Lucena KPI Forecasting - WMT
Analysis Period5/29/15 - 8/7/20
Lucena conducted multi-phase research to validate Equifax’s data and illustrate its predictive power in forecasting KPIs for certain publicly traded constituents. This report provides graphical and statistical representations of the effectiveness of Equifax’s consumer credit data in forecasting future quarter over quarter growth in revenue.
Quarterly gross revenue
WMT - Walmart Inc
WMT - Revenue Projections to Actuals
*KPI Attribution is a combination of ‘Seasonality’, ‘Trend’, & ‘Residual’, which sum to 100%.
Y over Y Quarterly Change % (Forecast vs Actuals)
*Values are in billions
Using Regression Analysis, Lucena constructed multiple models composed of Equifax, Macro, Fundamental, and Alternative Data factors. As can be seen, Equifax data is well suited for KPI forecasting and can be further optimized by combining it with other data sets.
Data Sources & Features
- Consumer credit dataset for the United States population.
+ Delinquency Rate
+ Utilization Rate
+ Debt to Income
+ Inquiries TTM
+ Payment to Balance Ratio
+ Available Credit+ Payment Due
+ Credit Score
+ Number of Accounts
+ Number of New Accounts
+ Total Balance
+ Number of Consumers
Lucena Machine Learning models are built using SVM, Random Forest, Knn, Logistic Regression, and other traditional Machine Learning techniques. An ensemble of uncorrelated multi-factor models are created and weighted based on their recent accuracy. Final forecast values are based on calculating the mean from the weighted sum of all models.
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Lucena is a technology company and not a certified investment advisor. Do not take the opinions expressed explicitly or implicitly in this communication as investment advice. The opinions expressed are of the author and are based on statistical forecasting based on historical data analysis. Past performance does not guarantee future success. In addition, the assumptions and the historical data based on which an opinion
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