Legal

The 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.

A – Overview; Hypothetical Backtesting Methodology

SAVE Advisers used Quantitative Investment Strategy Techniques (“QISs”) to develop the SAVE® Growth 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 within 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 2.5%.

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:

1. 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.

2. 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.

3. 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 2.5%. 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 71.17%.

The Strategy’s hypothetical backtest covers the period from January 01, 2006 to August 18, 2022. 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 Federal Funds Rate; this reference rate reduces the daily returns from each underlying by the relevant Federal Funds 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.

B – SAVE Growth Portfolio Strategy Underlyings and Groups

The underlyings are split into three Groups:

• Group 1 consists of Investment Classes such as U.S. treasuries of various durations, inflation protected treasuries, corporate bonds of various durations.
• Group 2 consists of Investment Classes such as large-cap U.S. equities, sectorial U.S. Equities, global equities and developed market equities.
• Group 3 consists of Investment Classes such as commodities, emerging markets and high yield bonds.

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.

C – SAVE Growth Portfolio Strategy Hypothetical Backtested Performance

The 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 Growth Portfolio Strategy from January 01, 2006 to August 18, 2022.

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 August 18, 2021, August 19, 2017 and August 20, 2012 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.

D – SAVE Strategy-Linked Security Hypothetical Backtested Returns

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 August 18, 2021. 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 purchase budget for each strategy-linked security reflects the most recent deposit rates with our banking partner. This budget is subject to change.
• SAVE Advisers’ fees accrue at a rate of 0.35% per annum on the then-current market exposure (initially $1,000) provided by each strategy–linked security for the Market Savings accounts. For the Debit Invest Card SAVE Advisers’ fees accrue at a rate of 0.59% per annum on the then-current market exposure -- from the match spend -- provided by each strategy–linked security. The “Fee Rebate” program (described further in the Appendix hereto) has been applied to each of them.
• The purchase price for each strategy-linked security is $15.80 for each $1000 of market exposure, which is based on Black-Scholes modelling as well as an analysis of hypothetical pricing offered from counterparties, inclusive of a reasonable projection of their fees.
• The returns ignore the effects of Federal, State and local taxation.
• SAVE Advisers’ used all computational conventions used to compute the hypothetical backtesting of the Strategy above under “A – Overview; Hypothetical Backtesting Methodology.”

The following “Distribution of One-, Five-, and Ten-Year Hypothetical Back-tested Net Returns” charts show the hypothetical returns for each period (1, 5, or 10 years).
Each period calculation begins in January 2006 and ends 1, 5, or 10 years later, then a new period calculation commences for the next 1, 5, or 10 years. If a period calculation is not completed (i.e. 2 years of a 5-year period are missing), then it will not show on the chart. X axis: Each year interval is representative of hypothetical back-tested performance for investments with a trade date beginning within the year listed on the X-axis, and maturing 1, 5, or 10 years after the trade date - dependent on which tab you've selected on the chart. If a period calculation is not yet complete, that period's starting year interval will not yet be represented on the X-axis.

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. Each one-year return is computed by observing a set of hypothetical one-year strategy-linked securities computed as described above. For the Market Savings accounts 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.

E – SAVE Growth Portfolio Strategy Hypothetical Backtested Total Exposure of the Strategy to Underlyings

The Strategy seeks to target a volatility of 2.5%. 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 71.17%.

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 August 18, 2021, August 19, 2017 and August 20, 2012 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.

F – SAVE Growth Portfolio Strategy Hypothetical Backtested Investment Class Supporting Detail

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 following chart shows the relative weighting of each underlying in each of the 3 Groups -- and the Groups relative weights to each other -- included in total exposure used in the generation of the hypothetical backtested performance. As described above historically the average total exposure is 71.17%

The table below shows for each underlying minimum, average, and maximum weight during the backtested period.

H – Appendices

Appendix I: Fee Rebates

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 yield a return of zero or less. 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.

Appendix II: Addition of Underlyings

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 August 18, 2022.↩}

^{6. 180-days ended August 18, 2022.↩}

^{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.↩}

^{10. The Debit Invest programs do not have the option of revinvestment, therefore the 5 and 10-year calculation does not apply.↩}