# 金融代写|投资组合代写Investment Portfolio代考|FINC3017

## 金融代写|投资组合代写Investment Portfolio代考|Model Uncertainty

The analyst usually encounters at least three kinds of uncertainty in conducting an analysis. Model uncertainty pertains to whether a selected model is structurally and/or conceptually correct. Parameter uncertainty arises because a quantitative model’s parameters are invariably estimated with error. Input uncertainty concerns whether the inputs are correct. Any or all of these may give rise to erroneous forecasts and/or cause the unwary analyst to overestimate the accuracy and reliability of his forecasts.

The effects of parameter uncertainty can be mitigated through due attention to estimation errors. Input uncertainty arises primarily from the need to proxy for an unobservable variable such as “the market portfolio” in the CAPM. Whether or not this is a serious issue depends on the context. It is a problem if the analyst wants to test the validity of the underlying theory or identify “anomalies” relative to the model. It is less of an issue if the analyst is merely focused on useful empirical relationships rather than proof of concept/ theory. Model uncertainty is potentially the most serious issue because the wrong model may lead an analyst to fundamentally flawed conclusions.

Our discussion of the limitations of historical data touched on a model that led many investors far astray in the late 1990 s. Up to that point, the implicit model used by many, if not most, institutional investors for setting long-term equity expectations was, “The ex ante expected return is, was, and always will be a constant number $\mu$, and the best estimate of that number is the mean over the longest sample available.” As the market soared in the late 1990 s, the historical estimate of $\mu$ rose steadily, leading investors to shift more heavily into equities, which fueled further price appreciation and more reallocation toward equities, and so on, until the technology bubble burst. Ironically, belief in the sanctity of historical estimates coincided with the diametrically opposed notion that the “new economy” made historical economic and market relationships obsolete. There seemed to be no limits to growth or to valuations, at least in some segments of the market. But, of course, there were. This description of the technology bubble illustrates the breakdown of a particular forecasting model. It is not a literal description of anyone’s thought process. For various reasons, however -competitive pressures, status quo/availability/prudence biases-many investors acted as if they were following the model.

## 金融代写|投资组合代写Investment Portfolio代考|The Role of Economic Analysis

History has shown that there is a direct yet variable relationship among actual realized asset returns, expectations for future asset returns, and economic activity. Analysts need to be familiar with the historical relationships that empirical research has uncovered concerning the direction, strength, and lead-lag relationships between economic variables and capital market returns.

The analyst who understands which economic variables may be most relevant to the current economic environment has a competitive advantage, as does the analyst who can discern or forecast changes in acceleration and deceleration of a trend.

Economic output has both cyclical and trend growth components. Trend growth is of obvious relevance for setting long-term return expectations for asset classes such as equities. Cyclical variation affects variables such as corporate profits and interest rates, which are directly related to asset class returns and risk. In the following sections, we address trend growth, business cycles, the role of monetary and fiscal policies, and international interactions.

The economic growth trend is the long-term average growth path of GDP around which the economy experiences semi-regular business cycles. The analyst needs to understand and analyze both the trend and the cycles. Though each could exist without the other, they are related.

It might seem that trends are inherently easier to forecast than cycles. After all, trends are about long-term averages, whereas cycles are about shorter-term movements and turning points. The assumption that trends are easier to forecast would be true if trend growth rates were constant. But trend growth rates do change, which is what makes forecasting them relevant for investment analysis. Some changes are fairly easy to forecast because they are driven by slowly evolving and easily observable factors such as demographics. Trend changes that arise from significant “exogeneous shocks” to underlying economic and/or market relationships are not only impossible to foresee but also difficult to identify, assess, and quantify until the change is well-established and retrospectively revealed in the data. Virtually by definition, the effect of truly exogenous shocks on the level and/or growth rate of the economy will not have been built into asset prices in advance-although the risk of such events will likely have been reflected in prices to some degree.

## 金融代写|投资组合代写Investment Portfolio代考|The Role of Economic Analysis

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