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

Table of Contents  Introduction  Requirements  Software Design  Features  Financial Models  Security  Challenges

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

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

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

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

Software Design

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!

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

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

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

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

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)

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.

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