# Archives

## Technical Analysis – Principles, Applications, and Assumptions

This is the boring article about technical analysis: no pictures.  Sorry.  The others have pictures.  They’re, therefore, more exciting. Principles of Technical Analysis The primary principle underlying technical analysis is that prices (for stocks, bonds, commodities, currencies, mutual funds, ETFs, whatever) are determined by supply and demand.  Therefore, changes in supply or demand will lead […]

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## Parametric Tests vs. Nonparametric Tests

Parametric tests are straightforward and are the focus of most of . . . well . . . all of the other hypothesis testing articles I’ve written here. They’re concerned, not surprisingly, with questions about the parameters of a particular population or populations: What’s the mean monthly return on this ETF? What’s the standard deviation […]

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## Sampling and Estimation

Often it’s impractical (or even impossible) to collect data on every member of a population in which you are interested.  When it is, an alternative approach that may prove adequate is to collect data on a sample from that population: a subset of the population which you hope will have characteristics sufficiently close to those […]

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## Central Limit Theorem

It’s probably (sorry) not an exaggeration (or, at least, not much of one) to say that the central limit theorem is the single most important theorem in probability theory.  It’s so important that it has its own abbreviation: CLT.  (Clever, eh?) In a nutshell, the CLT says that if you add up a bunch of […]

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## Technical Analysis

Let’s get this discussion going on clear footing from the outset: I’m no fan of technical analysis. I understand that it has shown some promise in the analysis of currency exchange rates, but on the whole I believe that it’s a flawed approach: far too subjective, and whose only true advantage comes from the hope […]

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## Monte Carlo Simulation

In a nutshell, Monte Carlo simulation uses random numbers to approximate the solutions to a variety of problems.  For our purposes, these problems will generally involve trying to approximate complicated probability distributions for such problems as calculating: A portfolio’s value at risk (VaR) The probability that an investor will outlive her assets The probability that […]

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