This is a summary of links featured on Quantocracy on Monday, 09/18/2017. To see our most recent links, visit the Quant Mashup. Read on readers!
-
The Lie of Averages [Flirting with Models]Averages are often used to summarize data: but sometimes fitting for the average means fitting nothing at all. Expected returns are a meaningful input to portfolio construction, but are unlikely to be the returns actually realized. Reality rarely looks average. The world is dynamic and forecasts can change. Not only should we expect that things will not be average, but we should expect that our
-
Factor Allocation 101: Equal vs Volatility-Weighted [Factor Research]Equal-weight and volatility-weighted allocations are two common factor allocation frameworks Risk-return ratios are not higher with volatility-weighted allocations Different reasons can explain the superiority of equal-weight allocations INTRODUCTION In July we published a research report Factors & Volatility-Based Risk Management were we analysed Value, Size and Momentum based on
-
The Weakest Week (Updated) [Quantifiable Edges]From a seasonality standpoint, there isnt a more reliable time of the year to have a selloff than this upcoming week. In the past I have referred to is as The Weakest Week. Since 1961 the week following the 3rd Friday in September has produced the most bearish results of any week. Below is a graphic to show how this upcoming week has played out over time. 2017-09-17 image1 As you can see
-
Research Review | Portfolio Management [Capital Spectator]Asset Allocation in a Low Yield Environment John Huss (AQR Capital Mgt.), et al. August 17, 2017 The year 2016 saw bond yields fall to unprecedented low levels in major developed markets, with nominal yields on 10-year German and Japanese government bonds even turning negative. While yields have risen off their lows in 2017, we are still in a very low rate environment. Does this demand exceptional
-
Why Machine Learning Funds Fail [Quantpedia]The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become apparent in this presentation. Over the past two decades, I have seen many faces come and go, firms