Creating Effective Revenue Forecast Models for CCAR · Creating Effective Revenue Forecast Models...

Creating Effective Revenue Forecast Models for CCAR

Michael Woods, Principal Data Scientist March 25, 2015

Company Confidential & Proprietary

Complexity is the Challenge

2

c

Dataset

# of possible models created from the Dataset

exceeds two trillion

# of possible models created from the

Dataset next year after it grows another 40% exceeds eight trillion


Inadequate Response to Complexity

3

Quants

Conventional Analytical Approaches

y

x

Overfitting


Clean Slate Requirements

4

Speed Defensibility Accuracy


A Man-Machine Workflow

5

Algorithms + Compute

Group of Variables

Data Statistical Tests

Models Business Input

Business Validation

Variable Selection

Model Selection

Variable Identification

Model Identification

Machines


Machine Intelligence

+ +

Topological Data Analysis

Scalable Compute

Machine Learning, Geometric + Statistical Algorithms

6


Transforming Data to Insight

7

Fed Macro Variables

~300

Transforms ~900

Lagged ~2700

Distillable Model

Company Confidential & Proprietary 8

Correlate Variables with Business Performance

Correlation with Revenue

High Low


Identify Potentially Explanatory Variables

9

Eurozone Interest Rates Corr AbsCorrEurozonei1MLibor 0.78 0.78UKi1MLibor 0.71 0.71Eurozonei5YrSwap 0.67 0.67EurozoneUnemployment -0.66 0.6620-Bond GO Index_Lag_1 0.62 0.62UKGB10Yr 0.51 0.51

Gro

up

A

A

Macro Growth Corr AbsCorrEurozoneGDP_Index 0.66 0.66UKGDP_Index 0.59 0.59USUnemployment -0.57 0.57UKHousing_Lag_2 0.47 0.47G

rou

p B

B

Emerging Risk Corr AbsCorrASIAHangseng -0.67 0.67MexicoFxrate -0.51 0.51EMEAMXEMEA_Lag_2 0.49 0.49UKFxrate -0.48 0.48G

rou

p C

C

Funding Stress Corr AbsCorrTED 0.85 0.85LIBOR less OIS 0.76 0.76LIBOR_Less_FED 0.67 0.67Eurozonei1MLibor_delta -0.34 0.34G

rou

p E

E

Credit Spreads Corr AbsCorrUSCDS_US10YR_BBB_pctChange 0.70 0.70USCDXnonIG_pctChange 0.70 0.70USCDS_US10YR_A_pctChange 0.68 0.68USCDS_US25YR_BBB_pctChange 0.65 0.65USCDXIG_pctChange 0.58 0.58

Gro

up

D

D


Business Selects Variables

10

A


Gro

up

A

B Macro Growth Corr AbsCorrEurozoneGDP_Index 0.66 0.66UKGDP_Index 0.59 0.59USUnemployment -0.57 0.57UKHousing_Lag_2 0.47 0.47G

rou

p B

E


rou

p E

DCredit Spreads Corr AbsCorrUSCDS_US10YR_BBB_pctChange 0.70 0.70USCDXnonIG_pctChange 0.70 0.70USCDS_US10YR_A_pctChange 0.68 0.68USCDS_US25YR_BBB_pctChange 0.65 0.65USCDXIG_pctChange 0.58 0.58

Gro

up

D


Gro

up

A

Macro Growth Corr AbsCorrEurozoneGDP_Index 0.66 0.66UKGDP_Index 0.59 0.59USUnemployment -0.57 0.57UKHousing_Lag_2 0.47 0.47G

rou

p B

Credit Spreads Corr AbsCorrUSCDS_US10YR_BBB_pctChange 0.70 0.70USCDXnonIG_pctChange 0.70 0.70USCDS_US10YR_A_pctChange 0.68 0.68USCDS_US25YR_BBB_pctChange 0.65 0.65USCDXIG_pctChange 0.58 0.58

Gro

up

D


rou

p E


Generate Permutation Group of Models

11


Select for Empirical Validity

12


Business Selects Final Model

13


A Man-Machine Workflow

14

Algorithms + Compute

Group of Variables

Data Statistical Tests

Models Business Input

Business Validation

Variable Selection

Model Selection

Variable Identification

Model Identification

Machines

Corporate Headquarters 4400 Bohannon Drive Suite #200 Menlo Park, CA 94025 ayasdi.com

Creating Effective Revenue Forecast Models for CCAR · Creating Effective Revenue Forecast Models...

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