Last updated on: October 20, 2020
All returns below are based on hypothetical back-tested performance. Hypothetical back-tested performance is no guarantee of future performance and actual results will vary. For more detailed information please see “G. Risk Factors” below.
SAVE Advisers used Quantitative Investment Strategy Techniques (“QISs”) to develop the SAVE® Moderate Portfolio Strategy (“Strategy”). The investment goal of the Strategy is to maximize a portfolio’s expected return for a given amount of portfolio risk, or equivalently, minimize risk for a given level of expected return, by selecting the proportions of various investment classes rather than selecting individual securities. The Strategy is a rules-based strategy that captures return across various markets by allocating its exposures across multiple asset classes (each, an “Investment Class”) in a diversified and risk balanced manner. SAVE Advisers selected an exchange traded fund (an “ETF” or an “underlying”) to represent each Investment Class included within the Strategy.
SAVE Advisers then used QIS’s to group the Investment Classes into groups (each a “Group”, as described below. SAVE Advisers selected the Groups by performing correlation analysis among the different underlyings. This allowed us to create three Groups such that with a Group the underlyings are highly correlated with each other, uncorrelated or negatively correlated with members of other Groups, or both.
The Strategy employs two risk control principles -- one at the Investment Class level and one at the overall portfolio level. To employ these principles dynamically, the Strategy rebalances the portfolio daily. At the individual Investment Class level, QISs are used to limit exposures to assets having poor performance, thereby limiting potential losses from those Investment Classes. At the overall portfolio level, a volatility control mechanism is employed that aims to keep the Strategy “volatility1” constant over time. The goal of the volatility control mechanism is to limit the Strategy’s portfolio level exposure to historically volatile assets during times of high volatility. Volatility control mechanisms such as the ones employed by the Strategy are based on the observation that historically high volatility coincides with times of market stress and accelerated losses. The Strategy targets a volatility of 3%.
The Strategy can be summarized in three steps: the first being the assessment of the underlying’s trends; the second weighting the underlyings via adjusted risk contribution. The third step is the final overall weighting determined by a portfolio level volatility control adjustment. These are each described in detail below:
Trend Assessment: The first step is to assess the trend of each of the potential underliers in the Strategy. This is done by comparing the current level of each underlier with the previous 126 levels available for the same underlier and calculating what percentage of the previous levels are below the current level. These levels are obtained by adjusting the price to include dividends and exclude the reference rate (defined below, meaning that the total return, reduced by the reference rate for the underlier is used—see the discussion of excess daily returns below). This number is then scaled by the ratio of the underlier’s long-term volatility and short-term volatility such that increased volatility adjusts down the trend assessment. The result is a trend measure adjusted for the specific underlier risk. Higher numbers for the trend indicate better (i.e., stronger) trends for the underlier and we use this in the next step to give higher trending underliers larger positions relative to their risk profile.
Adjusted Risk Contribution: The second step is to size the intended allocation for each underlying of the Strategy. To do this we start from an equal risk contribution allocation, where the marginal contribution to risk2 of each underlying is equal across all underlyings. We then adjust this allocation so that underlyings that are considered trending (have a trend score greater than a predetermined threshold) can have a higher marginal contribution to risk, while at the same time underlyings that are not trending (have a trend score lower than a different predetermined threshold) will have a lower marginal contribution.
The size of the position of each underlying within a Group is proportional to its marginal contribution to the risk within the Group. And the size of the position of each underlying within the overall Strategy is proportional to its marginal contribution to the risk of the overall Strategy, adjusted for the most common trend score of the underliers of that Group.
Volatility Control: The last step in the Strategy is the volatility control. We use the computed positions from the previous step to assess a Strategy-level risk: what would have been the realized volatility of the strategy if those were the realized allocations. This strategy-level volatility is the volatility of a portfolio of the underliers, using the allocation provided by the previous step. This provides a better estimate of future volatility of the Strategy than actual realized volatility. We calculate this strategy-level volatility over different periods of time to assess what would have been the maximum volatility achieved for that intended position and we use that volatility to scale the total exposure of the strategy, leveraging or reducing participation, in order to achieve a desired risk target level.
The strategy risk target is a volatility of 3%. The Strategy achieves this target by modulating the total exposure3 of the Strategy to underlyings. At times, the Strategy may increase the total exposure above 100% (to a maximum of 150%) to increase volatility to the target. Other times it will reduce the total exposure below 100%. Only the fraction exposed to the underliers will accrue returns, while the remainder, if below 100%, will not. Historically the Strategy’s average total exposure to underlyings is 88.10%.
The Strategy’s hypothetical backtest covers the period from January 01, 2006 to October 20, 2020. Prior to the Strategy start date, a lookback window of historical data that begins on January 1, 2004 is used to calculate certain elements of the Strategy -- such as trend, correlation, and volatility--that require over a year of input data.
At January 01, 2006, 17 out of the 30 underlyings had enough observations to be included in the Strategy. The Strategy is dynamic as to the number of underlyings, meaning that within each Group, it adjusts to the number of available underlyings for the Group allocation. When a new underlying is introduced, two-years of data is required for the Strategy computations.4
Returns used to compute relevant statistics for all underlyings are “excess daily returns”-- meaning the underlying total return is reduced by a reference rate. The reference rate used to compute excess returns is the relevant historical 3-month LIBOR rate; this reference rate reduces the daily returns from each underlying by the relevant LIBOR rate. In addition, the excess return is derived from the total return for each underlier which includes dividend payments as part of the return. This means, in general, that the effect of receiving dividends is included in the Strategy.
Trend, which is used to determine which underlyings are given more weight in the Strategy, is computed using a lookback window of 6-months and is based on measurements of excess daily returns during that period as well as volatility (as defined below).
Volatility, which means the variation in the price changes over a period of time, is used to adjust the trend computation as well as determine the total exposure of a portfolio to underlyings. In the volatility calculation, recent returns have more weight than past returns.
Correlation, which generally means the measure of how the returns of the underliers are related to each other, is used to determine the marginal contribution to risk in the Strategy. Correlation is determined using an expanding window, which means, that we include as many returns as we possibly can in its calculation, as opposed to use a fixed window, like 6-months.
The marginal contribution to risk for an underlier is how much the overall risk of the portfolio changes by a change in weight in the underlier. This takes into account both the underlier risk, measured by its volatility – how much its returns vary over time – and its correlation with other underliers – how the underliers tend to move together.
The underlyings are split into three Groups:
The following table lists all the ETFs, its Group, and its maximum weighting in the Strategy overall, and its maximum change in allocation allowed in a day.
|Underlying||Underlying Ticker||Group||Maximum Underlying Weighting in Strategy||Maximum Underlying Daily Change||Exchange|
|1||iShares® 20+ Year Treasury Bond ETF||TLT UQ Equity||Group 1||25%||2.5%||Nasdaq Global Market|
|2||iShares® 7-10 Year Treasury Bond ETF||IEF UQ Equity||Group 1||25%||2.5%||Nasdaq Global Market|
|3||iShares® TIPS Bond ETF||TIP UP Equity||Group 1||10%||1.0%||NYSE Arca, Inc.|
|4||iShares® IBOXX Investment Grade Bond ETF||LQD UP Equity||Group 1||25%||2.5%||NYSE Arca, Inc.|
|5||Vanguard Short-Term Corporate Bond ETF||VCSH UQ Equity||Group 1||10%||1.0%||Nasdaq Global Market|
|6||iShares Intermediate-Term Corporate Bond ETF||IGIB UQ Equity||Group 1||10%||1.0%||Nasdaq Global Market|
|7||Vanguard Total International Bond ETF||BNDX UQ Equity||Group 1||10%||1.0%||Nasdaq Global Market|
|8||iShares Core U.S. Aggregate Bond ETF||AGG UP Equity||Group 1||25%||2.5%||NYSE Arca, Inc.|
|9||Vanguard Large-Cap ETF||VV UP Equity||Group 2||25%||2.5%||NYSE Arca, Inc.|
|10||Vanguard Small-Cap ETF||VB UP Equity||Group 2||25%||2.5%||NYSE Arca, Inc.|
|11||Vanguard Info Tech ETF||VGT UP Equity||Group 2||10%||1.0%||NYSE Arca, Inc.|
|12||Invesco S&P 500 Low Volatility ETF||SPLV UP Equity||Group 2||10%||1.0%||NYSE Arca, Inc.|
|13||Vanguard FTSE Europe ETF||VGK UP Equity||Group 2||10%||1.0%||NYSE Arca, Inc|
|14||iShares® MSCI Japan ETF||EWJ UP Equity||Group 2||25%||2.5%||NYSE Arca, Inc.|
|15||Vanguard FTSE Emerging Markets ETF||VWO UP Equity||Group 2||25%||2.5%||NYSE Arca, Inc.|
|16||Vanguard Real Estate ETF||VNQ UP Equity||Group 2||25%||2.5%||NYSE Arca, Inc.|
|17||Vanguard Total World Stock ETF||VT UP Equity||Group 2||10%||1.0%||NYSE Arca, Inc.|
|18||iShares Edge MSCI USA Momentum Factor ETF||MTUM UF Equity||Group 2||10%||1.0%||CBOE Global Markets|
|19||Vanguard Dividend Appreciation ETF||VIG UP Equity||Group 2||10%||1.0%||NYSE Arca, Inc.|
|20||Health Care Select Sector SPDR® ETF||XLV UP Equity||Group 2||25%||2.5%||NYSE Arca, Inc.|
|21||Consumer Staples Select Sector SPDR® ETF||XLP UP Equity||Group 2||25%||2.5%||NYSE Arca, Inc.|
|22||Financial Select Sector SPDR® ETF||XLF UP Equity||Group 2||25%||2.5%||NYSE Arca, Inc.|
|23||iShares® J.P. Morgan USD Emerging Markets Bond ETF||EMB UQ Equity||Group 3||25%||2.5%||Nasdaq Global Market|
|24||SPDR® Bloomberg Barclays High Yield Bond ETF||JNK UP Equity||Group 3||25%||2.5%||NYSE Arca, Inc.|
|25||iShares S&P GSCI Commodity-Indexed Trust||GSG UP Equity||Group 3||25%||0.75%||NYSE Arca, Inc.|
|26||SPDR® Gold Shares ETF||GLD UP Equity||Group 3||25%||2.5%||NYSE Arca, Inc.|
|27||iShares Preferred and Income Securities ETF||PFF UQ Equity||Group 3||10%||1.0%||Nasdaq Global Market|
|28||iShares MSCI India ETF||INDA UF Equity||Group 3||10%||1.0%||CBOE Global Markets|
|29||VanEck Vectors® Gold Miners ETF||GDX UP Equity||Group 3||25%||2.5%||NYSE Arca, Inc.|
|30||Utilities Select Sector SPDR® ETF||XLU UP Equity||Group 3||25%||2.5%||NYSE Arca, Inc.|
All returns below are based on hypothetical back-tested performance. Hypothetical back-tested performance is no guarantee of future performance and actual results will vary. For more detailed information please see “G. Risk Factors” below.
The graph below shows the hypothetical backtested performance for the SAVE Moderate Portfolio Strategy from January 01, 2006 to October 20, 2020.
|As of October 20, 2020||Hypothetical Backtested Strategy Performance|
|Annualized Realized Volatility8||2.98%|
|Return over Risk9||1.64|
The following table shows the one-, five- and ten-year hypothetical backtested average returns of the Strategy for the corresponding time periods. Each return for a given time period is computed by observing a set of hypothetical one-, five- and ten-year time periods for each trading day commencing January 01, 2006 and ending October 21, 2019, October 21, 2019 and October 21, 2019 in the case of the one-, five- and ten-year periods, respectively. For each such period and set of observations we determined the average hypothetical backtested average returns of the Strategy for each period and set of observations which is the percentage appearing below.
|Hypothetical Backtested Average Returns of the Strategy|
The following charts titled “One-, Five- and Ten-Year Hypothetical Backtested Net Returns” show the returns of hypothetical strategy-linked securities giving $1,000 of market exposure to the Strategy for the corresponding time periods. Each return on the one-year chart is computed by observing a hypothetical one-year strategy-linked security giving $1,000 of market exposure to the Strategy with assumed issue dates on each trading day commencing January 01, 2006 and ending October 21, 2019. Each return on the Five- and Ten-Year charts assumes that five (ten) one-year hypothetical strategy-linked security were bought in sequence, and that the returns of each one-year security was fully invested into the next security in the sequence.
The following are the key assumptions:
The following graphs titled “Distribution of One-, Five- and Ten-Year Hypothetical Backtested Net Returns” show the distribution of hypothetical returns as described in the above charts for the corresponding period of time.
The following table shows the one-year hypothetical backtested average net returns of hypothetical strategy-linked securities. Each one-year return is computed by observing a set of hypothetical one-year strategy-linked securities computed as described above. We derived the Five- and Ten-Year hypothetical backtested average net returns by assuming the sequential reinvestment 5 (10) times into one-year hypothetical strategy-linked securities, by sequentially reinvesting the principal and any returns of each such security into a new hypothetical strategy-linked security effective on the maturity date of the predecessor security. For each such set of hypothetical strategy-linked securities we determined the average hypothetical backtested net returns by averaging the hypothetical net return of each security in the set, which are the percentages appearing below.
|Hypothetical Backtested Average Net Returns of Hypothetical Strategy-Linked Securities|
The Strategy seeks to target a volatility of 3%. As described above, the Strategy effects this by modulating the total exposure of the Strategy to underlyings. At times, the Strategy may increase the total exposure above 100% (to a maximum of 150%) to increase volatility to the target. Other times it will reduce the total exposure below 100%. In either case only the part exposed to the Strategy will participate in the returns of the underliers.
The graph below shows the total exposure of the Strategy to underlyings used for the hypothetical backtested performance. The total exposure is capped at 150%. Historically the average total exposure is 88.10%.
The following chart shows the average total exposure of the Strategy to underlyings for the indicated time periods. Each percentage is computed by observing the daily exposures over a One-, Five- and Ten-year period commencing January 01, 2006 and ending October 21, 2019, October 21, 2019 and October 21, 2019 in the case of the One-, Five- and Ten-year periods, respectively. For each such period and set of observations we determined the average total exposure of the Strategy to underlyings for each period and set of observations. We then averaged each set of observations in each indicated time period which is the percentage appearing below.
|Average Total Exposure of the Strategy to Underlyings by Time Period|
Of the total exposure of the Strategy allocated to underlyings described above, the Strategy uses trend assessment and adjusted risk contribution to allocate among underlyings.
The next three charts show the Strategy allocations included in total exposure among underlyings in each of the three Strategy Groups. The fourth chart shows the relative Strategy allocations of the total exposure to underlyings of each of the three Groups.
The following chart shows the relative weighting of each underlying in Group 1 included in total exposure used in the generation of the hypothetical backtested performance used herein.
The following chart shows the relative weighting of each underlying in Group 2 included in total exposure used in the generation of the hypothetical backtested performance used herein.
The following chart shows the relative weighting of each underlying in Group 3 included in total exposure used in the generation of the hypothetical backtested performance used herein.
The chart below shows the Group relative weights included in total exposure of the Strategy to underlyings used in the generation of the hypothetical backtested performance used herein. As described above historically the average total exposure is 88.10%
The table below shows for each underlying minimum, average, and maximum weight during the backtested period.
|Underlying||Minimum Percentage of Total Exposure||Average Percentage of Total Exposure||Maximum Percentage of Total Exposure|
Risk of hypothetical backtested performance – There are risks arising from reliance on hypothetical backtested performance information and projected returns. The Strategies do not have any material history. As a result, all performance returns on this Site are based on hypothetical backtested performances and do not reflect actual investment results and are not guarantees of future results. Such projected performance is subject to a number of limitations and assumptions designed to determine the probability or likelihood of a particular investment outcome based on a range of possible outcomes. It is possible that any of those assumptions may prove not to be accurate. In addition, performance of the Strategies and each pair of a strategy–linked security and FDIC-insured deposit as (each, a “Program”) may differ materially from investment gains projected, described, or otherwise referenced in forward-looking statements.
In particular, the hypothetical backtested performance information incorporates a budget for the strategy-linked security based on recent yields of the FDIC-insured deposit instruments. While SAVE Advisers will periodically update the projected returns, understand that the actual budget used for the strategy-linked security in a given pair may differ from that used in the hypothetical backtested performance information.
In addition, SAVE Advisers may revise the Strategies, Investment Classes and underlyings and while SAVE Advisers will concurrently update the hypothetical backtested performances, prior hypothetical backtested performances may no longer reflect these revisions. Hypothetical backtested performance is no guarantee of future performance and actual results will vary.
Risks of the Strategies – SAVE Advisers management approach uses QISs, the design of the Strategies, selection of Investment Classes and underlyings to represent those Investment Classes. There may be deficiencies in the design or operation of all the forgoing which may amplify underperformance (or the possibility of no returns) of a Program. These deficiencies may occur, for example because the markets fail to track the historical patterns on which all the forgoing is based or the failure or shortcomings of processes, people or systems. Investments selected using QISs may perform differently than expected as a result of the factors used in designing the Strategies, the weight placed on each underlying, the timing of the implementation of rebalancing relative to the factors’ historical trends and actual performance, and technical issues in the construction and implementation of the Strategies (including, for example, data problems and/or software issues). In addition, the Strategy may allocate exposure to the underlyings in excess of 100%, which effectively creates leverage. This leverage may amplify losses should the underliers lose value during the leveraged period. Moreover, the effectiveness of the Strategies may diminish over time, including as a result of changes in the market and/or changes in the behavior of other market participants. The Strategies’ return mapping is based on historical data regarding particular Investment Classes and underlyings as well as their correlation, which may not be predictive of future price movements, particularly if unusual or disruptive events cause market movements, the nature or size of which are inconsistent with the historical performance of individual markets and their relationship to one another or to other macroeconomic events. Therefore, the actual performance of the Strategies (and thus the performance of the Programs) may underperform (or result in no returns). These returns may further deviate materially from historical backtested performance. There is no guarantee that the use of the Strategies will result in effective investment decisions for any client account.
Risk of revisions to the Strategies – SAVE Advisers may periodically revise the Strategies based on their performance and other factors. These revisions may include changes to the Investment Classes and underlyings. There is risk that any such revision will not be effective or may amplify underperformance (or the possibility of no returns) of a Program.
Diversification risk – The SAVE Advisory Service assumes the beneficial nature of diversification. While using a diversified portfolio to reduce risk is a widely accepted investment principle, diversification cannot reduce risk to zero, and the returns on a diversified portfolio during any given time period may be lower than the returns on one or more investments concentrated in an industry, sector, or geographic region that was profitable during that time period. In addition, the Strategies’ return mapping is based on historical data regarding relative performance and the correlation of Investment Classes which may not be predictive of future price movements.
Investment Class investment risk – The Strategies select Investment Classes which in turn are represented by the underlyings. Underlyings used for this purpose pose risks of: (i) trading at a discount or premium to their underlying net asset value, if any; (ii) not fully tracking the market segment or strategy that underlies their investment objective, resulting in performance that differs from expectations; and (iii) an additional layer of fees and costs payable to the investment advisers and other relevant parties (which are unaffiliated with SAVE Advisers).
Multiple levels of fees and expenses — As described above, the strategy– linked securities track a Strategy which in turn is computed based on the levels of various underlyings imbedded in the related Strategy. These underlyings are currently ETFs which in turn charge fees and expenses which will reduce returns. In addition, the counterparty issuing the strategy-linked securities will imbed a profit in in each strategy-linked security.
No active management – The risk arising from the lack of active management. SAVE Advisers uses Strategies which are rules-based. Rules-based methodologies and passive investing may yield lower returns than active investing because active investing allows taking discretionary positions in single name securities while passive investing may not thereby forego any potential gains (or avoidance of losses) that could result from such active management.
SAVE Advisers will automatically rebate 100% of its wrap fee for each pair of a strategy–linked security and an FDIC-insured deposit as (each, a “Program”) if at the strategy–linked security’s scheduled maturity deduction of the wrap fee (whether or not previously paid) for that Program would reduce the maturity proceeds of that Program below the client’s initial investment in that Program. This rebate feature means that for each Program, SAVE Advisers takes a fee only if clients receive positive net performance from that Program. All clients are automatically enrolled in the SAVE Advisers fee rebate program. SAVE Advisers may discontinue the fee rebate program for all clients at any time. However, discontinuation of the fee rebate will be effective only for each Program purchased after announcement of the discontinuation of the rebate program.
The chart below shows the time at which SAVE Advisers included into the Strategy each of the 13 underlyings after January 01, 2006 (meaning that the underlying accrued the requisite historical closing prices). By August 2009, December 2011 and January 2014, 80%, 90% and 100%, respectively of the underlyings had the requisite historical closing prices for inclusion in the Strategy.
SAVE Advisers believes that in, general, the addition of the underlyings over this time period did not materially affect—as measured by correlation—the Strategy’s daily return. Specifically, when we recompute the Strategy without including the last 20% (10%) of underlyings, the recomputed strategy has a return correlation of 0.989 (0.994) with the actual Strategy over the relevant introduction period.
1. Volatility in this context generally means a measure of the variation in the level of the Strategy over a given historical time period.↩
2. The marginal contribution to risk for an underlier is how much the overall risk of the portfolio changes by a change in weight in the underlier. This takes into account not only the underlier risk, measured by its volatility – how much its returns vary over time – and its correlation with other underliers – how the underliers tend to move together.↩
3. Total exposure means the percentage of the investment that is allocated to underlyings in the Strategy. For example, if a given investment strategy-linked security were to provide $1,000 of market exposure, total exposure to the underlyings in the Strategy could range from $0.00 (0%) to $1,500 (150%) based on changing market conditions.↩
4. By August 2009, December 2011 and January 2014, 80%, 90% and 100%, respectively of the underlyings had the requisite historical closing prices for inclusion in the Strategy. SAVE Advisers believes that in, general, the addition of the underlyers over this time period did not materially affect—as measured by correlation—the Strategy’s daily return. See Appendix II for further details on the introduction of underlyings.↩
5. 30-days ended October 20, 2020.↩
6. 180-days ended October 20, 2020.↩
7. Geometric mean of the total returns within the backtest period.↩
8. Annualized variation of daily returns since inception.↩
9. The ratio between Annualized Performance and Annualized Realized Volatility, which measures return relative to risk.↩