Brian O'Reilly Case Scenario

Brian O’Reilly is a capital markets consultant for the Tennessee Teachers’ Retirement System (TTRS). O’Reilly is meeting with the TTRS board to present his capital market expectations for the next year. Board member Kay Durden asks O’Reilly about the possibility that data measurement biases exist in historical data. O’Reilly responds:

“One bias results from the use of appraisal data in the absence of market transaction data. Appraisal values tend to be less volatile than market determined values for identical assets. As a result, measured volatilities are biased downward and correlations with other assets tend to be exaggerated.”

Board member Arnold Brown asks O’Reilly about the use of high-frequency (daily) data in developing capital market expectations. O’Reilly answers, “Sometimes it is necessary to use daily data to obtain a data series of the desired length. Ironically, high-frequency data improves the precision of sample variances, covariances, and correlations but not the precision of the sample mean. High-frequency data are more sensitive to asynchronism across variables.”

Durden states that he recently read an article on psychological biases related to making accurate and unbiased forecasts. She asks O’Reilly to inform the board about the anchoringand prudence biases. O’Reilly offers the following explanation:

“The anchoring bias is the tendency for forecasts to be overly influenced by the memory of catastrophic or dramatic past events that are anchored in a person’s memory. The confirming evidence trap is the bias that leads individuals to give greater weight to information that supports a preferred viewpoint than to evidence that contradicts it.”

The board asks about forecasting expected returns for major markets, given that price earnings ratios are not constant over time and that many companies are repurchasing shares instead of increasing cash dividends. O’Reilly responds that the Grinold–Kroner model accounts for those factors and then makes the following forecasts for the European equity market:

• The dividend yield will be 1.95%.

• Shares outstanding will decline 1.00%.

• The long-term inflation rate will be 1.75% per year.

• An expansion rate for P/E multiples will be 0.15% per year.

• The long-term corporate earnings growth premium will be 1% above expected real GDP growth.

• Expected real GDP growth will be 2.5% per year.

• The risk-free rate will be 2.0%.

Question

With respect to his explanation of appraisal data bias, O’Reilly is most likely:

A. correct.

B. incorrect, because the measured volatilities biased upward.

C. incorrect, because correlations with other assets tend to be understated.

Solution

C is correct. O’Reilly’s explanation of appraisal data bias is incorrect because calculated correlations with other assets tend to be understated not exaggerated. O’Reilly is correct in that appraisal values tend to be less volatile than market determined values for identical assets and therefore measured volatilities are biased downward.

A is incorrect. O’Reilly’s comment regarding calculated correlations is incorrect.

B is incorrect. O’Reilly’s comment regarding measured volatilities is accurate.

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

1. Discuss challenges in developing capital market forecasts

Durden states that he recently read an article on psychological biases related to making accurate and unbiased forecasts. She asks O’Reilly to inform the board about the anchoring and prudence biases. O’Reilly offers the following explanation:

“The anchoring bias is the tendency for forecasts to be overly influenced by the memory of catastrophic or dramatic past events that are anchored in a person’s memory. The confirming evidence trap is the bias that leads individuals to give greater weight to information that supports a preferred viewpoint than to evidence that contradicts it.”

Which of O'Reilly's explanations regarding the anchoring bias and prudence bias is most likely incorrect?

A. for both.

B. only for the prudence bias.

C. only for the anchoring bias.

Solution

A is correct. Both of O’Reilly’s explanations are incorrect. His description of the prudence bias is incorrect—he is referring to the confirmation bias. The prudence bias is the tendency to temper forecasts so that they do not appear extreme or the tendency to be overly cautious in forecasting. His explanation of the anchoring bias is also incorrect. The anchoring bias is the tendency of the mind to give disproportionate weight to the first information it receives on a topic: initial impressions, estimates, or data, anchor subsequent thoughts and judgments.

B is incorrect. His description of the prudence bias is incorrect—he is referring to the confirmation bias.

C is incorrect. O’Reilly’s statement regarding the anchoring trap is incorrect. This is a description of availability bias.

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

1. Discuss challenges in developing capital market forecasts

Board member Arnold Brown asks O’Reilly about the use of high-frequency (daily) data in developing capital market expectations. O’Reilly answers, “Sometimes it is necessary to use daily data to obtain a data series of the desired length. Ironically, high-frequency data improves the precision of sample variances, covariances, and correlations but not the precision of the sample mean. High-frequency data are more sensitive to asynchronism across variables.”

With respect to his answer to Brown’s question, O’Reilly is most likely:

1. A.correct.

2. B.incorrect with respect to asynchronism.

3. C.incorrect with respect to variances and correlations.

Solution

A is correct. O’Reilly’s answer is entirely correct as stated.

B is incorrect. O’Reilly’s answer is entirely correct as stated. High-frequency data are more sensitive to asynchronism.

C is incorrect. O’Reilly’s answer is entirely correct as stated. High-frequency data produce more precise variances and co-variances (and less precise means).

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

1. Discuss challenges in developing capital market forecasts

Asynchronism, in the context of financial markets and High-Frequency Trading (HFT), refers to the occurrence where different financial data points or events are not synchronized in time. This misalignment can have significant implications, especially in HFT, where trading decisions are made based on data analyzed at extremely high speeds.

1. Necessity for Data Length: He mentions that sometimes it is necessary to use daily data to obtain a data series of the desired length. This is generally correct, as longer time series can be constructed with more frequent data.

2. Precision of Sample Variances, Covariances, and Correlations: He states that high-frequency data improves the precision of sample variances, covariances, and correlations. This is also correct. High-frequency data can provide more data points, which often leads to more precise estimates of these statistical measures.

3. Precision of the Sample Mean: He notes that the precision of the sample mean is not improved with high-frequency data. This is a nuanced point. While more data points can reduce the standard error of the mean, high-frequency data can also introduce more noise, which might not improve the precision of the mean in a practical sense.

4. Sensitivity to Asynchronism Across Variables: Finally, he mentions that high-frequency data are more sensitive to asynchronism (lack of simultaneousness) across variables. This is true, as high-frequency data can capture discrepancies in the timing of data recording across different variables, which can affect the analysis.

The board asks about forecasting expected returns for major markets, given that price earnings ratios are not constant over time and that many companies are repurchasing shares instead of increasing cash dividends. O’Reilly responds that the Grinold–Kroner model accounts for those factors and then makes the following forecasts for the European equity market:

• The dividend yield will be 1.95%. - D/P

• Shares outstanding will decline 1.00%. - %ΔS

• The long-term inflation rate will be 1.75% per year. - %ΔE

• An expansion rate for P/E multiples will be 0.15% per year. - %ΔP/E

• The long-term corporate earnings growth premium will be 1% above expected real GDP growth. ❌

• Expected real GDP growth will be 2.5% per year. - %ΔE

• The risk-free rate will be 2.0%. - ❌

Given O’Reilly’s forecasts for the European market, the expected long-term equity return using the Grinold–Kroner model is closest to:

• A.7.35%.

• B.6.35%.

• C.8.35%.

C is correct. The Grinold–Kroner model estimates the expected return on equity as follows:

where

E(Re) = expected rate of return on equity

D/P = expected dividend yield

%ΔE = expected percent change in total earnings

%ΔS = expected percent change in number of shares outstanding

%ΔP/E = expected percent change in the price-to-earnings ratio

(%ΔE – %ΔS) = the growth rate of earnings per share

%ΔE = nominal earnings growth = real earnings growth + long-term inflation + corporate premium

=2.50% + 1.75% + 1.00%

=5.25%

Alternatively, the expected return from the Grinold–Kroner model can be expressed as the sum of:

Expected cash flow (Income) return: D/P – %ΔS = 1.95 – (1.00) = 2.95

Expected nominal earnings growth return = %ΔE = 5.25 (as shown above)

Expected Pricing return: %ΔP/E = 0.15

Expected Return = 8.35%

A is incorrect. It incorrectly omitted the decline in shares outstanding from the calculation.

B is incorrect. It incorrectly added the decline in shares outstanding instead of subtracting it.

Capital Market Expectations, Part 2: Forecasting Asset Class Returns Learning Outcome

1. Discuss approaches to setting expectations for equity investment market returns

Note: ChatGPT was wrong on this calculation

ChatGPT Reference: https://chat.openai.com/c/e5633349-1bb4-465d-9066-85bad0d83702