Financial markets are not purely rational. Emotions play a large part in stock pricing. H2O Sentiment Analysis captures these emotions, the “animal spirits” coined by Keynes, through social media post messages. We employ a novel way to capture and quantify sentiment based on authors' credibility, namely tracking the accuracy of past recommendations. Our results provide evidence that there is strong and useful information on investor sentiment and likely stock market movements. Our research (done in collaboration with the Università della Svizzera italiana) has demonstrated that we can use this information in order to make predictions about stock price changes and to implement trading strategies based on sentiment analysis that perform, on average, better than traditional investment strategies like Buy and Hold or Moving Averages.

1. H2O Consulting
Cristian Bissattini, MBA

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

5. 5
Black,
Sholes,
Merton
Option pricing
Sharpe,
Lintner,
Black
CAPM
Markowitz
Portfolio Principles
Modigliani
& Miller
Arbitrage principles
Neoclassical Finance Model

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

11. Neoclassical Finance Model
11

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

16. Behavioral Finance Theory
Uncertainty
Risk
Fear
Gain
16

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

20. Behavioral Finance Theory
Risk
Perception
Prospect
Theory
Cognitive
Errors
20

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

27. Cognitive Errors
Overconfidence
Anchoring
Regret minimizing
Representativeness
Frame dependence
27
Loss aversion
Defense
mechanisms

28. Behavioral Finance Theory
Sources:
www.forrester.com/findresearch
BlackRock
28

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

37. Empirical Validation
Bull
Market
Investors
are
optimistic
Sentiment
index
raises
37
Human
behavior
(prospect
theory)
Lock in
gains
Market
peak

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