A caccia di emozioni per anticipare i mercati: finanza ... · 5/16/2013 · A caccia di emozioni...
Transcript of A caccia di emozioni per anticipare i mercati: finanza ... · 5/16/2013 · A caccia di emozioni...
A caccia di emozioni per anticipare i mercati: finanza comportamentale e sentiment analysis
H2O Consulting Cristian Bissattini, MBA
A caccia di emozioni per anticipare i mercati: finanza comportamentale e sentiment analysis
H2O Consulting
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H2O Consulting
Lugano (Switzerland)
www.h2oconsulting.ch
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Our Strategic Concept
Modern Portfolio Theory (MPT)
Markowitz (1952)
Behavioral Portfolio Theory (BPT)
Kahneman and Tversky (1979)
H2O Consulting presents RiskAdvisor® platform that combines Modern Portfolio Theory with Behavioral Portfolio Theory
Our Strategic Concept
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Define the client’s risk profile with behavioral models (Kahneman and Tversky, 1979)
Bulid the set of optimal asset allocations (portfolio models) with quantitative models (Markowitz, Black-Litterman, Monte Carlo Simulation, Risk Parity)
Match the asset allocation with your client’s risk profile (best fitting)
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Neoclassical Finance Model
CA
PM
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rtfo
lio P
rincip
les
Optio
n p
ricin
g
Arb
itra
ge p
rincip
les Modigliani
& Miller
Markowitz
Sharpe, Lintner, Black Black,
Sholes, Merton
Neoclassical Finance Model
All investors are rational, well-informed
and hope for maximizing profit
Market prices immeditely refllect all available information
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All private information
All public information
Information in past stock
prices
Efficient Market Hypothesis
Weak form
Semi-strong form
Strong form
Neoclassical Finance Model
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$3 dividend per share
a year from today
10% dividend expected growth rate per year
(foreseeable future)
15% required return
(iPear’s risk)
$3
0.15 - 0.10
= $60
Constant Growth Scenario
Share Price
An investor is considering the purchase of a share of the iPear Inc.
Neoclassical Finance Model
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Financial Turmoil
Internet bubble
Black Monday Crash Great
Crash
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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)
Prospect Theory
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The combination of risk-aversion with risk-seeking is represented by the value function
- 10 +20
- 20 +10
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Cognitive Errors
Overconfidence Anchoring Representativeness Loss aversion
Regret minimizing Frame dependence Defense mechanisms
Behavioral Finance Theory
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Sources:
www.forrester.com/findresearch
BlackRock
Sentiment Analysis
H2O Consulting
Università della Svizzera italiana
Sentiment Analysis
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Trust Calculation
Sentiment
Individual Recommendation
Aggregation Social Media
Online News
Message Board
Sources Social
Intelligence
Web Crawling Technology
Data Processing
Semantic Analysis
Classification Algorithm
Sentiment Analysis
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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.
Sentiment Analysis
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3-Scale Index
Model
5-Scale Index
Model
Strong Buy 1 2
Buy 1 1
Hold 0 0
Sell -1 -1
Strong Sell -1 -2
Trust Calculation
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Period from August 28, 2012 to October 23, 2013, on the 30 Dow Jones Index (DJIA) stocks
0.720.740.760.780.800.820.840.860.880.900.920.94
Microsoft Corp (MSFT)
0.916
0.887
0.876
0.876
0.876
0.875
0.875
0.844
0.832
0.828
0.78 0.80 0.82 0.84 0.86 0.88 0.90 0.92 0.94
****t_suckz (MSFT)
****icultalias (AA)
****tmimi (BAC)
****orkingman (MSFT)
****ab33 (INTC)
****buco2012 (MSFT)
****joiner (BAC)
****nvestor (HPQ)
****_refund (MSFT)
****lers_nightmare (MSFT)
Top 10 (DJIA)
A novel way to generate sentiment based on author’s credibility
calculated on accuracy of his past messages
Empirical Validation
From August 28, 2012 to May 16, 2013
on the 30 DJIA stocks
*** p-value < 0.001 ** p-value < 0.01 * p-value < 0.05
Coefficients are reported in basis points (0.01%)
3-scale index model
(Weighted)
5-scale index model
(Weighted)
Stock N° Observations
(Trading Days)
Adj
R-Square
Adj
R-Square
MMM 34 -2.5 0.69 3.9 0.73
AA 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 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 13.4** 0.62
MCD 113 18.6 0.37 7.8 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
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Empirical Validation
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3-scale index model (Weighted) 5-scale index model (Weighted)
Stock N° Observations (Trading Days)
Adj R-Square
Adj R-Square
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
Empirical Validation
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3-scale index model (Weighted)
5-scale index model (Weighted)
Stock 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*
Empirical Validation
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3-scale index model
(Weighted) 5-scale index model
(Weighted)
Stock 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*
1)
2)
Empirical Validation
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3-scale index model 5-scale index model
Stock 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
Sentiment Trading Strategy
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If Sentiment at trading day t is
greater than Upper Limit
If Sentiment at trading day t is
lower than Lower Limit
BUY
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.
August 28, 2012 May 16, 2013
$1 million
Sentiment Trading Strategy
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From August 28, 2012 to May 16, 2013 (Initial Investment $1 million)
Sentiment Trading Strategy
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From August 28, 2012 to May 16, 2013 (Initial Investment $1 million)
Sentiment Trading Strategy
Portfolio Expected Return (CAPM): 24.1% ($241K)
From August 28, 2012 to May 16, 2013 (Initial Investment $1 million)
S&P500: 17.1%
Risk-free: 0%
Beta (portfolio): 1.41
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?
Yes. We can!
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Publications / About us
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http://ssrn.com/abstract=2309375
H2O Sentiment Analysis
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Instantly capture human emotion in financial markets as it happens.
Sentiment Analysis
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Track Real-Time Sentiment Analysis On Your
Mobile Device
H2O Consulting © 2013 All Rights Reserved
Thank You for Your Attention