYBRID FINANCEMODEL OF THEINVESTORS
Decision Making
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Probability to follow the Technical Analysis
Figure: Flow chart shows the Anti-Imitator investor and how his decision is taken based ontwo strategies. For instance, the minority in his neighborhood is holding. Therefore, whenthe TA informs the option to buy, the investor will decide between holding and selling.Otherwise, if the option is to sell, the investor will decide between holding and buying.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Wealth Distribution - 99%
10000 15000 20000 25000 30000 35000
1
10
100
HUB - IMITATOR
Imitators Random Traders Anti-Imitators
NUMB
ER O
F IN
VEST
ORS
WEALTH10000 15000 20000 25000 30000 35000
1
10
100
HUB - RANDOM TRADER
Imitators Random Traders Anti-Imitators
NUMB
ER O
F IN
VEST
ORS
WEALTH
10000 15000 20000 25000 30000 35000
0.1
1
10
100
HUB - ANTI_IMITATOR
Imitators Random Traders Anti-Imitators
NUMB
ER O
F IN
VEST
ORS
WEALTH
Figure: Profit of the system as function of the hubs - 99%. Left: Hub -Imitator; Center: Hub - Random Trader; Right: Hub - Anti-imitator.The results for the whole systemare: µanti = 31878.00 ± 23.35 R2 = 0.99345; µrandom =19937.00± 16.44 R2 = 0.99562; µimit = 7886.00± 11.41 R2 = 0.98123
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Wealth Distribution - 50%
15000 20000 25000
1
10
100
Imitators Random Trader Anti-Imitators
HUB - IMITATOR
NUMB
ER O
F IN
VEST
ORS
WEALTH15000 20000 25000
0.01
0.1
1
10
100
1000 Imitators Random Traders Anti-Imitators
HUB - RANDOM TRADER
NUMB
ER O
F IN
VEST
ORS
WEALTH
15000 20000 250000.1
1
10
100
1000 Imitators Random Trader Anti-Imitators
HUB - ANTI_IMITATOR
NUMB
ER O
F IN
VEST
ORS
WEALTH
Figure: Profit of the system as function of the hubs - 50%. Left: Hub -Imitator; Center: Hub - Random Trader; Right: Hub - Anti-imitator.The results for the whole systemare: µanti = 20577.95.00 ± 6.64 R2 = 0.99738; µrandom =20134.00± 5.49 R2 = 0.99967; µimit = 18963.00± 20.64 R2 = 0.98197
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Wealth Distribution - 1%
18500 19000 19500 20000 20500 21000 21500 220000.1
1
10
100
Imitators Random TraderS Anti-Imitators
NUMB
ER O
F IN
VEST
ORS
HUB - IMITATOR
WEALTH19500 20000 20500 21000
1
10
100
Imitators Random Trader Anti-Imitators
HUB - RANDOM TRADER
NUMB
ER O
F IN
VEST
ORS
WEALTH
18500 19000 19500 20000 20500 21000
0.1
1
10
100
HUB ANTI_IMITATOR
Imitator Random Traders Anti-Imitators
NUMB
ER O
F IN
VEST
ORS
WEALTH
Figure: Profit of the system as function of the hubs - 1%. Left: Hub -Imitator; Center: Hub - Random Trader; Right: Hub - Anti-imitator.The results for the whole systemare: µanti = 20081.95.00 ± 2.50 R2 = 0.9981; µrandom =20073.00 ± 7.16 R2 = 0.99644; µimit = 20077.00 ± 5.17 R2 = 0.99325
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Rate of Return - 99% TA
-100 -50 0 50 100 150 2001
10
100
1000
10000
100000
1000000
1E7
1E8
CASE-1 ANTI_IMITATOR-99%
TRAD
ING
VOLU
ME
RATE OF RETURN-1000 -500 0 500 1000
1
10
100
1000
10000
100000
1000000
1E7
1E8
MEAN FIELD - ANTI_IMITATOR
TRAD
ING
VOLU
ME
RATE OF RETURN
-200 -150 -100 -50 01
10
100
1000
10000
100000
1000000
1E7
1E8
CASE-1 IMITATOR-99%
TRAD
ING
VOLU
ME
RATE OF RETURN-1000 -800 -600 -400 -200 0 200 400 600
1
10
100
1000
10000
100000
1000000
1E7
1E8
MEAN FIELD - IMITATOR
TRAD
ING
VOLU
MERATE OF RETURN
-150 -100 -50 0 50 100 1501
10
100
1000
10000
100000
1000000
1E7
CASE-1 RANDOM_TRADER-99%
TRAD
ING
VOLU
ME
RATE OF RETURN-100 -50 0 50 100
1
10
100
1000
10000
100000
1000000
1E7
1E8
MEAN FIELD - RANDOM TRADER
TRAD
ING
VOLU
ME
RATE OF RETURN
Figure: Rate of Return. Left side: Applying the Case-1 from the Table 2and a probability of 99% to follow the technical analysis - Top-anti-imitatorsinvestors which is concentrated on the positive return side; Middle-imitatorsinvestors which is concentrated on the negative return side;Bottom-random-trades investors which is symmetric around the origin.Right side: the figures show, statistically, the same results as the onesshown at the left side when applying another technique to compute thedecision-make.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Rate of Return - 5% AT
-80 -60 -40 -20 0 20 40 60 80 100 1201
10
100
1000
10000
100000
1000000
1E7
CASE-1 ANTI_IMITATOR-5%TR
ADIN
G VO
LUME
RATE OF RETURN18500 19000 19500 20000 20500 21000
0
50
100
150
200
250
300
CASE-1 - ANTI_IMITATOR-5%
NUMB
ER O
F IN
VEST
ORS
WEALTH
WEALTH DISTRIBUTION GAUSSIAN FIT
-20 -10 0 10 201
10
100
1000
10000
100000
1000000
1E7
CASE-1 RANDOM_TRADER-5%
TRAD
ING
VOLU
ME
RATE OF RETURN19200 19400 19600 19800 20000 20200 20400 20600 20800
0
50
100
150
200
250
300
CASE-1 - RANDOM_TRADER-5%
NUMB
ER O
F IN
VEST
ORS
WEALTH
WEALTH DISTRIBUTION GAUSSIAN FIT
-60 -40 -20 0 20 40 601
10
100
1000
10000
100000
1000000
1E7
CASE-1 IMITATOR-5%
TRAD
ING
VOLU
ME
RATE OF RETURN19200 19400 19600 19800 20000 20200 20400 20600 20800
0
50
100
150
200
250
300
CASE-1 - IMITATOR-5%
NUMB
ER O
F IN
VEST
ORS
WEALTH
WEALTH GAUSSIAN FIT
Figure: Rate of Return x Wealth Distribution. The figures show the resultswhen we apply the Case-1 from the Table 2 and a probability of 5% tofollow the technical analysis. Left Side - Rate of Return: top-anti-imitators;middle-random-traders; bottom-imitators. Right Side - Wealth Distribution:top-anti-imitators; middle-random traders; bottom-imitators.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Rate of Return - 99% AT - Case-2
-50 -40 -30 -20 -10 0 10 20 30 40 501
10
100
1000
10000
100000
1000000
1E7
CASE-2 - ANTI_IMITATOR-99%TR
ADIN
G VO
LUME
RATE OF RETURN17200 17400 17600 17800 18000 18200 18400 18600 18800 19000
0
50
100
150
200
WEALTH DISTRIBUTION GAUSSIAN FIT
CASE-2 - ANTI_IMITATOR-99%
NUMB
ER O
F IN
VEST
ORS
WEALTH
-50 -40 -30 -20 -10 0 10 20 30 40 501
10
100
1000
10000
100000
1000000
1E7
CASE-2 - IMITATOR-99%
TRAD
ING
VOLU
ME
RATE OF RETURN22000 22200 22400 22600 22800 230000
20
40
60
80
100
120
140
160
CASE-2 - IMITATOR-99%
NUMB
ER O
F IN
VEST
ORS
WEALTH
WEALTH DISTRIBUTION GAUSSIAN FIT
-50 -40 -30 -20 -10 0 10 20 30 40 501
10
100
1000
10000
100000
1000000
1E7
CASE-2 - RANDOM_TRADER-99%
TRAD
ING
VALU
E
RATE OF RETURN19200 19400 19600 19800 20000 20200 20400 20600 20800
0
50
100
150
200
250
300
CASE-2 RANDOM_TRADER-99%
NUMB
ER O
F IN
VEST
ORS
WEALTH
WEALTH DISTRIBUTION GAUSSIAN FIT
Figure: Rate of Return x Wealth Distribution. The figures show the results whenwe apply the Case-2 from the Table 2 and a probability of 99% to follow thetechnical analysis. Left Side - Rate of Return: top-anti-imitators;middle-imitators; bottom-random traders. Right Side - Wealth Distribution:top-anti-imitators; middle-imitators; bottom-random traders.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Rate of Return - 99% AT - Case-3
-60 -40 -20 0 20 40 60 80 100 1201
10
100
1000
10000
100000
1000000
1E7
CASE-3 ANTI_IMITATOR-99%TR
ADIN
G VO
LUME
RATE OF RETURN19000 20000 21000 22000 23000 24000 25000
0
20
40
60
80
100
120
WEALTH DISTRIBUTION GAUSSIAN FIT
CASE-3 ANTI_IMITATOR-99%
NUMB
ER O
F IN
VEST
ORS
WEALTH
-200 -150 -100 -50 0 501
10
100
1000
10000
100000
1000000
1E7
CASE-3 IMITATOR-99%
TRAD
ING
VOLU
ME
RATE OF RETURN14000 15000 16000 17000 18000 190000
20
40
60
80
100
120
140
160
CASE-3 IMITATOR-99%
NUMB
ER O
F IN
VEST
ORS
WEALTH
WEALTH DISTRIBUTION GAUSSIAN FIT
-80 -60 -40 -20 0 20 40 60 801
10
100
1000
10000
100000
1000000
1E7
CASE-3 RANDOM_TRADER-99%
TRAD
ING
VOLU
ME
RATE OF RETURN17000 18000 19000 20000 21000 220000
20
40
60
80
100
120
140
160
CASE-3 RANDOM_TRADER-99%NU
MBER
OF
INVE
STOR
S
WEALTH
WEALTH DISTRIBUTION GAUSSIAN FIT
Figure: Rate of Return x Wealth Distribution. The figures show the results whenwe apply the Case-3 from the Table 2 and a probability of 99% to follow thetechnical analysis. Left Side - Rate of Return: top-anti-imitators;middle-imitators; bottom-random traders. Right Side - Wealth Distribution:top-anti-imitators; middle-imitators; bottom-random traders.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Wealth Distribution in function of the Hub
1% 5% 30% 50% 70% 95% 99%68
10121416182022242628
HUB WEALTH DISTRIBUTION - CASE-1
WEA
LTH(
10^3
)
PROBABILITY FOR TECH ANALYSIS
IMITATOR RANDOM-TRADER ANTI-IMITATOR
1% 5% 30% 50% 70% 95% 99%68
10121416182022242628
HUB WEALTH DISTRIBUTION - CASE-2
IMITATOR RANDOM-TRADER ANTI-IMITATOR
WEA
LTH
(10^
3)
PROBABILITY FOR TECH ANALYSIS
1% 5% 30% 50% 70% 95% 99%
15
16
17
18
19
20
21
22
HUB WEALTH DISTRIBUTION - CASE-3
WEA
LTH(
10^3
)
PROBABILITY FOR TECH ANALYSIS
IMITATOR RANDOM TRADER ANTI-IMITATOR
1% 5% 30% 50% 70% 95% 99%68
10121416182022242628
HUB WEALTH DISTRIBUTION - CASE-4
IMITATOR RANDOM-TRADER ANTI-IMITATOR
WEA
LTH(
10^3
)
PROBABILITY FOR TECH ANALYSIS
Figure: The graphics show the wealth of the Hub of the system as a function of theprobability adopted to follow the technical analysis strategy for each psychological profileof the Hub. Left top: Case-1; Right top: Case-2;Left bottom: Case-3; Right-bottom: Case-4 (inverted tendency of the Case-3)
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Conclusion
1% 5% 30% 50% 70% 95% 99%8
101214161820222426283032 IMITATOR
RANDOM-TRADER ANTI-IMITATOR
SYSTEM WEALTH DISTRIBUTIONW
EALT
H (1
0^3)
PROBABILITY TO FOLLOW TECH ANALYSIS
Figure: The graphic shows shows the average wealth for every kind of psychologicalbehavior as a function of the probability adopted to follow the technical analysis strategyapplying the Case-1 from Table 2.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Conclusion
1% 5% 30% 50% 70% 95% 99%60008000
1000012000140001600018000200002200024000260002800030000
SYSTEM WEALTH DISTRIBUTION - 5 LINKS
WEA
LTH
PROBABILITY TO FOLLOW TECH ANALYSIS
IMITATOR RANDOM-TRADER ANTI-IMITATOR
Figure: The graphic shows the wealth of the whole system as a function of the probabilityadopted to follow the technical analysis strategy for each psychological profile of theinvestors applying the Case-1 from Table II. Each one of them shows the average value ofthe system when the hub was set to be anti-imitator, imitator, then random-trader.
22Asymmetric return rates and wealth distribution influenced by the
introduction of technical analysis into a behavioral agent based model F.M.Stefan, A.P.F. Atman – TO BE PUBLISHED at Physica A: StatisticalMechanics and its Applications
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
Referência
[1] Réka Albert and Albert-Lászlo Barabàsi. Statistical mechanicsof complex networks. Reviews of Modern Physics, 74:47–97,jan 2002.
[2] Albert-Lászò Barabàsi and Réka Albert. Emergence of scalingin random networks. Science, 286:509–512, oct 1999.
[3] F. M. Stefan and A. P. F. Atman. Is there any connectionbetween the network morphology and the fluctuations of thestock market index? PHYSICA A-Statistical Mechanics and itsApplications, 419:630–641, 2015.
Asymmetric returnrates and wealth
distributions inducedby introduction of
technical analysis intoa behavioral agent
based model
fischer.stefan@emge.
edu.br
INTRODUCTION
AIM
RESULTS
CONCLUSION
REFERENCE
References
ACKNOWLEDGEMENT
ACKNOWLEDGEMENT