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Quantocracy’s Daily Wrap for 08/11/2018

This is a summary of links featured on Quantocracy on Saturday, 08/11/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Optimal Portfolio Construction Using Machine Learning [Quant Insti]

    In this post, we will learn about the Stereoscopic Portfolio Optimization framework and how it can be used to improve a quantitative trading strategy. Well also review concepts such as Gaussian Mixture Models, K-Means Clustering, and Random Forests. Our objective is to determine whether we can reject the null hypothesis that the SPO model is not a viable option for creating optimal short-term
  • Endogenous market risk [SR SV]

    Understanding endogenous market risk (setback risk) is critical for timing and risk management of strategic macro trades. Endogenous market risk here means a gap between downside and upside risk to the mark-to-market value that is unrelated to a trades fundamental value proposition. Rather this specific downside skew arises from the markets internal dynamics and indicates the
  • The Lognormal Mixture Variance Model [Jonathan Kinlay]

    The LNVM model is a mixture of lognormal models and the model density is a linear combination of the underlying densities, for instance, log-normal densities. The resulting density of this mixture is no longer log-normal and the model can thereby better fit skew and smile observed in the market. The model is becoming increasingly widely used for interest rate/commodity hybrids. SSALGOTRADING AD In

Quantocracy’s Daily Wrap for 08/10/2018

This is a summary of links featured on Quantocracy on Friday, 08/10/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stock Prediction with ML: Model Evaluation [Alpha Scientist]

    Use of machine learning in the quantitative investment field is, by all indications, skyrocketing. The proliferation of easily accessible data – both traditional and alternative – along with some very approachable frameworks for machine learning models – is encouraging many to explore the arena. However, these financial ML explorers are learning that there are many ways in which using ML to
  • Volatility Metrics [Jonathan Kinlay]

    All that began to change around 2000 with the advent of high frequency data and the concept of Realized Volatility developed by Andersen and others (see Andersen, T.G., T. Bollerslev, F.X. Diebold and P. Labys (2000), The Distribution of Exchange Rate Volatility, Revised version of NBER Working Paper No. 6961). The researchers showed that, in principle, one could arrive at an estimate of
  • Video Digest: Mean Reversion and Bond ETF Returns [Flirting with Models]

  • Are Low Equity Sector Correlations A Warning Sign For Stocks? [Capital Spectator]

    James Paulsen, chief investment strategist at Leuthold Group, sees trouble brewing in the growing disconnect between US equity sectors. He told CNBC earlier this week that correlations among US equities is unusually low and flashing a warning signal. Thats an especially dangerous sign when the stock markets valuation is so high. Lets dig deeper into the topic by crunching correlations on
  • Using Volatility to Predict Market Direction [Jonathan Kinlay]

    We can decompose the returns process Rt as follows: While the left hand side of the equation is essentially unforecastable, both of the right-hand-side components of returns display persistent dynamics and hence are forecastable. Both the signs of returns and magnitude of returns are conditional mean dependent and hence forecastable, but their product is conditional mean independent and hence
  • Career Opportunity for Quant Traders [Jonathan Kinlay]

    We are looking for 3-4 traders (or trading teams) to showcase as Strategy Managers on our Algorithmic Trading Platform. Ideally these would be systematic quant traders, since that is the focus of our fund (although they dont have to be). So far the platform offers a total of 10 strategies in equities, options, futures and f/x. Five of these are run by external Strategy Managers and five are run

Quantocracy’s Daily Wrap for 08/09/2018

This is a summary of links featured on Quantocracy on Thursday, 08/09/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Algorithmic Trading System Development [Auquan]

    Often a Quantitative Researcher will develop trading models in Python or R. These models are then passed off to Quantitative Developers, who implement them in trading systems with Java or C++. Usually, a Quantitative Trader will then execute trades with the help of these systems. I have had the opportunity to work with the Interactive Brokers Java API for years as a researcher, developer, and
  • The Carry Factor and Global Risks [Alpha Architect]

    The carry factor is the tendency for higher-yielding assets to provide higher returns than lower-yielding assets it is a cousin to the value factor, which is the tendency for relatively cheap assets to outperform relatively expensive ones. A simplified description of carry is the return an investor receives (net of financing) if prices remain the same. The classic application is in currencies

Quantocracy’s Daily Wrap for 08/08/2018

This is a summary of links featured on Quantocracy on Wednesday, 08/08/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • SPY Mean Reversion With John Ehlers Adaptive RSI [Flare 9x]

    It has been a busy few months. I have been exploring market indicators that John Ehlers has created which he publicly made available in his book: Cycle Analytics for Traders : Advanced Technical Trading Concepts. The key theme of his book is applying digital signal processing filters to better process market data. He utilizes various high and low pass filters which only allow certain frequencies
  • Our Conversation with @MebFaber [Flirting with Models]

    This post is the first of a series where we will be providing some of our own thoughts and commentary the conversations we had in the first season of our new podcast. This post covers our conversation with Meb Faber, which you can listen to here. 2:09 – Meb hijacks the show to ask a very important question. Nathan Faber (NF) no relation that I know of: If you follow @choffstein on

Quantocracy’s Daily Wrap for 08/07/2018

This is a summary of links featured on Quantocracy on Tuesday, 08/07/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Warning: Stock and Bond Correlation Assumptions are Regime Dependent! [Alpha Architect]

    It aint what you dont know that gets you into trouble. Its what you know for sure that just aint so. attributed to Mark Twain. Mark Twain had some great insights. The quote above can apply to just about every aspect of life, including investing. This axiom is particularly relevant when one is making simplifying assumptions because you are implicitly stating that you know something
  • July 2018 Trend Following [Wisdom Trading]

    July 2018 Trend Following: DOWN -1.98% / YTD: -7.85% Please find this months report of the Wisdom State of Trend Following. Performance is hypothetical. Chart for July: Wisdom State of Trend Following – July 2018 And the 12-month chart: Wisdom State of Trend Following 12 months – July 2018 Below are the summary stats: Horizon Return Ann. Vol. Last month -1.98% 17.37% Year To Date -7.85% 15.7%
  • State of Trend Following in July [Au Tra Sy]

    Slightly positive month for the State of Trend Following, with the YTD slightly negative. Please check below for more details. Detailed Results The figures for the month are: July return: 0.57% YTD return: -2.19% Below is the chart displaying individual system results throughout July: StateTF July And in tabular format: System July Return YTD Return BBO-20 0.62% 8.33% Donchian-20 0.77% 6.03%

Quantocracy’s Daily Wrap for 08/06/2018

This is a summary of links featured on Quantocracy on Monday, 08/06/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean Reversion and Bond ETF Returns [Flirting with Models]

    In July 2016, we argued that bond investors should be quick to celebrate the strong returns they had realized year-to-date. The combination of a defined maturity and known coupon rate creates a gravitational pull for bond returns. Using a global bond ETF universe, we develop a simple model to forecast future 1-year returns. The model suggests that mean reversion is a strong forecaster of future
  • Momentum Variations [Factor Research]

    The simplicity of the Momentum factor can be intellectually challenging Various alternative Momentum versions highlight remarkable similar return profiles The robustness is an attractive characteristic of the investment strategy INTRODUCTION What do selfies, the Kardashians, Crocs, blue cheese, and Boris Johnson have in common? They all rank within the top 50 things that split the opinion of
  • An Extensive Test of Market Timing Strategies in the Gold Market [Quantpedia]

    While the literature on gold is dominated by studies on its diversification, hedging, and safe haven properties, the question When to invest in gold? is generally not analyzed in much detail. We test more than 4,000 seasonal, technical, and fundamental timing strategies for gold. While we find large gains in economic terms relative to the buy-and-hold benchmark for several strategies, the
  • Getting Down To Business! [System Trader Success]

    In the previous parts of this 3-part article (see part 1 and part 2), I introduced you to algo trading, and then discussed features of algo trading, along with advantages and disadvantages. Algo trading can definitely help you compete with the big boys, but it is not automatically a supertrader creator. There is no easy way to trade, and algo trading is no exception. Rest assured there

Quantocracy’s Daily Wrap for 08/05/2018

This is a summary of links featured on Quantocracy on Sunday, 08/05/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 08/04/2018

This is a summary of links featured on Quantocracy on Saturday, 08/04/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What variance swaps tell us about risk premia [SR SV]

    Variance swaps are over-the-counter derivatives that exchange payments related to future realized price variance against fixed rates. Variance swaps help estimating term structures for variance risk premia, i.e. market premia for hedging against volatility risk based in the difference between market-priced variance and predicted variance. The swap rates conceptually produce more accurate estimates
  • Mutual fund performance and survivorship bias [Mathematical Investor]

    As we have noted in previous Mathematical Investor blogs (see this blog for instance), surprisingly few mutual funds beat their respective benchmark (typically some market index). Even fewer consistently beat their benchmarks year after year. A new report from S&P Dow Jones sheds light on this phenomenon. It tabulates, for each year from 2001 through 2017, the percent of mutual funds in

Quantocracy’s Daily Wrap for 08/03/2018

This is a summary of links featured on Quantocracy on Friday, 08/03/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Conference: Financial Revolution – Sentiment Analysis, AI and Machine Learning – Oct 30, 2018, Zurich

    Artificial Intelligence is deemed to be the main driver of the 4th Industrial Revolution. IDC predicts that investment in AI will grow from $12bn in 2017 to $57.6 by 2021, while Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017. As a result, companies from every industry have been spurred on to seize the trend and innovate
  • A Q&A Discussion with Vanguard Researchers on the “Fair Value CAPE Ratio” [Alpha Architect]

    As everyone whos been invested for the last ten years knows, post-financial crisis stock returns have been incredible. The chart below highlights the total returns for the S&P 500 Index, the MSCI EAFE Index, the MSCI EEM Index, and the MSCI ACWI Index. The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually
  • Tactical Asset Allocation in July [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent tactical asset allocation strategies, net of transaction costs. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Learn more about what we do or let AllocateSmartly help you

Quantocracy’s Daily Wrap for 08/01/2018

This is a summary of links featured on Quantocracy on Wednesday, 08/01/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • RSI2 Strategy: Double returns with a simple rule change [Alvarez Quant Trading]

    While playing around with a 2 period RSI (Relative Strength Index) mean reversion strategy, I came up with a very simple rule change with a much larger impact on the results than expected. I doubled the compounded annual growth rate and cut the maximum drawdown in half. That never happens. In my optimization runs the best CAR went from lows 10s to the low 20s with this rule change. The
  • Consistent Momentum [Sutherland Research]

    Its been some time since I last posted so what better way to start than by quantifying and exploring a momentum strategy that was first introduced to me by the good guys at Quantpedia (www.quantpedia.com). If you havent heard of this site before, then I encourage you to check it out. For a nominal fee you get access to an incredible array of trading strategies that have been sourced and
  • Momentum Solutions for Retirement [Dual Momentum]

    As the surge of boomer retirements continues, commentators have given new thought to what safe withdrawal rates are for retirement accounts. The topic is especially significant given two additional factors. First, retirement balances are shockingly low for boomers (Ghilarducci 2015)[1]. Second, market fundamentals do not suggest that either bonds or equities will generate reliably strong rates of