Minglu Li Case Scenario

REDD Partners specializes in forecasting and consulting in particular sectors of the equity market. Minglu Li is one of the company’s analysts specializing in the consumer credit industry. A new consumer credit mechanism was being tested on a small scale using a smartphone application to pay for items instead of the traditional credit card. The application had proved successful in the use of microloans in developing countries and was now being applied to a much broader consumer base. The new challenge for Li’s team is to develop a model for the expected return for these new consumer credit companies, which are called “smart credit” companies because they combine the consumer credit industry and what had traditionally been considered the telecommunications industry.

Although smart credit company returns data are sparse, a five-year monthly equally weighted index called the “Smart Credit Index” (SCI) was created from the existing companies’ returns data. The number of companies in the index at a given time varies because of firms failing and also merging over time.

Li’s team also examines survey data projecting the future performance of the consumer credit and telecommunications industries over the same time period for which the actual performance data was collected. They found that projections in the survey data tended to be more volatile than the actual performance data. However, Li’s team decided not to make any adjustments to the survey data because a definitive procedure could not be determined.

Given the effect of short-term interest rates on consumer credit, Li’s team decides to determine what the short-term interest rate is expected to be in the future. The central bank’s last official statement identified 2.5% as the appropriate real rate, assuming no other factors. Li’s team then estimates potential factors that may make the central bank behave differently from the 2.5% rate in the statement, shown in Exhibit 1.

Exhibit 1

Estimated Central Bank Factors

GDP growth forecast	2.00%
GDP growth trend	1.00%
Inflation forecast	1.50%
Inflation target	3.50%
Earnings growth forecast	4.00%
Earnings growth trend	2.00%

Based on Taylor’s rule, with an assumption of equal weights applied to forecast versus trend measures, the short-term rate is expected to increase from the current 1.23%, and the yield curve is expected to flatten for longer maturities.

For further insight, Li decides to consult an in-house expert on central banking, Randy Tolliver. Tolliver states the economy is likely in the early expansion phase of the business cycle based on the yield curve and consistent with this phase of the business cycle, monetary policy is becoming less stimulative.

Although smart credit company returns data are sparse, a five-year monthly equally weighted index called the “Smart Credit Index” (SCI) was created from the existing companies’ returns data. The number of companies in the index at a given time varies because of firms failing and also merging over time.

The SCI data most likely exhibits which type of bias?

1. A.Time-period

2. B.Survivorship

3. C.Data-mining

Solution

B is correct. The SCI data is an index that is not composed of the same number of firms each period because of firm failures and combinations through time, which is indicative of a survivorship bias.

A is incorrect. Time-period bias applies to inferences taken from data.

C is incorrect. Data-mining bias applies to inferences taken from data.

Capital Market Expectations, Part 1: Framework and Macro Considerations Learning Outcome

1. Discuss challenges in developing capital market forecasts

Li’s team also examines survey data projecting the future performance of the consumer credit and telecommunications industries over the same time period for which the actual performance data was collected. They found that projections in the survey data tended to be more volatile than the actual performance data. However, Li’s team decided not to make any adjustments to the survey data because a definitive procedure could not be determined.

The comparison between the survey data and actual performance data for the consumer credit and telecommunications industries most likely exhibits:

1. A.ex post risk being a biased measure of ex ante risk.

2. B.a status quo bias.

3. C.an availability bias.

Solution

A is correct. As stated, the projections in the survey data tended to be more volatile than the actual outcomes over the same time period. This result indicates that the ex post risk (i.e., the volatility of the actual data) tends to have a downward bias relative to the ex ante risk displayed by the survey data. This tendency is evidence of ex post risk being a biased measure of ex ante risk.

B is incorrect. The status quo bias is a bias toward recent observations as being predictive of the future.

C is incorrect. The availability bias is a bias toward allowing expectations to be unduly influenced by a previous event that left a significant impression.

Capital Market Expectations, Part 1: Framework and Macro Considerations Learning Outcome

1. Discuss challenges in developing capital market forecast

by ChatGPT

A. Ex post risk being a biased measure of ex ante risk. This option seems the most relevant. "Ex post" risk refers to the risk measured after the fact, based on actual performance data. "Ex ante" risk, on the other hand, is the risk anticipated in advance, often reflected in survey projections or forecasts. The volatility found in the survey data projections compared to the actual performance data suggests that the expectations (ex ante risk) were more fluctuating and possibly less accurate than the real outcomes (ex post risk). This discrepancy indicates a potential bias in measuring future risks based on expectations or forecasts.

B. A status quo bias does not seem applicable here. Status quo bias refers to the preference for the current state of affairs, and the avoidance of change. The description provided does not indicate a preference for the current state or a resistance to change.

C. An availability bias is also less likely in this context. Availability bias refers to the tendency to overestimate the importance or likelihood of events that are more readily "available" in memory, often due to recent exposure or emotional impact. This bias doesn't directly relate to the difference in volatility between projected and actual performance data.

Given the effect of short-term interest rates on consumer credit, Li’s team decides to determine what the short-term interest rate is expected to be in the future. The central bank’s last official statement identified 2.5% as the appropriate real rate, assuming no other factors. Li’s team then estimates potential factors that may make the central bank behave differently from the 2.5% rate in the statement, shown in Exhibit 1.

Exhibit 1

Estimated Central Bank Factors

GDP growth forecast	2.00%
GDP growth trend	1.00%
Inflation forecast	1.50%
Inflation target	3.50%
Earnings growth forecast ❌	4.00%
Earnings growth trend ❌	2.00%

Based on Taylor’s rule, with an assumption of equal weights applied to forecast versus trend measures, the short-term rate is expected to increase from the current 1.23%, and the yield curve is expected to flatten for longer maturities.

Based on how the Taylor rule is applied by Li’s team, the central bank’s estimated optimal short-term real rate is closest to:

1. A.2.8%.

2. B.1.5%.

3. C.2.0%.

Solution

C is correct. The Taylor rule sets the optimal short-term rate as

Neutral rate + 0.5 × (GDP growth forecast – GDP growth trend) + 0.5 × (Inflation forecast – Inflation target)

Applying numbers from Exhibit 3,

2.0% = 2.5% + 0.5 × (2.0% ‒ 1.0%) + 0.5 × (1.5% ‒ 3.5%).

A is incorrect. The difference between the earnings trend and forecast is included with each difference between forecast and trend being weighted by 0.333.

2.8% = 2.5% + 0.333 × (2.0% – 1.0%) + 0.333 × (1.5% -– 3.5%) + 0.333 × (4.0% – 2.0%)

B is incorrect. The inflation and GDP adjustments are not multiplied by 0.5.

Capital Market Expectations, Part 1: Framework and Macro Considerations Learning Outcome

1. Discuss the effects of monetary and fiscal policy on business cycles

Taylor Rule = neutral rate + 0.5 x GDP & Inflation (forecast - trend)

Note: ChatGPT failed here

Based on Taylor’s rule, with an assumption of equal weights applied to forecast versus trend measures, the short-term rate is expected to increase from the current 1.23%, and the yield curve is expected to flatten for longer maturities.

For further insight, Li decides to consult an in-house expert on central banking, Randy Tolliver. Tolliver states the economy is likely in the early expansion phase of the business cycle based on the yield curve and consistent with this phase of the business cycle, monetary policy is becoming less stimulative.

Tolliver’s statement is most likely:

1. A.correct.

2. B.incorrect with the phase of the business cycle.

3. C.incorrect with regard to monetary policy.

Solution

A is correct. The yield curve is becoming steeper for short-term rates and flattening for longer-term rates which is consistent with the early expansion phase of the business cycle. Also, consistent with the early expansion phase of the business cycle, monetary policy is becoming less stimulative.

B is incorrect. The yield curve is becoming steeper for short-term rates and flattening for longer-term rates which is consistent with the early expansion phase of the business cycle.

C is incorrect. Consistent with the early expansion phase of the business cycle, monetary policy is becoming less stimulative.

Capital Market Expectations, Part 1: Framework and Macro Considerations Learning Outcome

1. Discuss the effects of monetary and fiscal policy on business cycles

(I tot yield curve flattened -> end of cycle, turned out it's specifically mentioning on 'longer' term maturities)

ChatGPT Reference: https://chat.openai.com/c/6bef29bd-7592-4af4-abab-bdff367935ed