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# Category Archives: Random portfolios

## Betas of the low vol cohorts

How did the constraints affect portfolio betas, and how did the betas change over time? Previously “Low (and high) volatility strategy effects” created 6 sets of random portfolios — the so-called low vol cohorts — as of 2007 and showed their performance up to about a month ago. “Rebalancing the low vol cohorts” looked at … Continue reading

Posted in Quant finance, R language, Random portfolios
Tagged beta in finance, low volatility investing
1 Comment

## Maximum weight of the low vol cohorts

Maximum weight was constrained to 4% at the start of 2007, how does that grow when unhindered? Previously “Low (and high) volatility strategy effects” created 6 sets of random portfolios as of 2007 and showed their performance up to about a month ago. “Rebalancing the low vol cohorts” looked at how much turnover was required … Continue reading

Posted in Quant finance, Random portfolios
1 Comment

## Rebalancing the low vol cohorts

How much turnover is required to get portfolios back to their constraints? Previously “Low (and high) volatility strategy effects” created 6 sets of random portfolios as of 2007 and showed their performance up to about a month ago. This post explores how much turnover it takes to get the portfolios to obey their constraints at … Continue reading

Posted in Portfolio Probe, Quant finance, Random portfolios
Tagged rebalancing, turnover
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## Low (and high) volatility strategy effects

Does minimum variance act differently from low volatility? Do either of them act like low beta? What about high volatility versus high beta? Inspiration Falkenblog had a post investigating differences in results when using different strategies for low volatility investing. Here we look not at a single portfolio of a given strategy over time, but … Continue reading

Posted in Quant finance, R language, Random portfolios
Tagged beta in finance, low volatility investing
1 Comment

## Portfolio optimization inside out

A possible way to search for constraints that improve optimization. The perspective The usual way of thinking about portfolio optimization is to first consider the utility and then restrict to where the constraints are satisfied. A perfectly reasonable view. We use random portfolios to get a different point of view: first ensure that the constraints … Continue reading

## Alpha decay in portfolios

How does the effect of our expected returns change over time? This is not academic curiosity, we want to know in the context of our portfolio if we can. And we can — we visualize the effect of expected returns in situ. First step The idea is to look at the returns of portfolios that … Continue reading

Posted in Quant finance, R language, Random portfolios
Tagged alpha decay, expected returns, MACD, realized alpha
5 Comments

## Performance measurement is about decisions

The return of a hypothetical fund was 17.9% in 2010. We want to know if that is good or bad. The benchmark method The assets in the portfolio are constituents of the S&P 500, so we can compare our fund return to the return of the index. Figure 1: 2010 returns of: the fund and … Continue reading

Posted in Performance, R language, Random portfolios
Tagged investment performance measurement, luck versus skill
4 Comments

## Some new ideas in financial mathematics

Financial mathematicians have built an increasingly elaborate structure around the idea of “the market” … In this article, I intend to challenge some of these foundational concepts with the intention of destabilizing the intellectual structure that has been erected on top of them and to demonstrate how a randomly generated portfolio can beat “the market,” … Continue reading

## Finding good active managers

Investors need to distinguish between good and bad active fund managers. Relatively new technology makes this much easier. The usual methods benchmark One of the common approaches is to compare funds versus a benchmark. Often much effort goes into choosing just the right index to be the benchmark. Should we use MSCI or something else? … Continue reading

## The effect of beta equal 1

Investment Performance Guy had a post about beta equal 1. It made me wonder about the properties of portfolios with beta equal 1. When I looked, I got a bigger answer than I expected. Data I have some S&P 500 data lying about from the post ‘On “Stock correlation has been rising”‘. So laziness dictated … Continue reading

Posted in Quant finance, R language, Random portfolios
Tagged beta equal 1, beta in finance, Capital Asset Pricing Model, CAPM
12 Comments