Level 1 is more about breadth of topics than depth of knowledge. A successful CFA Level I candidate does not have to be a quantitative expert or mathematician. Yes, quantitative skills are important in the investment profession and if you do not currently possess these abilities you need to in time. However, the “deadline” for acquisition is not necessarily December 2012.

Break the four quant study sessions into reasonable chunks and make sure you know the following:

* Normal distribution(ND)* – defined by mean and variance/standard deviation (stdev)– 50% of the distribution is above the mean and 50% below – you have all seen the picture. Mean = average=expected value. Know the properties of the ND. What percentage of the distribution lies within one stdev (68%) of the mean? Two stdev? 95%. Three stdev? 99%.

What is “Variance”? It is the sum of squared distances (i.e. dispersion or difference) from the mean – why do we square the distance from the mean? Direction (positive or negative) of the dispersion from the mean is not of primary importance, at least not yet. The fact that a random variable’s value differs from that of the mean is what is important. Once the variance is known, take its square root to arrive at stdev.

* EXAM TIP*: make sure you maintain the distinction between these two – if a problem provides one measure (variance) more likely than not the answer will require using the other (stdev). For example, if a confidence interval is required and variance is given, take its square root before computing the interval. Test writers never miss this opportunity to trip up candidates. Do not be fooled.

* Lognormal distribution *– is used to model asset prices. Please note the distinction between asset prices and asset returns. Asset prices are bound by zero on the downside, prices will never have a negative value. Conversely, asset returns can have either positive or negative values. Taking the natural log of a random variable does not need to be a cause for consternation.

* Shortfall Risk *– is the risk that portfolio value will fall below some minimum acceptable level over some time horizon. The risk addressed by “safety-first” rules of which Roy’s is an example.

* Tracking Error *– tells us how well a portfolio’s return tracks its benchmark or an index.

** Monte Carlo simulation **– is very useful for researchers who need to test their models. Will you have to use or “solve” a MC Sim on any level of the CFA exam? Not likely. Understand what it is – a tool to price complex securities, produce frequency distributions for portfolio returns, test pricing model assumptions, etc. – and why investment managers find it helpful.