This document summarizes an analysis of assets and liabilities for a pension fund. The analysis used historical data to predict future assets and liabilities through 2023 using nonlinear regression models. It found the assets and liabilities were best modeled with cubic equations. The analysis also examined the yearly changes in assets and liabilities and the realized assets to liabilities ratio from 1979 to 2011. Finally, it provided suggestions for how future groups could expand the analysis.

1. Asset/Liabilities
Analysis
By: James Regan, Kelsey Hopper, Nolan
Dowd, Brett Edelbeck, Patrick Brodesser
Advisors:Tom McCallum and Albert Cohen

2. Overview
 ChangesWe’ve Made
 MERS Overview
 Assets and Liabilities Predictions
 Future Groups
 Comments, Concerns, Questions

3. Changes We’ve made!
 Introduced more historical data into model
 Strengthens our analysis and shows us previously unseen
information
 New form of testing, using time series analysis
 Explored several more options and looked at
patterns/trends to help our predictions

4. MERS DATA
 Using the appendix(MERS) and pension data, we matched
most of the asset/liability values in the MERS Report. (See
Excel doc)
 To find amount of benefits paid out(payroll), used formula:
Benefit Multiplier x FAC xYears of Service +(80% max FAC)
 Difficulties matching valuation assets and payroll to MERS
report

5. Asset and Liabilities Predictions
 Predicted both assets and liabilities using a non-linear
regression model
 Gathered estimates from both models and took their
ratio(assets/liabilities) to obtain a predicted funded ratio

6. 0
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
70,000,000
80,000,000
90,000,000
100,000,000
110,000,000
120,000,000
130,000,000
140,000,000
150,000,000
160,000,000
170,000,000
180,000,000
190,000,000
200,000,000
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
Assets vs. Liabilities(1979-2011)
Act. Acrr. Liabilities
Valuation Assets

7. Equations for Lines
• assets= -6,421.306year^3 + 38,440,308.951year^2 - 76,701,416,415.023year +
51,012,118,586,785.500
• R² = 0.995
• liabilities = -4,701.535year^3 + 28,227,540.619year^2 - 56,485,383,617.588year +
37,672,974,716,973.600
• R² = 0.999

8. 50,000,000
65,000,000
80,000,000
95,000,000
110,000,000
125,000,000
140,000,000
155,000,000
170,000,000
185,000,000
200,000,000
215,000,000
1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
Assets vs. Liabilities(1997-2011) w/ order3 assets and simple liabilities
trend
Act. Acrr. Liabilities
Valuation Assets

9. Equations for Lines
• assets = 5705.9year^3 - 3E+07year^2 + 7E+10year - 5E+13
• R² = 0.9842
• liabilities = 6E+06year - 1E+10
•R² = 0.9976

10. -2,000,000
0
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Yearly Changes(1979-2010)
L(t+1)-L(t)
A(t+1)-A(t)

11. 0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
Realized
Assets/Liabilities Ratio(1979-2011)
100% Funded Line

12. 0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
Predicted assets/liability ratio(1979-2018)
y = 0.0001x3 - 0.004x2 + 0.0177x + 0.8222
R² = 0.9693
• percentfunded = 0.0001year^3 - 0.004year^2 +
0.0177year + 0.8222
• R² = 0.9693

13. Things Future Groups Could Do!
 “It’s the future, the possibilities are endless”
 Look at investment data from MERS to better predict an asset
return
 Monte Carlo simulation
 Read closer into inflection points to deduce causal effects
 Inflection point- where a graph switches from increasing to
decreasing
 Find a way to incorporate mortality tables
 Would need to be given more information on individual employees
per division
 Mortality tables would show when certain liabilities’ would be freed
up.

14. Comments/Concerns/Questions
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