VOLUME 29 | NUMBER 4 | FALL 2017

Journal of APPLIED CORPORATE FINANCE

In This Issue: Financial Regulation

Has Financial Regulation Been a Flop? (or How to Reform Dodd-Frank) 8 Charles W. Calomiris,

Statement of the Financial Economists Roundtable 25 Charles W. Calomiris, Columbia University; Larry Harris, Uni- Bank Capital as a Substitute for Prudential Regulation versity of Southern California; Catherine Schrand, University of Pennsylvania; Roman L. Weil, University of Chicago

High Frequency Trading and the New Stock Market: 30 Merritt B. Fox, Columbia Law School, Lawrence R. Glosten, Sense and Nonsense Columbia Business School, and Gabriel V. Rauterberg, Michigan Law School

Shadow Banking, Risk Transfer, and Financial Stability 45 Christopher L. Culp, Johns Hopkins Institute for Applied Economics, and Andrea M. P. Neves, Seven Consulting

Why European Banks Are Undercapitalized and What Should Be Done About It 65 John D. Finnerty, , and Laura Gonzalez, California State University, Long Beach

Bloomberg Intelligence Roundtable on 72 Participants: Clifford Smith, Gregory Milano, Joel Levington, The Theory and Practice of Capital Structure Management Asthika Goonewardene, Gina Martin Adams, Michael Holland, and Jonathan Palmer. Moderated by Don Chew.

Leverage and Taxes: Evidence from the Real Estate Industry 86 Michael J. Barclay, University of Rochester, Shane M. Heitzman, University of Southern California, Clifford W. Smith, University of Rochester

Formulaic Transparency: The Hidden Enabler of Exceptional U.S. Securitization 96 Amar Bhidé, Tufts University

How Investment Opportunities Affect Optimal Capital Structure 112 Stanley Myint, BNP Paribas and University of Oxford, Antonio Lupi, BNP Paribas, and Dimitrios P. Tsomocos, University of Oxford

How to Integrate ESG into Investment Decision-Making: 125 Robert G. Eccles, Arabesque Partners, and Results of a Global Survey of Institutional Investors Mirtha D. Kastrapeli, and Stephanie J. Potter, State Street

Maximum Withdrawal Rates: A Novel and Useful Tool 134 Javier Estrada, IESE Business School Maximum Withdrawal Rates: A Novel and Useful Tool by Javier Estrada, IESE Business School*

tandard evaluation of retirement strategies proportion of the portfolio that is withdrawn at the beginning involves the consideration of a large number of of retirement. Subsequent withdrawals are typically thought S simulated or historical retirement periods and of as being annually adjusted by inflation, thus enabling a the subsequent estimation of their failure rate; retiree to maintain his purchasing power constant during that is, the proportion of those periods in which the strategies retirement. In his seminal article,2 William Bengen suggested failed to sustain a withdrawal plan, thus depleting a portfolio that a strategy based on a 4% IWR was ‘safe’ in the sense before a retiree’s death. This failure rate, though ubiquitous that, historically, it never depleted a portfolio in less than 30 in retirement planning, has several flaws.1 years. The debate on the pros and cons of the ‘4% rule’ has For the purposes of the discussion here it suffices to been raging on since then. highlight that two strategies may have the same failure rate For the purposes of the discussion here, it is important and yet they may leave behind very different bequests. Put to highlight that some of the arguments in favor of the 4% differently, the failure rate may lead to an apples-to-oranges rule are based on its low failure rate in the U.S., at least comparison, which is where the novel tool discussed in this for relatively aggressive portfolios. Similarly, some of the article comes in: The maximum withdrawal rate predeter- arguments against it are based on its much higher failure rate mines a desired bequest, thus enabling an apples-to-apples in other countries. In my 2017 Journal of Retirement article, comparison of retirement strategies. I report historical failure rates for 11 asset allocations and 21 This maximum withdrawal rate has at least two applica- countries over the 1900-2014 period and also introduce the tions useful for retirees. First, it provides a comprehensive concept of shortfall years, which aims to refine and comple- way to evaluate retirement strategies, free from the flaw of ment the failure rate, and report its values for the same asset comparing strategies that leave very different bequests; and allocations, countries, and sample period. second, it provides a way to assess how likely is a retiree to To see how the failure rate provides an apples-to-oranges sustain a target sequence of inflation-adjusted withdrawals. comparison, consider Table 1, which reports evidence for the Given that depleting a portfolio too soon is one of the main U.S. market over the 1900-2014 period, for 11 asset alloca- risks faced by retirees, this tool may play the critical role of tions between 100-0 (all stocks) and 0-100 (all bonds), with assessing that risk. nine stock-bond allocations in between. All strategies are based on a starting portfolio of $1,000, a 4% IWR, subse- The Maximum Withdrawal Rate quent withdrawals annually adjusted by inflation, annual Retirement strategies can be classified as generating fixed rebalancing, and 86 rolling 30-year retirement periods or variable withdrawals during the retirement period. The between 1900-1929 and 1985-2014.3 former imply constant withdrawals, typically in real terms, The first row of Table 1 shows the failure rate for the and are related to the discussion here; the latter imply with- 11 asset allocations considered; for the four most aggressive drawals that vary over time depending on changing market strategies, the historical failure rate was 3.5%. These four conditions or life expectancy and are not the focus of this strategies, however, left substantially different bequests, article. regardless of whether they are measured with the mean or Strategies based on fixed withdrawals are fully charac- the median terminal wealth. The 100-0 and the 70-30 strate- terized by their initial withdrawal rate (IWR); that is, the gies left a median bequest of $2,881 and $1,460, a range over

*I would like to thank Jack Rader and Vladimir Valenta for their comments. Patricia 2. William Bengen, “Determining Withdrawal Rates Using Historical Data.” Journal of Palgi provided valuable research assistance. IESE’s Center for International Finance (CIF) Financial Planning, 7, 4, 171-180, 1994. kindly provided support for this research. The views expressed below and any errors that 3. The data used throughout this article is from the Dimson-Marsh-Staunton (DMS) may remain are entirely my own. database for the U.S. market, described in detail in their book; see Elroy Dimson, Paul 1. For a discussion of these flaws, see Moshe Milevsky, “It’s Time to Retire Ruin (Prob- Marsh, and Mike Staunton, Triumph of the Optimists – 101 Years of Investment Re- abilities).” Financial Analysts Journal, 72, 2, 8-12, 2016; Javier Estrada, “Refining the turns, Princeton University Press, 2002 and, more recently, Elroy Dimson, Paul Marsh, Failure Rate,” Journal of Retirement, 4, 3, 63-76, 2017; Javier Estrada, “From Failure and Mike Staunton; Credit Suisse Global Investment Returns Sourcebook 2016. Credit to Success: Replacing the Failure Rate.” Journal of Retirement, forthcoming, 2018. Suisse Research Institute, Zurich. Annual returns for stocks and long-term government bonds are real and account for capital gains/losses and cash flows.

134 Journal of Applied Corporate Finance • Volume 29 Number 4 Fall 2017 Table 1 Failure Rates and Bequests

This table shows failure rates (in %) for 11 static asset allocations with stock-bond proportions between 100-0 (all stocks) and 0-100 (all bonds), over 86 rolling 30-year retirement periods, beginning with 1900-1929 and ending with 1985-2014; it also shows the mean and median for the distribution of terminal wealth or bequest (in real $). All strategies are based on a starting portfolio of $1,000, a 4% IWR, subsequent annual withdraw- als adjusted by inflation, and annual rebalancing to the stock-bond allocations in the first row.

Stocks-Bonds 100-0 90-10 80-20 70-30 60-40 50-50 40-60 30-70 20-80 10-90 0-100 Failure 3.5 3.5 3.5 3.5 4.7 8.1 15.1 25.6 40.7 64.0 65.1 Mean 3,232 2,789 2,383 2,009 1,667 1,356 1,082 848 665 536 433 Median 2,881 2,457 1,925 1,460 1,171 793 520 249 40 0 0

140% larger, in real terms, than the starting portfolio. Put Usefulness of the Maximum Withdrawal Rate differently, these two strategies can hardly be thought of as The MWR as presented above can only be calculated ex-post, having delivered similar performance just because they had once the returns of the portfolio during the retirement period the same failure rate; their mean and median bequests were become known; put differently, it is a measure of the best a dramatically different. retiree could have done had he known the future returns of Enter then the maximum withdrawal rate. Given a his portfolio. Obviously, in practice, nobody knows what the portfolio P0 at the beginning of retirement, a T-year retire- returns of a portfolio will be; still, the MWR is a useful tool ment period, a terminal wealth or desired bequest of $0, for retirees for at least two reasons. First, it provides a compre- and the goal of keeping purchasing power constant during hensive way to evaluate retirement strategies, free from the retirement, the maximum withdrawal rate (MWR) is formally flaw of comparing strategies that leave very different bequests; given by and second, it provides a way to assess how likely is a retiree to sustain a target sequence of inflation-adjusted withdraw- (1+R )(1+R )∙∙∙(1+R ) MWR= 1 2 T als. This second application makes the MWR an essential risk assessment tool, providing an evaluation of the likelihood of (1+R1)∙∙∙(1+RT)+(1+R2)∙∙∙ depleting a portfolio earlier than desired. (1+R )+(1+R )∙∙∙(1+R )+∙∙∙+(1+R ) T 3 T T To explore these two applications of the MWR, consider where Rt denotes the real return in retirement period t. the evidence in Table 2, which describes 11 distributions of MWRs for the U.S. market over the 1900-2014 period, for This expression requires some interpretation. Note, first, that the same asset allocations considered in Table 1.5 The param- MWR is the proportion of the portfolio withdrawn at the eters that characterize the distributions of MWRs include the beginning of retirement; hence, it is an initial withdrawal rate. mean, median, and standard deviation (SD), as well as the 1% Second, the MWR (measured in percent), together with the (P1), 5% (P5), and 10% (P10) cutoff MWRs in the lower tail, value of the portfolio at the beginning of retirement, deter- and the 10% (P90), 5% (P95), and 1% (P99) cutoff MWRs mine the initial withdrawal W * (measured in dollars); that is, in the upper tail. * * W = MWR∙P0. Third, by definition,W remains constant, The MWR provides a comprehensive way to evaluate in real terms, throughout the retirement period. Finally, the retirement strategies, simply because the higher is a strat- MWR as written above assumes that the retiree has the goal egy’s MWR, the higher is the standard of living it enables; of leaving no bequest, but it is trivial to rewrite it for any other this is the application emphasized by Clare et al in their 2016 desired bequest. working paper. In this regard, the figures in Table 2 show Previous research has considered a similar framework.4 the seemingly unsurprising result that more aggressive strate- gies imply higher mean and median MWRs. However, two important caveats should be considered.

4. For a derivation of an expression similar to the one above and called the sustain- per, 2016. Andrew Miller does not present a formal model but discusses the idea behind able spending rate, see David Blanchett, Maciej Kowara, and Peng Chen, “Optimal the expression above and calls it, as is done here, the maximum withdrawal rate. See Withdrawal Strategy for Retirement Income Portfolios,” Retirement Management Jour- Andrew Miller, “Improving Withdrawal Rates in a Low-Yield World,” Journal of Financial nal, 2, 3, 7-20, 2012. Two other papers refer to the same concept as the perfect Planning, 29, 4, 52–58, 2016. withdrawal amount: Dante Suarez, Antonio Suarez, and Daniel Walz, “The Perfect With- 5. The MWR from the expression above is calculated for each 30-year retirement drawal Amount: A Methodology for Creating Retirement Account Distribution Strategies,” period and asset allocation considered. Given the 1900-2014 sample period, 86 rolling Financial Services Review, 24, 331-357, 2015; and Andrew Clare, James Seaton, Pe- (overlapping) retirement periods are considered, beginning with 1900-1929 and ending ter Smith and Stephen Thomas, “Sequencing, Perfect Withdrawal Rates and Trend Fol- with 1985-2014. This yields 86 MWRs for each asset allocation considered, and there- lowing Investing Strategies: Making the Known Unknown Less Unknown,” Working pa- fore a total of 946 MWRs.

Journal of Applied Corporate Finance • Volume 29 Number 4 Fall 2017 135 Table 2 The Distribution of Maximum Withdrawal Rates

This figure shows summary statistics for the distribution of MWRs for 11 asset allocations with stock-bond pro- portions between 100-0 (all stocks) and 0-100 (all bonds), over 86 rolling 30-year retirement periods, begin- ning with 1900-1929 and ending with 1985-2014. All strategies are based on a starting portfolio of $1,000, annual withdrawals adjusted by inflation, and annual rebalancing to the stock-bond allocations in the first row. The summary statistics that describe the distributions of MWRs across the 86 retirement periods considered are the mean, median, standard deviation (SD), 1% (P1), 5% (P5), and 10% (P10) cutoff MWRs in the lower tail, and 10% (P90), 5% (P95), and 1% (P99) cutoff MWRs in the upper tail. All figures in %.

Stocks-Bonds 100-0 90-10 80-20 70-30 60-40 50-50 40-60 30-70 20-80 10-90 0-100 Mean 7.1 6.9 6.6 6.4 6.1 5.8 5.5 5.1 4.8 4.5 4.1 Median 6.6 6.5 6.2 5.8 5.5 5.2 4.9 4.5 4.1 3.7 3.4 SD 2.3 2.1 1.9 1.8 1.7 1.6 1.6 1.6 1.6 1.7 1.7 P1 3.5 3.8 3.9 3.9 3.8 3.7 3.5 3.4 3.2 2.9 2.4 P5 4.1 4.0 4.2 4.1 3.9 3.9 3.8 3.6 3.3 3.0 2.6 P10 4.4 4.4 4.4 4.4 4.2 4.0 3.9 3.7 3.5 3.1 2.7 P90 10.4 9.9 9.4 8.8 8.5 8.3 8.0 7.7 7.4 7.1 6.7 P95 11.1 10.4 10.2 9.7 9.5 9.4 9.4 9.2 8.8 8.7 8.6 P9 12.5 11.4 10.9 10.6 10.5 10.5 10.4 10.3 10.2 10.1 9.9

First, although it is the case that the higher the allocation in retirement, aims to keep his purchasing power constant to stocks, the higher is the mean and median MWR, that is during this period, and plans to leave no bequest. If this not the only information a retiree may focus on. Note that, retiree were considering an initial withdrawal of $55,000 in very adverse scenarios, such as those occurring with 1% from a $1 million portfolio (hence a 5.5% MWR), he would (P1) and 5% (P5) probability, the highest MWRs correspond face a 50% probability of failure. Similarly, if this retiree were to stock allocations between 70% and 80%. In other words, considering an initial withdrawal of $38,000, $39,000, or retirees for whom tail risk is important may not find the $42,000 from the same $1 million portfolio, he would face 100-0 allocation to be the most attractive. 1%, 5%, or 10% probabilities of failure, as the P1, P5, and Second, in a forthcoming paper in the Journal of Retire- P10 figures indicate. ment, I report that in seven of the 21 countries considered, Furthermore, although this table shows some selected either the mean or the median MWR are not highest for the summary statistics and cutoff points, probabilities of failure 100-0 allocation. In other words, in one third of the countries for any target withdrawal and asset allocation could be in the sample (including Germany, Japan, and Switzerland, estimated directly from the empirical (or simulated) distri- to name but three), the most aggressive strategy does not lead bution of MWRs. To illustrate, Figure 1 shows 86 initial to the highest MWR.6 Furthermore, in most countries, either withdrawals from a $1 million portfolio, based on the 86 P1, or P5, or both, are not highest at the 100-0 allocation. MWRs calculated for the 60-40 allocation in the 86 retire- Besides being useful as a tool to comprehensively evaluate ment periods considered here. retirement strategies, the MWR is also useful to assess how The information in Figure 1 is similar but more detailed likely is a retiree to sustain a target sequence of inflation- (for the 60-40 allocation) than that in Table 2. It shows, for adjusted withdrawals; this is the application emphasized example, that if a retiree considers an initial withdrawal of by Suarez et al. in their 2015 article. This likelihood can be $40,000, the probability of failure would be roughly 5%; estimated directly from the (historical or simulated) distribu- in fact, in 82 of the 86 historical retirement periods consid- tion of MWRs, thus enabling an individual to assess one of ered a retiree was able to initially withdraw more than such the main risks he faces in retirement. amount. An initial withdrawal of $70,000, on the other hand, To illustrate, consider Table 2 again and for the sake would have roughly a 70% probability of failure, thus making of concreteness focus on the 60-40 allocation. Consider, it a very risky choice. Finally, an initial withdrawal of over as already mentioned, a retiree that expects to live 30 years $100,000 would have an extremely high probability of failure;

6. Javier Estrada, “Maximum Withdrawal Rates: An Empirical and Global Perspec- tive,” Journal of Retirement, forthcoming, 2018.

136 Journal of Applied Corporate Finance • Volume 29 Number 4 Fall 2017 Figure 1 Initial Withdrawals – 60-40 Allocation

This exhibit shows 86 initial withdrawals from a $1 million portfolio, based on the 86 MWRs calculated for the 60-40 stock-bond allocation, for 86 rolling (overlapping) 30-year retirement periods between 1900-1929 and 1985-2014. The vertical axis shows initial withdrawals and the horizontal axis percentiles of the cumulative distribution. 110000

100000

90000

0000

70000

0000

0000

Initial Withdrawal 40000

0000

20000

10000

0 0 10 1 20 2 0 40 4 0 0 70 7 0 90 9 100

historically, it was feasible in only two of the 86 retirement The MWR has at least two applications useful for retir- periods considered. ees: It provides a comprehensive way to evaluate retirement A similar analysis could obviously be made for any other strategies, free from the flaw of comparing strategies that leave target withdrawal and any other asset allocation. As long as very different bequests; and it provides a way to assess failure the distribution of MWRs has not changed substantially, risk by determining how likely is a retiree to sustain a target then the figures in Table 2, for the 11 strategies considered, sequence of inflation-adjusted withdrawals. and those in Figure 1, for the 60-40 allocation, could be used Most tools widely used in retirement analysis are specifi- by retirees to assess the probability of failure of any target cally designed to either evaluate retirement strategies, or initial withdrawal, and, therefore, to evaluate one of the main to assess the likelihood of sustaining a target sequence of risks faced during retirement. withdrawals, but not both. This is the main advantage of the MWR; it is a single tool that can be used to explore two of Conclusion the most important issues in retirement analysis. Research on Failure rates as a proxy for the expected probability of failure this novel tool is still incipient, but its attractive characteristics have become a standard way of evaluating retirement strate- make it a serious candidate to be the subject of much more gies. However, this useful tool does have some shortcomings, research in the near future. one of which being that a strategy with a low historical fail- ure rate may have also left large unintended bequests. Enter then the MWR, which by definition leaves no bequest (or, javier estrada is Professor of Finance at the IESE Business School alternatively, a chosen level of bequest), thus enabling an in Barcelona. apples-to-apples comparison of retirement strategies.

Journal of Applied Corporate Finance • Volume 29 Number 4 Fall 2017 137 ADVISORY BOARD EDITORIAL Yakov Amihud Carl Ferenbach Donald Lessard Charles Smithson Editor-in-Chief High Meadows Foundation Massachusetts Institute of Rutter Associates Donald H. Chew, Jr. Technology Mary Barth Kenneth French Laura Starks Associate Editor Stanford University John McConnell University of Texas at Austin John L. McCormack Purdue University Amar Bhidé Martin Fridson Joel M. Stern Design and Production Tufts University Lehmann, Livian, Fridson Robert Merton Stern Value Management Mary McBride Advisors LLC Massachusetts Institute of Michael Bradley Technology G. Bennett Stewart Assistant Editor Stuart L. Gillan EVA Dimensions Michael E. Chew University of Georgia Stewart Myers Richard Brealey Massachusetts Institute of René Stulz London Business School Richard Greco Technology The Ohio State University Filangieri Capital Partners Michael Brennan Robert Parrino Sheridan Titman University of California, Trevor Harris University of Texas at Austin University of Texas at Austin Los Angeles Columbia University Richard Ruback Alex Triantis Robert Bruner Glenn Hubbard University of Maryland University of Virginia Columbia University G. William Schwert Laura D’Andrea Tyson Charles Calomiris Michael Jensen University of Rochester University of California, Columbia University Berkeley Alan Shapiro Christopher Culp Steven Kaplan University of Southern Ross Watts Johns Hopkins Institute for University of Chicago California Massachusetts Institute Applied Economics of Technology David Larcker Betty Simkins Howard Davies Stanford University Oklahoma State University Jerold Zimmerman Institut d’Études Politiques University of Rochester de Paris Martin Leibowitz Clifford Smith, Jr. Morgan Stanley University of Rochester Robert Eccles Harvard Business School

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