Capital Market Expectations 1 Flashcards
This is to learn about the framework
What are capital market expectations, and what are they for?
They are expectations of the risk and return prospects of asset classes.
We create long term expectations for each identified asset class. We have projections of long term asset returns and we learn insights that help us too. The expectations drive our decision making and strategic asset allocation.
What is the ultimate objective of the projections?
Since precision is impossible, what do we focus on?
To help make informed investment decisions, specifically asset allocation decisions - its the primary determinant of long run performance.
Determining long run levels is hard, but precision is impossible, it is better to focus on internal consistency across asset classes (cross sectional consistency) and intertemporal consistency
Cross sectional consistency: A feature of expectations setting which means that estimates for all classes reflect the same underlying assumptions and are generated with methodologies that reflect or preserve important relationships among the asset classes, such as strong correlations. It is the internal consistency across asset classes.
Intertemporal consistency: (A feature of expectations setting which means that estimates for an asset class over different horizons reflect the same assumptions with respect to the potential paths of returns over time. It is the internal consistency over various time horizons.)
A Framework for Developing Capital Market Expectations:
- Specify the set of expectations needed, including the time horizon(s) to which they apply.
This means define the universe of asset classes (small as possible) that will be distinct allocations in client portfolio - develop expectations for the horizon.
- Research the historical record.
Find all the useful characteristics, this gives us a range for the future and find factors driving returns. Slice data into dimensions like geography.
- Specify the method(s) and/or model(s) to be used and their information requirements.
Be aware the time horizon will affect relationship among variables and forecasting results. For instance DCF is good for long run equities but if going short run, the new approach should be calibrated to converge to long run forecast as horizon extends.
- Determine the best sources for information needs.
Strive to find best data, use best frequency and source. These steps form foundation.
- Interpret the current investment environment using the selected data and methods, applying experience and judgment.
Implement your process. This is a daily activity, apply consistent judgement to ensure mutually consistent projections across asset classes and horizons.
- Provide the set of expectations needed, documenting conclusions.
At designated times, synthesize, document and defend your views. The projections should be accompanied by the reasoning and assumptions behind them.
- Monitor actual outcomes and compare them with expectations, providing feedback to improve the expectations-setting process
We measure our previously formed expectations against actual results to assess the level of accuracy the process is delivering.
Good forecasts: i) unbiased, objective
ii) efficient - less errors
iii) internally consistent cross sectionally and intertemporally.
What are some limitations of economic data?
- By the time some data is released, it is quite old, the IMF release data with a 2 year lag.
- Data is revised, either the last point or the whole benchmark, and in doing so we can’t be trapped thinking that the revised accurate data was what was known back when it happened, which makes a historical relationship look present when it isnt
- Statistics Denmark rebased their CPI and made 2016 the new starting point where from 1980 it reaches 100 in 2016.
What can go wrong with historical data and how should you use it?
- The past data may not reflect the future, and even if it does, our statistics calculated might be poor estimates of the desired metrics.
- The mean and variance might not be constant through time. There could be different regimes. The Fed had a period of expansionary buying up bonds and MBS.
- More data isnt always better. Higher freq can help variance / correlations it won’t help the mean.
- We should use the longest data with stationarity we can get.
- If we have lots of variables, we require more obs than variables to know the sample covariance matrix. Otherwise they spuriously show no volatility.
- Be aware of asynchronous data - different time zones - this distorts correlations and show lead-lag relationships.
Using Ex Post risk to measure Ex Ante risk
Looking back, did the asset prices reflect the risk of something bad happening? We need to be aware that a rare event may not be observable in past data yet. We often underestimate future risk and overestimate returns. Conversely, using a finite lookback period that has extreme events may skew our projections by making a rare event look common.
Anchoring bias:
Status quo bias:
Confirmation bias:
Overconfidence bias:
Prudence bias:
Availability bias:
Anchoring bias is the tendency to give disproportionate weight to the first information received or first number envisioned, which is then adjusted. Such adjustment is often insufficient, and approximations are consequently biased. Analysts can try to avoid anchoring bias by consciously attempting to avoid premature conclusions.
Status quo bias reflects the tendency for forecasts to perpetuate recent observations—that is, to avoid making changes and preserve the status quo, and/or to accept a default option. This bias may reflect greater pain from errors of commission (making a change) than from errors of omission (doing nothing). Status quo bias can be mitigated by disciplined effort to avoid “anchoring” on the status quo.
Confirmation bias is the tendency to seek and overweight evidence or information that confirms one’s existing or preferred beliefs and to discount evidence that contradicts those beliefs. This bias can be mitigated by examining all evidence with equal rigor and/or debating with a knowledgeable person capable of arguing against one’s own views.
Overconfidence bias is unwarranted confidence in one’s own intuitive reasoning, judgment, knowledge, and/or ability. This bias may lead an analyst to overestimate the accuracy of her forecasts and/or fail to consider a sufficiently broad range of possible outcomes or scenarios. Analysts may not only fail to fully account for uncertainty about which they are aware (sometimes described as “known unknowns”) but they also are very likely to ignore the possibility of uncertainties about which they are not even aware (sometimes described as “unknown unknowns”).
Prudence bias reflects the tendency to temper forecasts so that they do not appear extreme or the tendency to be overly cautious in forecasting. In decision-making contexts, one may be too cautious when making decisions that could damage one’s career or reputation. This bias can be mitigated by conscious effort to identify plausible scenarios that would give rise to more extreme outcomes and to give greater weight to such scenarios in the forecast.
Availability bias is the tendency to be overly influenced by events that have left a strong impression and/or for which it is easy to recall an example. Recent events may likewise be overemphasized. The effect of this bias can be mitigated by attempting to base conclusions on objective evidence and analytical procedures.
The trend growth rate and asset returns for bonds and equities:
- Average nominal or real default free bond yields are linked to the trend rate or nominal or real growth ie bond yields get pulled toward this level over longer horizons that weaken cyclical / short term forces. So its a way to estimate bond returns.
- In the long run, the growth rate of the total value of equity in an economy is linked to the growth rate of GDP. In finite horizons, we add capital’s share of income and P/E but they cant increase or decrease forever. This doesnt account for divs.
Econometric Models Approach: S&Ws?
What are the two main types?
Strengths:
Models can be quite robust, with many factors included to approximate reality.
New data may be collected and consistently used within models to quickly generate output.
Delivers quantitative estimates of impact of changes in exogenous variables.
Imposes discipline/consistency on analysis.
Weaknesses:
Complex and time-consuming to formulate.
Data inputs not easy to forecast.
Relationships not static. Model may be mis-specified.
May give false sense of precision.
Rarely forecasts turning points well.
- The estimated system of equations is used to forecast the future values of economic variables, with the forecaster supplying values for the exogenous variables. For example, such a model may require the forecaster to enter exchange rates, interest rates, commodity prices, and/or policy variables. The model then uses the estimated past relationships to forecast the future.
- Econometrics is the application of statistical methods to model relationships among economic variables. Structural models specify functional relationships among variables based on economic theory. The functional form and parameters of these models are derived from the underlying theory. Reduced-form models have a looser connection to theory.
Leading Indicator–Based Approach S&Ws
Strengths:
Usually intuitive and simple in construction.
Focuses primarily on identifying turning points.
May be available from third parties. Easy to track.
Weaknesses:
History subject to frequent revision.
“Current” data not reliable as input for historical analysis.
Overfitted in-sample. Likely overstates forecast accuracy.
Can provide false signals.
May provide little more than binary (no/yes) directional guidance.
- Analysts use both individual LEIs and composite LEIs, reflecting a collection of economic data releases combined to give an overall reading. The OECD composite LEI for each country or region is based on five to nine variables such as share prices, manufacturing metrics, inflation, interest rates, and monetary data that exhibit cyclical fluctuations similar to GDP, with peaks and troughs occurring six to nine months earlier with reasonable consistency. Individual LEIs can also be combined into a so-called diffusion index, which measures how many indicators are pointing up and how many down. For example, if 7 out of 10 are pointing upward, then the odds are that the economy is accelerating.
Checklist Approach S&Ws
Strengths:
Limited complexity.
Flexible.
Structural changes easily incorporated.
Items easily added/dropped.
Can draw on any information, from any source, as desired.
Breadth: Can include virtually any topics, perspectives, theories, and assumptions.
Weaknesses:
Subjective. Arbitrary. Judgmental.
Time-consuming.
Manual process limits depth of analysis. No clear mechanism for combining disparate information.
Imposes no consistency of analysis across items or at different points in time. May allow use of biased and/or inconsistent views, theories, assumptions.
- Checklist assessments are straightforward but time-consuming because they require continually monitoring the widest possible range of data. The data may then be extrapolated into forecasts via objective statistical methods, such as time-series analysis, or via more subjective or judgmental means. An analyst may then assess whether the measures are in an equilibrium state or nearer to an extreme reading.
Why is it hard to predict CME using business cycles?
The phases of the cycle are unpredictable in their timing and length. It can be hard to predict movement that is due to the cycle, prices reflect expectations and attitutde to risk which can be as hard to predict in itself.
If our horizon matches the length of the expansion or contraction. Any shorter and returns probably driven by transitory events, any longer and a turning point in cycle probably eventuates and returns will be closer to average cycle.
Initial Recovery
Early Expansion
Late Expansion
Slowdown
Contraction
Initial recovery. This period is usually a short phase of a few months beginning at the trough of the cycle in which the economy picks up, business confidence rises, stimulative policies are still in place, the negative output gap is large, and inflation is typically decelerating. Recovery is often supported by an upturn in spending on housing and consumer durables.
Capital market effects: Short-term rates and government bond yields are low. Bond yields may continue to decline in anticipation of further disinflation but are likely to be bottoming. Stock markets may rise briskly as fears of a longer recession (or even a depression) dissipate. Cyclical assets—and riskier assets, such as small stocks, higher-yield corporate bonds, and emerging market equities and bonds—attract investors and typically perform well.
Early expansion. The economy is gaining some momentum, unemployment starts to fall but the output gap remains negative, consumers borrow and spend, and businesses step up production and investment. Profits typically rise rapidly. Demand for housing and consumer durables is strong.
Capital market effects: Short rates are moving up as the central bank starts to withdraw stimulus put in place during the recession. Longer-maturity bond yields are likely to be stable or rising slightly. The yield curve is flattening. Stocks trend upward.
Late expansion. The output gap has closed, and the economy is increasingly in danger of overheating. A boom mentality prevails. Unemployment is low, profits are strong, both wages and inflation are rising, and capacity pressures boost investment spending. Debt coverage ratios may deteriorate as balance sheets expand and interest rates rise. The central bank may aim for a “soft landing” while fiscal balances improve.
Capital market effects: Interest rates are typically rising as monetary policy becomes restrictive. Bond yields are usually rising, more slowly than short rates, so the yield curve continues to flatten. Private sector borrowing puts pressure on credit markets. Stock markets often rise but may be volatile as nervous investors endeavor to detect signs of looming deceleration. Cyclical assets may underperform while inflation hedges such as commodities outperform.
Slowdown. The economy is slowing and approaching the eventual peak, usually in response to rising interest rates, fewer viable investment projects, and accumulated debt. It is especially vulnerable to a shock at this juncture. Business confidence wavers. Inflation often continues to rise as firms raise prices in an attempt to stay ahead of rising costs imposed by other firms doing the same.
Capital market effects: Short-term interest rates are high, perhaps still rising, but likely to peak. Government bond yields top out at the first clear sign of a slowing economy and may then decline sharply. The yield curve may invert, especially if the central bank continues to exert upward pressure on short rates. Credit spreads, especially for weaker credits generally widen. The stock market may fall, with interest-sensitive stocks such as utilities and “quality” stocks with stable earnings performing best.
Contraction. Recessions typically last 12 to 18 months. Investment spending, broadly defined, typically leads the contraction. Firms cut production sharply. Once the recession is confirmed, the central bank eases monetary policy. Profits drop sharply. Tightening credit magnifies downward pressure on the economy. Recessions are often punctuated by major bankruptcies, incidents of uncovered fraud, exposure of aggressive accounting practices, or a financial crisis. Unemployment can rise quickly, impairing household financial positions.
Capital market effects: Short-term interest rates drop during this phase, as do bond yields. The yield curve steepens substantially. The stock market declines in the earlier stages of the contraction but usually starts to rise in the later stages, well before the recovery emerges. Credit spreads typically widen and remain elevated until signs of a trough emerge and it becomes apparent that firms will be able to roll over near-term debt maturities.
The horizon structure of inflation:
Asset prices in general and bond yields in particular generally build in compensation for a positive average inflation rate. The long run sees the central bank keep it near target. If we know the cycle influences inflation, then for shorter periods we have inflations expectations. If we are at the peak, short term inflation is high but long term we expect inflation to be lower, so the expectations are downward sloping.
The effect of inflation on cash, bonds, stocks, and property.
- Cash: short-term interest-bearing instruments. As long as short-term interest rates adjust with expected inflation, cash is essentially a zero-duration, inflation-protected asset that earns a floating real rate. Inflation above or below expectation contributes to temporary fluctuations in the realized real return. Because central banks aim to stabilize actual and expected inflation, they tend to make the real rate on cash procyclical around a long-term level consistent with their target inflation rate. Hence, cash is relatively attractive (unattractive) in a rising (declining) rate environment. Deflation may make cash particularly attractive if a zero-lower-bound is binding on the nominal interest rate. Otherwise deflation is simply a component of the required short-term real rate.
- Bonds: Because the cash flows are fixed in nominal terms, the effect of inflation is transmitted solely through the discount rates (i.e., the yield curve). Rising (falling) inflation induces capital losses (gains) as the expected inflation component of yields rises (falls). If inflation remains within the expected cyclical range, shorter-term yields rise/fall more than longer yields but have less price impact as a result of shorter duration. If, however, inflation moves out of the expected range, longer-term yields may rise/fall more sharply as investors reassess the likelihood of a change in the long-run average level of inflation. Persistent deflation benefits the highest-quality bonds because it increases the purchasing power of the cash flows, but it is likely to impair the creditworthiness of lower-quality debt.
- Stocks: As long as inflation stays within the expected cyclical range, there should be little effect on stocks because both expected future cash flows (earnings and dividends) and associated discount rates rise/fall in line with the horizon structure of inflation expectations. Signs that inflation is moving out of the expected range, however, indicate a potential threat. Unexpectedly high and/or rapidly rising inflation could mean that the central bank needs to act to slow the economy, whereas very low and/or falling inflation (possibly deflation) threatens a recession and a decline in asset prices. Within the stock market, higher inflation benefits firms that are able to pass along rising costs, whereas deflation is especially detrimental for asset-intensive, commodity-producing, and/or highly leveraged firms.
- Real estate: Short- to intermediate-term nominal cash flows are generally dictated by existing leases, with the speed of adjustment depending on the type of real estate asset held. As long as inflation remains within the expected range, renewal of leases will likely generate rental income rising with expected inflation, accompanied by stable asset values. Higher-than-expected inflation is likely to coincide with high demand for real estate, expectations that rental income will rise even faster than general inflation, and rising property values. The impact may be quite idiosyncratic, however, depending on the length of leases, the existing supply of similar properties, and the likelihood of new supply hitting the market when leases come up for renewal. On the other hand, unexpectedly low inflation (or deflation) will put downward pressure on expected rental income and property values, especially for less-than-prime properties, which may have to cut rents sharply to avoid rising vacancies.
