Capital Market Expectations Framework and limitations

After assessing an investors overall risk tolerance, we determine which asset classes are appropriate to meet the objectives as outlined in the IPS. In defining our capital market expectations regarding those classes of assets, we are doing what is known as beta research or macro economic analysis In the following section, we look at a standard framework for preparing capital market expectations Capital Market Expectations

A Framework for Developing Capital Market Expectations Specify the final set of expectations that are needed, including the time horizon to which they apply. Accomplishing this step requires one to formulate his specific needs in terms of a relevant set of asset classes that are of concern, giving appropriate regard to the constraints of the client. Capital Market Expectations

Research the historical record. For many markets, the historical record contains useful information on the investment characteristics of the asset, suggesting at least some possible ranges for future results. One should identify the factors that affect asset class returns and understand the what, when, where, why, and how of these return drivers. Capital Market Expectations

Specify the valuation method or model that will be used and its information requirements and be able to justify the selection. Information requirements (economic and financial market data needs, for example) depend on the decision about method(s) the effectiveness of forecasting approaches and relationships among variables may be related to the investor’s time horizon. Capital Market Expectations

Determine the best sources for information needs Determine the best sources for information needs. Executing this step well requires that the analyst research the quality of alternative data sources. Factors such as data collection principles and definitions, error rates in collection, calculation formulas, and for asset class indices, qualities such as investability, correction for free float, turnover in index constituents, and biases in the data are relevant Capital Market Expectations

Besides taking care with data sources, select the appropriate data frequency. E.g. long-term data series should not be used for setting short-term trading expectations or evaluating short-term volatility. Daily series are of more use for setting shorter-term capital market expectations. Quarterly or annual data series are useful for setting long-term capital market expectations. Capital Market Expectations

Interpret the current investment environment using the selected data and methods, applying experience and judgment. work from a common set of assumptions in interpreting different elements of the investment and economic scene so that conclusions are mutually consistent. This step often requires judgment and experience to interpret apparently conflicting signals within the data. Capital Market Expectations

Formulate the capital markets expectations, documenting assumptions and rationales used in the analysis. We take all of our analyses of the economic and market environment into forward- looking views on capital markets, developing any required quantitative forecasts. In other words, the questions formulated in Step 1 are answered in Step 6. Capital Market Expectations

Monitor actual outcomes and compare them to expectations, providing feedback to improve the expectations- setting process. we want to use experience to improve the expectations-setting process. We measure our previously formed expectations against actual results to assess the level of accuracy that the expectations- setting process is delivering. Capital Market Expectations Generally, good forecasts are: unbiased, objective, and well researched; efficient, in the sense of reducing the magnitude of forecast errors to a minimum; internally consistent. Internal inconsistency can take a number of forms. For example, domestic bond and domestic equity expectations developed by different analysts using different inflation projections would not be internally consistent. A restructuring of a portfolio based on those expectations would, at least in part, merely reflect an unresolved difference in assumptions. In some cases, inconsistent forecasts may result in conclusions that are implausible or impossible.

Challenges in Forecasting Limitations of Economic Data The time lag with which economic data are collected, processed, and disseminated can be an impediment to their use. Definitions and calculation methods change too. One or more official revisions to the initial values are common Index providers re-base their indices Capital Market Expectations

Data measurement errors and biases Transcription errors. These are errors in gathering and recording data. Such errors are most serious if they reflect a bias. Survivorship bias. Survivorship bias arises when a data series reflects only entities that have survived to the end of the period. Without correction, statistics derived from series subject to survivorship bias can be misleading in the forward-looking context of expectations setting. Appraisal (smoothed) data. For certain assets without liquid public markets, appraisal data are used in lieu of market price transaction data. Appraised values tend to be less volatile than market-determined values for the identical asset would be.

The Limitations of Historical Estimates With justification, we frequently look to history for information in developing capital market forecasts. But although history is usually a guide to what we may expect in the future, the past cannot be simply extrapolated to produce future results uncritically. An historical estimate should be considered a starting point for analysis. The analysis should include a discussion of what may be different from past average results going forward.

Ex Post Risk Can Be a Biased Measure of Ex Ante Risk In interpreting historical prices and returns over a given sample period for their relevance to current decision making, we need to evaluate whether asset prices in the period reflected the possibility of a very negative event that did not materialize during the period. Looking backward, we are likely to underestimate ex ante risk and over- estimate ex ante anticipated returns. Only the ex ante risk premium is important in decision making.

Biases in Analysts’ Methods Data-mining bias. Data-mining bias is introduced by repeatedly “drilling” or searching a dataset until the analyst finds some statistically significant pattern. Time-period bias. Time-period bias relates to results that are time period specific. Research findings are often found to be sensitive to the selection of starting and/or ending dates. Capital Market Expectations

The Failure to Account for Conditioning Information We should ask whether there are relevant new facts in the present when forecasting the future. Where such information exists, we should condition our expectations on it.

Misinterpretation of Correlations In financial and economic research, the analyst should take care in interpreting correlations. When a variable A is found to be significantly correlated with a variable B, there are at least three possible explanations: A predicts B; B predicts A; a third variable C predicts A and B. Without the investigation and modeling of underlying linkages, relationships of correlation cannot be used in a predictive model.

Psychological Traps The anchoring trap is the tendency of the mind to give disproportionate weight to the first information it receives on a topic. The status quo trap is the tendency for forecasts to perpetuate recent observations—that is, to predict no change from the recent past. The confirming evidence trap is 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.

The overconfidence trap is the tendency of individuals to overestimate the accuracy of their forecasts. Lack of comments from others is taken as agreement. The prudence trap is the tendency to temper forecasts so that they do not appear extreme, or the tendency to be overly cautious in forecasting. The recallability trap is the tendency of forecasts to be overly influenced by events that have left a strong impression on a person’s memory. The easiest to remember (often extreme events) are overweghted

Model and input Uncertainty Model uncertainty refers to selecting the correct model (use DCF or relative value model to determine expected stock return) Input uncertainty refers to knowing the correct the correct input values for the model

Forecasting Tools The formal tools, that can used in forecasting, include; Statistical tools Discounted cashflow methods The risk premium approach Financial equilibrium models

Statistical tools Descriptive statistics summarize the data Inferential statistics use the data to make forecasts If the past data is stationary, the parameters driving the past and the future are unchanged. Thus historical estimates are reasonable estimates of the future. Over a single period, arithmetic average is used If a portfolio has a 50/50 chance of making or losing 10%, in any given period. Thus on average the portfolio is unchanged a100, 0% return Over a multi period geometric average is preferred. Unannualized, the GR of the portfolio is (1.1)(0.9)-1 = -1%

Another approach is to use equity risk premium plus a current bond yield to estimate the expected return on equities. Shrinkage estimates can be applied to the future estimate if the analyst believes simple historical results do not fully reflect expected future conditions. A shrinkage estimate is a weighted average estimate based on history and some other projections.

For example, suppose the historical covariance between two assets is 180 and analyst has used a model to project covariances. If the model estimated covariance is 220 and the analyst weighs the historical covariance by 60% and the target b 40%, the shrinkage estimate is thus 196 ((180*60%)+ (220*40%))

Time series models can also be used in forecasting; They are frequently used to make estimates of near term volatility For example; Assuming the following is a time series model If for instance, we suppose and the standard deviation in returns is 15% in period t-1, and the random error is 0.04, then the forecasted variance in time t is

Capital Market Expectations

Multi factor models can be used in a top down analysis to forecast returns based on sensitivities and risk factors A two factor model would take the form of;

Example

Capital Market Expectations

Estimate the covariance between market C and D Estimate the variance for market C and D Capital Market Expectations

Capital Market Expectations

Discounted Cash Flow models The model says that the intrinsic value of an asset is the present value of future cash flows

Grinold-Kroner Model takes this model one step further by including a variable that adjusts for stock repurchases and changes in the market valuations (change in price-earnings P/E ratio) The model states that the expected return on a stock is its dividen yield plus inflation rate plus the real earnings growth rate minus the change in stock outstanding plus change in P/E ratio

Capital Market Expectations

The variables in the model can be grouped into three components; the expected income return, the expected nominal growth in earnings, and the expected repricing return

Expected income return is the cash flow yield in that market; Capital Market Expectations

Repricing return is captured by the expected change in the P/E ratio Expected nominal earnings growth is the real growth in the stock price plus expected inflation Repricing return is captured by the expected change in the P/E ratio Capital Market Expectations

Suppose an analyst estimates that a 2 Suppose an analyst estimates that a 2.1% dividend yield, real earnings growth of 4%, long term inflation of 3.1% , a repurchase yield of -0.5%, and P/E repricing of 0.3%: Capital Market Expectations

Expected current yield (income return)= 2.1% - 0.5% = 1.6% Expected capital gain yield = real growth + inflation +re-pricing 4%+3.1+0.3% =7.4% Capital Market Expectations

Estimating Fixed Income Returns Discounted cash flow analysis It supports the use of the YTM as an estimate of expected returns Risk premium approach (build up model) The approach starts with a low risk yield and then adds compensation for risks

Capital Market Expectations

Financial Equilibrium Models Assumes that supply and demand in global asset markets are in balance International Capital Asset Pricing Model (ICAPM). The Singer and Terhaar begins with the ICAPM

The expression states that the risk premium for an asset is equal to its correlation with the global market portfolio multiplied by the standard deviation of the asset multiplied the sharpe ratio for the global portfolio. Capital Market Expectations

Capital Market Expectations

Capital Market Expectations

The singer Terhaar analysis adjusts the ICAPM for market imperfections such as segmentation and illiquidity. When there is free flow of capital the market is said to be integrated, otherwise it is said to be segmented. Presence of investment barriers increases the risk premium for securities in segmented markets. Recall that the covariance for two markets

If there is one factor driving the returns (i If there is one factor driving the returns (i.e the global portfolio) then the equation reduces to Capital Market Expectations

Capital Market Expectations

If the market is fully integrated, equity risk premium If fully segmented;

Weight the integrated and segmented risk premiums by the degree of integration and segmentation The expected return in each market figures

The beta in each market Capital Market Expectations

Economic Analysis An investment strategist who anticipates changes to the inputs to the Taylor rule can use the rule to anticipates changes in short term interest rates by the central banks A central bank can use the Taylor rule to determine the appropriate level for short term interest rates;