1. By: David Johnston, James Mataras, Jesse Pirnat, Daniel Sanchez, Eric Shaw, Sean Vazquez, Brad Warren Stevens Institute of Technology Department of Quantitative Finance: Professor Calhoun Department of Computer Science: Professor Klappholz

2. Table of Contents • Introduction • Requirements • Software Design • Features • Financial Models • Security • Challenges

3. Introduction • System will enable more efficient and effective portfolios and risk management • Providing tools and analytics to drive investment decisions • Tools to support portfolio construction, position and trade analysis, risk metrics, and monitoring performance • System features a market model to help identify risks and trading opportunities • Client may leverage these tools to build a custom strategy based on quantitative analysis

4. Objectives • Provide access to stock data without the need to pay for a subscription data source • Manage virtual trading accounts • Track portfolio performance • Risk forecasts and analytics • Analyze potential trades

5. Functional Requirements • Portfolio Tracking • Input a portfolio and enter trades in the system • From the online feed for market data, system will provide updated quotes and charting capability for viewing portfolio performance • Portfolio will be made up of cash and long equities • Will not maintain a margin account • Risk Management • System shall provide portfolio risk metrics • Volatility forecasts • Scenario analysis • Sensitivities and correlations • Value-at-Risk • User may also drill down to position-level granularity

6. Functional Requirements Cont. • Trade Analysis • With the risk management technology, the user shall be able to: • Analyze potential trades • Assess risk and return • See the effects on the portfolio as a whole

7. Software Design

8. Features and Usability • Multi platform usability • iPhone, Android, Tablet, Laptop/Desktop • Instant Access to Profile data and online data • Save all data on your profile online • Detailed graphing interface • No installation required!

9. Factor Model • Stock returns are explained by a set of factors • As well as an idiosyncratic component • We use 4 factors • Total market return • Market cap • Value • Momentum • Makes for a tractable model • Dimensionality reduction • Intuition

14. Regime Switching • Define a latent variable for the current regime • Returns in each regime are determined by a factor model with different parameters • Model dynamics using a Hidden Markov Model (HMM) • Regime transitions are described by a Markov process • Model calibration is data driven, using Machine Learning • Bayesian inference • Expectation-Maximization algorithm • Advantages over traditional factor models • Traditional factor models are Gaussian and stationary • Regime switching can generate behavior that better approximates empirical market dynamics • E.g. fat tails, heteroskedasticity, leverage effect, time-varying correlations

16. Forecasts and Analytics • Monte Carlo simulation based on model • Forecasts the distribution of returns • Perform risk analysis based on simulation results • Return and volatility forecasts • Value at Risk • Marginal impact of individual positions and potential trades

17. Security Requirements • Confidentiality • User Information • Account Password • Anonymity • Integrity • Data loss prevention • Data modification restrictions • Accessibility • In production: accessible anywhere at any time • Currently: only accessible on Stevens Campus

18. Security Risks • User Accounts • Session Management • Authentication Mechanisms • Data Communication • Source Verification • HTTPS • Input Validation • XSS, JavaScript Injection, SQL Injection etc. • Denial of Service (DoS)

19. Challenges: Implementation • Interfacing with live data • Integrating different technologies and algorithms in application • Wt C++ Framework • Caused significant setbacks • Was not familiar with any of the standard web tools and technologies: HTML5, CSS3, JavaScript, jQuery • Time constraints after moving off original Wt C++ web toolkit • Did not know about responsive web design • Gained familiarity and understanding of the traditional web technologies • Gained exposure to widely used Twitter Bootstrap framework and learned how responsive web design is done.

20. Challenges: Financial Modeling • Model research and development • Literature research and applied quantitative analysis • Algorithm implementation • Efficiency is critical • Data acquisition • Bloomberg Terminal • Data cleaning • Size of historical dataset