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Quantocracy’s Daily Wrap for 06/20/2017

This is a summary of links featured on Quantocracy on Tuesday, 06/20/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • An Out of Sample Update on DDN s Volatility Momentum Trading Strategy and Beta Convexity [QuantStrat TradeR]

    The first part of this post is a quick update on Tony Coopers of Double Digit Numericss volatility ETN momentum strategy from the volatility made simple blog (which has stopped updating as of a year and a half ago). The second part will cover Dr. Jonathan Kinlays Beta Convexity concept. So, now that I have the ability to generate a term structure and constant expiry contracts, I decided
  • Dynamic Asset Allocation for Practitioners, Part 2: The Many Faces of Price Momentum [Invest Resolve]

    In our last post, we covered the importance of a well-designed investment universe as a precondition for thoughtful diversification. In this second article on Dynamic Asset Allocation for Practitioners we will explore several methods for measuring price momentum to compare and contrast their utility under different portfolio concentration and asset universe specifications. What is momentum?
  • You Don’t Want to Buy Vol, You Want to Sell Vol! [Meb Faber]

    That headline was a response I received from a handful of friends regarding my last post on buying puts as tail risk insurance. And I agree. Well, sort of. Its been long known that there exists a premium for selling insurancehey, otherwise why would anyone do it? Now what if you could combine the best of both? Selling vol to capture the premium but buying vol to protect against big down
  • Isolating the Monkey Effect [Markov Processes]

    Continuing our exploration into the smart beta segment (Part 1, Part 2), in this third post we introduce a simple IQ Test that can help investors and managers measure the smartness of the increasing number of non-cap-weight rules-based products on the market. There are numerous arguments in circulation saying that smart beta in general isnt particularly smart. A prominent one,

Quantocracy’s Daily Wrap for 06/19/2017

This is a summary of links featured on Quantocracy on Monday, 06/19/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to come up with quant trading ideas? [Cuemacro]

    I used to play the violin. I really enjoyed it. However, there was something that was very clear to anyone listening to me playing, who perhaps would not have enjoyed it quite as much. The sound which came out of the violin, might have been technically similar to the music on a sheet in front of me, but it didnt sound perhaps quite like the composer intended. The tuning wasnt that
  • Is Your Multi-Asset Strategy Really Multi-Asset? [Flirting with Models]

    The term multi-asset appears in many investment strategies and applies to both balanced funds and target date retirement funds. However, multi-asset strategies may be concentrated in a limited set of asset classes, and the performance of these asset classes may be driven by an even more limited set of risk factors. By looking through the lenses of performance, asset classes, risk factors,
  • Academic Research Insight: The Strategic Timing of Earnings News [Alpha Architect]

    Title: FURTHER EVIDENCE ON THE STRATEGIC TIMING OF EARNINGS NEWS: JOINT ANALYSIS OF WEEKDAYS AND TIMES OF DAY Authors: RONI MICHAEY, AMIR RUBIN, ALEXANDER VEDRASHKO Publication: JOURNAL OF ACCOUNTING AND ECONOMICS, 2016 (version here) What are the research questions? Do managers act to strategically time negative earnings announcements? Is there a strategic weekday (Monday through Friday) and/or
  • Machine Learning In Python for Trading [Quant Insti]

    At the end of my last blog, I had asked a few questions. Now, I will answer them all at the same time. I will also discuss a way to detect the regime/trend in the market without training the algorithm for trends. But before we go ahead, please use a fix to fetch the data from Google to run the code below. data from Google to run the code Trading Using Machine Learning In Python Part-2Click To

Quantocracy’s Daily Wrap for 06/18/2017

This is a summary of links featured on Quantocracy on Sunday, 06/18/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Algorithmic Options Trading, Part 2 [Financial Hacker]

    In this second part of the Algorithmic Options trading series well look more closely into option returns. Especially into the methods of combining different option types for getting user-tailored profit and risk curves, which gives options an interesting advantage over other financial instruments. Options traders know combinations with funny names like Iron Condor or Butterfly, but

Quantocracy’s Daily Wrap for 06/16/2017

This is a summary of links featured on Quantocracy on Friday, 06/16/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Nervous About The Market? It Might Be Time for This Strategy [Meb Faber]

    When the tech bubble collapsed back in 2000, the Nasdaq fell from 5,132 to just 1,470 a few months later. Many popular stocks found their market prices gutted. For example, Cisco lost 86% of its market cap, while Amazon fell over 90% from $107 to $7. Losses such as these decimated investor portfolios. In 2008, it happened again. The average diversified U.S. stock fund fell a whopping 38 percent.
  • Research Review | 16 June 2017 | Yield Curve Analysis [Capital Spectator]

    Monetary Policy Uncertainty and Bond Risk Premium Fuwei Jiang (Central University of Finance and Economics) and Guoshi Tong (Renmin University) October 1, 2016 We show that uncertainty of monetary policy (MPU) commands a risk premium in the US Treasury bond market. Using the news based MPU measure in Baker, Bloom, and Davis (2016) to capture monetary policy uncertainty, we find that MPU forecasts
  • Active Share: Does it Predict Fund Performance? [Alpha Architect]

    The Holy Grail for mutual fund investors is the ability to identify in advance, which of the active mutual funds (or ETFs nowadays) will outperform in the future. The evidence suggests this task is almost impossible. To date, the overwhelming body of academic research has demonstrated that past performance not only doesnt guarantee future performance (as the required SEC disclaimer states), but

Quantocracy’s Daily Wrap for 06/15/2017

This is a summary of links featured on Quantocracy on Thursday, 06/15/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Scalars, Vectors, Matrices and Tensors – Linear Algebra for Deep Learning (Part 1) [Quant Start]

    Back in March we ran a content survey and found that many of you were interested in a refresher course for the key mathematical topics needed to understand deep learning and quant finance in general. Since deep learning is going to be a big part of this year's content we thought it would be worthwhile to write some beginner tutorials on the key mathematical topicslinear algebra, calculus

Quantocracy’s Daily Wrap for 06/14/2017

This is a summary of links featured on Quantocracy on Wednesday, 06/14/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fractal Adaptive Moving Average | Trading Strategy (Setup) [Oxford Capital]

    I. Trading Strategy Developer: John Ehlers. Source: Ehlers, J., FRAMA: Fractal Adaptive Moving Average. Concept: Trend following trading strategy based on adaptive price filters. Research Goal: To verify performance of the Fractal Adaptive Moving Average (FRAMA). Specification: Table 1. Results: Figure 1-2. Trade Setup: Long Trades: Close[i 1] > Entry_Upper_Band[i 1]. Short Trades:
  • “Passive” Investing: Theory and Practice in a Global Market [Alpha Architect]

    Purely passive investing is theoretically plausible, but practically impossible. That said, the practical implementations can often be good enough. As a theoretical index investor, you deploy capital, take a long snooze, and wake up some day to consume your portfolio. Unfortunately, the world doesnt work like. Allocations change, life happens, and as we cover in this blog post, there are
  • Portfolio Weighting Schemes for Commodity Futures Risk Premia [Quantpedia]

    We examine whether and to what extent successful equities investment strategies are transferrable to the commodities futures market. We investigate a total of 7 investment strategies that involve optimization and mean-variance timing techniques. To account for the unique characteristics of the commodity futures market, we propose a novel method of classification based on momentum or term structure
  • Podcast: Optimal bet sizing – lessons from biased coin flip experiment w/ Victor Haghani [Chat With Traders]

    Victor Haghani began his career at Salomon Brothers in 1984, starting out in a research role before joining their prop trading desk. In 1992, Victor left Salomon to become one of the founding partners of Long Term Capital Management LTCM was an incredibly successful hedge fund, up until 1998, when it failed in a spectacular fashion. Causing the Federal Reserve to step in and organize a bailout,

Quantocracy’s Daily Wrap for 06/12/2017

This is a summary of links featured on Quantocracy on Monday, 06/12/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factors & Financial Planning [Flirting with Models]

    In asset management research, we often assume an investor has an infinite horizon, no spending requirements, and no tax consequences. While this may be appropriate for some institutions, it is rarely appropriate for individual investors, leaving financial advisors to fill the gaps. Many factor (smart-beta) products focus on their potential for excess (risk-adjusted) returns. The return is
  • Academic Research Insight: Factors and the Road to Retirement [Alpha Architect]

    Title: A WEALTH MANAGEMENT PERSPECTIVE ON FACTOR PREMIA AND THE VALUE OF DOWNSIDE PROTECTION Authors: LOUIS SCOTT AND STEFANO CAVAGLIA Publication: THE JOURNAL OF PORTFOLIO MANAGEMENT, SPRING 2017 (version here) What are the research questions? The article links two current hot topics: goal based investing and factor premia. Can factor premia (value, size, momentum and quality) help the aspiring

Quantocracy’s Daily Wrap for 06/11/2017

This is a summary of links featured on Quantocracy on Sunday, 06/11/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Real Time Factor Performance [Dual Momentum]

    According to S&P DJ Indices, 92% of all actively managed stock funds failed to beat their benchmarks over the past 15 years. This should come as no surprise. Similar results were published more than 20 years ago. This information has caused a move away from active stock selection and toward index funds or systematic approaches. Money managers have recently moved more in the direction of
  • Is Bitcoin A New Asset Class? [Capital Spectator]

    The astonishing bull market (bubble?) in Bitcoin has drawn attention to the cryptocurrency from all corners. One of the questions thats reasonating: Should Bitcoin be treated as an asset class, on par with stocks, bonds, real estate and commodities? A Forbes article last year, citing a study by ARK Investment Management, offered 4 Reasons Why Bitcoin Represents A New Asset Class. The

Quantocracy’s Daily Wrap for 06/10/2017

This is a summary of links featured on Quantocracy on Saturday, 06/10/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quant trading strategies which float [Cuemacro]

    had many different types of toys. My favourite was Lego. It was kind of cool (and even cooler these days judging by the ever wackier Lego sets you can buy these days). You can create a new toy each day out of Lego. All you needed was some imagination. However, there was always an Achilles heel to anything made of Lego. They couldnt float, because of the gaps between the plastic*. So how could

Quantocracy’s Daily Wrap for 06/07/2017

This is a summary of links featured on Quantocracy on Wednesday, 06/07/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Dynamic Asset Allocation for Practitioners, Part 1: Universe Selection [Invest Resolve]

    In 2012 we published a whitepaper entitled Adaptive Asset Allocation: A Primer in which we built upon the simple, robust momentum framework proposed by Mebane Faber in his 2009 study Relative Strength Strategies for Investing. Our approach utilized a portfolio optimization overlay to this framework which served to stabilize and strengthen the dynamic mix of high-momentum assets,
  • Yahoo Finance Alternatives [Foss Trading]

    I assume that you're reading this because you are one of many people who were affected by the changes to Yahoo Finance data in May (2017). Not only did the URL change, but the actual data changed as well! The most noticeable difference is that the adjusted close column is now only split-adjusted, whereas it used to be split- and dividend-adjusted. Another oddity is that only the close prices
  • State of Trend Following in May [Au Tra Sy]

    Negative month for the State of Trend Following report, putting the YTD well in the red. Please check below for more details. Detailed Results The figures for the month are: May return: -3.14% YTD return: -7.44% Below is the chart displaying individual system results throughout May: StateTF May And in tabular format: System May Return YTD Return BBO-20 -8.18% -14.54% Donchian-20 -5.35% -16.26%
  • Factors vs. Sectors in Asset Allocation [Quantpedia]

    This paper compares and contrasts factor investing and sector investing, and then seeks a compromise by optimally exploiting the advantages of both styles. Our results show that sector investing is effective for reducing risk through diversification while factor investing is better for capturing risk premia and so pushing up returns. This suggests that there is room for potentially fruitful
  • Rough Path Theory and Signatures Applied To Quantitative Finance – Part 3 [Quant Start]

    This is the third in a new advanced series of posts written by Imanol Prez, a PhD researcher in Mathematics at Oxford University and an expert guest contributor to QuantStart. In this post Imanol applies the Theory of Rough Paths to the task of handwritten digit classificationa common task for testing the effectiveness of machine learning models. – Mike. As we discussed in the last article,