Moving at the Speed of Light

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CPR&CDRα

The BIG data customer frequently requires making, analyzing, creating or running:

1). Predictive non-linear, non-parametric models.

2). Path-dependent Monte Carlo simulations.

3). Very complex iterative global optimization.

4). Quantum covariance matrix calculations.

5). Query n-dimension stratification or slicing and dicing by variables and filters.

6). Real-time conditional probability decision with highly skewed fat-tails.

7). Probabilistic economic value-added outcomes in time series.

8). Adaptive feedback loop for any incremental or derivative alpha knowledge.

BIG Data Customer Focus

Utilize high performance patented virtual computing and storage technologies managing BIG MORTGAGE DATA sets to our value-added workflow processes with embedded adaptive control feedback to achieve maximum performance results and efficiency.

Manage and architect 2,000 CPU and GPU sysgovernor, computing nodes, and more than 1000TB storage capacity and advanced mathematical modeling tools( Including Quantum Field Theory, Pattern Recognition, Manifold Topology and Differential Geometry) to quantify the eigenfunction of the data structures.

Specialize in maximizing investors profits and alphas by building real-time based Monte Carlo Simulations pricing model by using millisecond resolution timestamp of market data for pricing loans or mortgage-backed securities, asset-backed securities, futures and options, as well as risk management analysis.

Deliver customized value-added solution for mortgage issuers and servicers, banks, investment banks, finance companies, broker-dealers, rating agencies and most importantly, the fixed income investor. Offers our clients with the critical mass of resources and experience to get the job done in a timely manner.

Who We Are

We specialize in maximizing investors profits or alphas by building a real-time Knowledge Decision Architecture (KDA) based with Monte CarloSimulations pricing model for mortgage-backed securities, asset-backedsecurities, futures and options, as well as risk management analysis usingmillisecond resolution timestamp of market data. We also delivercustomized value-added solution for mortgage issuers and servicers,banks, investment banks, finance companies, broker-dealers, ratingagencies and most importantly, the fixed income investor. In addition, weoffer our clients with the one of the industries most robust data resourcesand experience to get the job done in a timely manner.

What We Do

Knowledge Decision Matrix Paradigm

(+)

(-)

Profit

Decision

Knowledge

Index Field

Raw Data

UBX Network System Architecture

UBXUBiquitous indeX

Patented Technology

Real Time Query &

Reporting Analytics

Valuation Models &

Monte Carol Simulations

Advanced Mathematical

Physics Library

Patented Sorting Index

Algorithm

On-Demand Services

Mortgage POD/DOD/ROD: Prepayment/Default/Remittance On-Demand

A portal service provides slice and dice of Agency prepayment data for MBS analytics

VOD: Valuation On-Demand for ATOMS & SDP asset class A portal service provides all asset classes Monte Carlo Simulations (MCS) OAS

and Scenarios valuations

SOD: SCW On-Demand A portal service for Structured Cashflow Waterfall (SCW) product issuance,

analytics, and surveillance

Equity EOD: Equity Derivative On-Demand

A portal service for ETF & its Derivatives via Monte Carlo Simulation

Prepay/Default/Severity Models

Projects monthly prepayment, delinquency, default and loss severity rates of new (at purchase) or seasoned (portfolio) loans.

Takes into account of loan, borrower and collateral risk characteristics as well as macro economic variables on rates and home prices.

Based on a hybrid delinquency transition rate and competing risks survivorship model where the prepay & default risk parameters are estimated from historical loan-level data.

Based on a proprietary highly non-linear non-parametric methodology with parameters estimated from non-agency loan-level data.

Prepay and default are jointly estimated in a competing risk framework.

Real-time Analysis• High efficiency, real-time• Provide market real-time snapshot to capture market movements.

Flash Report

• Customize on-demand• Provide customized services for our clientsIOS Report

• Comprehensive, clear• Provide various statistics of market indicators to catch market dynamics.

Servicer -Specpool PROFITWe can provide timely and accurate market information, which serves as the crucial reference for tens of trilliondollars trading within seconds by Wells Fargo and other world's top financial institutions, and make huge profits.

Monte Carlo Simulations Workflow

Structured Cashflow Waterfalls

(SCW)

Equity Pricing

+Prepayment &

Default Models

+

Interest Rate and HPA

Models: MC simulations or Rep Paths for stress testing

Collateral

(Resident

Collateral

(Residential Mortgage

Loans)

Equity On-Demand

Equity Valuation

Prepay

Delinquency

Roll Rates

Default

Loss Severity

Equity

+

Equity Derivatives

Macro Economics Factors &

Assumptions : Rates & HPA

Pricing

MSR

Risk Mgmt.

FASB 157

IAS 39

Hedging

Securitization

Input Models Output Calculators Applications

Slice and Dice Engine applied in Pooling, Optimization, and Surveillance Complete database for agency (FN, FH, GN) Pass-Through’s

– Fully expanded Mega-pools, Giants, Platinum’s, STRIPs, CMO’s Complete Loan Performance, Lewtan, and Intex loan level database for prepayment

and default analysis:– mapped to groups, bonds, and Intex, Lewtan ground groups – Macro-Economic data integrated: HPI’s, unemployment, etc

Time Series and Aging Curves: web-based GUI – Roll rate analysis– Various breakout analysis– Portfolio feature: simple or with weights

S-Curve: pre-defined or user-supplied rate incentives with lag-weights

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Collateral Data Management

ATOMS™ Index

Available TBA-Only MBS Supply (ATOMS™) Market Index is designed so that market participants will have a practical, open standard real-time process to track the MBS market in terms of Daily Returns, Risk Adjusted Yields (RAY) and Prepayment (P) scores.

ATOMS™ is the first open standard index contains around 200,000 pools of market constituent that can use to track the composition and performance of the freely-tradable generic TBA-eligible population of fixed rate MBS pools.

These pools RAY, Intrinsic Value (IV) and Market Implied Price (MIP) are stored in sorted order index, and time series analysis can be performed.

These pools are guaranteed by Fannie Mae (FNMA), Freddie Mac (FHLMC), and Ginnie Mae (GNMA).

ATOMS™ Index Using our patent Score Technology

In addition to the features described above, the ATOMS™ Index is fundamentally a scientific and mathematical based index because it utilizes two very unique and ground-breaking technologies:

Score Technology which is comprised of R-Score (Repayment) and D-Score (Default). In essence, the R-Score ranks the voluntary prepayment behavior, while the D-Score ranks the involuntary prepayment behavior.

QED Model which is proprietary quantum electro-dynamic model utilizing quantum field theory and mathematical Lie symmetry analysis.

These two fundamental parts of the ATOMS™ Index will be described in detail in the following slides.

ATOMS™ Index - R & D Score Formulation

The 𝒕𝒕-month R-Score is based on the average of the model vector 𝒖𝒖𝒄𝒄𝒄𝒄𝒄𝒄 over the next 𝒕𝒕 months

The 𝒕𝒕-month D-Score is based on the average of the model vector 𝒖𝒖𝒄𝒄𝒄𝒄𝒄𝒄 over the next 𝒕𝒕 months

R & D Scoring Predictive Power

There are many statistical tools to measure the ranking power of scores. The most widely used tool in scoring industry is the KS statistic.

Figure 1 shows the scoring KS performance between R-Score and the “s curve” (current coupon rate minus prevailing rate) in ranking voluntary prepayment behavior for FN/FH 30YR Fixed Products for Purchase segment through Retail channel.

Figure 2 shows the scoring KS performance between D-Score and the FICO score ranking involuntary prepayment behavior for FN/FH 30YR Fixed Products for Purchase segment through Retail channel.

R & D Scoring Back Testing Agency Loan Result - June 2014

The following two charts include two test cases, which back tested agency population from June 2014. Twenty percentiles are display for their relative performance of the R-scores and D-scores. The agency population was for all unpaid mortgage loans and the performance is for the proceeding three months.

Mortgage Risk-adjusted Yields are established in order to quantify the risk-reward relationship for a given mortgage loan, based on the intrinsic credit/prepay factors, as well as market-driven factors such as liquidity, regulatory risk, counterparty risk, and maintenance carry.

In addition to Risk-adjustment, our latest version of RAY (called the Structured RAY) also accounts for the credit convexity that can occur within structured products such as Agency MBS, Non-Agency MBS, P2P deals, CMBS, Project Loans, and all other securitizations!

In order to determine the Structured RAY for our set of loans, we use the following mathematical formulation to obtain yields that match current market-calibrated levels:

Mortgage Loan Risk-Adjusted Yield (RAY)

Mortgage Loan Risk-Adjusted Yield (RAY)

RAY = MktYld_calib + [MinRiskFactor - (InvolPrepayRiskFactor + VolunPrepayRiskFactor)/(InvolPrepayRiskMin + VolunPrepayRiskMin)]*MktVol_calib,

Such that: • MktYld is the baseline yield for that origination/pool calibrated from market data. • MinRiskFactor is the lowest risk factor (e.g. highest score) that can be obtained in such origination/pool• InvolPrepayRiskFactor is the individual mortgage loan’s involuntary prepay risk factor (higher number indicates lower risk) – this is where we utilize D-Score• VolunPrepayRiskFactor is the individual mortgage loan’s voluntary prepay risk factor (higher number indicates lower risk) – this is where we utilize R-Score• InvolPrepayRiskMin and VolunPrepayRiskMin are the factors where the lowest risk for involuntary and voluntary risk would occur (highest sum indicates lowest risk) • MktVol is the volatility factor for the difference in loan risk versus “ideal risk” and is calibrated from market data.

ATOMS™ A Matrix

ATOMSTM Index Valuation Time Series Matrix:

Where a11 is defined by a given ATOM[Cusip(1)]@T(1) => Jan 2014 => T(y) => As of todayax1 is defined by a given ATOM[Cusip(x)]@T(1) => Jan 2014 => T(y) => As of todaya11 @ T(1) Given Px[MIP, T(1), P(1), RAY(1), AIV(1)]Where P(1) = 0.9 * R(1) + 0.1 * D(1)

Get Duration, Convexity, OAS, Yield, WAL from OAS pricing methodology.

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ATOMS™ Time Series Matrix

ATOMSTM Index Valuation Time Series Matrix:

Where a11 is defined by a given ATOM[Cusip(1)]@T(1) => Jan 2014 => T(y) => As of todayax1 is defined by a given ATOM[Cusip(x)]@T(1) => Jan 2014 => T(y) => As of todaya11 @ T(1) Given Px[MIP, T(1), P(1), RAY(1), AIV(1)]Where P(1) = 0.9 * R(1) + 0.1 * D(1)

Get Duration, Convexity, OAS, Yield, WAL from OAS pricing methodology.

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x mn xy

a a aa a a

ATOMS™ Index Daily Returns, PScore, R&D Score & RAY

Structure Derivative Products

In structured finance, a Structured Derivative Products (SDP™), also known as a market -linked investment, is a pre-packaged investment strategy based on derivatives, such as a single security, a basket of securities, options, indices, commodities, debt issuance and/or foreign currencies, and to a lesser extent, swaps. The variety of products just described is demonstrative of the fact that there is no single, uniform definition of a structured product. A feature of some structured products is a "principal guarantee" function, which offers protection of principal if held to maturity.

The SDPTM is a trademark structured derivatives product that market participants can use to understand the composition and performance of the freely-tradable generic population of structured derivatives securities issued by Fannie Mae (FNMA), Freddie Mac (FHLMC), and Ginnie Mae (GNMA). The SDPTM is constituted of 300,000 US Agency structured derivatives securities which comply waterfall logic of distribution of principal and interest, with underlying collateral that are not purposely held from the overall secondary market supply by central banking authorities or TBA-eligible.

Structure Derivative Products S Matrix

SDPTM Valuation Time Series Matrix:

Where s11 is defined by a given SDP[Cusip(1)]@T(1) => Jan 2008 => T(y) => As of todaysx1 is defined by a given SDP[Cusip(x)]@T(1) => Jan 2008 => T(y) => As of todays11 @ T(1) Given Px[MIP, T(1), P(1), RAY(1), AIV(1)]Where P(1) = 0.9 * R(1) + 0.1 * D(1)

Get Duration, Convexity, OAS, Yield, WAL from OAS pricing methodology.

11 1

1

ij y

x mn xy

s s ss s s