This is a summary of links featured on Quantocracy on Monday, 03/18/2019. To see our most recent links, visit the Quant Mashup. Read on readers!
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Trend Following in Cash Balance Plans [Flirting with Models]Cash balance plans are retirement plans that allow participants to save higher amounts than in traditional 401(k)s and IRAs and are quickly becoming more prevalent as an attractive alternative to defined benefit retirement plans. The unique goals of these plans (specified contributions and growth credits) often dictate modest returns with a very low volatility, which often results in conservative
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Smart Beta Asset Allocation Models [Factor Research]Most smart beta strategies outperformed the market since 1990, but few have in recent years Diversifying across strategies mitigates the risk of underperformance Various asset allocation models for creating multi-factor portfolios highlight similar results INTRODUCTION The appearance of smart beta ETFs has simplified the life of investors as they no longer need to suffice themselves with plain
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10 Ways to Combine Quant and Fundamental Approaches that Work (and 10 that don’t) [Two Centuries Investments]Can quantitative and fundamental approaches be successfully combined? In my estimate, this has been a top 5 industry question for a long time, including this conference at which Ill be speaking at tomorrow The short answer is: Yes More-so, I believe quantitative approaches cannot work without being guided by fundamental principles and insightful questions. Even if the model is fully technical
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How to collect market tick data [Cuemacro]A lunch break is probably more of a necessity from a break perspective than anything else. You could make the break aspect as short as possible, by getting a ready made takeaway. However, that kind of negates the whole break aspect of it all. I end up going through various cycles of what I have for lunch at work, largely due to the plethora of food choices available near my office in London.
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How to estimate risk in extreme market situations [SR SV]Estimating portfolio risk in extreme situations means answering two questions: First, has the market entered an extreme state? Second, how are returns likely to be distributed in such an extreme state? There are three different types of models to address these questions statistically. Conventional extreme value theory really only answers the second question, by fitting an appropriate