CFA Institute

Today’s spiking inflation, rising interest rates, and billion-dollar investment frauds are good reminders that a clear understanding of the past helps us better anticipate the future. Indeed, it can provide a reliable foundation on which we can develop portfolio-allocation decisions. That’s why we need to have a firm grasp on different equity markets’ past relative and absolute performance. After all, what drives the market cycle if not the relative under- and outperformance of different assets? With that in mind, the equity market universe is variously categorized by region, country, sector, market capitalization, and style. The well-established geographic segmentation divides the world into frontier, emerging, and developed markets. What determines a country’s designation? The MSCI Market Classification Framework focuses on three criteria: economic development, size and liquidity, and market accessibility. The latter is a function of three components: openness to foreign ownership, ease of capital inflows and outflows, and the efficiency and stability of the institutional framework. It hasn’t always been easy or cheap to invest at the regional or country level, but thanks to technological advances and the development of exchange-traded funds (ETFs), retail investors now have much better, if not limitless, access to the various segments of the equity market universe. So, how have these geographic segments performed relative to one another in recent market cycles? Benchmark Investability Market Index Investable ETF MSCI ACWI and Frontier Markets Index Not Available MSCI ACWI iShares MSCI ACWI ETF (ACWI) MSCI World Index iShares MSCI World ETF (URTH) MSCI Emerging Markets iShares MSCI Emerging Markets ETF (EEM) MSCI Frontier Markets Not Available MSCI Frontier & Select Emerging Markets iShares MSCI Frontier and Select EM ETF (FM) Introduced on 7 April 2003, the iShares MSCI Emerging Markets ETF (EEM) had a cumulative return of 381% through 31 December 2010. By comparison, the iShares Core S&P 500 ETF (IVV) generated a 66% return over the same sample period. So, in that particular market cycle, emerging markets did much better than their developed counterparts. EEM vs. IVV 7 April 2003 to 31 December 2010 But let’s take the examination one step further and use intermarket analysis to compare the relative performance, or relative strength, of EEM and IVV. This way we can differentiate among distinct trend periods by identifying the major inflection points on their price charts. The relative strength of EEM to IVV over the sample period shows a key pivot in early 2011, as detailed in the following chart. Relative Strength of EEM vs. IVV12 April 2003 to 7 December 2022 Emerging markets as a category did better than the S&P 500 until that 2011 inflection point. Since then, the downward slope demonstrates their underperformance. Frontier markets generated better returns than other geographic segments at various times as well. For example, from 2002 to 2007, the MSCI Frontier Markets Index beat both the MSCI EM and MSCI World Indexes. Some frontier markets may again eclipse both developed and emerging markets. This kind of intermarket analysis has applications well beyond geographic segments. We can use it to identify similar inflection points relative to the S&P 500 in the energy, technology, and commodities sectors. For example, the Energy Select Sector SPDR Fund (XLE) lagged the S&P 500 for years. But the relationship pivoted in early 2021, and the energy sector proxy has since outpaced the benchmark index. The technology sector’s relationship with the S&P 500 flipped in the opposite direction. After years of beating the S&P 500, our tech proxy, the Technology Select Sector SPDR Fund (XLK), began to fall behind early in 2022. As for commodities, the S&P GSCI Commodity-Indexed Trust ETF went from lagging to outperforming the S&P 500 in late April 2020. Analysis like this can inform tactical asset allocation decisions. We can adjust the percentage of the portfolio held in an asset class or across asset classes based on these changing market opportunities. Of course, we have to understand both the index and the relative performance of its underlying constituents. Country factors, for example, may have more influence on returns than industry factors, while returns in a frontier market country may result more from country-specific than global factors. As such, active investors should examine the individual country characteristics rather than simply allocate to a broad frontier or emerging market index. Single country indexes are becoming more accessible through ETFs, but not all are equally investable. For example, Saudi Arabia’s stock exchange, the Tadawul, operated long before the iShares MSCI Saudi Arabia ETF (KSA) began trading in 2015. There is a larger lesson to these inflection points (when for whatever reason, the switch is flipped and the dynamic between geographic segments changes): Relationships in the global equity market universe are not static. There will be critical pivots in the future just as there were in the past. One segment or another will have periods of sustained outperformance relative to their peers. That’s why adjusting our allocations to emerging or frontier markets may at times enhance risk-adjusted returns. If you liked this post, don’t forget to subscribe to Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images/ Luigi Masella / EyeEm Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

Pause to Think: Using Mental Models to Learn and Decide. 2024. Jaime Lester. Columbia Business School Publishing. Pause to Think offers a perfect balance of light and serious reading. It is direct, delightful, and articulate in suggesting specific ways to improve approaches to anything we attempt to do. The author presents mental models in accessible, entertaining ways that prove both memorable and immediately useful. Jaime Lester is a hedge fund industry veteran and an adjunct professor at Columbia Business School. He has divided this powerful book into two sections — Part I: Important Concepts and Part II: Important Frameworks — with an interlude. He suggests we take a slow rather than fast approach to getting things thoughtfully done. Being human means being imperfect. Using this awareness, we can counteract impetuous actions and biases. Consistency and that overused word, “mindfulness,” should reduce flawed outcomes. The identification of many of the cognitive biases discussed by Lester is rooted in investment research, particularly in the behavioral finance discipline that emerged in the 1970s. According to behavioral finance, human psychology and emotion cause fluctuations in securities prices, which are often determined irrationally. The rise of robo-advisors is frequently described as a solution to these biases, intended to compel investors to assess their risk, determine their most suitable allocation, and stick to the program. At the conclusion of each section in Part I, the author offers some exercises to try. They involve excellent thought questions on topics such as investing and science, economics and business, and probability and statistics. I found that these reinforced the concepts discussed and enabled me to evaluate my own biases. Some of the exercises also assess basic mathematical literacy. Try them and you will find that the lessons are worthy of mastering. Have a calculator at hand if your mental math is rusty. If your eyes are too tired to read, or you choose to give Lester’s excellent narrative a break, turn to the adorable illustrations by Albertus Ang Hartono, aka Everwinter, that emphasize the intent of the book. You will capture the book’s sense of porpoise! See the illustration on p. 127, in connection with efficient learning, to understand this seabound-mammal reference. In Part II of the book, Lester is unreservedly opinionated in addressing important frameworks for investing. Most readers will fully agree with his five keys to successful investing but will argue with his recommendations, such as ignoring the value of the investment portfolio, setting up automatic savings and investment options, and avoiding all high-fee investment products. Micromanaging or over-policing values does not make sense, but periodic review of the asset allocation and holdings certainly does. Setting up automatic savings and investment options must include some flexibility associated with risk tolerance and liquidity requirements that can change when one least expects. “Set it and forget it” does not fulfill the needs of responsible, prudent investors. And lastly, fees of considerable size could be justified, based on the investment product and its objectives. One would not expect to pay much at all for a passive exchange traded fund but would expect to pay a premium fee for a special-purpose hedge fund. Lester repeats and emphasizes that “you will make more money choosing stocks randomly than paying a professional investor to choose them for you” (p. 160). Many clients of investment advisors are nonetheless required to employ professional managers, or else they are unskilled or time-constrained and admit they need help from a professional. The investment professional coaches and converses with the investor, provides a sounding board for investment concerns, manages expectations, and consistently delivers performance reports to ensure that the investment program is on track with its agreed-upon objective. But fear not! Lester brings the book to a truly bullish conclusion, addressing the framework of happiness. His focus is on professional happiness that evolves into happiness, period. He encourages us all, young and aging, to reduce time and energy devoted to activities that are unlikely to make us happy, including seeking the perfect job. Increase time and energy, the author urges, toward activities that are likely to make us happy. My favorite of Lester’s prescriptions for maximizing happiness is to maintain an appropriate context and perspective by incorporating mindfulness and gratitude in the daily routine. As I pause to think why this is so important, I turn to our universal pandemic experience and its outcome for each of us, personally and professionally. I think that Lester has struck gold in delivering a unique book that touches the minds and hearts of investment professionals — one that we all should pause to read. source

Passive environmental, social, and governance (ESG) investing has become increasingly active. It’s time for a rethink. Assets have poured into passive ESG indexes in recent years. As more investors look to align sustainability goals with their investments, global ESG assets under management (AUM) have soared from $2.2 trillion in 2015 to $18.4 trillion in 2021. Yet ESG assets aren’t all that’s on the rise. With ever-changing methodologies and expanding exclusion lists, the active risk of popular ESG indexes is also climbing. This begs the question: Does the term “passive” even apply to ESG investing? Given the lack of consensus on sustainability criteria and the evolving data sets and exclusions, the answer is no. ESG investing is inherently active and investors should evaluate ESG strategies accordingly. That means focusing on the drivers of risk and return. But just what is propelling the rise in index tracking errors and how can investment managers integrate factors and deliver risk-efficient portfolios with proven sources of return that aren’t found in “passive” offerings? Passive indexes are popular because they track broad market benchmarks at low cost. With similar inclusion criteria and construction methodologies, such capitalization-weighted indexes share common features and are built around a broad consensus. Passive ESG indexes do not exhibit the same characteristics. Indeed, four popular ESG indexes display vastly different levels of ESG uplift and tracking error, as Exhibit 1 demonstrates. While the drivers of active risk vary among indexes, most are sourced from idiosyncratic risk. Exhibit 2, which lists the top five overweights and underweights of each index, illustrates this. The MSCI US ESG Leaders Index has a 5.3% overweight to Microsoft and -7.0% underweight to Apple. Such active weights resemble traditional active management more than passive investing. Similar divergence even occurs within the same index family. The MSCI US ESG Leaders and MSCI US ESG Universal indexes, for example, share only Nvidia among their top five overweights. Alphabet, with combined Class A and Class C shares, meanwhile, comprises the second largest overweight in Leaders and the third largest underweight in Universal even though the indexes use the same ESG ratings. The differences are even more dramatic across index providers. Three of the top five overweights in the S&P 500 ESG Index are among the top five underweights in the MSCI US ESG Leaders Index. Two of the latter’s top holdings — Tesla and Johnson & Johnson — are underweights in the former. Apple is the most extreme example, with an absolute difference between the two indexes of nearly 10% — -7.0% vs. +2.8%. By comparison, Apple’s weight in the cap-weighted MSCI US and S&P 500 indexes differs by fewer than 10 basis points (bps) over the last five years. No wonder the MSCI US ESG Leaders and S&P 500 ESG indexes have a 2.5% relative tracking error, three times more than their cap-weighted counterparts. The lack of overlap among ESG indexes and the wide dispersion in risk levels means that investors must be diligent in aligning their ESG objectives with their chosen strategy. They must also ensure that the level of ESG is commensurate with the active risk taken. And they have to remember that the evolving ESG landscape requires constant oversight. Less ESG for More Risk? While higher ESG content has always necessitated higher active risk, recent trends have exacerbated this trade-off. Exhibit 3 plots the aggregate cap-weighted ESG ratings of the MSCI World and MSCI US indexes along with the number of exclusions for the MSCI World ESG and US ESG Leaders indexes. The broad-based ESG rating improvements over the past five years suggest that companies are addressing their ESG risks. But over this same time period, more and more companies are being excluded from ESG indexes. These trends are difficult to reconcile, as higher aggregate ESG ratings ought to lead to fewer exclusions not more. In practice, the exclusions have proven easy to add yet difficult to remove. These changes, along with numerous methodology revisions, have increased “passive” ESG index tracking error levels over the past five years. Exhibit 4 demonstrates the increasingly active nature of these passive ESG benchmarks. While the COVID-19 crisis is a factor in this development, it does not fully account for the rise in tracking error. Tracking error jumped in late 2018, well before the onset of pandemic-related market turmoil in 2020. The ESG rating uplifts also declined in response to the higher ratings of the underlying indexes, as shown in Exhibit 3. What it all adds up to is more concentrated portfolios with lower ESG uplifts and more active risk. The irony of course is that the integration of ESG data is often promoted as a way to decrease portfolio risk. The key to capturing ESG benefits, and capturing them efficiently, lies in modern portfolio construction techniques. Improving ESG Efficiency How can we improve a portfolio’s efficiency when integrating ESG content? First, we need to establish a baseline. In Exhibit 5, we plot the achievable active risk levels at varying degrees of ESG uplift based on each index provider’s unique ESG ratings and after excluding those companies flagged by MSCI controversy criteria. As we can see within the chart, each ESG index falls well outside of what would be considered efficient. The distance of each index from the efficient frontier is a product of two developments: additional business involvement exclusions and suboptimal portfolio construction. To quantify the effect of each, we plotted a hypothetical ESG portfolio within the MSCI World ESG frontier in Exhibit 6. The ESG Portfolio excludes the same set of companies as the MSCI World ESG Leaders Index, while targeting a higher (20%) ESG rating uplift and similar levels of carbon reduction. Given the distance of the ESG Portfolio from the efficient frontier, we see that multiple ESG objectives can be achieved with a minimal increase in active risk. The ESG Portfolio has less than half the active risk of the MSCI World ESG Leaders Index. We attribute that 76 bps vs. 198 bps divergence to portfolio construction.

The Role of Low Volatility Strategies in Investment Portfolios  Low volatility equity strategies appeal to investors for many reasons. First, they help keep our portfolios invested in equities during periods of market turmoil. Second, when well-constructed, they often exhibit higher risk-adjusted returns than their higher volatility counterparts. While general surveys of low volatility strategies show that they do indeed shield investors from market-driven risk, what is frequently overlooked is that these same strategies can be insufficiently diversified or risk controlled. To that end, we will examine the critical components of an effective low volatility portfolio construction process. These elements enable the construction of low volatility portfolios with more diversification and significantly better risk-adjusted returns than the standard low volatility strategy. Low Volatility Strategies: Three Potential Drawbacks   Low volatility stocks can deliver a premium over the longer term. And while they may provide both volatility reduction and capital protection in bear markets relative to cap-weighted indices, not all low volatility strategies accomplish this to the same degree. Indeed, many commercially available low volatility strategies suffer from common drawbacks. 1. A Lack of Diversification Inverse volatility and minimum variance optimization are two common methodologies in low volatility strategies. In inverse volatility portfolios, a stock’s portfolio weight is proportional to its risk. Such portfolios penalize high volatility stocks and reward their low volatility counterparts. They can also be highly concentrated. The same criticism applies to the minimum variance optimization technique, which, without various constraints, can also unduly overweight the portfolio in one or more stocks. 2. Negative Exposure to Other Rewarded Factors Value, Momentum, High Profitability, and Low Investment, in particular, are among the factors that have rewarded investors over the years, but low volatility strategies can underweight such factors and constitute a drag on the long-term risk adjusted performance. 3. Excess Risk through Sector and Regional Exposures Low volatility portfolios may have persistent sector or regional exposures that can open them up to undo macroeconomic risks.  A Better Way to Build Low Volatility Portfolios  There are several remedies to these diversification- and risk-related challenges in low volatility portfolios. To address the excess weighting issue, we can build more diversified low volatility portfolios by selecting weights based on several optimization frameworks and introducing robust weight constraints. Every model has parameter estimation risks due to its particular architecture. By averaging across multiple models, we can reduce much of the model risk that comes with relying on a single framework. In addition, without a considerable amount of at times ad hoc constraints, such as min-max weights on stocks or sectors, a given model may produce overly concentrated or otherwise insufficiently diversified portfolios. To address this issue, we use so-called norm weight constraints that avoid concentration better than ad-hoc, sample-dependent constraints. (We also employ principal component analysis — PCA, a statistical technique — to de-noise the covariance matrices with which we construct our portfolios.) Another way to address diversification in a low volatility strategy is to increase a portfolio’s factor intensity. This measure, when applied to a single stock, is simply the sum of individual factor exposures, or betas, in a portfolio. So, if we are selecting stocks for a low volatility portfolio, we prefer those with high exposure to the low volatility factor, but we also want to filter out stocks with significant negative exposure to other rewarded factors. By implementing such filtering, our low volatility stocks will have, to the maximum degree possible, positive exposure to Value, Momentum, and other rewarded factors. As a result, in environments where the low volatility factor is underperforming, the other factors may be able to “pick up the slack” and shield the portfolio from some of the damage that the portfolio might incur without such filtering. Every rewarded equity factor has exposure to macroeconomic factors. Which factor loads on the most macroeconomic risk will depend, of course, on the macroeconomic environment, or regime. Country- or region-specific drivers explain much of a portfolio’s macro risk, so we can mitigate that risk by constructing portfolios that are geographically neutral relative to a cap-weighted benchmark. Because macro risks are also often sector driven, selecting low volatility stocks within sectors can mitigate macro risk. Sectors are important considerations since low volatility strategies can overweight specific sectors, such as Utilities, that are sensitive to interest rate and other forms of risk. In terms of empirical results, the exhibit below shows that a low volatility portfolio with factor intensity filters delivers a significant risk-adjusted return compared with both cap-weighted and standard low volatility indexes. This holds for both US and Developed Markets low volatility strategies. Low Volatility Equity Strategy Performance and Risk Measures  US Statistics 21 June 2002 to 30 September 2023(RI/USD)  Cap-Weighted  Robust Low VolatilityStrategy  MSCI MinimumVolatility Annualized Returns  9.41%  9.85%  8.92% Annualized Volatility 19.35% 15.81%  16.17% Sharpe Ratio 0.42 0.54 0.47  Maximum Drawdown 54.6% 43.0% 46.6% Developed Market Statistics  21 June 2002 to 30 September 2023(RI/USD) Cap-Weighted Robust Low VolatilityStrategy MSCI MinimumVolatility Annualized Returns 8.32%  9.45%  7.96%  Annualized Volatility 16.16%  12.79%  12.09%  Sharpe Ratio 0.43  0.63  0.55  Maximum Drawdown 57.1%  45.6%  47.7%  The process described above results in significantly higher factor intensities for both US and Developed Market portfolios, as the following charts demonstrate. Factor Intensity in Low Volatility Equity Strategies  US Factor Intensities  21 June 2002 to30 September 2023(RI/USD) Robust Low VolatilityStrategy MSCI MinimumVolatility Factor Intensity (Int) 0.43  0.21  Developed Market Factor Intensities  21 June 2002 to30 September 2023(RI/USD)  Robust Low VolatilityStrategy MSCI MinimumVolatility Factor Intensity (Int)  0.47  0.25  This approach also reduces macro exposures across geographies as the tables below indicate.  Macro Exposures in Low Volatility Strategies  US Exposures 21 June 2002 to 30 September 2023(RI/USD)  Robust Low VolatilityStrategy MSCI MinimumVolatility Short Rates  –1.23 –1.43 Term Spread  –3.16 –3.16 Default Spread  1.35 1.41 Breakeven Inflation  –3.75 –4.17 Developed Market Exposures 21 June 2002 to30 September 2023(RI/USD) Robust Low VolatilityStrategy MSCI Min Vol Short Rates –1.21 –1.95 Term Spread –3.17 –4.00 Default Spread 1.62 2.28 Breakeven Inflation –4.21 –6.04 Conclusion  Low volatility equity portfolios can be valuable additions to investor portfolios. They

Investors across the globe use the Sharpe Ratio, among other risk-adjusted metrics, to compare the performance of mutual fund and hedge fund managers as well as asset classes and individual securities. The Sharpe Ratio attempts to describe the excess return relative to the risk of the strategy or investment — that is, return minus risk-free rate divided by volatility — and is among the primary gauges of fund manager performance. But hidden within the Sharpe Ratio is the assumption that volatility — the denominator of the equation — captures “risk” in its entirety. Of course, if volatility fails to entirely reflect the investment’s risk profile, then the Sharpe Ratio and similar risk-adjusted measures may be flawed and unreliable.  What are the implications of such a conclusion? A common one is that the distribution of returns must be normal, or Gaussian. If there is significant skewness in the returns of the security, strategy, or asset class, then the Sharpe Ratio may not accurately describe “risk-adjusted returns.” To test the metric’s effectiveness, we constructed monthly return distributions for 15 global stock market indices to determine if any had such exacerbated skewness as to call into question the measure’s applicability. The distribution of returns went as far back as 1970 and were calculated on both a monthly and annual basis. The monthly return distributions are presented blow. Annual return results were qualitatively similar across the various indices studied. We ranked all 15 indices by their skewness. The S&P 500 came in close to the middle of the pack on this measure, with an average return of 0.72% and a median return of 1% per month. So, the S&P distribution skews just a bit to the left. S&P 500 Monthly Return Distributions, Since 1970 The complete list of indices ranked by their skewness is presented in the chart below. Ten of the 15 indices exhibit left skewness, or crash risk: They are more prone to pronounced nose-dives than they are to steep upward climbs. The least skewed distributions were those of France’s CAC 40 and the Heng Seng, in Hong Kong, SAR. Monthly Returns by Global Index Index Mean Median Min. Max. STD Skewness ASX 200 0.58% 1.01% -42.3% 22.4% 0.048 -1.3 TSX 0.60% 0.88% -22.6% 16% 0.044 -0.77 FTSE 0.53% 0.91% -27.6% 13.7% 0.045 -0.73 Russell 2000 0.84% 1.60% -21.9% 18.3% 0.055 -0.55 S&P 500 0.72% 1.00% -21.8% 16.3% 0.044 -0.45 DAX 0.67% 0.74% -25.4% 21.4% 0.056 -0.39 Nikkei 0.54% 0.91% -23.8% 20.1% 0.055 -0.37 MXX 1.23% 1.16% -29.5% 20.4% 0.066 -0.34 MOEX 1.29% 1.63% -30% 33% 0.079 -0.29 CAC 40 0.64% 0.98% -22.3% 24.5% 0.056 -0.11 Hang Seng 1.17% 1.23% -44.1% 67.3% 0.090 0.33 NSE 1.50% 1.05% -24% 42% 0.076 0.53 KRX 0.90% 0.49% -27.3% 50.7% 0.074 0.80 BVSP 5.63% 1.94% -58.8% 128.6% 0.184 2.51 SSE 1.65% 0.63% -31.2% 177.2% 0.151 6.26 The Shanghai Composite has exhibited the greatest degree of right skewness over time, tending to crash up more than down, and otherwise generating average returns of 1.65% per month and median returns of 0.63% per month. Shanghai Composite (SSE) Monthly Return Distribution, Since 1990 On the opposite end of the spectrum is the Australian ASX. The ASX has the most left skewness of all the indices, with an average monthly return of 0.58% and median monthly return of 1.01% since 1970. Australian Stock Exchange (ASX) Monthly Return Distributions, Since 1970 In the end, the BSVA in Brazil, the Shanghai Composite in China, and, to a lesser extent the ASX in Australia just have too much skewness in their returns to validate the Sharpe Ratio as an appropriate measure for their risk-adjusted performance. As a consequence, metrics that account for skewness in returns may be better gauges in these markets. Of the other indices, seven had fairly symmetrical distributions and five had moderately skewed ones. All told, this suggests that the Sharpe Ratio still has value as a performance metric and that it may not be as obsolete or ineffective as its critics contend. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images/NPHOTOS Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

Climate crises disproportionately affect women and girls across all demographics, but especially in developing economies. Subject to persistently higher poverty (and extreme poverty) than men, women and girls are also more likely to be impacted by climate disasters. In fact, 80% of those displaced by climate change are women, according to UN statistics. Due to ongoing worldwide gender gaps, climate events exact a toll on women’s job security and education as well as their access to health care, potable water, and food resources, among other necessities. Yet, research shows that with their local knowledge, women have much to contribute to climate change adaptation even if gender gaps in legal resources and economic participation hamper their involvement. As environmental, social, and governance (ESG) investing continues its rapid growth, applying a gender lens to public funds focused on climate solutions is critical. Why? Because higher levels of women in leadership (WIL) benefit corporate performance, operations, and risk management. Indeed, gender diverse ACWI Index companies are better at reducing carbon emissions, according to a 2021 MSCI report, while 2020 research found that gender diversity on the boards of US companies correlated with higher renewable energy consumption, which in turn, boosted financial performance. Gender Lens Equity Funds: Steady Growth Gender lens investing directs resources to women-focused initiatives, women-owned businesses, and firms that demonstrate a commitment to gender and broad-based equality internally and through their external relationships, products, and services. Thirty-two gender lens equity funds are available to individual investors. There are 14 global and 18 regional funds in the Parallelle Finance coverage universe with mandates to invest in higher WIL and related gender metrics. These funds hold anywhere from 30 to more than 400 stocks. As of 31 March 2022, their assets under management (AUM) totaled $4.1 billion, having grown by 51% in 2021. The 12 Largest Gender Lens Equity Funds, in US Millions, as of 31 March 2022 Are Climate Funds Investing with a Gender Lens? Renewable energy is a cornerstone of global efforts to address climate change. Renewable energy funds invest in solar, wind, and other clean energy producers as well as related technology and services providers. The 17 US-listed and three European- or UK-listed funds in our dataset have AUMs ranging from $5.6 billion, for the iShares Global Clean Energy exchange-traded fund (ETF), to less than $5 million, as of 31 March 2022. The average track record for the funds is six years. The 12 Largest Renewable Energy Funds in the Dataset, in US Millions, as of 31 March 2022 These funds are not capturing the benefits of diverse leadership and wider corporate equality. Only 11% of US portfolio managers are women. That figure hasn’t notably improved in 20 years. According to the available data, only 13% of the portfolio managers at renewable energy funds are women, and 14 of the 20 funds have no women on their portfolio management teams. In contrast, our research found that over 50% of gender lens equity fund portfolio managers are women. There are 110 unique top 10 holdings among the 20 renewable energy funds. The chart below lists the 21 firms that overlap with the leading gender lens equity indexes and datasets. Only seven appear on any of the Solactive Equileap gender lens equity indexes, which are constructed from Equileap analysis of leadership and workforce equality metrics, pay equity and transparency, and workplace benefits and policies at public companies. Among the top clean energy holdings of the 400 companies on the Bloomberg Gender Equality Index, only 16 appear on both lists and only 5 among the Forbes-Statista list of female-friendly companies. Top Holdings Overlaps: Renewable Funds and Gender Lens Indexes, Datasets, and Equity Funds Of the top renewable energy holdings, only three — Enbridge, Meridian Energy, and Schneider Electric — appear among the 164 unique top 10 holdings of gender lens equity funds. These three are also on at least one of the index and dataset lists. The results are clear: Unless renewable energy companies improve their WIL and other equality metrics, the sector will miss out on the related performance and operational benefits. The Way Forward: Incorporate Equality Criteria Climate change will set gender equality back 20 years, according to BCG forecasts. Why? Because climate change disproportionately affects women and because women are underrepresented in the global economy. Indeed, women are being left out of the industries emerging in response to climate change, and with forecasts of global net-zero investments ranging from $100 to $150 trillion by 2050, according to BCG, that is bad news for both women and net zero. The data doesn’t lie: WIL is material to all sectors and industries. Climate-focused equity and fixed-income funds must apply WIL and broad-based equality criteria. That should include: Investing in women-led clean energy innovators, producers, and product and services providers. Seeking greater gender equality in leadership, workforce, pay, and workplace policies across all demographics in all their funds and encouraging fund holdings to develop supplier diversity programs. Investing in innovations to reduce climate-related displacement. Applying shareholder advocacy tools to advance corporate gender equality. For more analysis from Marypat Smucker, CFA, visit Parallelle Finance. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images / SDI Productions Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

Most investable assets and strategies tend to cluster into two broad groups: productive and scarce. This is an important concept for the asset-owner-level portfolio construction and asset allocation process, since productive assets and scarce assets tend to have, respectively, concave and convex profiles with respect to the major risk factors. Practitioners who are aware of the concave and convex nature of productive and scarce assets and strategies can better hedge their risks in deflationary, and — especially — inflationary crises. We suggest that portfolios including both productive and scarce assets can deliver performance similar to the S&P 500 with less risk than portfolios holding only productive assets.   The Nature of Productive and Scarce Assets The raison d’être of productive assets is to finance, support, and provide the means for productive activities in the economy, in exchange for promised future cash flows. For example, equities promise future dividends, and credit promises future coupons. The issuance of equity and debt is externally constrained mostly by what the capital markets can bear. Shocks hit these assets with a downward asymmetry, suggesting concave supply curves. The returns of these assets arise from the economic growth they exist to finance. Scarce assets and strategies, on the other hand, exist for reasons other than financing productive activities. They exist in limited supply or capacity and may or may not promise any regular cash payments. Examples of scarce assets include gold, some other commodities and natural resources, high-end art, and other collectibles. “Safe” government bonds with low or negative yields, reserve currencies, and some global macro strategies are also scarce assets. The returns of these assets arise from their scarcity, which is often associated with convex supply curves. How to Quantify Productivity and Scarcity Since directly modeling — or even conceptualizing — the “supply curves” in many cases may be difficult or impossible, we instead measure the asymmetric risk statistics of observed asset returns. The findings are detailed in our paper, “The Concave and Convex Profiles of Productive and Scarce Assets.” We use coskewness to measure the convexity of asset returns with respect to a set of major risk factors: inflation, rates, credit, and equity. We also use the standard skewness to measure the “convexity of an asset to itself,” or “auto-convexity.” These coskewness and convexity measures tell us the tendency of an asset to appreciate or depreciate when risk factors become volatile. In our paper, we quantify the investable assets’ and strategies’ place in the productive-to-scarce spectrum based on their skewness and coskewness with respect to the major risk factors. Such an overall spectrum is laid out, for top-level asset classes, in Exhibit 1. Empirically, equities, duration-hedged credit — and more generally “higher beta” and “positive carry” strategies — tend to be concave with respect to the major risk factors, and auto-concave (negatively skewed), belonging in the productive group. By contrast, “safe” government bonds, gold, the US dollar versus a broad basket, and fast-moving momentum strategies, tend to be empirically convex with respect to the major risk factors, and auto-convex (positively skewed), belonging in the scarce group. We observe that an asset or strategy must have some intuitive economic scarcity to be convex. Exhibit 1. Productivity, Scarcity, Convexity, Concavity Convex or scarce assets and strategies tend to have low beta to equities. But low beta does not guarantee convexity, as we demonstrate empirically in our paper. The coskewness to inflation risk serves to complement and enrich the traditional set of risk metrics, such as equity beta and bond duration. Though many assets may feature low correlation to inflation, their coskewness may be more significant and persistent, showing large potential losses (or gains, for scarce assets) during periods of macroeconomic instability. Inflation itself is a highly skewed, non-normal process with high-impact tail events. Exhibit 2 depicts monthly excess returns of high yield bonds and Barclay CTA Index, plotted against the S&P 500 excess returns. High yield bonds exhibit concave response to the S&P 500 returns, while Barclay CTA Index is convex with respect to the S&P 500. Exhibit 2. High Yield Bonds Concave to S&P 500, CTAs are Convex Notes: Left panel: High Yield Bonds vs S&P500, Right panel: Barclay CTA index vs S&P500. Period 1990-2022. Horizon=1M. Quadratic model fit is depicted for each asset. Exhibit 3 depicts monthly returns of four productive and four scarce assets, plotted against the inflation risk factor. The upper panel shows the broad US equity market (S&P 500), investment grade bond returns (duration hedged), high yield bonds, and the Bloomberg Commodity Index, each plotted against CPI month-over-month. Except for the Bloomberg Commodity Index, the assets show generally weak correlations to inflation, but all have a marked negative convexity. By contrast, the four assets in the lower panel, namely US Treasuries, Gold, the US Dollar index (DXY), and a simple four-asset momentum strategy (with one-month lookback), show a convex response to inflation innovations. We believe that the convex responses arise from an underlying scarcity of the asset or strategy. In practice, the convexity metrics can tell us which assets are likely to perform above and beyond their linear or beta exposure, during times of great risk and uncertainty—that is, in a crisis. Exhibit 3. Productive Assets are Concave to CPI, Scarce Assets are Convex Notes: Upper panel for four productive assets, Lower panel for four scarce assets. Period 1973-2022. Horizon=1M. Quadratic model fit is depicted for each asset. Convex and concave responses to price action are very familiar from textbook option payoffs: most assets are empirically concave or convex with respect to major risk factors. Recalling the pioneering work of Arrow and Debreu, as well as Black and Scholes, and Merton, these convexities are central to asset-return profiles in a world of multi-dimensional risks and uncertain outcomes. Exhibit 4. Concave and convex Black-Scholes option prices and payoffs In Practice From an investor’s perspective, productive assets generally provide exposure to nominal GDP growth, while scarce assets are key for resilience in recessionary and inflationary environments. In a traditional 60/40 portfolio, for example, stocks

This year, 2023, is the heralded third year of the presidential election cycle. It has kicked off with nervous markets eyeing political drama in Washington, DC: contentious votes to elect the Speaker of the House of Representatives, a combative State of the Union presidential address, partisan threats not to raise the nation’s debt ceiling, and posturing and positioning by lawmakers ahead of the 2024 election. Nevertheless, according to presidential election cycle theory, which was first referenced in the Stock Trader’s Almanac, this third year of the four-year cycle should generate well-above-average stock returns. So, what are the strengths, limitations, and nuances of presidential election cycle theory and what does the current political context foretell regarding whether 2023 will follow the predicted trend? The conventional election cycle theory narrative, and why it augurs so well for 2023, goes as follows: “Presidents do the heavy lifting in their first and second year in office and then pivot to preparing for reelection in the fourth year by being friendly to markets in the third year.” Though the data around this may be compelling, the overall narrative warrants some refinement. The Presidential Election Cycle and S&P 500 Returns “Trifecta” is when a single party holds the presidency and majorities in both houses of Congress.Source: Bloomberg Since 1928, the third year of the presidential cycle has produced positive S&P 500 returns 78% of the time, generating 13.5% average returns vs. an all-year average of 7.7%. We did not find other coincident indicators in the monetary and fiscal policy signals — rising vs. falling rate environments, for example — that might also offer insights on 2023, but we do believe a party’s degree of government control may be a critical factor. A single party held the “trifecta” of the presidency and House and Senate majorities two-thirds of the time in the first and second years of the cycle, as the Democrats did in 2021 and 2022, but only about one-third of the time in the third and fourth years. This is a familiar phenomenon in US politics: The president’s party often faces setbacks in the midterm elections. But it also implies that the relevant election cycle may be congressional rather than presidential. Markets may simply be rewarding gridlock. Third years that followed a switch from unified to split government averaged 15.0% returns compared to 10.7% for third years in which the trifecta was preserved. The gridlock question seems important given the likely contrast between 2022’s ambitious legislative agenda and anxiety about potential deadlock in 2023. The sequence also may be important in anticipating how presidential election cycle theory will play out in 2023. With their glass-half-full outlook, commentators tend to focus on above-average returns in the third year and pay less attention to below-average returns in the second. Second years have accounted for more than one-third of the S&P 500’s total negative return years since 1928 and an even higher proportion of years with materially negative returns, or those like 2022, with worse than –10% performance. The rebound pattern from bearish second to bullish third years is the key sequencing feature. Two down years in a row have only occurred eight times since 1928, and only once, in 1930 and 1931, during the Great Depression, did it happen in the second-to-third-year sequence. So, the second-to-third-year signal may be especially powerful and predictive following such a gloomy 2022. As such, all the leading indicators of presidential election cycle theory — third year, split government, and dismal second year — would seem to augur well for 2023. But are there any current conditions that might inhibit this predicted strength? Put simply, while the markets may respond well to gridlock, a complete breakdown in government functioning may be a bridge too far. While fiscal restraint in 2023 after the sizable government largesse of 2022 could have its benefits, total government paralysis and dysfunction — not raising the debt ceiling and not funding the government –may be too much for the markets and economy to bear. Of course, debt ceiling debates are nothing new in US politics and have yet to lead to catastrophe. But just because they haven’t doesn’t mean that they won’t. So, is this time different? If it is, a particular congressional cohort could be the threshold reason. In the tightly divided House of Representatives with its narrow Republican majority, the House Freedom Caucus wields considerable influence and can impede legislation in the interest of both diminishing the scope of government and reducing spending. These efforts can also have a performative element that helps caucus members raise their profiles and campaign funds and otherwise build their brands. This latter component may be what most differentiates 2023 from previous third years in the presidential election cycle. In 2023, as in 2011 and 2013, political drama’s path to economic significance runs squarely through the debt ceiling and federal budget negotiations. The House Freedom Caucus has positioned itself as a key power base in that regard and extracted considerable concessions during the efforts to elect Rep. Kevin McCarthy as Speaker of the House. Among the more significant of these were securing the single-member motion to vacate and gaining several of the nine Republican seats on the 13-seat Rules Committee. These constitute an effective blocking position, or a veto, that will make it very difficult to pass any legislation to raise the debt ceiling without the group’s agreement or acquiescence. There are few examples in recent political history of similar groups gaining such influence. The difference here is just how performative congressional politics today have become. Amid the rise of social media and a host of other politically centrifugal forces, the threshold for differentiation has moved ever higher, especially amid the 2020 election’s lead-up and aftermath. To understand just how this performative power may have become the differentiating factor in this presidential cycle’s third year, it helps to conduct a recall experiment. Think of 10 members of Congress. How many of them do you remember for their accomplishments? How many do you

The following is adapted from the recent book Small Actions: Leading Your Career to Big Success, by Eric Sim, CFA, and Simon Mortlock. Nobody likes rejection. Which is why, over the years, I’ve developed a three-step process to transform situations from hopeless to hopeful. I call it the 3Ps approach: Perseverance Perspective Positivity Let me give you some examples of how to put the 3Ps into action. Leaving by 7 pm Getting a table on a weekend at some of Hong Kong’s most popular restaurants can feel, with only mild exaggeration, like winning the lottery. But hope springs eternal, and like all those people who patiently queue up at lottery counters hoping to buy the winning ticket, I can’t help but try my luck at my favorite Italian restaurant chain. Its thin-crust pizzas and aglio e olio pasta are just that irresistible. My family loves to have Sunday dinner at the busy Kennedy Town branch. Of course, we usually make spur-of-the-moment decisions to go, and it’s all but impossible to reserve a table on the same day. But that’s where the 3Ps came in. One Sunday afternoon, I called the restaurant. “Good afternoon!” a woman with a cheerful voice answered. “Do you have a table for four tonight?” I asked hopefully. “No sir, we’re fully booked,” she replied with a tinge of regret. “How about at 6 pm?” I countered. “Sir, we’re fully booked,” she repeated, probably thinking, “Which part of ‘fully booked’ do you not understand, sir?” But I wasn’t deterred. “What if we leave by 7 pm?” I asked. There was a slight pause on the other end of the line. “Let me check,” she said. A few seconds later, she replied, “Yes sir, we have a table.” I used the 3Ps to change her mind. Here’s how it works: Perseverance: Show Your Effort I didn’t hang up after she said “fully booked.” Instead, I came up with a counter proposal. When I suggested leaving the restaurant early, I showed her I could be flexible on timing. Perspective: Understand the Other Person’s Priority The restaurant employee’s main concern wasn’t catering to my needs; it was ensuring that customers who had reservations were seated by the allotted time. She didn’t care whether I wanted a table to celebrate my child’s birthday or my boss’s resignation. Getting angry, saying how much business I’d given the restaurant, or threatening never to go there again weren’t going to work with her. Instead, I helped her do her job by offering her the restaurant hostess equivalent of an options trade in finance. I gave her a contract establishing her right (but not obligation) to chase me out at 7 pm. But that Sunday night, I wasn’t shooed away: The restaurant had enough room, so the option holder didn’t need to exercise her option. Positivity Call me an eternal optimist, but I always hope I can flip a situation from unfavorable to favorable. Many people would have given up at “we’re fully booked.” Not me. I sought a compromise that was a win–win solution for both sides. The restaurant is rarely full during the early evening, so I helped it use its resources more efficiently. Can I Drop By? The ability to change a “no” to a “yes” is even more critical in our careers. When I was working for a bank, a corporate client based in Taipei asked for a renminbi (RMB) construction loan to build an office tower in Shanghai. This was a 10-year loan, and my colleague from the loans department priced it accordingly, using the People’s Bank of China’s (PBOC’s) five-year or longer rate, which was then 5.94%. In the cutthroat world of finance, that wasn’t enough. Another bank offered the client a more “creative” loan structure. Instead of the standard 10-year loan, the bank proposed a six-month arrangement that would be continually extended until the loan was paid off at the end of 10 years. This shorter loan period had a much lower interest rate of 4.86%. My colleague came to me for advice on how to resurrect the deal. I suggested a loan in US dollars (USD) as well as a USD–RMB currency hedge to create a synthetic RMB loan with an all-in interest rate of 4.5%. It was cheaper than the other bank’s offer but was still a 10-year loan. We proposed our solution to the client’s finance team. They liked it and submitted the idea to their CFO. The feedback was positive. I had saved the deal! Or so I thought. A week later, the client told us that they couldn’t accept our proposal. Their CFO had already verbally committed to the other bank before he heard our innovative offer. We were devastated. I couldn’t understand why the client had gone with our competitor’s pricier solution, so I asked if I could “drop by” for a coffee meeting in Taipei. Over our lattes, I explained that under Mainland regulations, banks in China weren’t allowed to price a long-term construction loan using the six-month PBOC lending rate. Should the “creative” bank run into trouble with the regulator, its clients could be impacted. The finance manager from the client firm took what I said to heart. I left the meeting and flew back to Hong Kong the same afternoon. The next day, the client called to say we’d won the deal. Again, the 3Ps worked. Perseverance: Show Your Effort I continued to engage with the client even after they turned down our solution. Perspective: Understand the Person’s Priority There were two potential “no’s” here. First, the client could have refused to take the face-to-face meeting. Had I stressed the business trip was just to see them, they might have turned down the meeting. Taking it might have made them feel obligated to reverse their decision. But when I asked, “Can I drop by?” they didn’t feel as pressured. I gave them the option to say they weren’t going to change their loan decision. This brings me to the

Introduction The imperatives of climate change demand enhanced risk management in the commercial real estate (CRE) loan market: Investors and lenders must refine their strategies and conduct meticulous property-level risk assessments as part of their credit analysis. Community and regional banks are particularly susceptible to climate-related financial risk due to their CRE loan balance sheet exposure and must navigate unpriced climate risks to ensure balanced and resilient loan portfolios. To maintain portfolio health and overall stability, these institutions must exercise ongoing vigilance in their risk monitoring. In this conversation, we — Adam W. Sandback, CFA, FRM, CPA, CMA, and Andrew Eil — assess how climate could affect CRE loans in the most at-risk areas in the United States. Climate Risk and Community and Regional Bank CRE Loan Portfolios: What Are the Implications? Adam W. Sandback, CFA, FRM, CPA, CMA: Property values in California, Texas, and Florida are poised to decline due to rising sea levels, heat waves, water stress, and increasingly frequent and severe natural disasters, among other unaccounted climate risks. Insurers have retreated from natural peril coverage in key geographic markets. The shift to remote work and the extensive CRE debt held by privately owned community and regional banks, combined with persistently high interest rates, heightens the systemic risks — a concern that the US Federal Reserve highlighted in its May and October 2023 reports. Andrew Eil: Recent real estate activity suggests climate concerns aren’t deterring buyers. Florida’s CRE market is thriving, and residential values soared 80% over five years and more than 170% in a decade to March 2023. The housing markets in Texas cities and California also remain robust. Historically, there’s little precedent for climate risk affecting CRE value, yet with unprecedented climate events becoming frequent, this emerging risk may soon challenge historical norms. For example, certain highly vulnerable areas in Houston, Miami, and Norfolk, Virginia, are exhibiting local softness in market values that are starting to price in sea level rise as a risk. Sandback: Post-2008 financial reforms have widened the risk-modeling chasm between large and small banks; the latter, more vulnerable to climate-related risks in CRE loans due to large and concentrated portfolios, struggle with less-stringent regulation, the constraints of personnel expertise, and inadequate technology for complex in-house modeling. The recent regional banking crisis underscores this issue. Andrew, how should privately owned community and regional banks implement manageable climate-risk modeling and enhance data management and system controls to mitigate potential capital losses in a manner their resources can support? Eil: Community and regional banks, like their larger counterparts, must integrate climate risk management because they face similar if not greater climate risk exposure due to geographic and asset class concentration in their portfolios. New standard disclosure guidelines, such as the Task Force on Climate-Related Financial Disclosures (TCFD) and International Financial Reporting Standards (IFRS) S2 Climate-Related Disclosures on governance, strategy, risk management, and metrics and targets, are a good place to begin. This involves integrating climate change into risk appetite statements, aligning strategies and risk management policies with climate realities, and adjusting risk models to account for climate factors. Even without extensive specialized teams, banks can undertake climate risk assessments and monitor related financial risks cost effectively as climate risk data, analytics, and expertise rapidly become more affordable and accessible amid the maturing market and the proliferation of open-source data and tools. Sandback: Rising interest rates and climate risks are converging, threatening to destabilize the commercial real estate (CRE) loan market, especially for community banks where such loans constitute half of their assets. This circumstance augurs an uptick in delinquencies and potential forced sales at reduced prices. Yet, despite $1.4 trillion in CRE loans facing maturity by 2027, evidence of climate-related discounts remains elusive. What explains the absence of visible climate discounting in the market? Eil: One explanation is that pressing near-term concerns, such as housing affordability, low taxes, and jobs, drive consumers to the Sun Belt and climate-vulnerable locations. Another is that CRE is more regional and less neighborhood based than residential real estate, where climate factors do show up in market indicators in some areas. Given the recent ubiquity of extreme climate events, such as heat waves, droughts, floods, wildfires, and severe storms — US natural disasters with losses of $1 billion or more occurred at a record pace in 2023, despite mild hurricane and wildfire seasons — we can expect that at some point these trends will be reflected in CRE market dynamics. Indeed, a 2022 Redfin survey found that 62% of respondents consider climate and extreme weather as factors when they decide where to live. Sandback: The combined impact of regulations and climate change could undermine property values and loan repayments, raising the risk of defaults, especially in climate-sensitive states like California, Florida, and Texas. Could these regulatory changes lead to increased defaults in these areas in the coming years, given that climate change’s effects on CRE will become more visible? Eil: Real estate values tend to hinge on market demand and consumer outlook, but climate-related regulations may also increasingly affect them. Climate-related government policies toward the real estate sector extend far beyond required retrofits to such domains as mandatory disclosure of home flood risk exposure, adopted by both New Jersey and New York in the summer of 2023, and state-level regulation to ensure that insurance against climate-related perils is both available and affordable, such as was recently proposed in California. Local governments facing climate hazards are also prioritizing investments in climate-resilient infrastructure and enacting climate-smart building codes, which may influence perceptions of the safety and desirability of communities from now on, driving market sentiment. Climate risk disclosure and management, as well as government backstops for the real estate and insurance sectors, should help to reduce risk exposure and avoid market panic and acute repricing events. Sandback: Given more frequent extreme weather, retrofitting buildings in climate-vulnerable states becomes essential, but financial and regulatory hurdles make it challenging. The benefits, which may far exceed the costs according to OECD research, are elusive