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H2O Consulting
Cristian Bissattini, MBA
Innovation from Swiss tradition

H2O Consulting

H2O Consulting
Lugano (Switzerland)
www.h2oconsulting.ch

2
The Prize in Economic Sciences 2013

Eugene F. Fama
University of Chicago

Robert J. Shiller
Yale University, New Haven

Lars Peter Hansen
University of Chicago

 There is no way to predict the price of stocks and bonds over the next few days or weeks.
But it is quite possible to foresee the broad course of these prices over longer periods,
such as the next three to five years. 
3
The Prize in Economic Sciences 2013

4
5

Black,
Sholes,
Merton

Option pricing

Sharpe,
Lintner,
Black

CAPM

Markowitz

Portfolio Principles

Modigliani
& Miller

Arbitrage principles

Neoclassical Finance Model
Neoclassical Finance Model
Strong form
All private
information
All public
information

All investors are
rational, well-informed
and hope for
maximizing profit

Market prices
immeditely refllect all
available information

Information
in past stock
prices

Efficient Market Hypothesis
Semi-strong
form
Weak form

6
Neoclassical Finance Model
Markets are normally distributed with daily stock return
lying under their theoretical bell shaped curve
Stock prices reflect the discounted value of expected
cash-flows
It is not possible to beat the market over time
without taking excess risk
Sentiment does not play a role in this classic framework.

7
Neoclassical Finance Model
Gordon Dividend Discount Model (DDM)

The value of a stock is the present value of all of the
expected future dividends.

Zero growth

Constant growth

8

Differential growth
Neoclassical Finance Model
An investor is considering the purchase of a share of the iPear Inc.

$3
dividend per share
a year from today

$3

= $60

10%
0.15 - 0.10

dividend expected growth rate per year
(foreseeable future)

Share Price

15%
required return
(iPear’s risk)

Constant Growth Scenario
9
Neoclassical Finance Model

Great
Crash

1929

Black
Monday
Crash

October 1987

Internet
bubble

1990s

Financial
Turmoil

October 2008

Neoclassical Financial Model is unable to explain extreme cases
of bubbles and crashes

It seems timely to define a human sentiment function
in stochastic discount factor (SDF)
10
Neoclassical Finance Model

11
Neoclassical Finance Model
 Adherents of geometric Brownian motion or log normally distributed stock
returns (one of the foundation blocks of modern finance) must ever after
face a disturbing fact: assuming the hypothesis that stock index returns are
log normally distributed with about a 20% annualized volatility, the
probability that the stock market could fall 29% (the decline in S&P futures
on October 19th, 1987) in a single day is 10-160. So improbable is such an
event that it would not be anticipated to occur even if the stock market were
to last for 20 billion years, the upper end of the currently estimated duration
of the universe. Indeed, such an event should not occur even if the stock
market were to enjoy a rebirth for 20 billion years in each of 20 billion big
bangs. 
Mark Rubinstein
in Comments on the 1987 Stock Market Crash

12
Behavioral Finance Theory
 People in standard finance are
rational. People in behavioral
finance are normal 

 If you don’t know who you are,
the stock market is an expensive
place to find out 

Psychology

Economics

Adam Smith
Scottish moral philosopher and a pioneer of political economy

Meir Statman
Professor of Finance (Leavey School of Business, Santa Clara University)

13
Behavioral Finance Theory
Brain’s
biological and
physiological
limits
Simplification of
reality
Approximation of
information
(heuristics and
cognitive filters)

Errors and
biases

14
Behavioral Finance Theory
Germany

Austria

12%

1%

Donate organs

Don’t donate

12% Donors

99% Donors
Opt-in system vs Opt-out system

The Role of Inertia
15
Behavioral Finance Theory

Uncertainty

Risk

Fear
Gain

16
Behavioral Finance Theory

In the Mind of the Market: Theory of Mind Biases Value Computation during Financial Bubbles (Benedetto De Martino)

17
Keynesian Beauty Contest Theory
1

2

3

18
Robert J. Shiller (1981)

19
Behavioral Finance Theory

Risk
Perception

Prospect
Theory

Cognitive
Errors

20
Behavioral Finance Theory

Source: based on the risk
formula by risk researcher
Dr. Peter M. Sandman.

21
Risk Aversion vs Seeking Aversion
Option A
50% Win €250’000
50% Win €0

Option B
Offer: €125’000

Expected Value of option A is: 0.5 · €250’000 = €125’000
The two options are equivalent in term of expected value

78% of people choose option B
Risk Aversion!
Source: Psicologia e Investimenti Finanziari
Paolo Legrenzi (2006)

22
Risk Aversion vs Seeking Aversion

For the same expected value, are investors
always risk-averse?

23
Risk Aversion vs Seeking Aversion
Option A
50% Loss €250’000
50% Loss €0

Option B
Certain Loss: €125’000

Expected Value of option A is: 0.5 · -€250’000 = -€125’000
The two options are equivalent in term of expected value

61% of people choose option A!
Risk Seeking!
Source: Psicologia e Investimenti Finanziari
Paolo Legrenzi (2006)

24
Risk Aversion vs Seeking Aversion
For the same expected value, are investors
always risk-averse?
When we are faced with a sure gain
we tend to be risk averse
but
When we are faced with a certain loss
we tend to be risk seeking

25
Prospect Theory
The combination of risk-aversion with risk-seeking is
represented by the value function

- 20 - 10

+10 +20

26
Cognitive Errors

Overconfidence

Anchoring

Regret minimizing

Representativeness

Frame dependence

27

Loss aversion

Defense
mechanisms
Behavioral Finance Theory

Sources:
www.forrester.com/findresearch
BlackRock

28
Our Strategic Concept
H2O Consulting launches RiskAdvisor® platform that combines
Modern Portfolio Theory with Behavioral Portfolio Theory

Modern Portfolio Theory
(MPT)
Markowitz (1952)

Behavioral Portfolio
Theory (BPT)
Shefrin, Statman (2000)

29
Sentiment Analysis
H2O Consulting
Università della Svizzera italiana
30
Sentiment Analysis
Web Crawling
Data
Semantic Classification
Technology Processing Analysis
Algorithm

Trust
Calculation

Online News

Aggregation

Social Media

Sentiment

Sources

Individual
Recommendation
Message Board

31

Social
Intelligence
Sentiment Analysis

Our dataset consist of 447’393 messages,
on the 30 Dow Jones Index (DJIA) stocks,
posted on the Yahoo! Finance message board
in the period August 2012 to May 2013,
of which 55’217 with sentiment tag
and 5’967 distinct authors.

32
Trust Calculation
A novel way to generate sentiment
based on author’s credibility
calculated on accuracy of
his past messages
Top 10 (DJIA)
0.78 0.80 0.82 0.84 0.86 0.88 0.90 0.92 0.94

Microsoft Corp (MSFT)
0.94
0.92
0.90
0.88
0.86
0.84
0.82
0.80
0.78
0.76
0.74
0.72

****t_suckz (MSFT)
****icultalias (AA)
****tmimi (BAC)
****orkingman (MSFT)
****ab33 (INTC)
****buco2012 (MSFT)
****joiner (BAC)
****nvestor (HPQ)
****_refund (MSFT)
****lers_nightmare (MSFT)

0.916
0.887
0.876
0.876
0.876
0.875
0.875
0.844
0.832
0.828

Period from August 28, 2012 to October 23, 2013,
on the 30 Dow Jones Index (DJIA) stocks
33
Empirical Validation
3-scale index model
(Weighted)
Stock

N° Observations
(Trading Days)

5-scale index model
(Weighted)

Adj
R-Square

Adj
R-Square

MMM

-2.5

0.69

3.9

0.73

152

24.1

0.40

11.2

0.38

AXP

30

-10.9

0.33

-0.99

0.35

T

162

37.3***

0.40

23.7***

0.41

BAC

174

131.9***

0.46

55.7***

0.46

BA

172

48.2**

0.21

18.3*

0.19

CAT

168

37.3*

0.50

23.2*

0.51

CVX

110

3.9

0.55

4.3

0.57

CSCO

From August 28, 2012 to
May 16, 2013
on the 30 DJIA stocks

34

AA

153

22.9

0.12

11.8

0.14

DD

80

17.2

0.37

12.3

0.39

XOM

147

1.3

0.75

2.7

0.65

GE

90

20.0

0.24

4.6

0.27

HPQ

174

119.8**

0.14

56.7**

0.16

HD

97

3.2

0.23

-2.7

0.24

INTC

174

90.1***

0.38

40.3***

0.35

IBM

139

6.0

0.17

6.2

0.19

JNJ

104

-11.1

0.35

-6.1

0.36

JPM

155

27.7**

0.62

MCD

113

18.6

0.37

13.4**
7.8

0.62
0.35

MRK

89

19.0

0.05

4.3

0.05

MSFT

174

116.4***

0.52

53.9***

0.52

PFE

155

38.5***

0.35

20.9***

0.38

PG

66

0.9

0.31

5.0

0.35

KO

110

9.5

0.29

8.1

0.28

TRV

12

N/A

N/A

N/A

N/A

UTX

62

20.6

0.50

13.0*

0.50

UNH

32

2.3

0.31

3.8

0.40

VZ

127

6.8

0.26

6.4

0.27

WMT

170

52.6***

0.23

27.8***

0.24

DIS

82

-1.9

0.23

-2.6

0.23

*** p-value < 0.001 ** p-value < 0.01 * p-value < 0.05
Coefficients are reported in basis points (0.01%)

34
Empirical Validation

3-scale index model (Weighted)

Stock

5-scale index model (Weighted)

Adj
R-Square

Adj
R-Square

N° Observations
(Trading Days)

T

162

37.3***

0.40

23.7***

0.41

BAC

174

131.9***

0.46

55.7***

0.46

BA

172

48.2**

0.21

18.3*

0.19

CAT

168

37.3*

0.50

23.2*

0.51

HPQ

174

119.8**

0.14

56.7**

0.16

INTC

174

90.1***

0.38

40.3***

0.35

JPM

155

27.7**

0.62

13.4**

0.62

MSFT

174

116.4***

0.52

53.9***

0.52

PFE

155

38.5***

0.35

20.9***

0.38

WMT

170

52.6***

0.23

27.8***

0.24

35
Empirical Validation

3-scale index model
(Weighted)

5-scale index model
(Weighted)

N° Obs.
(Trading Days)

N° posts

BAC

174

10’090

131.9***

-99.0**

55.7***

-44.9**

HPQ

174

5’146

119.8**

-93.6*

56.7**

-49.8*

INTC

174

6’545

90.1***

-53.6**

40.3***

-19.3

MSFT

174

8’337

116.4***

-31.6*

53.9***

-13.6*

Stock

36
Empirical Validation

Bull
Market

Investors
are
optimistic

Sentiment
index
raises

37

Human
behavior
(prospect
theory)

Lock in
gains

Market
peak
Empirical Validation
3-scale index model
(Weighted)
Stock

BAC
HPQ
INTC
MSFT

N° Obs.
(Trading
Days)
174
174
174
174

5-scale index model
(Weighted)

131.9***
119.8**
90.1***
116.4***

55.7***
56.7**
40.3***
53.9***

N° posts

10’090
5’146
6’545
8’337

-99.0**
-93.6*
-53.6**
-31.6*

1)

2)
38

-44.9**
-49.8*
-19.3
-13.6*
Empirical Validation
3-scale index model
Stock

5-scale index model

N° Observations
(Trading Days)

T

162

37.3***

25.0

23.7***

12.6

BAC

174

131.9***

60.4

55.7***

33.2

BA

172

48.2**

16.5

18.3*

7.9

CAT

168

37.3*

20.6

23.2*

16.2

HPQ

174

119.8**

102

56.7**

62.0

INTC

174

90.1***

82.5*

40.3***

46.8*

JPM

155

27.7**

26.0*

13.4**

16.4**

MSFT

174

116.4***

101.4**

53.9***

57.4**

PFE

155

38.5***

32.6**

20.9***

21.7**

WMT

170

52.6***

0.12

27.8***

-0.4

39
Sentiment Trading Strategy
May 16, 2013

August 28, 2012

$1 million

If Sentiment at
trading day t is
greater than
Upper Limit

BUY

If Sentiment at
trading day t is
lower than
Lower Limit

SELL

3-scale index model

5-scale index model

Upper Limit

0.97

1.00

Lower Limit

-0.83

-1.70

Upper and lower limits have been estimated through a best-fitting process on time series,
with proprietary genetic algorithms.
40
Sentiment Trading Strategy

From August 28, 2012 to May 16, 2013 (Initial Investment $1 million)
41
Sentiment Trading Strategy

From August 28, 2012 to May 16, 2013 (Initial Investment $1 million)
42
Sentiment Trading Strategy
Can we build an active investment strategy,
using our sentiment trading rule and source of information,
in order to generate greater risk-adjusted returns than
a passive, naïve, yet achievable, investment strategy?
S&P500: 17.1%
Risk-free: 0%
Beta (portfolio): 1.41

Portfolio Expected Return (CAPM): 24.1% ($241K)
From August 28, 2012 to May 16, 2013 (Initial Investment $1 million)

Yes. We can!
43
Publications / About us

http://ssrn.com/abstract=2309375

44
H2O Sentiment Analysis
Instantly capture human emotion
in financial markets
as it happens.

45
Sentiment Analysis
Track Real-Time Sentiment
Analysis On Your
Mobile Device

46
Thank You for Your Attention

H2O Consulting © 2013 All Rights Reserved

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Emotions Affect Markets in Predictable Ways: Behavioral Finance and Sentiment Analysis

  • 2. Innovation from Swiss tradition H2O Consulting H2O Consulting Lugano (Switzerland) www.h2oconsulting.ch 2
  • 3. The Prize in Economic Sciences 2013 Eugene F. Fama University of Chicago Robert J. Shiller Yale University, New Haven Lars Peter Hansen University of Chicago  There is no way to predict the price of stocks and bonds over the next few days or weeks. But it is quite possible to foresee the broad course of these prices over longer periods, such as the next three to five years.  3
  • 4. The Prize in Economic Sciences 2013 4
  • 6. Neoclassical Finance Model Strong form All private information All public information All investors are rational, well-informed and hope for maximizing profit Market prices immeditely refllect all available information Information in past stock prices Efficient Market Hypothesis Semi-strong form Weak form 6
  • 7. Neoclassical Finance Model Markets are normally distributed with daily stock return lying under their theoretical bell shaped curve Stock prices reflect the discounted value of expected cash-flows It is not possible to beat the market over time without taking excess risk Sentiment does not play a role in this classic framework. 7
  • 8. Neoclassical Finance Model Gordon Dividend Discount Model (DDM) The value of a stock is the present value of all of the expected future dividends. Zero growth Constant growth 8 Differential growth
  • 9. Neoclassical Finance Model An investor is considering the purchase of a share of the iPear Inc. $3 dividend per share a year from today $3 = $60 10% 0.15 - 0.10 dividend expected growth rate per year (foreseeable future) Share Price 15% required return (iPear’s risk) Constant Growth Scenario 9
  • 10. Neoclassical Finance Model Great Crash 1929 Black Monday Crash October 1987 Internet bubble 1990s Financial Turmoil October 2008 Neoclassical Financial Model is unable to explain extreme cases of bubbles and crashes It seems timely to define a human sentiment function in stochastic discount factor (SDF) 10
  • 12. Neoclassical Finance Model  Adherents of geometric Brownian motion or log normally distributed stock returns (one of the foundation blocks of modern finance) must ever after face a disturbing fact: assuming the hypothesis that stock index returns are log normally distributed with about a 20% annualized volatility, the probability that the stock market could fall 29% (the decline in S&P futures on October 19th, 1987) in a single day is 10-160. So improbable is such an event that it would not be anticipated to occur even if the stock market were to last for 20 billion years, the upper end of the currently estimated duration of the universe. Indeed, such an event should not occur even if the stock market were to enjoy a rebirth for 20 billion years in each of 20 billion big bangs.  Mark Rubinstein in Comments on the 1987 Stock Market Crash 12
  • 13. Behavioral Finance Theory  People in standard finance are rational. People in behavioral finance are normal   If you don’t know who you are, the stock market is an expensive place to find out  Psychology Economics Adam Smith Scottish moral philosopher and a pioneer of political economy Meir Statman Professor of Finance (Leavey School of Business, Santa Clara University) 13
  • 14. Behavioral Finance Theory Brain’s biological and physiological limits Simplification of reality Approximation of information (heuristics and cognitive filters) Errors and biases 14
  • 15. Behavioral Finance Theory Germany Austria 12% 1% Donate organs Don’t donate 12% Donors 99% Donors Opt-in system vs Opt-out system The Role of Inertia 15
  • 17. Behavioral Finance Theory In the Mind of the Market: Theory of Mind Biases Value Computation during Financial Bubbles (Benedetto De Martino) 17
  • 18. Keynesian Beauty Contest Theory 1 2 3 18
  • 19. Robert J. Shiller (1981) 19
  • 21. Behavioral Finance Theory Source: based on the risk formula by risk researcher Dr. Peter M. Sandman. 21
  • 22. Risk Aversion vs Seeking Aversion Option A 50% Win €250’000 50% Win €0 Option B Offer: €125’000 Expected Value of option A is: 0.5 · €250’000 = €125’000 The two options are equivalent in term of expected value 78% of people choose option B Risk Aversion! Source: Psicologia e Investimenti Finanziari Paolo Legrenzi (2006) 22
  • 23. Risk Aversion vs Seeking Aversion For the same expected value, are investors always risk-averse? 23
  • 24. Risk Aversion vs Seeking Aversion Option A 50% Loss €250’000 50% Loss €0 Option B Certain Loss: €125’000 Expected Value of option A is: 0.5 · -€250’000 = -€125’000 The two options are equivalent in term of expected value 61% of people choose option A! Risk Seeking! Source: Psicologia e Investimenti Finanziari Paolo Legrenzi (2006) 24
  • 25. Risk Aversion vs Seeking Aversion For the same expected value, are investors always risk-averse? When we are faced with a sure gain we tend to be risk averse but When we are faced with a certain loss we tend to be risk seeking 25
  • 26. Prospect Theory The combination of risk-aversion with risk-seeking is represented by the value function - 20 - 10 +10 +20 26
  • 29. Our Strategic Concept H2O Consulting launches RiskAdvisor® platform that combines Modern Portfolio Theory with Behavioral Portfolio Theory Modern Portfolio Theory (MPT) Markowitz (1952) Behavioral Portfolio Theory (BPT) Shefrin, Statman (2000) 29
  • 30. Sentiment Analysis H2O Consulting Università della Svizzera italiana 30
  • 31. Sentiment Analysis Web Crawling Data Semantic Classification Technology Processing Analysis Algorithm Trust Calculation Online News Aggregation Social Media Sentiment Sources Individual Recommendation Message Board 31 Social Intelligence
  • 32. Sentiment Analysis Our dataset consist of 447’393 messages, on the 30 Dow Jones Index (DJIA) stocks, posted on the Yahoo! Finance message board in the period August 2012 to May 2013, of which 55’217 with sentiment tag and 5’967 distinct authors. 32
  • 33. Trust Calculation A novel way to generate sentiment based on author’s credibility calculated on accuracy of his past messages Top 10 (DJIA) 0.78 0.80 0.82 0.84 0.86 0.88 0.90 0.92 0.94 Microsoft Corp (MSFT) 0.94 0.92 0.90 0.88 0.86 0.84 0.82 0.80 0.78 0.76 0.74 0.72 ****t_suckz (MSFT) ****icultalias (AA) ****tmimi (BAC) ****orkingman (MSFT) ****ab33 (INTC) ****buco2012 (MSFT) ****joiner (BAC) ****nvestor (HPQ) ****_refund (MSFT) ****lers_nightmare (MSFT) 0.916 0.887 0.876 0.876 0.876 0.875 0.875 0.844 0.832 0.828 Period from August 28, 2012 to October 23, 2013, on the 30 Dow Jones Index (DJIA) stocks 33
  • 34. Empirical Validation 3-scale index model (Weighted) Stock N° Observations (Trading Days) 5-scale index model (Weighted) Adj R-Square Adj R-Square MMM -2.5 0.69 3.9 0.73 152 24.1 0.40 11.2 0.38 AXP 30 -10.9 0.33 -0.99 0.35 T 162 37.3*** 0.40 23.7*** 0.41 BAC 174 131.9*** 0.46 55.7*** 0.46 BA 172 48.2** 0.21 18.3* 0.19 CAT 168 37.3* 0.50 23.2* 0.51 CVX 110 3.9 0.55 4.3 0.57 CSCO From August 28, 2012 to May 16, 2013 on the 30 DJIA stocks 34 AA 153 22.9 0.12 11.8 0.14 DD 80 17.2 0.37 12.3 0.39 XOM 147 1.3 0.75 2.7 0.65 GE 90 20.0 0.24 4.6 0.27 HPQ 174 119.8** 0.14 56.7** 0.16 HD 97 3.2 0.23 -2.7 0.24 INTC 174 90.1*** 0.38 40.3*** 0.35 IBM 139 6.0 0.17 6.2 0.19 JNJ 104 -11.1 0.35 -6.1 0.36 JPM 155 27.7** 0.62 MCD 113 18.6 0.37 13.4** 7.8 0.62 0.35 MRK 89 19.0 0.05 4.3 0.05 MSFT 174 116.4*** 0.52 53.9*** 0.52 PFE 155 38.5*** 0.35 20.9*** 0.38 PG 66 0.9 0.31 5.0 0.35 KO 110 9.5 0.29 8.1 0.28 TRV 12 N/A N/A N/A N/A UTX 62 20.6 0.50 13.0* 0.50 UNH 32 2.3 0.31 3.8 0.40 VZ 127 6.8 0.26 6.4 0.27 WMT 170 52.6*** 0.23 27.8*** 0.24 DIS 82 -1.9 0.23 -2.6 0.23 *** p-value < 0.001 ** p-value < 0.01 * p-value < 0.05 Coefficients are reported in basis points (0.01%) 34
  • 35. Empirical Validation 3-scale index model (Weighted) Stock 5-scale index model (Weighted) Adj R-Square Adj R-Square N° Observations (Trading Days) T 162 37.3*** 0.40 23.7*** 0.41 BAC 174 131.9*** 0.46 55.7*** 0.46 BA 172 48.2** 0.21 18.3* 0.19 CAT 168 37.3* 0.50 23.2* 0.51 HPQ 174 119.8** 0.14 56.7** 0.16 INTC 174 90.1*** 0.38 40.3*** 0.35 JPM 155 27.7** 0.62 13.4** 0.62 MSFT 174 116.4*** 0.52 53.9*** 0.52 PFE 155 38.5*** 0.35 20.9*** 0.38 WMT 170 52.6*** 0.23 27.8*** 0.24 35
  • 36. Empirical Validation 3-scale index model (Weighted) 5-scale index model (Weighted) N° Obs. (Trading Days) N° posts BAC 174 10’090 131.9*** -99.0** 55.7*** -44.9** HPQ 174 5’146 119.8** -93.6* 56.7** -49.8* INTC 174 6’545 90.1*** -53.6** 40.3*** -19.3 MSFT 174 8’337 116.4*** -31.6* 53.9*** -13.6* Stock 36
  • 38. Empirical Validation 3-scale index model (Weighted) Stock BAC HPQ INTC MSFT N° Obs. (Trading Days) 174 174 174 174 5-scale index model (Weighted) 131.9*** 119.8** 90.1*** 116.4*** 55.7*** 56.7** 40.3*** 53.9*** N° posts 10’090 5’146 6’545 8’337 -99.0** -93.6* -53.6** -31.6* 1) 2) 38 -44.9** -49.8* -19.3 -13.6*
  • 39. Empirical Validation 3-scale index model Stock 5-scale index model N° Observations (Trading Days) T 162 37.3*** 25.0 23.7*** 12.6 BAC 174 131.9*** 60.4 55.7*** 33.2 BA 172 48.2** 16.5 18.3* 7.9 CAT 168 37.3* 20.6 23.2* 16.2 HPQ 174 119.8** 102 56.7** 62.0 INTC 174 90.1*** 82.5* 40.3*** 46.8* JPM 155 27.7** 26.0* 13.4** 16.4** MSFT 174 116.4*** 101.4** 53.9*** 57.4** PFE 155 38.5*** 32.6** 20.9*** 21.7** WMT 170 52.6*** 0.12 27.8*** -0.4 39
  • 40. Sentiment Trading Strategy May 16, 2013 August 28, 2012 $1 million If Sentiment at trading day t is greater than Upper Limit BUY If Sentiment at trading day t is lower than Lower Limit SELL 3-scale index model 5-scale index model Upper Limit 0.97 1.00 Lower Limit -0.83 -1.70 Upper and lower limits have been estimated through a best-fitting process on time series, with proprietary genetic algorithms. 40
  • 41. Sentiment Trading Strategy From August 28, 2012 to May 16, 2013 (Initial Investment $1 million) 41
  • 42. Sentiment Trading Strategy From August 28, 2012 to May 16, 2013 (Initial Investment $1 million) 42
  • 43. Sentiment Trading Strategy Can we build an active investment strategy, using our sentiment trading rule and source of information, in order to generate greater risk-adjusted returns than a passive, naïve, yet achievable, investment strategy? S&P500: 17.1% Risk-free: 0% Beta (portfolio): 1.41 Portfolio Expected Return (CAPM): 24.1% ($241K) From August 28, 2012 to May 16, 2013 (Initial Investment $1 million) Yes. We can! 43
  • 44. Publications / About us http://ssrn.com/abstract=2309375 44
  • 45. H2O Sentiment Analysis Instantly capture human emotion in financial markets as it happens. 45
  • 46. Sentiment Analysis Track Real-Time Sentiment Analysis On Your Mobile Device 46
  • 47. Thank You for Your Attention H2O Consulting © 2013 All Rights Reserved