Quantocracy’s Daily Wrap for 05/11/2017
This is a summary of links featured on Quantocracy on Thursday, 05/11/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

Why your fund isn t doing well: skill, active weight and fees [KKB Research]Month upon month, we see that articles and studies out about active management under performing their benchmarks, some say it use due to fees and others due to managers having no skill. Personally I think its a bit of both, I will demonstrate mathematically how fund constraints combined with fees can make outperformance very difficult. First we will define active share, the active share of a

Do Trading Costs Destroy Factor Investing? [Alpha Architect]There are a number of recent studies that propose a more rigorous criteria for evaluating the practical significance of factors published in academic research journals. First, Harvey, Liu, and Zhu (2015) argue that a tstat of 3 should be replacing the old 2 as a rule for statistical significance. In 2017, Campbell Harvey was quoted claiming the following: Half the financial products (promising

Risk Premia Market Timing? [Quant Bear]Here it goes, finally a strategy backtest (sort of) on this blog (what an intro). In their 1973 paper Risk, Return and Equilibrium: Empirical Tests, Fama and MacBeth introduce a method for estimating betas and risk premia for any risk factors that determine asset prices. Under the assumption that the only common risk factor that drives asset prices is the market itself, we will use their

Shrinkage in statistics [Eran Raviv]Shrinkage in statistics has increased in popularity over the decades. Now statistical shrinkage is commonplace, explicitly or implicitly. But when is it that we need to make use of shrinkage? At least partly it depends on signaltonoise ratio. Introduction The term shrinkage, I think, is the most underappreciated umbrella term in statistics. The reason is that it is often masked under different

Can We Use Mixture Models to Predict Market Bottoms? (Part 3) [Black Arbs]Thus far in the series we’ve explored the idea of using Gaussian mixture models (GMM) to predict outlier returns. Specifically, we were measuring two things: The accuracy of the strategy implementation in predicting return distributions. The return pattern after an outlier event. During the exploratory phase of this project there were some interesting results worthy of more investigation. The

Luck in Trading and Favorable Distributions [Build Alpha]The role of luck in (algorithmic) trading is ever present. Trading is undoubtedly a field that experiences vast amounts of randomness compared to mathematical proofs or chess, for example. That being said, a smart trader must be conscious of the possibility of outcomes and not just a single outcome. I spoke about this in my http://ift.tt/2pqzRjE interview, but I want to reiterate the point as I

Do Mutual Fund Managers Have StockPicking Skill in Lottery Stocks? [Quantpedia]Are portfolio managers skilled in stockpicking? It is a popular subject for academic research and majority of papers show that active funds underperform their respective benchmarks. But… It doesn’t mean professionals do not know how to pick stocks. It can simply mean that a lot of managers are too afraid (or are limited by risk or fund size) to increase their funds’ active share.
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May 12, 2017 at 06:47AM
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