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Eureka! A Valuation-Based Asset Allocation Strategy That Might Work

By Wesley R. Gray, Ph.D. We’ve had a few posts showing that asset allocation systems relying on market valuation indicators (e.g., Shiller CAPE ratios) as a timing signal may end up in disappointment… Nonetheless, we’ve continued on the quest to improve tactical asset allocation using market valuation data. The data speaks clearly when it comes to the association between valuations and long-term realized returns – high valuations are associated with low long-term realized returns. However, as Michael Kitces highlights, tactically allocating using valuation information is challenging . Moreover, there are arguments that the association between CAPE and LT returns may be more complex than was previously thought. In short, valuation-based asset allocation strategies haven’t been that exciting, but… The folks at Gestaltu inspired us with a unique twist on basic valuation-based timing methodologies: … we chose the cyclically adjusted earnings yield as the valuation metric, which is just the reciprocal of the Shiller PE. We then adjusted the yield value for the realized year-over-year inflation rate to find the real earnings yield. Finally, we used an ‘expanding window’ approach to find the percentile rank of the real earnings yield to eliminate as much lookahead bias as possible. Note that because we are using real earnings yield rather than nominal earnings yield, markets can get cheap or expensive in three ways: changes in inflation changes in earnings changes in price Gestaltu’s post used 1/CAPE as the valuation metric, or the “earnings yield,” as a baseline indicator; however, they “adjusted the yield value for the realized year-over-year (yoy) inflation rate” by subtracting the year-over-year inflation rate from the rate of 1/CAPE. To summarize, the metric looks as follows if the CAPE ratio is 20 and realized inflation (Inf) is 3%: Real Yield Spread Metric = (1/20)-3% = 2% Fairly simple. Strategy Background: We performed our own replication of the first two strategies from the post: Average Valuation-based asset allocation: Own S&P 500 when valuation < long-term average, otherwise hold cash. In other words, if last month's CAPE valuation is in the 50 percentile or higher, buy U.S. Treasury bills (Rf); otherwise stay in the market. 80th Percentile Valuation-based asset allocation: Own S&P 500 when valuation < 80th percentile, otherwise hold cash. In other words, if last month's CAPE valuation is in the 80 percentile or higher, buy U.S. Treasury bills (Rf); otherwise stay in the market. Some adjustments are applied in the replication: The Bureau of Labor Statistics (BLS) publishes the CPI on a monthly basis since 1913; however, the data is one-month lagged (possibly longer). For example, the CPI for January won't be released until February. So, when we subtract the year-over-year inflation rate from the rate of 1/CAPE, we do a 1-month lag to avoid look-ahead bias. We use the S&P 500 Total Return index as a buy-and-hold benchmark. Our back test period is from 1/1/1934 to 12/31/2014, while the article looks over the period from 1/1/1934 to 12/31/2012. The results are gross of any fees. All returns are total returns and include the reinvestment of distributions (e.g., dividends). Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Our Replication Results The first table shows the results from the Gestaltu post: The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Our backtest results show similar CAGRs, but higher volatilities than the results from Gestaltu. This could be due to changes in experiment design. Overall, the "Abs Return 80%" strategy outperforms buy-and-hold, while the "Abs Return 50%" strategy underperforms buy-and-hold. We include a long-term moving average rule for reference (S&P 500 if above the 12-month MA, risk-free if below the 12-month MA). Summary statistics are below: (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Bottom line: The system looks promising. Robustness Tests: Adjusting the Starting Point for the Look-Back Window Gestaltu set 1/1/1924 as the starting date, and then uses an expanding window as the look-back period. We investigate how changing the start date affects the results. The results shown are from 1/1/1934 to 12/31/2014. The table below shows the results of the "Abs Return 80%" strategy using different starting dates for the expanding window: 1924, 1900, and 1881. The starting date for the expanding window calculation can create marginal differences in the results. For example, the Sharpe ratios vary from 0.57 to 0.63. Overall, the results appear robust to the expanding look-back window start date. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Bottom line: The system looks promising. Robustness Tests: Rolling Look-Back Window In this section, we try a 10-year rolling look-back period calculation. For example, we measure the percent rank of CAPE on 12/31/2014 relative to the past 10 years (12/31/2004 to 12/31/2014); while an expanding window (results already shown above) would measure the percent rank of CAPE on 12/31/2014 relative to the whole time period (from the start date to 12/31/2014). The results below highlight that a rolling-window technique yields similar results to the expanding-window technique. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Bottom line: The system looks promising. Robustness Tests: Inflation-Adjusted P/E Ratio In this section, we use the old-fashioned price-to-earnings ratio in place of the CAPE ratio. We use a rolling 10-year window look-back method and adjust inflation with a 1-month lag. Full Sample Results: 1/1/1934 to 12/31/2014 Inflation-adjusted P/E strategies work better than simple Moving Average rules and buy-and-hold. They also work better than CAPE-based strategies. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Bottom line: The system looks promising. First-Half Results: 1/1/1934 to 12/31/1974 Inflation-adjusted P/E strategies work well in the first half. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Bottom line: The system looks promising. Second-Half Results: 1/1/1975 to 12/31/2014 Inflation-adjusted P/E strategies work well in the second half. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Bottom line: The system looks promising. Robustness Tests: Different Thresholds In this section, we look at different percentile thresholds to determine the timing signal. For example, the results are strong when the timing signal is based on the average or the 80th percentile, but what happens if we use different signals? We use a rolling 10-year window look-back method and adjust inflation with a 1-month lag. Full Sample: 1/1/1934 to 12/31/2014 Higher thresholds increase maximum drawdowns (relative to lower thresholds, such as the 50th and 80th percentiles). The results are better than pure buy-and-hold, but this does highlight a potential robustness issue. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Bottom line: The system may have robustness issues. Robustness Tests: Staggered Allocations In this robustness test, we vary holding percentages based on the percentile rank of earnings yield - realized inflation. For example, if last month's E/P - CPI is in the 12th percentile based on the past, then we allocate 12% to stock and 88% to T-bills. We use a rolling 10-year window look-back method and adjust inflation with a 1-month lag. Full Sample: 1/1/1934 to 12/31/2014 Staggered allocations strategies are better than buy-and-hold. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Bottom line: The system looks promising. Robustness Tests: Changing the Real Inflation Component of the Signal In the post, " Market Valuations based on CAPE - A Deeper Dive ", we take the 1/CAPE and subtract the inflation adjusted 10-year U.S. Treasury yield, so that we can examine how expensive the market is relative to real returns available via a bond alternative (a stock investor would prefer a higher spread, all else being equal). To summarize, the metric looks as follows if the CAPE ratio is 20, realized inflation (Inf) is 3%, and the 10-Year Treasury is 5%: Real 10-Year Spread Metric = (1/20)-(5-3)% ~ 3% Full Sample: 1/1/1934 to 12/31/2014 This new measure doesn't work - at all. Understanding why a seemingly small change in technique destroys the results is puzzling and worthy of more investigation... (click to enlarge) The results are hypothetical, and are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Bottom line: The system may have robustness issues. Conclusion After enduring years of frustration trying to identify a valuation-based asset allocation technique - that actually worked - I think the team at Gestaltu is on to an interesting concept. By simply looking at real spreads between equity valuations and realized inflation (high spreads are good for equity; low spreads are bad for equity), one can devise a timing rule that captures most of the upside, but protects on the downside. Of course, this is all historical data and could very well be an exercise in data mining. That said, the concept of buying equity assets when they have much higher yields than current inflation is intuitively appealing. We'll continue our investigations into the subject, but we wanted to give a quick view into some of our high-level research on the subject. Original Post

Are Some Decisions To Allocate To U.S. Equities Due To Survivorship Bias?

By David Foulke The CFA Institute Magazine recently published an interview (a copy is here ) with C. Thomas Howard, CEO of Athena Investment Services. Howard has some pretty explicit views on why investors should allocate all of their assets to U.S. stocks: The primary driver of long-horizon wealth is expected returns. Why would you invest in anything but stocks? Why isn’t your portfolio 100% stocks? Do you believe stocks are going to have the highest return? By the way, stocks have averaged 10% a year for a long period of time. Bonds have averaged about 6%. The difference between a portfolio that’s 100% stocks and one that is a mixture of stocks and one that is a mixture of stocks and bonds over long periods of time is huge, possibly millions of dollars. Why would I want to buy anything but the highest expected return, asset-wise? U.S. stocks have offered the best returns for a long time, and therefore the U.S. stock market is where you want to be invested. This is an interesting argument. Certainly, Howard is right that the U.S. stock market has been the best place to be invested. For instance, Mehra and Prescott in their 1985 paper, “The Equity Premium Puzzle” (a copy can be found here ), demonstrated how the risk premium on U.S. Equities from 1889-1978 averaged roughly 6%. The paper was notable in that it suggested that existing general equilibrium models were unable to explain the size of this premium, which was dramatically higher than for other economies. Academics struggled to explain the persistently strong U.S. stock market. This is the “puzzle” to which the paper’s title refers. In 1998, Reitz proposed that investors in U.S. markets might be more risk averse due to the potential occurrence of large drawdowns, or “crashes.” In a risky market that could crash dramatically, risk averse investors might expect high equity returns as compensation for bearing the risk of such crashes. Perhaps this explained high returns in the U.S. As academics pondered the effect of possible crashes on risk premia, they increasingly questioned that it was risk aversion to crashes that was driving returns. Some thought these unexplainable returns might have something to do with whether a market simply survived, which by definition meant that it consistently recovered from periodic drawdowns over long time frames. Was their some bias introduced to a market’s returns that was associated with the mere fact of its survival? In their paper, “Global Stock Markets in the Twentieth Century” (a copy can be found here ), the authors Jorion and Goetzmann explored this question. They examined 39 global stock markets from 1921 through 1996 and, as before, saw evidence of the outperformance of the U.S. stock market, which provided a real return of 4.32% over the period, the highest of all countries. During this period, however, several of these 39 markets experienced interruptions to their functioning, caused by forces such as war, political instability, hyperinflation, and so forth. The authors compared what happened when they considered both “loser” markets, and how long they were viable, in addition to the survivors, like the U.S. and others, who were “winners” over long periods. The figure below plots annual returns against the length of the history of each market: (click to enlarge) There appears to be a clear relationship between returns and longevity of markets, with longer-lived markets generating higher returns. Over the period, the median return for all 39 countries was 0.75%, representing the return earned by holding a globally diversified portfolio since 1921. Notably, there were 11 “winner” countries, which had continuous returns going back to 1921. For this group, the median return was dramatically higher, at 2.35%. Also, note that the U.S. appears at the upper right of the figure. These results suggest that returns for the U.S. 1) are uncommon at 4.3% versus 0.8% for all other countries, and 2) could be explained by survival, as could higher returns for the other survivors. If you happened to invest in a country that survived, you would have earned higher returns. The paper also examined Reitz’s hypothesis. Recall that Reitz had suggested that investors demanded a higher return as compensation for the risk of a crash. If this were true, then you would expect to see the “losers” exhibit higher equity premia. As the figure above illustrates, the opposite appears to be the case. A regression of these points would slope upward to the right. The returns of the winners may thus be conditional on their survival. If you think about investing in a particular country as like drawing a ball from an urn, then how meaningful is it to say that we can expect future returns to resemble past returns in that country, if those past returns are a result of survivor bias? Survivor bias refers to how we can focus on survivors in a data set, and ignore failures, which provide additional information about risk. Hindsight may be 20/20, but predicting the future is not, and if we condition on only the surviving winners, we ignore the possibility that we may be investing in a previous winner that may turn into a loser in the future. In a PBS interview (a copy is here ) Jack Bogle stated the following: Good markets turn to bad markets, bad markets turn to good markets. So the system is almost rigged against human psychology that says if something has done well in the past, it will do well in the future. That is not true. And it’s categorically false. And the high likelihood is when you get to somebody at his peak, he’s about to go down to the valley. The last shall be first and the first shall be last. Indeed, why should it be easy to predict which markets will survive? As Bogle points out, it may be precisely the past winners who are about to fail. Or as Jeremy Siegel stated in his paper, “The Equity Premium: Stock and Bond Returns since 1802”: Certainly investors in…1872…did not universally expect the United States to become the greatest economic power in the next century. This was not the case in many other countries. What if one had owned stock in Japanese or German firms before World War II? Or consider Argentina, which, at the turn of the century, was one of the great economic powers. It’s probably likely that Argentinian investors predicted continued economic dominance at the turn of the century. They were wrong. The outcome of World War II, which today looks obvious, could have played out in many different ways, and the U.S. might very well have turned into a loser. The Japanese certainly thought they would emerge as the dominant power after the war, or they wouldn’t have fought the war. Same for Germany. If the outcome of WW II had been different, we might today be studying the stock markets of Japan, Germany or other European countries, instead of the U.S. Who is to say the U.S. will not enter a hyperinflationary period or a sustained major war? Such an outcome for the U.S. is obviously not without precedent elsewhere. When we look at past U.S. returns, we are looking at a market that did not fail, but does it follow that it cannot fail in the future? Conditioning on past survival can subject investors to risks, which they are not accounting for. Even with strong past returns, we need to consider survivor bias, and that we are necessarily betting on a winner. Interestingly enough, Warren Buffett and Jack Bogle offer investors puzzling investment advice in the face of the results presented by Jorion and Goetzmann and a simple knowledge of survivor bias. First, Warren’s advice: Put 10% of the cash in short-term government bonds and 90% in a very low-cost S&P 500 index fund. (I suggest Vanguard’s.) Next, Jack Bogle’s advice : I wouldn’t invest outside the U.S. If someone wants to invest 20 percent or less of their portfolio outside the U.S., that’s fine. I wouldn’t do it, but if you want to, that’s fine. We have to question whether the advice from Buffett/Bogle considers the reality of survivor bias or their own personal bias. Original Post

The Impact Of Cash Flow On Asset Allocation Decisions

Guest Post: By Chris Scott Investors trying to make decisions on how to invest their savings face many complications that are frequently ignored in research papers on asset allocation. Often, it is assumed that a fixed lump sum of money is invested. But this is rarely the case in real-world investing for the individual investor. Typically, an investor will be either accumulating funds or drawing down funds, which results in regular cash flows into or out of investment accounts. These cash flows can have a significant impact on the investment results obtained, and therefore, should influence asset selection and asset allocation decisions. The objective of asset selection/asset allocation is to maximize return for a given amount of risk. When evaluating investment assets and making asset allocation decisions, asset volatility is a bad thing. Higher volatility typically means more risk. Higher volatility also reduces the geometric mean of returns (compound returns). When there are no cash flows into or out of an investment, this reduction in return from volatility drag (VD) can be estimated by: Or, to be more precise, we can calculate: VD is one of the reasons for generally trying to avoid or limit assets with high levels of volatility. However, periodic cash flow into an account changes the impact volatility can have on geometric returns. With regular contributions to an investment account, you are dollar-cost averaging into the investment. When the price of the investment increases, you purchase fewer shares. When the price of the investment decreases, you purchase more shares. To see how periodic cash flows into an account affect the geometric return of volatile investments, I ran a monte carlo simulation utilizing a normally distributed zero return investment with varying levels of volatility. Regular periodic cash flows of a fixed size were invested on a monthly basis. The size of the monthly cash flow tested ranged from 0.1% to 100% of the total initial account value. Each simulation trial was run for 60 months. After 100,000 trials for each set of parameters, the results of the trials were averaged. The graph below shows that even modest regular cash flows into an investment reduces the negative impact volatility drag can have on geometric returns. (click to enlarge) These are hypothetical results, and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. How do things change when using historical returns which exhibit serial auto-correlation, fat tails, and skewness? I repeated the simulation using monthly US stock market returns from 1926 to the present (returns are from the Ken French data library ). The returns were de-meaned and scaled to a desired standard deviation (average return is subtracted from each monthly return to produce a time series with an arithmetic average of zero, then multiplied by a scalar to increase/decrease the standard deviation). This results in 1000 5-year overlapping periods. The outcome shows that with actual returns, there is slightly more reduction in volatility drag compared to the monte carlo simulation. For reference, the monthly standard deviation for US stock market returns since 1926 has been 5.4%. (click to enlarge) These are hypothetical results, and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. So we can see that positive cash flow into an investment reduces the negative impact of volatility on returns, but that doesn’t mean an investor should blindly seek out volatility when there are significant positive cash flows into an account. Volatility hurts geometric returns. Ultimately, achieving good results is still about investing in assets with high expected returns. Typically, an investor will avoid or limit investments in high-volatility assets due to their risk. Positive cash flow into high expected return, high-volatility investments can reduce their perceived riskiness. To illustrate this, let’s consider the following assets: US cap-weighted stock market, US decile 10 momentum stocks – equal-weighted, and long-term US Treasury bonds (LTR). These three assets provide a set of risky assets with a range of returns and volatilities. Monthly Statistics LTR Mkt. Mom. Average Return 0.5% 0.9% 1.8% Standard Deviation 2.4% 5.4% 7.4% Using monthly returns from 1/1927 to 7/2014, rolling 5-year periods were simulated with varying levels of positive cash flow. (click to enlarge) These are hypothetical results, and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Historically, decile 10 momentum stocks produce fantastic returns, but with high volatility and drawdowns. Consistent with the previous simulations, average geometric returns improve with fixed regular monthly investments. With their lower volatility, long-term bonds show very little improvement, while decile 10 momentum stocks add 90 basis points of return for the high-cash flow scenario. The following charts represent drawdowns in a portfolio’s value, including the added funds invested. In other words, no adjustment is made for the increasing value of cash invested. So, if an investment experiences a decline of 10%, then additional cash of 5% is added to the portfolio that month, and it is treated as a 5% drawdown. (click to enlarge) These are hypothetical results, and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. (click to enlarge) These are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. (click to enlarge) These are hypothetical results, and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. (click to enlarge) The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. With funds being invested monthly, the depth and duration of drawdowns are significantly reduced. Of course, the investment risk hasn’t changed, and the improved drawdown metrics are mostly a function of adding funds. The investment still experiences severe drawdowns in market crashes and the returns during the crash are horrible, but the pain of the drawdowns is muted by the cash flows. While it seems easy to discount this effect, it can provide an important mental benefit to the investor. The illusion of a rapid recovery in value can make the high-return/high-volatility asset seem more tolerable, enabling the investor to allocate more to risky assets, and therefore improving long-term results. It should be obvious that the 100% cash flow scenario applies to a young investor with no savings who is starting to invest. However, long-time investors who have built up substantial portfolios may wonder if any of this is useful for an investor whose cash flow into their investments is now small relative to their total portfolio value. Even in the case where an investor’s additional contributions are small relative to the entire portfolio, there still can be high positive cash flow situations. Due to government tax rules, there are significant restrictions on moving funds between different classes of accounts (IRA, Roth IRA, 401k, etc.). For example, if you change jobs, you start over with a brand new 401k account. You can rollover your 401k from your previous employer to an IRA to take advantage of the better investment options available in a brokerage IRA. The new 401k account will start with a zero balance, with no opportunity for funding other than from monthly payroll contributions. The 100% cash flow scenario would also apply to this case, providing an opportunity to allocate more to higher-risk/higher-expected return assets in the new 401k than in the other accounts. Now let’s look at what happens when there are negative cash flows. As you would expect, negative cash flow from a volatile investment further reduces geometric returns. Using monthly returns from 1/1927 to 7/2014, rolling 5-year periods were simulated with varying levels of negative cash flow. (click to enlarge) Again, long-term treasuries show little impact to geometric returns, but the momentum stocks’ geometric returns were reduced by 26 basis points at the highest withdrawal rate. So, there’s an impact from negative cash flow, but at sane withdrawal rates, the return reduction is fairly small. The impact to the drawdown metrics is more dramatic, but not always in the way you would expect. (click to enlarge) Maximum drawdowns worsen with increasing negative cash flows. These Great Depression drawdowns illustrate the extreme case of things going bad for a retiree invested in equities! (click to enlarge) Long-term treasuries suffer from their low returns, showing significant average drawdowns as the withdrawal rate increases. (click to enlarge) While the withdrawals seem to have a minimal impact on the portfolio recovery time for equities, that’s not the case. This chart only represents the recovery time where the portfolio recovered by the end of a 5-year period. On the next chart, you can see the significant increase in periods that ended without recovering from the drawdown. (click to enlarge) In the end, even with negative cash flow, it’s still about investing in assets with high expected returns. One could argue that taking withdrawals would naturally result in a declining portfolio value, which is to be expected and okay as long as you don’t run out of money, and therefore the worsened drawdown statistics with withdrawals are not relevant. However, the mental impact to a newly retired investor of a portfolio that declines and doesn’t recover year after year after year can be significant. I’ve intentionally only used individual assets rather than diversified portfolios to illustrate the impact of cash flows on risk and returns. By combining uncorrelated assets into a diversified portfolio, the overall risk/return characteristics of the investment can be improved, while all the same principles and effects associated with cash flows still apply. Ultimately, it is up to each investor to determine how much risk they can take and still sleep at night. Having an understanding of how your portfolio – and the assets in it – behave when there are positive or negative cash flows is an important aspect of getting a good night’s sleep! Original Post