R16 CME Flashcards

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1
Q

R16

How CME fits into investment process

A
  • Step 1 - Construct IPS
  • Step 2 - Formulate CME - Macro (inflation, interest rates, asset classes etc) and Micro expectations (concerning individual assets)
  • Step 3 - Asset Allocations (SAA)
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2
Q

R16

7 Step Framework for CME

A
  1. Specify the final set of expectations that are needed, including the time horizon to which they apply.
  2. Research the historical record - what are drivers of past performance?
  3. Specify the method(s) and/or model(s) that will be used and their information requirements.
  4. Determine the best sources for information needs.
  5. Interpret the current investment environment using the selected data and methods, applying experience and judgment.
  6. Provide the set of expectations that are needed, documenting conclusions (answers step 1)
  7. Monitor actual outcomes and compare them to expectations, providing feedback to improve the expectations-setting process
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3
Q

R16

Problems of forecasting - 1

Limitations of Economic Data

A
  1. Limitations of Economic Data:
  • Time lag between collection of data and when it is distributed
  • Data can be revised - but revisions are not made at the time they are distributed
  • Data definitions and methodologies change over time.
  • Data indices are rebased over time - meaning that the specific time period used as the base of the index is changed
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4
Q

R16

Problems of forecasting - 2

Data Measurement Errors and Biases

A
  • Data Measurement Errors and Biases:
  1. Transcription errors. These are errors in gathering and recording data. Such errors are most serious if they reflect a bias.
  2. Survivorship bias.
  3. Appraisal (smoothed) data - Appraised values tend to be less volatile than market-determined values for the identical asset would be. The consequences are 1) the calculated correlations with other assets tend to be smaller in absolute value than the true correlations, and 2) the true standard deviation of the asset is biased downward
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5
Q

R16

Problems of forecasting - 3

The Limitations of Historical Estimates

A
  • The Limitations of Historical Estimates:
  1. Regime changes - Changes in the technological, political, legal, and regulatory environments, as well as disruptions such as wars and other calamities, can alter risk–return relationships. Gives rise to the statistical problem of nonstationarity (meaning, informally, that different parts of a data series reflect different underlying statistical properties)
  2. A long data series may be a statistical necessity BUT :

The risk that the data cover multiple regimes increases.

Time series of the required length may not be available.

In order to get data series of the required length, the temptation is to use high-frequency data (weekly or even daily). Data of high frequency are more sensitive to asynchronism across variables. As a result, high-frequency data tend to produce lower correlation estimates.

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6
Q

R16

Problems of forecasting - 4

Ex Post Risk Can Be a Biased Measure of Ex Ante Risk

A
  • Ex Post Risk Can Be a Biased Measure of Ex Ante Risk

Understates risk and overstate returns

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7
Q

R16

Problems of forecasting - 5

Biases in Analysts’ Methods

A
  • Biases in Analysts’ Methods:
  1. Data-mining bias - introduced by repeatedly “drilling” or searching a dataset until the analyst finds some statistically significant pattern. Such patterns cannot be expected to be of predictive value
  2. Time-period bias - Research findings are often found to be sensitive to the selection of starting and/or ending dates
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8
Q

R16

Problems of forecasting - 6

The Failure to Account for Conditioning Information

A
  • The Failure to Account for Conditioning Information:
  1. Prospective returns and risk for an asset as of today are conditional on the specific characteristics of the current marketplace and prospects looking forward
  2. We should not ignore any relevant information or analysis in formulating expectations. Indeed, the use of unconditional expectations can lead to misperceptions of risk, return, and risk-adjusted return.
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9
Q

R16

Problems of forecasting - 7

Misinterpretation of Correlations

A
  • Misinterpretation of Correlations
  1. Assumes a linear relationship between variables
  2. Without the investigation and modeling of underlying linkages, relationships of correlation cannot be used in a predictive model
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10
Q

R16

Problems of forecasting - 8

Psychological Traps (6)

[Applies to forecasters]

RASPOC

A

Psychological Traps

  1. Anchoring trap - The tendency of the mind to give disproportionate weight to the first information it receives on a topic. The analyst can try to address this trap by consciously attempting to avoid premature conclusions.
  2. Status quo trap - The tendency for forecasts to perpetuate recent observations—that is, to predict no change from the recent past. The status quo trap may be overcome with rational analysis used within a decision-making process.
  3. Confirming evidence trap - The bias that leads individuals to give greater weight to information that supports an existing or preferred point of view than to evidence that contradicts it.
  4. Overconfidence trap - The tendency of individuals to overestimate the accuracy of their forecasts. To overcome: Examine all evidence with equal rigor. Enlist an independent-minded person to argue against your preferred conclusion or decision. Be honest about your motives. To prevent this trap from undermining the forecasting endeavor is to widen the range of possibilities around the primary target forecast.
  5. Prudence trap - The tendency to temper forecasts so that they do not appear extreme; the tendency to be overly cautious in forecasting. To avoid the prudence trap, an analyst is again wise to widen the range of possibilities around the target forecast
  6. Recallability trap - The tendency of forecasts to be overly influenced by events that have left a strong impression on a person’s memory. To minimize the distortions of the recallability trap, analysts should ground their conclusions on objective data and procedures rather than on personal emotions and memories.
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11
Q

R16

Problems of forecasting - 9

Model Uncertainty

A
  • Model Uncertainty:
  1. Model uncertainty - Uncertainty concerning whether a selected model is correct.
  2. Input uncertainty - Uncertainty concerning whether the inputs are correct.
  • Input uncertainty and model uncertainty in particular often make it hard to confirm the existence of capital market anomalies (inefficiencies)
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12
Q

R16

Tools for formulating CME (5)

A
  1. Statistical Tools
  2. DCF Models
  3. Risk Premium Model
  4. Financial Equilibrium Models
  5. Survey and Panel Methods
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13
Q

R16

Statistical Tools for CME

A
  1. Use arthmetic average for single periods, but geometric average for mutliple periods
  2. Shrinkage Estimator - Estimation that involves taking a weighted average of a historical estimate of a parameter and some other parameter estimate, where the weights reflect the analyst’s relative belief in the estimates. Uses a target covariance matrix - A component of shrinkage estimation; allows the analyst to model factors that are believed to influence the data over periods longer than observed in the historical sample. Used when data set is very small. Reduces the incidence of historical outliers. Can be used for both covariance and mean returns forecasting.
  3. Time series model - forecasting a variable on the basis of lagged values of the variable being forecast and often lagged values of other selected variables. Useful in developing particularly short-term forecasts for financial and economic variables. Notably applied to estimating near-term volatility
  4. Volatility Clustering - will high or low volaility persist in short term.
  5. Multifactor models - When the factors are well chosen, a multifactor model approach may filter out noise. This structure has been found useful for modeling asset returns and covariances among asset returns
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14
Q

R16

DCF Models for CME

A
  • Gordon Growth model
  • Have the advantage of being forward-looking.
  • They do not address short-run factors such as current supply-and-demand conditions, so practitioners view them as more appropriate for setting long-term rather than short-term expectations
  • DCF models can be used for Fixed Income and equity but for fixed income we should use YTM. The YTM calculation makes the strong assumption that as interest payments are received, they can be reinvested at an interest rate that always equals the YTM
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15
Q

R16

DCF Models for CME - Gronold-Kroner

A

E(Re ) ≈ D/P −(ΔS) + i +g + ΔP/E

  • Expected income return: D/P − ΔS.
  • Expected dividend yield D/P
  • Repurchase yield: − ΔS.
  • Expected nominal earnings growth return: i + g.
  • Expected repricing return: ΔP/E
  • Compounded Annual growth rate or expected rate of return on equity: E(Re)
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16
Q

R16

Risk Premium Approach for CME

A

E(Ri) = RF + (Riskpremium)1 + (Riskpremium)2 +…+ (Riskpremium)K

  • Real risk-free interest rate
  • Inflation premium - The sum of the real risk-free interest rate and the inflation premium is the nominal risk-free interest rate, often represented by a governmental Treasury bill YTM
  • Default risk premium
  • Illiquidity premium
  • Maturity premium
  • Tax premium
  • Equity risk premium:

E(Re) = YTM on a long-term government bond + Equity risk premium

17
Q

R16

Financial Market Equilibrium Models for CME

A
  • Singer–Terhaar Full Integration

RPi = σiρi,M (RPMM)

  • Singer–Terhaar Segmented Markets

RPi = σi (RPM / σM)

  • Possible addition of illquidity premium
  • Steps to calculate:
  1. Estimate the perfectly integrated and the completely segmented risk premiums for the asset class using the ICAPM.
  2. Add the applicable illiquidity premium, if any, to the estimates from the prior step.
  3. Estimate the degree to which the asset market is perfectly integrated.
  4. Take a weighted average of the perfectly integrated and the completely segmented risk premiums using the estimate of market integration from the prior step
18
Q

R16

Survey and Panel methods for CME

A
  • Survey - A method of capital market expectations setting that involves surveying experts.
  • Panel - A method of capital market expectations setting that involves using the viewpoints of a panel of experts. The group queried and providing responses must be fairly stable.
  • Judgement - The expectations-setting process nevertheless can give wide scope to applying judgment. Other investors who rely on judgment in setting capital market expectations may discipline the process by the use of devices such as checklist
19
Q

R16

Business Cycle

A
  1. Initial recovery. Inflation still declining Stimulatory fiscal policies Confidence starts to rebound Short rates low or declining; bond yields bottoming; stock prices strongly rising
  2. Early upswing. Healthy economic growth; inflation remains low Increasing confidence. Short rates moving up; bond yields stable to up slightly; stock prices trending upward
  3. Late upswing. Inflation gradually picks up Policy becomes restrictive Boom mentality Short rates rising; bond yields rising; stocks topping out, often volatile
  4. Slowdown. Inflation continues to accelerate; inventory correction begins Confidence drops Short-term interest rates peaking; bond yields topping out and starting to decline; stocks declining
  5. Recession. Production declines; inflation peaks Confidence weak Short rates declining; bond yields dropping; stocks bottoming and then starting to rise
20
Q

R16

Taylor Rule

A

Roptimal = Rneutral + [0.5×(GDPgforecast − GDPgtrend) + 0.5×(Iforecast − Itarget)]

  • The Taylor rule gives the optimal short-term interest rate as the neutral rate plus an amount that is positively related to the excess of the forecasts of GDP and inflation growth rates above their trend or target values.
  • The belief is that when GDP forecast growth is below trend, lowering the interest rate will stimulate output by lowering corporations’ cost of capital. When forecast inflation is below target, lowering the interest rate is expected to help the inflation rate return to target through its stimulative effect on the money supply.
21
Q

R16

Economic Growth Trends

A
  • LT trend growth determined by:
  1. Population growth
  2. Business investment
  3. Exogenous Shocks
  • The trend growth in GDP is approximately the sum of the following:

growth from labor inputs, comprising:

  • growth in potential labor force size and*
  • growth in actual labor force participation, plus*

growth from labor productivity, comprising

growth from capital inputs and

TFP growth (i.e., growth from increase in the productivity in using capital inputs).

22
Q

R16

Permanent income hypothesis

A
  • The hypothesis that consumers’ spending behavior is largely determined by their long-run income expectations.
  • Temporary or unexpected (or one-time) events such as benefiting from an inheritance might temporarily increase an individual’s demand for items that might not ordinarily be purchased, but overall spending patterns remain largely determined by long-run expectations. However, if an unexpected event (e.g., winning the lottery) produced an ongoing series of incoming cash flows, it would be expected to permanently alter an individual’s spending patterns since the flows would be ongoing and could be depended upon over the long term.
  • Only when income disruptions occur over the long term may individuals capitulate and reduce their consumption—out of necessity
23
Q

R16

Government Structural Policies

A
  • To help facilitate long term economic growth:
  1. Fiscal policy is sound.
  2. The public sector intrudes minimally on the private sector. The most damaging regulations for business tend to be labor market rules (e.g., restricting hiring and firing) because such regulations tend to raise the structural level of unemployment
  3. Competition within the private sector is encouraged.
  4. Infrastructure and human capital development are supported
  5. Tax policies are sound. According to economic theory, taxes distort economic activity by reducing the equilibrium quantities of goods and services exchanged
24
Q

R16

Exogenous Shocks

A
  • Can spread to other economies - contagion. Diversification is lost in times of contagion.
  • Oil Shocks
  • Financial Crises - more dangerous in a low interest rate environment.
25
Q

R16

Country Risk Analysis

A
  1. How sound is fiscal and monetary policy? Fiscal deficit to GDP A persistent ratio above 4 percent is regarded with concern.
  2. What are the economic growth prospects for the economy? Annual growth rates of less than 4 percent generally mean that the country is catching up with the industrial countries slowly
  3. Is the currency competitive, and are the external accounts under control? Current account deficit is > 4% potentially damaging. 1–3% of GDP is probably sustainable, provided that the economy is growing.
  4. Is external debt under control? Ratio of foreign debt to GDP > 50% is dangerous territory, while 25–50% is the ambiguous area. Or use debt to current account receipts > 200% for this ratio puts the country into the danger zone, while a reading below 100% does not.
  5. Is liquidity plentiful? Reserves divided by short-term debt Safe level is over 200% while a risky level is under 100%
  6. Is the political situation supportive of the required policies?
26
Q

R16

Economic Forecasting

A
  1. Econometric models, the most formal and mathematical approach to economic forecasting. Uses ordinary lease squared regression.
  2. Leading indicators: variables that have been found to lead (precede) turns in the economy.
  3. Checklists, requiring the subjective integration of the answers to a set of relevant questions.
27
Q

R16

Econometric models

A

Econometric models

  • Rarely forecast recessions well, although they have a better record in anticipating upturns.
  • Can be complex and time consuming to construct
  • May require interpretation of results
28
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R16

Economic Indicators

A
  • Leading
  • Lagging
  • Coincident
  • Advantages:
  1. Simple and easy to understand
  2. Data often available from 3rd parties
  3. Can be taylored to individuals needs
  4. 3rd Party literature available
  • Disadvantages
  1. Results have been inconsistant
  2. Have given false signals
29
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R16

Checklist Approach

A
  • Subjective
  • Less complex and very flexible
  • Time consuming
  • Limited complexity
30
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R16

Forecasting Asset Class Returns

A
  • Cash and equivalents - Managers lengthen or shorten maturities according to their expectations of where interest rates will go next.
  • Nominal Default-Free Bonds - For investors buying and selling long-term bonds over a shorter time period, the emphasis is on how bond yields will respond to developments in the business cycle and changes in short-term interest rates
  • Defaultable Debt - During a business cycle, spreads tend to rise during a recession because companies are under stress from both weak business conditions and, typically, higher interest rates. Default rates typically rise during recessions.
  • Emerging Market Bonds - Higher default risk. Often in terms of non-domestic currency
  • Inflation-Indexed Bonds - Real Yields rise as real economy expands. Real yields fall as inflation rises. Real yield falls as investor demand grows. There are considered a seperate asset class.
  • Emerging Market Stocks - often positively correlated with business cycle in developed world.
  • Real Estate - growth in consumption, real interest rates, the term structure of interest rates, and unexpected inflation as systematic determinants of real estate returns.
31
Q

R16

Exchange Rate Forecasting (5)

A
  1. Relative PPP - The theory that movements in an exchange rate should offset any difference in the inflation rates between two countries. PPP is often not a useful guide to the direction of exchange rates in the short or even medium run (up to three years or so). PPP in broad terms does seem to be useful in the long run—say, over periods of five years or longer
  2. Relative economic strength forecasting approach - Better for short term rather than long term.An exchange rate forecasting approach that suggests that a strong pace of economic growth in a country creates attractive investment opportunities, increasing the demand for the country’s currency and causing it to appreciate. Focuses on investment flows rather than trade flow. The relative strength approach indicates the response to news on the economy but does not tell us anything about the level of exchange rates. The PPP approach indicates what level of the exchange rate can be regarded as a long-term equilibrium. By combining the two, we can generate a more complete theory.
  3. Capital flows forecasting approach - An exchange rate forecasting approach that focuses on expected capital flows, particularly long-term flows such as equity investment and foreign direct investment. Inflows of FDI into a country increase the demand for the country’s currency, all else being equal.
  4. Savings–investment imbalances forecasting approach - An exchange rate forecasting approach that explains currency movements in terms of the effects of domestic savings–investment imbalances on the exchange rate. Not really used for forecasting exchange rates, but it could be. If the economy becomes weak enough and domestic investments no longer exceed domestic savings, then the currency will weaken.
  5. Government Intervention - economists and the markets have been skeptical about whether governments really can control exchange rates with market intervention alone because of three factors: First, the total value of foreign exchange trading, in excess of US$1 trillion daily, is large relative to the total foreign exchange reserves of the major central banks combined. Second, many people believe that market prices are determined by fundamentals and that government authorities are just another player. Third, experience with trying to control foreign exchange trends is not encouraging in the absence of capital controls. Unless governments are prepared to move interest rates and other policies, they cannot expect to succeed.