Quantitative Core Equity

1. November 2004 Quantitative Core Equity Quantitative Management Associates

2. Table of Contents I. Organization & People II. Quantitative Core Equity Overview III. Underlying Research IV. Investment Process V. Trading VI. Results Appendix • Technical Information • Biographies • Fee Schedule • Composite Performance Returns

3. I. Organization & People

4. Quantitative Management AssociatesInvestmentManager Firmly Grounded in Academic Theory Assets Under Management$47 Billion* • Highly experienced, stable team of investment professionals • Time-tested investment principles • psychology of investor behavior • financial valuation theory • Proprietary quantitative research recognized by industry publications • Insights and empirical research incorporated in quantitative processes * Quantitative Management Associates LLC (QMA) directly manages more than $36 billion and allocates approximately $10 billion (assets as of 9/30/04) to other Prudential Investment Management, Inc. units. QMA operated for many years as a unit within Prudential Financial’s asset management business, known today as Prudential Investment Management. On July 1, 2004, QMA became an SEC-registered investment adviser. No changes in investment professionals and processes occurred as a result of this change in legal structure. ** Includes approximately $10 billion in assets for which equity and balanced management services are provided

5. Quantitative Management AssociatesSenior Team Focused on Research and Implementation • Stable, dedicated team of experienced investors • Research driven investment culture • Theoretical underpinning • Rigorous testing • $47 billion under management* • Quantitative Core Equity ($11.8 billion) • Value Equity ($2.0 billion) • Balanced Management ($19.9 billion)** • Equity Index Management ($22.6 billion) • Other ($0.1 billion) Quantitative Years Core Equity at Professionals Role Firm James Scott, PhDPresident Portfolio Manager 17Margaret Stumpp, PhDChief Investment Officer Portfolio Manager 17 Ted LockwoodManaging Director Portfolio Manager 16 John Van Belle, PhDManaging Director Portfolio Manager 21 Mitch Stern, PhD Vice President Portfolio Manager 7 Peter Xu, PhDPrincipal Portfolio Manager 7 Max Smith, PhDSenior Associate Research Analyst 15 Dan Carlucci, CFASenior Associate Research Analyst 20 Betty Tong Associate Research Analyst 23 Rich Crist Vice President Trader 21  As of 9/30/04 * Quantitative Management Associates (QMA) directly manages more than $36 billion and allocates approximately $10 billion (assets as of 9/30/04) to other Prudential Investment Management, Inc. units. ** Includes approximately $10 billion in assets for which equity and balanced management services are provided.

6. II. Quantitative Core Equity Overview

7. Quantitative Core Equity Objective* Achieve total return of 1.0 –1.5% over the benchmark with about 2% tracking error Philosophy • Investors make systematic, exploitable mistakes • They fall too easily for “stock stories” • They fail to react sufficiently to material news • The most effective way to exploit these mistakes is with a diversified, objective process • Different selection criteria are effective for different types of stocks • Risk should be focused on areas of greatest confidence * There can be no guarantee that this objective will be achieved.

8. Results Consistently Achieved Objective Over Time Quantitative Core Equity Annualized Performance as of 9/30/04 Please see ‘Composite Performance Returns’ section of the Appendix for full disclosures. Source of all data: QMA, Standard & Poor’s

9. III. Underlying Research

10. “Behavioral Bias, Valuation and Active Management,” Financial Analysts Journal, Vol 55 (July/August 1999) Compelling Research Recognized by Industry Publications • “Overconfidence Bias in International Stock Prices,” The Journal of Portfolio Management, (Winter 2003) • “Enhanced Equity Indexers: Common Traits and Surprising Differences,”Journal of Investment Management, (September 2003) • “News, Not Trading Volume, Builds Momentum,”Financial Analysts Journal, (March/April 2003)

11. Conceptual Framework • Value of companies with no growth opportunities depends on normalized earnings. P =E/k Valuation Theory Shows Where to Find Opportunities • Value of rapidly growing companies depends primarily on expectations of future growth. P =E/k + profitable growth P = Stock Price E = Normalized Earnings Per Share k = Equity Discount Rate

12. Valuation “Works” Best for Slowly Growing Companies … Quarterly payoff to stocks grouped by P/E and Long-Term EPS Growth Slow Growth Stocks Fast Growth Stocks Quarterly Excess Returns* P/E P/E * Based on the difference between each group of stocks returns and the average of all stocks returns. Source: Quantitative Management Associates, based on the largest 3000 US stocks (based on market cap valuation) in each quarter from 3/1985 - 6/2003. Past performance is not a guarantee of future results. Returns are gross of management fees and are only provided to illustrate the information implicit in our stock selection methodology.

13. Negative Revisions Negative Revisions Neutral Revisions Neutral Revisions Positive Revisions Positive Revisions … While “News” Is More Important for Rapidly Growing Companies Quarterly payoff to stocks grouped by Estimate Revisions and Long-Term EPS Growth Slow Growth Stocks Fast Growth Stocks Quarterly Excess Returns* * Based on the difference between each group of stocks returns and the average of all stocks returns. Source: Quantitative Management Associates, based on the largest 3000 US stocks (based on market cap valuation) in each quarter from 3/1985 - 6/2003. Past performance is not a guarantee of future results. Returns are gross of management fees and are only provided to illustrate the information implicit in our stock selection methodology.

14. Fast Slow Average Valuation Framework Also Works Internationally Valuation Is More Important For Slow Growth 1987 - 2000 “Good News” Is More Important For High Growth 1987 - 2000 High E/P - Low E/P (% excess Total Return) Strong News minus Weak News (% excess Total Return) Source: Scott, J., Stumpp, M., and Xu,P. “Overconfidence Bias in International Stock Prices” Journal of Portfolio Management, 29(2), Winter 2003. Past performance is not a guarantee of future results. This is shown for illustrative purposes only.

15. IV. Investment Process

16. Three-Step Process Adds Value Inputs STEP Outputs 1. Stocks classified by growth rate into categories Nightly download of data for approximately 3,000 US stocks Classify Stocks 2. Models for each category • Slow growth: emphasize “valuation” • Fast growth: emphasize “news” Calculate Expected Return Expected return for each stock and portfolio, calculated daily 3. Internally built optimizer • Overweight high expected return stocks • Limits exposure to other risks Periodically rebalance each portfolio to reflect risk/reward objectives Construct Portfolio

17. Classify Stocks Slow Growth Average Growth Fast Growth Calculate Expected Return Construct Portfolio 3000 Stock Universe Equal emphasis on both valuation and “news” Emphasize “news” • EPS Estimate Revisions • Price-Volume Behavior • Insider Trading • New Issues/Buybacks • Earnings Quality Emphasize valuation • Forward Price/Earnings • Change in Price/Earnings • Adjusted Price/Book Steps 1 and 2: Classify Stocks and Calculate Expected Returns

18. Higher Expected Return Stocks Have Outperformed Classify Stocks Calculate Expected Return Construct Portfolio Average quarterly equal-weighted sector-adjusted gross returns for all stocks in universe, 1998-Q1 through 2004-Q2. Source: Quantitative Management Associates, using data provided by Factset Data Systems. Past performance is not a guarantee of future results. Returns are gross of management fees and are only provided to illustrate the information implicit in our stock selection methodology.

19. Our Process Adapts to Changes in a Firm’s Business Slow Growth but Cheap (Buy) • Negative EPS revisions; • Low P/E, P/B; • EPS Quality OK. Fast Growth but bad news (avoid) • Negative EPS revisions; • Insider Selling; • Weak EPS Quality. Source: Quantitative Management Associates using data provided by Factset. Shown to illustrate the stock selection methodology and not intended to be a recommendation. Not all stocks held in the portfolio perform similarly. Past performance is not a guarantee of future results.

20. Classify Stocks Calculate Expected Return Construct Portfolio Step 3: Construct Portfolios Mindful of Risk Expected Returns • Calculated Daily Estimated Trading Costs Risk Constraints • Market capitalization • Industry • Sector • Active stock position • Liquidity • Style Proprietary Optimizer • Data integrity review • Select portfolio with appropriate risk/return profile • Review transactions before trading Portfolio 1 Portfolio 10 This is shown for illustrative purposes only.

21. Industry ± 0.75% Sector ± 0.75% Growth/Value (by growth bucket) ± 3.0% Size (by cap bucket) ± 3.0% No more than 20% of average daily trading volume No more than 10% in an individual trade More liquid stocks favored Classify Stocks Calculate Expected Return Construct Portfolio Representative Optimization Parameters Factor Restrictions Liquidity Stock Restrictions • No more than 0.75% underweight • No more than 0.75% overweight Levels vary under normal market conditions. Precise bounds may vary without notice.

22. Key Attributes of Portfolio In Line With Market Quantitative Core EquityRepresentative Portfolio Characteristics As of 9/30/04 Classify Stocks Calculate Expected Return Construct Portfolio As of 9/30/04 1 There is no guarantee that forecasts will be met. This is shown for illustrative purposes only. 2 Historical Beta calculated in Zephyr Style Advisor using monthly returns since inception (1/97 – 3/04). Sources of data: QMA, Frank Russell Company, Standard & Poor’s.

23. V. Trading

24. Carefully Manage Trading Costs Timely Data Expected returns/ Risks characteristics Estimated trading costs Optimizer Market Access Trading (Agency, Principal, Electronic Crossing Network) Ongoing Research Post trade analysis • Evaluation of broker performance • Evaluation of our trading techniques • Estimation of trading costs

25. Ongoing Research Has Reduced Transaction Costs Average Transactions Costs for All Quantitative Core Equity Trades, May 2001 – December 2003 Enhanced transaction-cost modeling Intra-day principal trading Incorporated real-time bid-ask spreads Quarterly average of total transaction costs including commission, spread, impact and delay. Results include both agency and principal trades. Data begins 5/15/01 Past trends are not a guarantee of future results. Source: QMA

26. Ongoing Research Leads to Periodic Model Enhancements • Incorporated real-time bid-ask spread and transaction costs in optimization to evaluate brokers and trading strategies • Added insider trading, share repurchase/issues and earnings quality • Additional data integrity screens • Introduced Long-Short Market Neutral model • Replaced earning surprises with analyst estimate revision as measure of news 1997 1998 1999 2001 2002 2004 • Product Inception • Changed optimizer from BARRA to CPLEX • Introduced international models • Refined market capitalization risk control

27. VI. Results

28. Quantitative Core Equity CompositeInvestment Performance Quantitative Core Equity Annualized Gross ReturnsAs of 9/30/04 Annual Returns Quantitative Core Equity Composite S&P 500 Year (Gross) Index Difference % 2004 (1/1-9/30)3.22% 1.51% +171 bps 2003 31.02 28.69 +233 2002 -20.60 -22.10 +150 2001 -12.97 -11.89 -108 2000 -5.46 -9.11 +365 1999 21.38 21.04 +34 1998 31.31 28.58 +273 1997 33.51 33.38 +13 (1/1/97 – 9/30/04) Past performance is not a guarantee of future results. Please see ‘Composite Performance Returns’ section of the Appendix for full disclosures. Source of Benchmark: Standard & Poor's Source of all other data: Quantitative Management Associates

29. Performance in One-Year Rolling Periods Relative Performance of Quantitative Core vs. S&P 500 Times that Quantitative Core Outperformed S&P 500 Times that Quantitative Core Underperformed S&P 500 # of Rolling One-Year Periods -5 to -6% -4 to -5% -3 to -4% -2 to -3% -1 to -2% 0 to -1% S&P 0 to 1% 1 to 2% 2 to 3% 3 to 4% 4 to 5% 5 to 6% 500 Relative Gross Performance of Quantitative Core vs. S&P 500 (Account Performance Minus S&P 500) (79 Month-end Observations From 12/31/97 – 6/30/04). Past performance is not a guarantee of future results. Source: Quantitative Management Associates and Standard & Poor’s.

30. Outperformance in Up Markets and Down Markets Percent of the Time Quantitative Core Outperforms S&P 500 in Up and Down Markets (Rolling One-Year Periods; 79 Month-end Observations from 12/31/97- 6/30/04) % of times Quantitative Core (Gross) Outperforms Past performance is not a guarantee of future results. Source: Quantitative Management Associates and Standard & Poor’s.

31. Outperformance in Growth, Value and Neutral Markets Percent of the Time Quantitative Core Outperforms S&P 500 in Growth and Value Markets (Rolling One-Year Periods; 79 Month-end Observations from 12/31/97- 6/30/04) % of times Quantitative Core (Gross) Outperforms Growth Favored Neutral Value Favored (Defined as Russell 1000 ValueminusRussell 1000 Growth) Past performance is not a guarantee future results. Sources: Quantitative Management Associates, Standard &Poor’s and Frank Russell

32. Value Added Primarily Through Security Selection Attribution Analysis 6/30/1999 – 6/30/2004 Annualized Value Added (%) --Attribution Factor -- Source: Factset. 5-Year consensus forecasted EPS growth rates from IBES. P/E calculated using latest 12 month trailing EPS.

33. Strategy Adds Value Across Capitalization Range and Internationally Annualized Value Added (Gross) Inception through 9/30/04 (%) Inception Date Benchmark (1/1/1997) S&P 500 (1/1/2000) Russell 3000 (7/1/1996) S&P 400 (6/1/2000) S&P 600 (4/1/2002) MSCI World (Free) (1/1/2002) MSCI EAFE Past performance is not a guarantee of future results. An investment cannot be made directly in an index. Source of data: QMA, Standard & Poor’s, Frank Russell and Morgan Stanley.

34. Why Quantitative Core? • Experienced, stable, dedicated team • Captures major insights of growth and value management into one portfolio • Adds value in different market environments • Quantitative approach ensures discipline and objectivity • Continuing research keeps process fresh

35. Appendix Technical Information Biographies Fee Schedule Composite Performance Returns

36. Classify Stocks Calculate Expected Return Construct Portfolio No Black Box: Review Transactions Before Trading Anticipate trading costs Monitor data integrity Understand what drives transactions RangesSize (1-5): 5 = SmallGrowth (0-3): 3 = Fast Contribution to  from news, (E/P), E/P and B/P Contribution to  from insider trading, buybacks and earnings quality Stocks shown to illustrate the investment process. They are not intended as recommendations or as a complete listing. Source: QMA

37. Classify Stocks Calculate Expected Return Construct Portfolio Evaluate Impact of Each Recommended Trade on Alpha and Risk Shown for illustrative purposes, and not intended to be a recommendation or as a complete listing. Source of Data: QMA

38. Analyze Every Broker Top performing agency broker, last 120 days Average agency trade cost = 0.17% Total costs 0.04% below expectations Shown for illustrative purposes, and not intended to be a recommendation or as a complete listing. Source of Data: QMA

39. Analyze Every Trade • Evaluate broker performance based upon “residual cost”, given difficulty of trade Cost below expectations. “Good trade.” Large, slightly unbalanced, program Shown for illustrative purposes only. This does not depict actual trades. Source of Data: QMA

40. Biographies James H. Scott, PhD is the President and co-head of Quantitative Management Associates (QMA). Jim is portfolio manager for enhanced equity index portfolios for institutional investors and mutual fund clients. Prior to joining the firm, Jim was a professor and head of the Finance Department at Columbia University Graduate School of Business. His academic career included positions at Stanford University, University of Wisconsin-Milwaukee, and Carnegie Mellon University. During this period, Jim also served as a consultant, corporate director, mutual fund trustee, and research fellow at the Federal Reserve Bank of Cleveland. He has written numerous articles that have appeared in The Journal of Portfolio Management, The Journal of Finance, and The Financial Analysts Journal, among other publications. Jim is a cum laude graduate from Rice University where he holds a BA in Economics. He holds a Masters and PhD in Economics from Carnegie Mellon University. He serves on the Business Board of Advisors for the Graduate School of Industrial Administration at Carnegie Mellon University, and is a Director of the Institute for Quantitative Research in Finance, and Chair of its Research Committee. He is also a member of the Board of Editors of The Financial Analyst Journal and of The Journal of Investment Management.  Margaret S. Stumpp, PhD is the Chief Investment Officer and co-head of Quantitative Management Associates (QMA). She is portfolio manager for enhanced equity index portfolios for institutional investors and mutual fund clients. Maggie is extensively involved in quantitative research in asset allocation, security selection and portfolio construction for Quantitative Management Associates. Prior to joining the firm, Maggie was employed by the AT&T Treasury department and by Price Waterhouse as a senior consultant. In both positions, she was responsible for providing expert testimony on economic and financial matters. She has published articles on finance and economics in numerous publications, including, The Financial Analysts Journal, The Journal of Portfolio Management, The Journal of Investment Management and Award Papers in Public Utility Economics. Maggie earned a BA cum laude with distinction in Economics from Boston University, and holds an AM and PhD in Economics from Brown University. Ted Lockwood is Managing Director for Quantitative Management Associates (QMA). Ted oversees the equity area, which includes quantitative equity, derivative, and index funds. He is also responsible for managing portfolios, investment research, and new product development. Previously, Ted was with AT&T and a member of the technical staff at AT&T Bell Laboratories. Ted graduated summa cum laude with a BE in Engineering from the State University of New York at Stony Brook, as well as an MS in Engineering and an MBA in Finance from Columbia University. Peter Xu, PhD is Principal for Quantitative Management Associates (QMA). He conducts equity market research, the results of which are used in the stock selection process for all quantitative core equity portfolios. He has published articles in various journals, including The Financial Analysts Journal, The Journal of Portfolio Management, Review of Quantitative Finance and Accounting, and Review of Pacific Basin Financial Markets and Policies. Previously, Peter taught in the business school at the University of Houston. He earned a BS in Nuclear Physics from Fudan University in Shanghai, an MA in Economics from Rice University, and a PhD in Finance from the University of Houston.

41. John Van Belle, PhD is Managing Director for Quantitative Management Associates (QMA). John manages global balanced portfolios, domestic balanced funds, and equity portfolios for foreign-based full service clients. Previously, John was a vice president in Currency Management Consulting Groups at both Bankers Trust and Citibank. He began his career in the research department at the Federal Reserve Bank of New York. Before that he taught Economics and Finance at the University of Virginia and Rutgers Graduate School of Management. He has published numerous articles in the fields of Economics and Finance. John earned a BS in Economics from St. Joseph's College and holds a PhD from the University of Virginia. Mitchell B. Stern, PhD is Vice President for Quantitative Management Associates (QMA). Mitch is responsible for research, development, and management of structured products. He also is a portfolio manager for the PIIMA (Prudential Investments Individually Managed Accounts) individual tax-managed portfolios and the Long-Short Market Neutral fund. Previously, Mitch was an Assistant Professor of Finance at Fairfield University and the University of Tennessee. He also has twelve years of experience as a consultant to portfolio managers and hedge funds on quantitative investment strategies. Mitch holds a BA cum laude in Economics from Brandeis University, and an MA and PhD in Financial Economics from the University of Virginia. Maxwell Smith, PhD is Senior Associate for Quantitative Management Associates (QMA). He is responsible for optimizing quantitative core equity portfolios and engages in research to improve the quantitative investment process. Previously, he was a municipal bond portfolio manager with Prudential Fixed Income. He joined Prudential Financial in 1989. Max earned a BS in Physics from CalTech, an MS in Physics from the University of Illinois, and holds a PhD in Finance from the University of British Columbia. Betty Sit Tong is Investment Associate for Quantitative Management Associates (QMA). She co-manages the global index portfolios benchmarked against MSCI developed index series. She is also responsible for trading foreign and domestic equities, foreign exchange, and derivative instruments. In addition to the developed index series, she has experience with funds benchmarked against the MSCI small cap and emerging market index series. Previously, Betty was employed by Prudential Equity Management Associates. She joined Prudential Financial in 1981. Betty earned a BA in Psychology from Princeton University. Daniel Carlucci, CFA, is Senior Associate and Portfolio Advisor for Quantitative Management Associates (QMA). He assists with the management of several quantitative portfolios, specifically the large-cap and and small-cap core portfolios as well as tax-managed portfolios for high net worth investors. Prior to his current assignment, Dan was an Investment Analyst with Quantitative Management Associates’ Value Equity team, where he assisted with the management of quantitative large-cap institutional portfolios. He joined Prudential Financial in 1984. Dan holds a BS in Financeand an MBA in Finance from Rutgers University. Richard L. Crist, ChFC, CLU is Vice President for Quantitative Management Associates (QMA). Rich is responsible for trading US and foreign equities for the group's quantitative core, quantitative value, and global balanced strategies. He manages US equity index funds and also trades inflation indexed government bonds, treasuries, foreign currencies, and futures contracts. Previously, he was an Accounting Supervisor with the Prudential Asset Management Company, which he joined in 1983. Rich earned a BS in Accounting from Montclair State College. He holds the Chartered Financial Consultant designation, and is a Certified Life Underwriter from the American College. Biographies

42. Quantitative Core EquityFee Schedules Commingled Fund (includes custody) 35 basis points on first………………….... $25 million 30 basis points on next…………………. $75 million25 basis points …………………………….. Thereafter Minimum account ………………….…….. $2 million Single Client Separate Account (excludes custody) 35 basis points on first………………….... $25 million 30 basis points on next………………….…$75 million25 basis points …………………………….. Thereafter Minimum account ………………….…….. $30 million

43. Quantitative Core Equity Composite

44. Quantitative Core Equity Composite