CFA Exam Question of the Day

Rationale:
The roll yield component of return arises from the difference between the price of a futures contract as it approaches expiration and the price of the next futures contract into which the position is rolled. When the futures curve is in contango (futures prices increasing with maturity), the roll yield is negative because an investor sells a relatively lower-priced expiring contract and buys a relatively higher-priced longer-dated contract. Conversely, in backwardation, the roll yield is positive. For an investor holding a one-month futures contract and planning to roll it into a two-month contract, the expected monthly roll yield is calculated as the difference between the current one-month futures price and the current two-month futures price, divided by the one-month futures price. This assumes the futures curve remains unchanged over the period. Using the provided data: One-month futures price = $79.25 Two-month futures price = $80.10 Monthly roll yield = (One-month futures price - Two-month futures price) / One-month futures price Monthly roll yield = ($79.25 - $80.10) / $79.25 = -$0.85 / $79.25 = -0.01072555... To annualize this monthly roll yield, we multiply by 12: Annualized roll yield = -0.01072555 * 12 = -0.1287066... Expressed in basis points (1% = 100 bps), this is -12.87066% * 100 = -1,287.066 bps, which rounds to -1,287 bps. The calculation leading to a positive 1,287 bps incorrectly assumes a positive roll yield. The futures curve for crude oil, as indicated by the increasing futures prices with longer maturities ($79.25 for one-month, $80.10 for two-month), is in contango. In a contango market, an investor rolling a short-term contract into a longer-term contract will experience a negative roll yield because they are selling a relatively cheaper contract and buying a relatively more expensive one. The result of -1,146 bps would arise if the calculation incorrectly used the current spot price in relation to the one-month futures price (e.g., (Spot - One-month futures) / Spot) and then annualized. Roll yield, in the context of rolling a futures position, specifically relates to the difference between futures contracts of different maturities, not directly involving the current spot price when the rollover is between two futures contracts. The value of -4,914 bps suggests an error in identifying the correct futures contracts for the rollover strategy. This result would be obtained by incorrectly using the six-month futures contract price instead of the two-month futures price in the calculation (i.e., (One-month futures - Six-month futures) / One-month futures, annualized). The question specifically asks about rolling the one-month contract into the two-month contract, requiring only those two contract prices for the calculation.

Rationale:
To determine the bond equivalent yield (BEY) for a U.S. Treasury Bill, it is necessary to convert the bank discount yield (BDY) by adjusting for two key factors: the yield basis and the day count convention. The BDY is based on the face value of the instrument and uses a 360-day year, whereas the BEY is based on the purchase price and uses a 365-day year. First, calculate the dollar discount from the face value: Discount = Face Value * BDY * (Days to Maturity / 360) Discount = $1,000,000 * 0.0275 * (180 / 360) = $1,000,000 * 0.0275 * 0.5 = $13,750. Next, calculate the purchase price of the T-bill: Purchase Price = Face Value - Discount = $1,000,000 - $13,750 = $986,250. Then, calculate the holding period yield (HPY), which is based on the purchase price rather than the face value: HPY = Discount / Purchase Price = $13,750 / $986,250 = 1.394170%. Finally, annualize the HPY using a 365-day year to arrive at the bond equivalent yield: BEY = HPY * (365 / Days to Maturity) = 1.394170% * (365 / 180) = 2.8271%. The 90-day T-bill yield and the standard deviation of excess returns for corporate debt are distractors and are not used in converting the bank discount yield to a bond equivalent yield. An answer of 2.7883% results from annualizing the holding period yield using a 360-day year instead of the 365-day year required for the bond equivalent yield basis: HPY * (360 / 180) = 1.394170% * 2 = 2.7883%. This fails to make the full conversion from the 360-day bank discount basis to the 365-day bond equivalent basis. An answer of 2.7931% reflects a computational error of similar magnitude in applying the day count or annualization adjustment, rather than a distinct, cleanly identifiable conceptual misapplication of the BEY formula. An answer of 2.7500% simply restates the quoted bank discount rate itself without any conversion at all, ignoring both the shift from a face-value basis to a purchase-price basis and the shift from a 360-day to a 365-day year, both of which are required to properly compare a T-bill's yield to instruments quoted on a bond equivalent basis.

Rationale:
The shift towards modular product architectures and standardized component interfaces fundamentally alters the competitive landscape. This development typically leads to products becoming more commoditized, which intensifies rivalry among existing firms and drives down prices. Furthermore, standardized interfaces reduce the costs and complexities for customers to switch between different suppliers, thereby increasing buyer bargaining power. Both intensified rivalry and increased buyer power exert downward pressure on prices and profit margins, leading to a sustained erosion of average industry profitability over the long term. The idea of enhanced operational efficiencies, while potentially true, overlooks the crucial impact of competitive forces. While modularity can reduce costs, if competition drives prices down even faster, overall industry profitability will decline, not increase. The claim of elevated barriers to entry is incorrect; modularity and standardization typically *lower* barriers to entry by allowing new entrants to assemble products from readily available components without needing to develop every part in-house, reducing the capital and R&D expenditure required. Finally, the notion of strengthened supplier bargaining power due to specialization is a common misinterpretation in this context. While some niche modular component suppliers might gain, the overall trend of standardization often leads to a more fragmented supply base and reduced switching costs for manufacturers, thereby *weakening* supplier power or at least not making it the dominant factor driving down overall industry profitability compared to the combined effects of buyer power and rivalry.

Rationale:
The indicator that most accurately and consistently functions as a lagging economic indicator is corporate profits after tax, with inventory valuation and capital consumption adjustments. These figures reflect the financial results of economic activity that has already occurred. They are a consequence of past sales, production, and investment decisions, confirming the state of the economy only after trends or turning points have been established. While expectations of future profits drive investment decisions, the *actual reported* profits are a historical measure, reflecting the culmination of prior economic forces rather than signaling future changes. Conversely, the interest rate spread (10-year Treasury yield minus federal funds rate) is a prominent leading indicator. A widening positive spread often precedes economic expansions, while an inverted yield curve (where short-term rates exceed long-term rates) has historically been a strong predictor of recessions, reflecting market expectations about future economic growth and inflation. New orders for durable goods (excluding defense and aircraft) also serve as a leading indicator. An increase in these orders signals future production, investment, and employment in manufacturing, as businesses prepare to fulfill new demand. Similarly, residential building permits are a classic leading indicator. An increase in permits signifies future construction activity, which has significant multiplier effects on the economy through demand for labor, materials, and financing, thereby anticipating future economic growth.

Rationale:
The Coefficient of Variation (CV) is a standardized measure of dispersion that allows for the comparison of risk per unit of return across different investments, particularly when their expected returns differ significantly. It is calculated as the ratio of an investment's standard deviation to its mean return. The formula is: CV = Standard Deviation / Mean Return. For the Global Alpha Equity Fund, the relevant data for its total returns are: - Mean Annualized Total Return = 850 basis points = 0.0850 - Annualized Standard Deviation of Total Returns = 1200 basis points = 0.1200 Applying the formula: CV = 0.1200 / 0.0850 = 1.41176... Rounded to three decimal places, the Coefficient of Variation for the Global Alpha Equity Fund's total returns is 1.412. The value of 0.708 represents the reciprocal of the Coefficient of Variation, where the mean return is incorrectly divided by the standard deviation. The value of 1.176 is derived by incorrectly using the Annualized Standard Deviation of the MSCI World Index (1000 basis points or 0.1000) in the numerator instead of the Global Alpha Equity Fund's own standard deviation. The value of 4.800 results from incorrectly using the Mean Annualized Return of the MSCI World Index (250 basis points or 0.0250) in the denominator instead of the Global Alpha Equity Fund's own mean return. The Coefficient of Variation is a crucial tool for assessing relative risk, especially when comparing investments with different expected returns, as it normalizes risk by the level of return.