IT in IT in Financial
Markets
IT in Financial Markets
IT in Financial Markets
Ali Javed
Adrienne Fernandez
Ekaterina Ianovskaia
E-investors
Agenda
 Top-down approach to security analysis
 First portfolio vs. last portfolio
 Tools used
 Surprise 1
 Surprise 2
 Neural Networks
 Challenges and risk mitigation
 Lessons learned
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Introduction
 Modern Portfolio Theory
 Don’t put all your eggs in the same
basket!
 use of diversification strategy
 diversify across industries and
companies
 choose large market capitalization
stocks
 Avoid risks
Strategy:
Top-down approach to
security analysis
Step 1: Economic Analysis
Step 2: Industry Analysis
Step 3:
Fundamental
analysis
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Step 1: Economic Analysis
 Research and data mining:
 Online
 Offline
 TV
 Newspapers
 discussions with professors
 Popularity hypothesis by Keynes:
Find what stocks will be popular among other investors
 Community based social investing websites like Zecco
 Findings:
 High economic instability
 Don’t invest in Japan
 U.S. low dollar stimulates exports and economic growth
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Step 2: Industry Analysis
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Historical trends show that the most
performing current industries are:
 Information Technology
 Financials
 Energy
 Industrials
 Health care
 Consumer Services
Fidelity.com
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First Portfolio
First Portfolio Industry Weights
Telecommunications
1%
Health
13%
Consumer Services
18%
Industrials
15%
Finance
19%
Electronic
technology
23%
Energy Minerals
11%
Step 3: Fundamental
Analysis
 Teamwork: Search different websites,
use Google docs to post findings
 Technical analysis: look at stock patterns
and analyzing the potential of growth of
these stocks.
 Choose many stocks performing better
than the benchmark S&P500 over the
past
 To diversify some of the risk, choose
several stocks with more steady returns,
which are far less volatile
 High earnings per share
 Mixed high and low betas
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First Portfolio
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Stock
Sector and Industry
Target Investment
Amount
1
CBS
Consumer Services: Media Conglomerates
294,642.86
12,100
7%
2
SIX Six Flags
Consumer services: movies/entertainment
294,642.86
4,718
7%
3
HMIN Home Inns & Hotels
Management Inc, ADR
Consumer services: Hotels/Resorts/Cruiselines,
ADR
200000
5,322
4%
4
AAPL
Electronic tech computer processing hardware
339285.7143
944
8%
5
LMT Lockheed Martin
Electronic tech: aerospace and defence
339285.7143
4,200
8%
6
RIMM
Electronic tech: telecom eqiupment
339285.7143
4,957
8%
7
CVX Chevron
Energy Minerals: integrated oil
517,857.14
4,970
12%
8
ALRN American Learning
Corp
Finance: insurance, brokers, services
18,073.76
6,364
0%
9
AXP American Express
Financial conglomerates: Finance
428,571.43
9,674
10%
10
GS Goldman Sachs Group Inc
Finance: Investment Banks/Brokers
410,497.67
2,496
9%
11
JAZZ Pharmaceuticals
Health technology: pharmaceuticals
196,428.57
7,480
4%
12
PFE Pfizer
Health technology: pharmaceuticals
196,428.57
9,936
4%
13
NVS Novartis
Health technology: pharmaceuticals
196,428.57
3,436
4%
14
EP El Paso corp
Industrial services: oil and gaz
214,285.71
11,710
5%
15
3M
Producer Manufacturing : Industrial
conglomerates
214,285.71
2,309
5%
16
TTM TATA Motors
Producer manufacturing: Machinery, ADR
250,000.00
9,571
6%
17
ALU Alcatel Lucent SA
Telecom equipment, Electronic technology, ADR
50,000.00
11,442
1%
Quantity
Weights
Last Portfolio
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Portfolio management
over time
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3.00%
2.00%
1.00%
0.00%
-1.00%
-2.00%
-3.00%
-4.00%
-5.00%
Return
Tools used
 CompuStat
 CRSP
 Data mining, stock charts
 CAPM
 Matlab
 Most useful tool: Excel solver
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Excel Solver: Step by step
 Step 1: Define what we need to find:
 maximum return
 minimum variance
 Step 2: Prepare the Spreadsheet
 Data and Constraints
 Step 3: Solve the model with the Solver
 Find optimal portfolio
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Covariance matrix
 Best Portfolio: diversified
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Excel Solver: Step by step
0.2
Efficient Frontier
ALRN
0.15
JAZZ
0.1
ALU
SIX
CBS
0.05
EP
0
0
-0.05
-0.1
0.05
LMT
PFE AAPL
NVS
AXP
LMT
0.1
TTM
RIMM
0.15
0.2
0.25
0.3
0.35
0.4
Surprise 1-Mad Money
 Followed recommendations
 Chose stocks according to our
analysis
 Diversification:
 According to days of
recommendation
 Outcome: positive!
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Surprise 1-Mad Money
Outcome
SKS : Monthly
SKS : Daily
MCD : Monthly
MCD : Daily
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ORCL : Monthly
ORCL : Daily
Surprise 1-Mad Money
Outcome
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Surprise 2 - Vice vs Virtue
 AGP (Amerigroup Corp)
 WBS (Webster Financial Corp)
 PLL (Pall Corp)
 Vice Companies:
 WMT (Wal-Mart Stores Inc)
 NOC (Northrop Grumman Corp)
 KBR (KBR Inc)
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Surprise 2 - Vice vs Virtue
Outcome
AGP: -$4,395.47
WBS: +$487.20
WMT: +$2,740.75
NOC: -$1,143.84
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PLL: +$2,844.60
KBR: -$1,255.68
Surprise 2 - Vice vs Virtue
Outcome
Vice:
Virtue:
WMT: 2,740.75
AGP:-4,395.47
NOC: -1,143.84
WBS: 487.2
KBR: -1,255.68
PLL: 2,844.60
341.23
-1,063.67
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Neural Networks
 Trial version : Limited number of inputs
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Neural Networks in stock
price forecast
 Data: 250 observations (1 year period) of WBS daily
stock prices and market indicators from Compustat
 Indicators:
 Fundamental: Returns, Volume
WBS
 Technical: Moving averages (30) (90)
 Market Index: S&P 500
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Neural Networks Result
MSE versus Epoch
WBS
1
0.9
0.8
0.7
MSE
0.6
0.5
Training MSE
0.4
Cross Validation MSE
0.3
0.2
0.1
0
1
5
9
13
17
21
25
29
Epoch
33
37
41
45
49
50-50-50 rule
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Neural Networks Result
Desired Output and Actual Network Output
WBS
20
19
Output
18
17
PRC
PRC Output
16
15
14
1
6
11
16
21
26
31
36
41
46
51
56
61
Exemplar
Performance
MSE
NMSE
MAE
Min Abs Error
Max Abs Error
r
PRC
1.961558161
2.969163734
1.274166299
0.144731369
2.386955525
-0.048827027
Predicted Volatility
well, but not
magnitude of
changes and price
level
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Neural Networks Result
MSE versus Epoch
AGP
0.9
0.8
0.7
MSE
0.6
0.5
0.4
Training MSE
0.3
Cross Validation MSE
0.2
0.1
0
1
5
9
13
17
21
25
29
33
37
41
45
49
Epoch
Best Networks
Epoch #
Minimum MSE
Final MSE
Training
50
0.030559798
0.030559798
Cross Validation
1
0.442329399
0.69339859
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Neural Networks Result
Desired Output and Actual Network Output
AGP
50
45
40
Output
35
30
25
PRC
20
PRC Output
15
10
5
0
1
6
11
16
21
26
31
36
Exemplar
Performance
MSE
NMSE
MAE
Min Abs Error
Max Abs Error
r
PRC
68.69420555
39.35646708
8.182435557
5.198936887
10.70313759
0.052262509
41
46
51
56
61
Predicted Price
Level better, not
Volatility.
Reason: Input
Factors
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Neural Networks Result
Not all factors that affect
one stock affect the other
•Bank Prime Loan Rate
•Sensitivity To The Market
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Event Analysis
 We did Event Analysis
using Eventus
 Walmart dividend increase
announcement for April 1st
 Assuming markets are
efficient or semi-efficient
 Traders can react to news
faster than we can.
 Was not useful in picking
stocks
Challenges and
risk mitigation
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 Difficulty to use some portfolio analysis tools
(Matlab)
 Difficulty to understand tools (Neuro Solutions)
 Selling Stocks (Glitches, time limit)
 Availability issues for our team (Google)
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Glitch
Lessons learned
 New stock portfolio investment tools
 Never use one tool in isolation
 Market is quicker than we are
 Instinct Vs Hope
 Timing is Key
 Market Efficiency? Eventus Vs Mad Money
 Detailed Analysis = Computer Power
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Last Portfolio
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Thank you !
Questions