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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. Agenda. Top-down approach to security analysis First portfolio vs. last portfolio Tools used Surprise 1 Surprise 2 Neural Networks

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

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  1. IT in IT in Financial MarketsIT in Financial MarketsIT in Financial Markets E-investors Ali Javed Adrienne Fernandez Ekaterina Ianovskaia

  2. 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

  3. 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

  4. Strategy:Top-down approach to security analysis

  5. 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

  6. Step 2: Industry Analysis Fidelity.com Historical trends show that the most performing current industries are: Information Technology Financials Energy Industrials Health care Consumer Services

  7. First Portfolio

  8. 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

  9. First Portfolio

  10. Last Portfolio

  11. Portfolio management over time

  12. Tools used CompuStat CRSP Data mining, stock charts CAPM Matlab Most useful tool: Excel solver

  13. 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

  14. Covariance matrix Best Portfolio: diversified

  15. Excel Solver: Step by step

  16. Surprise 1-Mad Money • Followed recommendations • Chose stocks according to our analysis • Diversification: • According to days of recommendation • Outcome: positive!

  17. Surprise 1-Mad MoneyOutcome    SKS : Monthly MCD : Monthly ORCL : Monthly SKS : Daily MCD : Daily ORCL : Daily

  18. Surprise 1-Mad MoneyOutcome   

  19. 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)

  20. 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

  21. Surprise 2 - Vice vs Virtue Outcome Vice: Virtue:

  22. Neural Networks Trial version : Limited number of inputs

  23. Neural Networks in stock price forecast WBS 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

  24. Neural Networks Result WBS 50-50-50 rule

  25. Neural Networks Result WBS Predicted Volatility well, but not magnitude of changes and price level

  26. Neural Networks Result AGP

  27. Neural Networks Result AGP Predicted Price Level better, not Volatility. Reason: Input Factors

  28. Neural Networks Result • Not all factors that affect one stock affect the other • Bank Prime Loan Rate • Sensitivity To The Market

  29. 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

  30. 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)

  31. Glitch

  32. 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

  33. Last Portfolio

  34. Thank you ! Questions

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