Ayasdi - Creating Effective Revenue Forecast Models for CCAR-PDF ...

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Creating Effective Revenue Forecast Models for CCAR Michael Woods, Principal Data Scientist March 25, 2015

Complexity is the Challenge

# 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

Dataset Company Confidential & Proprietary

2

Inadequate Response to Complexity Quants

Conventional Analytical Approaches

Overfitting y

x

Company Confidential & Proprietary

3

Clean Slate Requirements

Speed

Company Confidential & Proprietary

Accuracy

Defensibility

4

A Man-Machine Workflow

Data

Algorithms + Compute

Variable Identification

Group of Variables

Business Input

Variable Selection

Statistical Tests

Models

Model Identification

Business Validation

Model Selection

Machines

Company Confidential & Proprietary

5

Machine Intelligence

Scalable Compute

Machine Learning, Geometric + Statistical Algorithms

+

Company Confidential & Proprietary

Topological Data Analysis

+

6

Transforming Data to Insight

Fed Macro Variables ~300 Company Confidential & Proprietary

Transforms ~900

Lagged ~2700

Distillable Model

7

Correlate Variables with Business Performance

Correlation with Revenue High Company Confidential & Proprietary

Low 8

EurozoneUnemployment 20-Bond GO Index_Lag_1

UKGB10Yr

Corr AbsCorr 0.78 0.78 0.71 0.71 0.67 0.67 -0.66 0.66 0.62 0.62 0.51 0.51

B Group B

Eurozone Interest Rates Eurozonei1MLibor UKi1MLibor Eurozonei5YrSwap

Macro Growth EurozoneGDP_Index UKGDP_Index USUnemployment UKHousing_Lag_2

Corr AbsCorr 0.66 0.66 0.59 0.59 -0.57 0.57 0.47 0.47

A

Group C

C

E Funding Stress TED LIBOR less OIS LIBOR_Less_FED Eurozonei1MLibor_delta

Corr AbsCorr 0.85 0.85 0.76 0.76 0.67 0.67 -0.34 0.34

Company Confidential & Proprietary

Emerging Risk ASIAHangseng MexicoFxrate EMEAMXEMEA_Lag_2

UKFxrate

Corr AbsCorr -0.67 0.67 -0.51 0.51 0.49 0.49 -0.48 0.48

D Group D

Group E

Group A

Identify Potentially Explanatory Variables

Credit Spreads USCDS_US10YR_BBB_pctChange USCDXnonIG_pctChange USCDS_US10YR_A_pctChange USCDS_US25YR_BBB_pctChange USCDXIG_pctChange

Corr AbsCorr 0.70 0.70 0.70 0.70 0.68 0.68 0.65 0.65 0.58 0.58

9

EurozoneUnemployment 20-Bond GO Index_Lag_1

UKGB10Yr

Corr AbsCorr 0.78 0.78 0.71 0.71 0.67 0.67 -0.66 0.66 0.62 0.62 0.51 0.51

B Group B

Eurozone Interest Rates Eurozonei1MLibor UKi1MLibor Eurozonei5YrSwap

Macro Growth EurozoneGDP_Index UKGDP_Index USUnemployment UKHousing_Lag_2

Corr AbsCorr 0.66 0.66 0.59 0.59 -0.57 0.57 0.47 0.47

A

E Funding Stress TED LIBOR less OIS LIBOR_Less_FED Eurozonei1MLibor_delta

Corr AbsCorr 0.85 0.85 0.76 0.76 0.67 0.67 -0.34 0.34

Company Confidential & Proprietary

D Group D

Group E

Group A

Business Selects Variables

AbsCorr Credit Spreads Corr AbsCorr 0.70 USCDS_US10YR_BBB_pctChange 0.70 0.70 0.70 USCDXnonIG_pctChange 0.70 0.70 0.68 USCDS_US10YR_A_pctChange 0.68 0.68 0.65 USCDS_US25YR_BBB_pctChange 0.65 0.65 0.58 USCDXIG_pctChange 0.58 0.58

10

Generate Permutation Group of Models

Company Confidential & Proprietary

11

Select for Empirical Validity

Company Confidential & Proprietary

12

Business Selects Final Model

Company Confidential & Proprietary

13

A Man-Machine Workflow

Data

Algorithms + Compute

Variable Identification

Group of Variables

Business Input

Variable Selection

Statistical Tests

Models

Model Identification

Business Validation

Model Selection

Machines

Company Confidential & Proprietary

14

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