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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|FINC3017

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

分析师在进行分析时通常会遇到至少三种不确定性。模型不确定性与所选模型在结构和/或概念上是否正确有关。参数不确定性的出现是因为定量模型的参数总是被错误地估计。输入不确定性涉及输入是否正确。任何或所有这些都可能导致错误的预测和/或导致粗心的分析师高估其预测的准确性和可靠性。

通过适当注意估计误差,可以减轻参数不确定性的影响。输入不确定性主要来自于需要代理不可观察变量,例如 CAPM 中的“市场投资组合”。这是否是一个严重的问题取决于上下文。如果分析师想要测试基础理论的有效性或识别与模型相关的“异常”,这是一个问题。如果分析师只关注有用的经验关系而不是概念/理论的证明,那么问题就不大了。模型不确定性可能是最严重的问题,因为错误的模型可能导致分析师得出根本性错误的结论。

我们对历史数据局限性的讨论触及了一个在 1990 年代后期导致许多投资者误入歧途的模型。到目前为止,许多(如果不是大多数)机构投资者用于设定长期股票预期的隐含模型是,“事前预期回报是,过去是,并且永远是一个常数米,而对这个数字的最佳估计是可用最长样本的平均值。” 随着市场在 1990 年代后期飙升,历史估计米稳步上涨,导致投资者更多地转向股票,这推动了价格的进一步升值和更多的股票重新配置,等等,直到科技泡沫破裂。具有讽刺意味的是,相信历史估计的神圣性与“新经济”使历史经济和市场关系过时的截然相反的观念不谋而合。至少在某些市场领域,增长或估值似乎没有限制。但是,当然,有。这种对技术泡沫的描述说明了特定预测模型的崩溃。它不是对任何人的思维过程的字面描述。然而,出于各种原因——竞争压力、现状/可用性/审慎偏见——许多投资者的行为就好像他们遵循了模型一样。

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

历史表明,实际实现的资产回报、对未来资产回报的预期和经济活动之间存在直接但可变的关系。分析师需要熟悉实证研究发现的关于经济变量与资本市场回报之间的方向、强度和领先-滞后关系的历史关系。

了解哪些经济变量可能与当前经济环境最相关的分析师具有竞争优势,就像能够辨别或预测趋势加速和减速变化的分析师一样。

经济产出既有周期性增长成分,也有趋势增长成分。趋势增长对于为股票等资产类别设定长期回报预期具有明显的相关性。周期性变化会影响公司利润和利率等变量,这些变量与资产类别的回报和风险直接相关。在以下部分中,我们将讨论趋势增长、商业周期、货币和财政政策的作用以及国际互动。

经济增长趋势是经济经历半规则经济周期的GDP长期平均增长路径。分析师需要了解和分析趋势和周期。尽管每个都可以独立存在,但它们是相关的。

似乎趋势本质上比周期更容易预测。毕竟,趋势是关于长期平均值的,而周期是关于短期运动和转折点的。如果趋势增长率是恒定的,那么趋势更容易预测的假设将是正确的。但趋势增长率确实会发生变化,这就是预测它们与投资分析相关的原因。一些变化相当容易预测,因为它们是由缓慢演变且易于观察的因素驱动的,例如人口统计数据。由对潜在经济和/或市场关系的重大“外生冲击”引起的趋势变化不仅无法预见,而且难以识别、评估和量化,直到变化得到充分确立并在数据中追溯揭示。实际上,根据定义,

金融代写|投资组合代写Investment Portfolio代考

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