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

Octo

ber

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87

19

90

s

19

29

Octo

be

r 2

00

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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