Frequently Asked Questions

Economic Index Associates (EIA) is a developer and licensor of active index strategies that are replicable, investable, rules-based and transparent. EIA’s three founders (Robert Johnson, Gerald Jensen and Luis Garcia-Feijoo) are the authorities on the association of Fed monetary policy with security returns – combined they have published over 200 academic articles, which have over 9,000 citations. The basic premise of their original research is captured in their book - Invest With the Fed (McGraw-Hill, 2015). Leveraging its founders’ extensive research, the “Invest with the Fed” (IFED) methodology uses Fed policy signals and firm fundamentals to guide stock selection. The IFED strategy is a dynamic approach that selects portfolios that align with whatever market environment is signaled by prevailing Federal Reserve policy actions. EIA licenses its proprietary, patent pending IFED indexes as the basis for financial products. With EIA’s services, clients can also design customized portfolios that apply the IFED methodology. Through its registered investment advisor affiliate, EIA Investments, EIA also provides asset management and related advisory services for institutional and high net worth investors as well as major TAMP platforms and financial advisor networks.

Economic Index Associates (EIA) is the outgrowth of over three decades of research by widely published finance professors Robert Johnson, Gerald Jensen and Luis Garcia-Feijoo (EIA’s founders). The three researchers document how Fed policy signals correspond with return patterns for a variety of asset classes; their findings are published in prestigious academic and practitioner peer-reviewed journals. The basic premise of their original research is captured in their book - Invest With the Fed (McGraw-Hill, 2015). In response to ongoing interest from industry professionals on how to implement their research findings, EIA’s founders designed an active index methodology that incorporates their extensive research findings.

EIA's investment strategy is based on the premise that macroeconomic forces (i.e., market conditions) significantly influence the relation between risk factors and security prices. In identifying changes in market conditions, EIA’s approach relies on signaled shifts in Federal Reserve monetary policy as implemented through Fed actions on key interest rates. Market conditions are classified into one of three possible environments (expansive, restrictive and indeterminate) and a set of firm metrics is applied to help identify firms that perform best under each environment. The strategy relies on the premise that failure to consider the impact that the monetary environment has on security prices results in systematic mispricing of securities. For example, research shows that a strategy that targets small, out-of-favor stocks is very successful when the Fed is following an expansive monetary policy but underperforms when monetary policy is restrictive (see, Jensen, Gerald R., Robert R. Johnson and Jeffrey M. Mercer. “New Evidence on Size and Price-to-Book Effects in Stock Returns.” Financial Analysts Journal 53, November/December 1997, 34-42 and Jensen, Gerald R., Robert R. Johnson and Jeffrey M. Mercer. “The Inconsistency of Small-Firm and Value-Stock Premiums.” Journal of Portfolio Management 24, Winter 1998, 27-36). Thus, a smart-beta product that tracks small, out-of-favor stocks underperforms when monetary conditions are restrictive. In contrast, the EIA strategy uses a combination of twelve metrics to identify a portfolio that aligns appropriately with each environment. The guiding principles underlying EIA’s investment philosophy are as follows: Ethical conduct is essential EIA’s operations are guided by CFA Institute Code of Ethics and Standards of Practice; the founders are CFA Charterholders Time in the market is crucial Strategy is always fully invested, shifts holdings strategically based on changes in Fed policy Diversification avoids underperformance Strategy selects stocks using twelve diverse financial metrics, which helps to diversify the portfolio Excessive turnover hurts performance Strategy is dynamic; however, turnover is limited relative to most active strategies Following the herd destroys wealth Strategy is grounded in economic/financial theory and has a long-term focus Knee-jerk reactions are harmful Strategy is rules-based, it strategically shifts with market conditions and does not succumb to following the latest investment fad

The IFED indexes were created based on 30+ years of published, peer-reviewed academic research. EIA was formed in December 2018 to translate the founder’s research into rules-based active index strategies applicable for a wide range of investment products. The data used to develop the final IFED methodology was pre-2012; with the post-2012 period serving as the holdout period. Regression and attribution analyses have been conducted on back-test data for all IFED indexes going back as far as 1979. The results of these analyses align with the founder’s extensive peer-reviewed research findings and support the efficacy of the IFED methodology. In June 2020, EIA launched its first customized index, the Nasdaq IFED Large-Cap US Equity Index™ (IFED-L), to provide the basis for separately managed accounts. On September 14, 2021, UBS launched two ETNs on the New York Stock Exchange that track Nasdaq IFED-L. Their stock codes are IFED and FEDL. For the period June 9, 2020 – June 30, 2024, the Nasdaq IFED-L produced a total return of 107.76% and outperformed its benchmark (the S&P 500) by 27.77%.

Our academic research findings over the past 30 years applied exhaustive and extensive modeling to identify the critical combination of financial metrics that best differentiate company stock market performance during each of three market environments. Furthermore, independent research confirms that each of the twelve financial metrics is consistent with an underlying economic rationale that supports the metrics inclusion. In selecting our final model and conducting performance evaluation, we applied rigorous academic research standards to eliminate prominent data issues such as survivorship bias, selection bias and look-ahead bias. The IFED model relies on twelve selected firm-specific metrics; however, the metrics are not common across the three environments. The strategy relies on the observation that certain financial metrics have a magnified or “leveraged” effect on companies’ stock prices during particular market environments but have little or no effect during the other two environments. Our process involves weighting those metrics appropriately to take the maximum advantage of the existing environment. IFED scores across stocks range from -4 to 4, and the distribution is normalized. We developed the details of the IFED model based on economic theory and empirical findings using the post-2012 period as our holdout sample. Equities with insufficient financial data to derive the required metrics are eliminated from consideration when scoring firms. Based on our robust and meticulous model design, we are confident in the IFED model’s efficacy for future application.

The IFED strategy was designed as a long-run strategy based on established relationships in financial markets. The strategy relies on the underlying systematic association between market conditions and security returns; it adjusts portfolio composition to benefit from the existence of those established patterns. We do not expect that the strategy will outperform over all short-term periods. When idiosyncratic forces such as a pandemic, a terrorist attack or a financial crisis occur, the systematic patterns can be temporarily obscured. The IFED strategy utilizes firm financial metrics to select firms with features that “magnify” or “leverage” the firms’ exposures to the prevailing market environment, as signaled by monetary conditions. When monetary conditions shift, the strategy rotates out of firms with features that are out-of-favor and rotates into firms that reflect in-favor features. Thus, over time, the strategy has produced consistent out-performance. In general, the strategy was developed based on established relationships that have persisted over 60+ years of financial market history. Idiosyncratic factors may cause short-term disruptions in the strategy's performance, which may temporarily exist until normal conditions reemerge.

The IFED strategy relies on Federal Reserve monetary policy changes to identify shifts in market conditions; these changes serve as a guide in each index’s stock selection process. The IFED strategy classifies conditions into one of three different environments, expansive, restrictive or indeterminate. Research by EIA’s founders shows that the characteristics of companies that prospered during each of the three environments is dramatically different. Therefore, a shift in environment produces a substantial reallocation from stocks with characteristics that were aligned with the old environment to stocks with features that align with the new environment. The following graph illustrates the sector allocation of the IFED All Cap index (IFED-A). IFED-A alone is selected to condense the presentation and because it is the most encompassing of the IFED indexes. Thus, it best reflects the general characteristics of the IFED strategy. Note, IFED-A starts with the largest investment universe of the IFED indexes. Therefore, IFED-A provides the IFED strategy with the most flexibility to capture the systematic return patterns that the strategy relies on. The other IFED indexes demonstrate comparable sector weight data across time. Data for the other IFED indexes can be obtained upon request to EIA. While portfolio composition experiences substantial turnover at a rebalance, the strategy has held the risk of underperformance at bay. The strategy’s risk controls include: 1) keeping the portfolio fully invested in stocks, 2) several of the financial metrics that the strategy relies on are firm-specific quality metrics, and 3) aligning the index composition with the market environment to avoid a mismatch. Thus, the strategy favors high quality companies that have characteristics that are aligned with the prevailing environment. These features of the strategy explain its past success at generating alpha while controlling downside risk.

The following chart reports performance of 1,500 stocks comprising the IFED-A Universe. The stocks are ranked by IFED score, placed in quintiles according to their IFED score and weighted by IFED score within the quintile portfolio. The chart supports the IFED strategy’s efficacy over the 1999 through 2023 period based on two observations: Four of the five quintiles in the IFED Universe beat the S&P 1500, which indicates that weighting by IFED score, rather than market cap, was superior: and, The observed monotonic drop in performance across quintiles confirms the model’s ranking efficacy. Quintile 1 to quintile 5 are created based on relative stock IFED ranks that reflect the model’s assessment of stock attractiveness, this ranking is born out in past performance.

The IFED strategy has produced index portfolios of above average quality stocks that have broadly diversified exposures across risk factors and across industries. These features help to reduce the sensitivity of portfolio returns to market corrections. Downside risk analysis supports the strategy’s ability to mitigate extreme underperformance when markets correct. The following table reports the maximum drawdown and time to recovery for each of the IFED indexes relative to its respective benchmark over the period from 1999 through 2021. The results are shown for the full period and for the last five years. The maximum drawdown period for the full-period timeframe coincided with the 2008 financial crisis. Over this timeframe, the IFED indexes had a smaller drawdown for five of the six indexes and the one larger case was only slightly larger. Perhaps more importantly, the IFED indexes generally had a much shorter time to recover back to their starting levels. In particular, the All-Cap (IFED-A), Large-Cap (IFED-LG), Large/Mid-Cap (IFED-LM) and Small-Cap (IFED-S) recovered their losses much sooner than their corresponding benchmarks. The maximum drawdown period for the last five-year timeframe coincided with the Covid-19 pandemic. Over this timeframe, the IFED indexes had generally similar maximum drawdowns as the benchmarks. The IFED indexes, however, were generally superior to their benchmarks with respect to time to recover from the loss. This is especially true with respect to IFED-M, IFED-S and IFED-LV. For IFED-S and IFED-LV, investors recovered their losses in less than half the time compared to their benchmarks. This evidence strongly supports the contention that the alpha enhancement that IFED indexes produced throughout the past 23 years did not come at the expense of subjecting investors to a higher-than-average level of risk from a market correction.

Our research over the past 30 years indicates that shifts in the direction of Fed policy rates is the primary factor explaining security return patterns, not the level of rates nor the size of the rate changes. The rationale for this view is that the existing rate level is already factored into security prices. The IFED model reacts to signaled shifts in policy, rather than focusing on existing rate conditions. We believe that the focus on rate levels is common and explains the view that Fed policy was consistently and overwhelmingly accommodative over the decade following the financial crisis; we believe this view is misplaced. The IFED model incorporates subtle, but verified, shifts in policy rates that many market participants do not recognize; or the participants classify the rate changes as inconsequential. The IFED model has no threshold rate level that distinguishes between high versus low; and hence, the model does not suffer the same obsolescence problems as other models that assume mean reversion in rates. The model’s reaction to subtle policy shifts is an instrumental feature of the IFED strategy, which helps to explain its superior performance over time. Overall, the IFED strategy has produced significant alpha across time regardless of the level of interest rates. The graph below illustrates the relationship between the interest rate level (as proxied by the 5-year T-bond rate) and the performance of the IFED strategy [as proxied by the alpha for the IFED All Cap index (IFED-A)]. The S&P 1500 Composite serves as the benchmark in calculating IFED-A’s alpha. The graph clearly indicates that the IFED strategy produced significant alpha during periods of both rising and falling interest rates. For example, the two largest alphas, in year 2000 and year 2009, were produced in opposite rate movement periods, falling rates in 2000 and rising rates in 2009. Furthermore, performance was generally better during the 1999-2007 period when rates were higher; however, there were several years of significant alphas during the subsequent low-interest-rate environment. EIA research confirms that the IFED model worked effectively going back to the 1970s. This extended period incorporates the extremely high-rate environment existing through much of the 1980s. Thus, the model has demonstrated the ability to work effectively during timeframes with unusually high and unusually low interest rate levels.

The IFED strategy relies on a systematic relationship between shifts in market conditions (as signaled by monetary policy changes) and security returns. While the relationship has been shown to be relatively consistent over time, the strategy is not advocated as a short-term trading strategy. Given the myriad of factors that affect the security markets, we think it most appropriate to assess any investment strategy over a 3- to 5-year investment timeframe. The IFED strategy has consistently demonstrated positive alpha over longer timeframes. Obviously, there are shorter periods where idiosyncratic forces drive returns and these forces are not fully captured by our model, or indeed any model, e.g., trade wars, Covid-19, terrorist activities, global conflicts, etc. To provide further context, the following figures present information regarding 3- and 5-year rolling alphas for different IFED indexes vs their benchmarks. The graphs show that long-run outperformance was both large and persisted throughout most of the 24-year period. The IFED strategy lagged market performance in only three relatively short timeframes and in each case the amount of underperformance was minimal. In contrast, periods of outperformance extended for long timeframes and the level of outperformance was large. The following table presents the win-rate percentage for each IFED index for 3- and 5-year holding periods. The win-rate percentage identifies the percentage of time that each index outperformed its benchmark. In all but one case, the win rate exceeded 87% for the three-year holding period and for the five-year holding period the win rate was 100% for four of the six indexes. In all cases, the win-rate percentage increased as the holding period expanded. The average annual alphas produced over the rolling 3- and 5-year holding periods were equally impressive with the highest values approaching 11%. Even the IFED low volatility index produced an annual alpha approaching 4%.

The IFED strategy has produced significant alpha in both high and low inflation environments. This is consistent with the strategy’s design since the strategy relies on signaled shifts in Federal Reserve policy. Because the strategy relies on Fed policy changes, it implicitly incorporates an inflation factor. The Fed has a dual mandate to maintain price stability (i.e., keep inflation under control) and promote full employment. Therefore, Fed policy changes are made with current, and potential future, inflationary pressures as a primary concern. The graph below depicts the performance of the IFED All Cap index (IFED-A) in relation to the inflation rate. The depicted relationship between inflation and index returns is similar for the other IFED indexes. The above graph suggests that inflationary pressures have had relatively little influence on the performance of the IFED strategy. The greatest return during the 24 years was in 2009 when the inflation rate was declining. In contrast, the second greatest return occurred in 2003 when the inflation rate was increasing. Likewise, the lowest return (2008) occurred in a rising rate environment, whereas inflation was falling when the second lowest return occurred (2007). The entire period from 1999 through 2021 represents a period of relatively low inflation rates. In earlier research, we demonstrate that the IFED strategy performed well even during periods of extraordinary inflation (e.g., 1980s). Our earlier research classified monetary environments less precisely as it classified environments by month, rather than days. Therefore, that earlier research (available on EIA’s website) is referenced sparingly in this document.

The following table identifies the seven best years of relative performance for the IFED indexes during the period from 1999 through 2024. In general, the alphas in the table are very large and consistent across the indexes. This evidence supports our contention that the IFED strategy positions the portfolio to excel when normal return patterns prevail. The IFED strategy uses a monetary indicator to classify the market environment as Expansive, Restrictive or Indeterminate. The monetary indicator allows us to transition portfolio holdings prior to subsequent economic developments signaled by the indicator. Furthermore, note that the years of strong outperformance are interspersed throughout the 1999-2024 period. This observation supports our contention that the return patterns that motivated the IFED strategy were occasionally obscured by idiosyncratic factors; however, they ultimately revealed themselves with great results for the strategy. The following discussion summarizes the general circumstances that lead to each year’s outperformance.

The following table identifies the five worst years of relative performance for the IFED indexes during the period from 1999 through 2024. Note that for all five years, at least three indexes reported negative alphas; however, there were no years where negative relative performance occurred for all six indexes. The IFED strategy uses a monetary indicator to classify the market environment as Expansive, Restrictive or Indeterminate. The indicator is designed such that we can transition portfolio holdings prior to subsequent predicted economic developments that are signaled by the indicator. Note that in contrast to the very large alpha values reported during the eight years of strong performance (shown in the prior question), the values reported in the above table are generally quite small. This is consistent with the strategy’s design, as the strategy’s stock selection integrates quality aspects and is always fully invested. These aspects help to avoid substantial underperformance when unusual or chaotic forces drive equity returns.

The IFED strategy is an active, rules-based strategy captured in an index format. The strategy is distinctive in the marketplace as it introduces a dynamic, Fed policy-guided dimension to the more traditional static approaches. It is well recognized that traditional strategies, such as smart-beta products, go through extended time periods in which they are "out-of-favor." Our research establishes that these patterns of inferior performance correspond with changes in the market environment. Therefore, our strategy shifts portfolio composition such that we target firms with financial metrics that are positioned to prosper during the prevailing market environment. The adaptability of the strategy allows it to identify the set of securities that aligns with various market environments across market cycles.

EIA’s proprietary monetary indicator is motivated by economic theory and empirical research: it relies on a combination of Fed policy signals shown to have predictive viability with respect to fund availability and security return patterns. As supported below, there is strong empirical and economic support for using the IFED monetary indicator. Changes in Fed policy rates signal shifts in both monetary conditions and economic conditions. The Fed operates under a dual mandate whereby the Fed focuses on maintaining price stability (i.e., keeping inflation under control) and fostering full employment (i.e., promoting economic activity). Therefore, a shift in Fed policy occurs when the Fed determines that a change in policy is warranted to offset harmful developments it sees on the horizon for inflation and/or economic activity. Traditional economic-indicator-based rotation strategies rely on a direct measure (or measures) of economic activity to pinpoint their rotation timing. In contrast, by relying on Fed policy rates, the EIA strategy captures monetary conditions directly as well as capturing future economic activity via an indirect affiliation between Fed policy shifts and both current and future economic activity. Changes in Fed policy rates precede changes in economic variables because the Fed makes adjustments based on its forecasts, and further, changes in Fed policy rates (and interest rates in general) have ramifications for future economic activity. Therefore, the EIA strategy integrates a forward-looking aspect into the portfolio reallocation process. In contrast, other approaches that use the business cycle often rely on backward-looking economic variables. A change in Fed policy rates serves as a more effective signal of a genuine shift in market conditions. The Fed influences the availability of money and interest rates, and relative to most market participants, the Fed also has superior access to economic and monetary information. A temporary or transitory change in an economic variable may prompt a stock reallocation in rotation strategies that rely on economic variables. In contrast, the IFED strategy avoids such false signals because the Fed avoids shifting policy if it deems a change in an economic measure to be temporary or transitory. In addition, many economic variables are measured with error and on a significantly lagged basis, which greatly reduces their reliability as indicator variables. There is compelling empirical support linking changes in Fed policy rates with subsequent stock returns. In contrast, there is limited empirical evidence establishing a significant systematic link between reported economic variables and subsequent stock returns. EIA founder research compared the linkage between stock returns relative to business cycles versus monetary cycles. The published study found that monetary cycles maintain a far more consistent linkage even though business cycles are identified with a significant lag, that is, the monetary indicator showed superior predictive ability even though the business cycle indicator had a significant look-ahead bias. Business cycles can only be identified several months after turning points have taken place, whereas shifts in Fed policy signals are readily available to market participants when they occur. Follow this link to view published research supporting the efficacy of the IFED monetary indicator.

For this analysis, we report statistics only for the IFED All Cap index (IFED-A). IFED-A is selected to condense the presentation and because it is the most encompassing of the IFED indexes. Thus, it best reflects the general performance of the IFED strategy. Note, IFED-A starts with the largest investment universe of the IFED indexes. Therefore, IFED-A provides the IFED strategy with the most flexibility to capture the systematic return patterns that the strategy relies on. The other IFED indexes demonstrate comparable performance results. These results can be obtained upon request to EIA. The IFED strategy has been successful at capturing upside movements The IFED strategy has been successful at capturing upside movements when equity markets have prospered, as demonstrated by the high upside capture of the IFED All Cap index (IFED-A) as shown below. Specifically, over the 2019-2024 period, IFED-A captured 121% of the upside movement in quarterly returns. At the same time, IFED-A avoided periods of large underperformance as it captured only 94% of downside movements. This attribute of the strategy is consistent with its design as the strategy remains fully invested in equities and favors high quality firms that are positioned to prosper in the prevailing environment. Since high quality firms are favored, underperformance has been diminished even when normal patterns were obscured by idiosyncratic events. Overall, the IFED strategy has outperformed the prominent smart beta indexes. It has captured the biggest percentage of upside market movement without subjecting investors to greater than average downside risk. By capturing almost 30% more of the market’s upside than its downside movement, the IFED strategy has overshadowed other approaches, as the next highest net capture value is only 28%.

The IFED methodology uses monetary policy shifts as its indicator of changes in market conditions. This signal is then used as the basis for when to rotate the firm-specific metrics used and the metric weightings. Since the strategy is based on twelve firm-specific metrics, which are conditioned on the market environment, any sector or factor exposures are incidental to the strategy, i.e., not part of the strategy’s design. Factor and sector analysis on each IFED index confirms that the majority of alpha is sourced from ‘stock selection’ and not traditional factor (value, market, size, momentum) and/or sector selection and rotation. In other words, the IFED methodology has been proficient in selecting stocks for the prevailing market outlook. There are aspects of factor rotation in the strategy because the strategy emphasizes a diverse set of financial metrics (some of which are recognized risk factors) during each monetary environment. The graphs below illustrate the factor and sector attribution analyses for the IFED All Cap index (IFED-A). For expositional purposes, IFED-A is chosen as the only IFED index presented since it is the most encompassing of the IFED indexes. That is, it starts with the largest investment universe, which provides the strategy with the greatest flexibility in selecting stocks. The other IFED indexes produce attribution results that are not materially different from those for IFED-A and are available upon request to EIA. The two attribution analyses demonstrate that the majority of the IFED strategy’s outperformance was due to stock selection. Specifically, stock selection explained 80% of alpha, relative to the portion explained by factors. Size tends to be the most prominent factor contributing to returns, which is logical since smaller firms are generally more sensitive to changes in market conditions. Likewise, stock selection explained 89% of alpha relative to the portion explained by sectors. Information Technology and Consumer Discretionary are often the sectors that contribute to outperformance, which again is logical as these two sectors are typically sensitive to changing market conditions.

The following table contrasts the performance characteristics of the IFED Large-Cap Core index (IFED-LG) relative to the leading S&P 500 factor indexes for the period January 1999 through December 2021. For convenience, in this analysis, we label outperformance relative to the S&P 500 as alpha for IFED-LG and each of the factor indexes. IFED-LG produced an average annual alpha (relative to the S&P 500) that is positive and large at 11.83%. This is by far the highest alpha recorded, with the next highest average alpha reported for the S&P 500 High Dividend index at only 1.73%. Two of the S&P factor indexes report negative average annual alphas over the 23-year period. While the volatility of IFED-LG exceeds that of the S&P 500 factor indexes, the higher volatility should be considered with the following caveats. First, the standard deviation of the IFED indexes was inflated because of their relatively high upside deviation values. That is, much of the volatility was due to unusually large positive return outcomes. Second, the alpha is far superior to the alpha reported for the S&P factor indexes. Most investors would consider the tradeoff of substantially higher return to be well worth the slightly higher standard deviation. Consistent with its design objectives and the published research of EIA’s founders, the minimum alpha (vs S&P 500) was much lower for IFED-LG. Furthermore, the maximum annual alpha far exceeded that of the factor indexes. Finally, the skewness of annual returns was also in favor of IFED-LG. These measures confirm that IFED-LG had favorable downside risk and that much of its volatility can be attributed to unusually large return outcomes, which are never discouraged by investors. The assessment of Sortino ratios and upside and downside capture in the analysis below provides additional support for the superiority of the IFED strategy in avoiding downside risk over this period. IFED-LG produced a Sortino Ratio that exceeded that of the S&P factor indexes. Indicating the strategy provided superior returns for the downside deviation assumed. Furthermore, IFED-LG captured the highest amount of market upside (142.19%), while only capturing (67.75%) of the market's downside movements. IFED-LG produced the second lowest level of downside capture even though its upside capture exceeded the factor competition. Overall, IFED-LG captured market upside without significantly underperforming when the market dropped.

The IFED indexes have outperformed their respective benchmarks by a significant amount across all three environments. For example, the following table presents the alpha and various performance ratios for six IFED indexes by monetary environment for the period January 1, 1999 through June 30, 2024. Each of the six IFED indexes outperformed its benchmark in each monetary environment; however, the performance ranking across environments varied. Expansive periods were superior for IFED-A and IFED-S, whereas indeterminate periods were superior for IFED-LG, IFED-LM, IFED-M and IFED-LV. In general, we found that the environment with the best performance depended on the time period examined; however, over all long-term periods the three environments each produced positive outperformance. In general, the difference in the level of outperformance across environments can be attributed to idiosyncratic factors, which are not reflected in the model, such as pandemics, international trade issues, fiscal policy anomalies and military conflicts. In our research going back to the 1970s, each environment produced positive alpha and there was no statistically significant difference between the level of outperformance across environments.

There are several features of our monetary indicator that give us confidence that it will maintain its efficacy in the future: the monetary indicator has been largely the same since it was first introduced during the mid-1990s, and it has produced long-term outperformance since. This timeframe includes almost every type of market event imaginable. Furthermore, we backtested the efficacy of the indicator variable back to the 1970s and the superior performance holds in the earlier time frame as well. the indicator is based on policy signals that incorporate the two fundamental dimensions of Fed policy, the Fed’s signaled long-term policy intentions and its activities in the short-term financial markets. These two features should be relatively invariant to modifications in the Fed’s operating procedures since they reflect the Fed’s plans and its activities. given the empirical evidence we show that links our monetary indicator with the aggregate availability of funding, we believe the indicator has a strong economic motivation along with its link to security return patterns. Occurrences like extraordinary levels of fiscal stimulus and unprecedented events (e.g., pandemics) will continue to impact the financial markets and create noise in the identified relationship between the IFED monetary indicator and security returns. Our research, however, indicates that in the long-run monetary conditions (as we define them) have shown a systematic relation with security returns that overshadows other relations. We believe this long-run relation will be maintained in the future, but not necessarily over every short-term period. In our research, we were unable to establish any systematic link between fiscal policy measures and security returns, and furthermore, adding fiscal policy measures to our monetary indicator failed to improve the model’s explanatory power. We are unaware of any credible research that has had success on using fiscal policy in this capacity.

We believe the strategy is well-positioned to give investors an exposure to the U.S. stock market, and therefore, could serve as a core product meant to replace a general market exposure. Our findings show that the strategy has the following features, which support this contention. The long-run (3- to 5-year) performance of the strategy has beat its market benchmarks consistently in rolling evaluations of historical performance i.e., performance superiority exists 90%+ of the time for 3-year performance and 100% of the time for 5-year. The strategy positions the portfolio to excel when the normal relation between monetary conditions and return patterns prevails; however, the strategy has demonstrated limited downside risk when other forces drive returns. Hence, much of the strategy’s volatility has been upside deviation, which most investors do not consider to be risk. The strategy produced Sortino and drawdown measures that are superior to their benchmarks, particularly with respect to holding periods of one year or longer. The IFED strategy relies on twelve firm-specific metrics, several of which incorporate firm-quality aspects, thus when idiosyncratic forces have driven security returns the portfolio has mitigated losses since it was weighted toward quality firms. For example, in examining the five calendar years, over the prior 24 years, where the market return was negative (see graph below), the IFED All-Cap index (IFED-A) produced positive alpha in all five years with an average alpha of over 12%. The minimum alpha for IFED-A across the five market correction years was 1.51% Given that the strategy relies on twelve diverse firm metrics, it generally selects a portfolio that is diverse in its sector and factor representation, and thus, typically provides a broad exposure to the market. While we believe the IFED indexes are well positioned as a core equity product, the indexes have considerable active share. This means they could serve the role as a supplement to an investor’s core equity position for investors who are not interested in a complete portfolio substitution. Relative to the prominent factor indexes, which generally report correlations above 95% with the market, the IFED indexes have much lower correlations with the equity market and the factor indexes (see IFED index correlations below). We believe a major attraction of the IFED indexes is that they do not mimic the market.

The strategy is completely customizable. The IFED methodology assigns an IFED score to each security; securities with relatively high (low) IFED scores are expected to prosper (languish) during prevailing conditions. Therefore, the strategy can be treated as an overlay and used on the total US equity market or any subset thereof, including in combination with features such as ESG, risk factors and investment styles. With the IFED scores, clients have substantial flexibility to design customized strategies, such as targeting specific types of securities, creating long/short exposures, etc. EIA’s IFED Indexes may be customized by licensees in several ways including but not limited to the following: Alternative weighting methods for portfolio constituents (e.g., market cap weighted, equal weighted, dividend weighted, etc.); Specified constraints on security liquidity, eligible market cap, and maximum/minimum individual stock holdings; Different weighting constraints on industries or sectors (e.g., exclude certain sectors, target selected sectors, etc.); Different IFED score criteria (e.g., select top 10%, top 50%, etc.); User defined definition of breakpoints for stocks to be included in the index (e.g., largest 800 stocks by market capitalization); A licensee defined portfolio or index as the starting point for applying the IFED methodology; Application of the IFED strategy to a universe of selected ESG accepted firms; Application of the IFED strategy to an existing factor or smart beta strategy (e.g., low volatility, minimum volatility, growth, value, low beta, beta neutral, high dividend); Application to an active investment strategy, i.e., the firms selected by an active manager are weighted by their IFED score; and, Application of the IFED strategy to a long-short strategy, i.e., firms with highest and lowest IFED scores.

The IFED strategy relies on a confluence of Fed policy signals to classify market conditions into one of three categories, expansive, restrictive or indeterminate. The policy signals used are identified as Stance and Stringency and they reflect two unique and diverse aspects of Fed policy. Stance reflects the Fed’s long-term strategic intentions, whereas Stringency indicates the impact Fed actions are having on fund availability in the short-term market. Thus, the IFED strategy considers both the Fed’s signaled strategic intentions as well as its conviction and effectiveness in implementing those intentions. Stance and Stringency are invariant to shifts in the Fed’s operating procedures; however, the signals reflect any potential ramifications that changes in Fed operating procedures may have. For example, consistent with Stance, QE corresponded with the Fed’s signaled intention to pursue a more expansive policy going forward. In concert, Stringency indicated that the Fed was aggressively pursuing such a policy based on the excessive liquidity existing in the short-term market for funds. As the Fed works to reverse the effects of QE, Stance will consider the Fed’s intentions to reduce fund availability and Stringency will indicate its aggressiveness in that regard. The performance of the IFED strategy has been highly consistent over an extended period that has witnessed numerous shifts in Fed operating procedures including implementation of QE, changing operating targets, changing its degree of transparency, building its balance sheet and reducing its balance sheet. The performance consistency across time supports the strategy’s robustness relative to shifts in Fed operating procedures. Over time, the IFED strategy has produced superior performance across the various Fed regimes. For details, see IFED’s long-term performance history on EIA’s website.