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Quantocracy’s Daily Wrap for 08/15/2018

This is a summary of links featured on Quantocracy on Wednesday, 08/15/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Parameter Sensitivity Analysis [Flare 9x]

    In this post we demonstrate ideas to test for parameter sensitivity. Here we have a strategy with 5x parameters. 3x being look back periods for a specific indiactor. The other 2x being an entry level threshold and an exit level threshold. I decided to change the original parameters by up to 50% in either direction of the original. It might look something like this: # Original param1 = 116 param2 =
  • U.S. dollar exchange rate before FOMC decisions [SR SV]

    Since the mid-1990s the dollar exchange rate has mostly anticipated the outcome of FOMC meetings: it appreciated in the days before a rate hike and depreciated in the days before a rate cut. This suggests that since fixed income markets usually predict policy rate moves early and correctly their information content can be used to trade the exchange rate. A recent paper proposes a systematic
  • Size, Value and Equity Premium Waves [Quantpedia]

    This paper examines the link between microeconomic uncertainty and the size premium across different frequencies in an investment model with heterogeneous firms. We document that the observed time-varying dispersion in firm-specific productivity can account for a large size premium in the 1960's and 1970's, the disappearance in the 1980's and 1990's, and reemergence in the

Quantocracy’s Daily Wrap for 08/14/2018

This is a summary of links featured on Quantocracy on Tuesday, 08/14/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Factor-Based Approach to Disruptor-Based Sectors [Flirting with Models]

    As more thematic products come to the market, it can be difficult for investors to decide how to allocate to them, even if they believe in their future potential. The sector disruptors are a suite of products that focus on areas of the economy that are heavily influenced by new technologies. Taking a factor-based approach using, for example, low volatility and momentum has the potential to boost
  • The Best Research Paper Ever Written on Trading Costs [Alpha Architect]

    Trading costs are a hot topic these days. The topic has sparked investor attention because of the rise of systematic factor investing strategies available via the ETF structure. It seems as if everyone is a quant these days, slinging money around like drunken pirates, destroying the price discovery process along the way. A popular narrative is the following: There is too much capital chasing

Quantocracy’s Daily Wrap for 08/13/2018

This is a summary of links featured on Quantocracy on Monday, 08/13/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Robustness in Quantitative Research and Trading [Jonathan Kinlay]

    One of the most highly desired properties of any financial model or investment strategy, by investors and managers alike, is robustness. I would define robustness as the ability of the strategy to deliver a consistent results across a wide range of market conditions. It, of course, by no means the only desirable property investing in Treasury bills is also a pretty robust strategy, although
  • Factor Exposure: Smart Beta ETFs vs Mutual Funds [Factor Research]

    Investors can express factor views via smart beta ETFs or mutual funds Some mutual funds offer higher factor exposure than smart beta ETFs Given higher fees, strong views on expected factor performance are required INTRODUCTION Similar to wind and water eroding the strongest mountains over time, passive fund management has been gradually capturing market share from active managers globally. The
  • Modeling Asset Volatility [Jonathan Kinlay]

    I am planning a series of posts on the subject of asset volatility and option pricing and thought I would begin with a survey of some of the central ideas. The attached presentation on Modeling Asset Volatility sets out the foundation for a number of key concepts and the basis for the research to follow. Perhaps the most important feature of volatility is that it is stochastic rather than
  • Macro Conditions May Enhance Short-term Predictability of the Shiller P/E [Alpha Architect]

    Is there a relationship between real yields and short-term market valuation? Is there a relationship between inflation rates and short-term market valuation? Does the predictive power of the Shiller P/E improve by using yields and inflation? What are the Academic Insights? YES. The authors describe a mountain-shaped relationship between stock market valuations and inflation rates and real
  • The Law of Large Numbers – Practical Statistics for Algo Traders Part 2 [Robot Wealth]

    Even if youve never heard of it, the Law of Large Numbers is something that you understand intuitively, and probably employ in one form or another on an almost daily basis. But human nature is such that we sometimes apply it poorly, often to great detriment. Interestingly, psychologists found strong evidence that, despite the intuitiveness and simplicity of the law, humans make systematic
  • Pullbacks Heading Into Opex Week [Quantifiable Edges]

    Opex week often carries some bullish seasonality. Pullbacks into strong seasonal periods will often offer substantial edges. The study below utilizes this concept and examines pullbacks of at least 3 days just prior to opex week. 2018-08-12-1 Numbers here are strong, and suggest a possible upside edge. Of course, August opex week has NOT been great. (Click here to see opex week broken down by

Quantocracy’s Daily Wrap for 08/12/2018

This is a summary of links featured on Quantocracy on Sunday, 08/12/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Yield Curve Construction Models – Tools & Techniques [Jonathan Kinlay]

    Yield curve models are used to price a wide variety of interest rate-contingent claims. The existence of several different competing methods of curve construction available and there is no single standard method for constructing yield curves and alternate procedures are adopted in different business areas to suit local requirements and market conditions. This fragmentation has often led to

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