it in it in financial markets it in financial markets it in financial markets e-investors ali javed...
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IT in IT in Financial Markets IT in Financial Markets IT in Financial Markets
E-investors
Ali Javed Adrienne Fernandez Ekaterina Ianovskaia
E-INVESTORSAgenda
Top-down approach to security analysis
First portfolio vs. last portfolio
Tools usedSurprise 1Surprise 2
Neural Networks
Challenges and risk mitigation
Lessons learned
E-INVESTORS
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
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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
Historical trends show that the most performing current industries are:
Information Technology
Financials
Energy
Industrials
Health care
Consumer Services
Fidelity.com
E-INVESTORSFirst Portfolio
Consumer Services18%
Electronic technology23%
Energy Minerals12%
Finance19%
Health13%
Industrials15%
Telecommunications1%
First Portfolio Industry Weights
E-INVESTORSStep 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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Stock Sector and Industry Target Investment Amount Quantity Weights
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%
First Portfolio
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Last Portfolio
E-INVESTORSPortfolio management
over time
2/25
/07
2/27
/07
3/1/
07
3/3/
07
3/5/
07
3/7/
07
3/9/
07
3/11
/07
3/13
/07
3/15
/07
3/17
/07
3/19
/07
3/21
/07
3/23
/07
3/25
/07
3/27
/07
3/29
/07
3/31
/07
4/2/
07
4/4/
07
4/6/
07
-5.00%
-4.00%
-3.00%
-2.00%
-1.00%
0.00%
1.00%
2.00%
3.00%
Return
E-INVESTORSTools used
CompuStat
CRSP
Data mining, stock charts
CAPM
Matlab
Most useful tool: Excel solver
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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
Excel Solver: Step by step
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Best Portfolio: diversified
Covariance matrix
E-INVESTORSExcel Solver: Step by
step
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
-0.1
-0.05
0
0.05
0.1
0.15
0.2
AXP
CBS
SIX
AAPL
LMT RIMM
LMT
ALRN
JAZZ
PFENVSEP
TTM
ALU
Efficient Frontier
E-INVESTORSSurprise 1-Mad Money
Followed recommendationsChose stocks according to our
analysis
Diversification:According to days of
recommendation
Outcome: positive!
E-INVESTORSSurprise 1-Mad Money
Outcome
SKS : Monthly
SKS : Daily ORCL : Daily
ORCL : MonthlyMCD : Monthly
MCD : Daily
E-INVESTORSSurprise 1-Mad Money
Outcome
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Surprise 2 - Vice vs Virtue
Virtue Companies:
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
PLL: +$2,844.60
WMT: +$2,740.75
NOC: -$1,143.84 KBR: -$1,255.68
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Vice: Virtue: WMT:
2,740.75NOC: -
1,143.84KBR: -
1,255.68
341.23
AGP:-4,395.47
WBS: 487.2PLL:
2,844.60
-1,063.67
Surprise 2 - Vice vs Virtue
Outcome
E-INVESTORSNeural Networks
Trial version : Limited number of inputs
E-INVESTORSNeural 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
Technical: Moving averages (30) (90)
Market Index: S&P 500
WBS
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Neural Networks Result
1 5 9 13 17 21 25 29 33 37 41 45 49
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
MSE versus Epoch
Training MSE
Cross Validation MSE
Epoch
MSE
50-50-50 rule
WBS
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Neural Networks Result
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61
14
15
16
17
18
19
20
Desired Output and Actual Network Output
PRC
PRC Output
Exemplar
Outp
ut
Performance PRCMSE 1.961558161NMSE 2.969163734MAE 1.274166299Min Abs Error 0.144731369Max Abs Error 2.386955525r -0.048827027
Predicted Volatility well, but not magnitude of changes and price level
WBS
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Neural Networks Result
AGP
1 5 9 13 17 21 25 29 33 37 41 45 49
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
MSE versus Epoch
Training MSE
Cross Validation MSE
Epoch
MSE
Best Networks Training Cross ValidationEpoch # 50 1Minimum MSE 0.030559798 0.442329399Final MSE 0.030559798 0.69339859
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Neural Networks Result
Predicted Price Level better, not Volatility.
AGP
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61
0
5
10
15
20
25
30
35
40
45
50
Desired Output and Actual Network Output
PRC
PRC Output
Exemplar
Outp
ut
Performance PRCMSE 68.69420555NMSE 39.35646708MAE 8.182435557Min Abs Error 5.198936887Max Abs Error 10.70313759r 0.052262509
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
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Challenges and risk mitigation
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
E-INVESTORSLessons 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