IT in IT in Financial Markets IT in Financial Markets IT in Financial Markets

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

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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

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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