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Quantocracy’s Daily Wrap for 08/01/2019

This is a summary of links featured on Quantocracy on Thursday, 08/01/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Robust Moving Average [Eran Raviv]

    Moving average is one of the most commonly used smoothing method, basically the go-to. It helps us detect trend in the data by smoothing out short term fluctuations. The computation is trivial: take the most recent k points and simple-average them. Here is how it looks: Moving average example with k= 24 (simulated data) moving average As you can see we have a few points which are stretched out.
  • Tactical Asset Allocation in July [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent Tactical Asset Allocation (TAA) 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

Quantocracy’s Daily Wrap for 07/30/2019

This is a summary of links featured on Quantocracy on Tuesday, 07/30/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research Group Update & Website [Quants Portal]

    For those of you that dont know, the idea of creating an open-source package based on Advances in Financial Machine Learning stemmed from Ashu and my masters project at WorldQuant University. The initial goal was to build out a package that we could leverage to help us extend the literature with a novel contribution. A rather ambitious goal for a 3-month project, which resulted in 2 Capstone
  • Can Low Vol Strategies Be Improved [Alpha Architect]

    My Advisor Perspective article of June 17, 2019 discussed the regime shifting nature of the low volatility anomalylow volatility stocks have outperformed high volatility stocks, providing both higher returns while experiencing lower volatility. For example, in his 2012 paper, Enhancing a Low-Volatility Strategy is Particularly Helpful When Generic Low Volatility is Expensive, Pim van

Quantocracy’s Daily Wrap for 07/29/2019

This is a summary of links featured on Quantocracy on Monday, 07/29/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Extreme Value Theory [Asm Quant]

    Lets talk about tail risk modelling today. In this blog, I want to introduce Extreme Value Theory (EVT) which concerns itself with modelling of the tails of a distribution, and its key results. As we go along we will work through a toy example with basic R implementation. There are two popular parametric approaches to EVT that we will cover in this post. The first is called Block Maxima method.
  • Tips for an Aspiring Creative Quant [Two Centuries Investments]

    Alternative Title: What to Do if Your Boss is Terrified of New Ideas? Several younger quants have asked this question: The culture of our quant group is very skeptical about new ideas. They are terrified of data-mining, and random factors. How can we innovate in such environment? My thoughts on the need for innovation are here, here, and here. But today, I wanted to lay out four tips that
  • Timing Luck and Systematic Value [Flirting with Models]

    We have shown many times that timing luck when a portfolio chooses to rebalance can have a large impact on the performance of tactical strategies. However, fundamental strategies like value portfolios are susceptible to timing luck, as well. Once the rebalance frequency of a strategy is set, we can mitigate the risk of choosing a poor rebalance date by diversifying across all potential

Quantocracy’s Daily Wrap for 07/25/2019

This is a summary of links featured on Quantocracy on Thursday, 07/25/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How the Mathematics of Fractals Can Help Predict Stock Markets Shifts [Open Quants]

    In financial markets, two of the most common trading strategies used by investors are the momentum and mean reversion strategies. If a stock exhibits momentum (or trending behavior as shown in the figure below), its price on the current period is more likely to increase (decrease) if it has already increased (decreased) on the previous period. Section of the time series of the S&P 500 Index or

Quantocracy’s Daily Wrap for 07/24/2019

This is a summary of links featured on Quantocracy on Wednesday, 07/24/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • DeepTrading with TensorFlow VII [Todo Trader]

    For the first time, we will use the features of multiple financial instruments. In this case, we will use the main Forex pairs and the SP500 to perform our forecasts on Gold. This Jupyter notebook will be your guide for more complex calculations. Obviously, you can change the features and instruments as you want or you need. That will be your own research. I have given you the guide. Problem
  • Geometrical evaluation of Generative Adversarial Networks [Quant Dare]

    Generative Adversarial Networks are a quite powerful tool for generating synthetic samples. Visual inspection has been used as a traditional measure of performance. However, it is quite hard to inspect when a time series looks realistic or not! Which methodology can be used then? In order to measure sample quality, topological tools could provide us with some insight, as the geometrical properties
  • PMI & Equity Factor Performance [Factor Research]

    Value and Size have a positive relationship with the PMI, similar to the S&P 500 Indicates that risk sentiment is a core driver of factor performance Investors can consider incorporating variables like the PMI in a risk management framework INTRODUCTION A physicist, a chemist, and an economist are stranded on an island, with nothing to eat. A can of soup washes ashore. The physicist says,

Quantocracy’s Daily Wrap for 07/23/2019

This is a summary of links featured on Quantocracy on Tuesday, 07/23/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Two Sides of Factor Investing [Two Centuries Investments]

    Todays quantitative investors seem split between innovation on one hand and engineering on the other. The prior group is constantly looking for new factors that predict returns in areas like alternative data and machine learning – yet often fail to find them. The latter camp is focused on investing in the more established factors such as value, momentum and quality, where long-term patience,

Quantocracy’s Daily Wrap for 07/22/2019

This is a summary of links featured on Quantocracy on Monday, 07/22/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to build a Bitcoin Sentiment Analysis Strategy [Augmento]

    TL;DR: We built a profitable Bitcoin sentiment strategy yielding 2400% returns over 24 months. Adding trading fees made the strategy more realistic while finding optimal sentiment combinations and window sizes increased returns dramatically. In the previous article, we described how to build a strategy based on Augmento Bullish and Bearish Bitcoin sentiment, and backtested it on Bitmex XBTUSD. The
  • TAA and Transaction Costs [Allocate Smartly]

    New to Tactical Asset Allocation? Learn more: What is TAA? There are two hard costs that investors must consider when comparing a tactical asset allocation strategy to conventional buy & hold: (1) increased tax liability (if trading in a taxable account), and (2) increased trading costs (transaction costs and slippage). Because we track so many published models (50 and counting) were in a
  • Stocks Don’t Do So Hot – Most equities don’t beat 1m Treasury bills (h/t @thodoha) [Mark Rzepczynski]

    Stocks are risky investments. Let's be very clear, stocks are risky with positive skew. Of course, everyone knows that but some data published about two years really drove that home. (See my earlier post "Most stocks are losers – Median and skew tell an important story" about the paper "Do stocks Outperform Treasury Bills" by Hendrik Bessembinder)That path-breaking work
  • Time Series Decomposition & Prediction in Python [Python For Finance]

    In this article I wanted to concentrate on some basic time series analysis, and on efforts to see if there is any simple way we can improve our prediction skills and abilities in order to produce more accurate results. When considering most financial asset price time series you would be forgiven for concluding that, at various time frames (some longer, some shorter) many, many of the data sets we
  • Ensemble Multi-Asset Momentum [Flirting with Models]

    We explore a representative multi-asset momentum model that is similar to many bank-based indexes behind structured products and market-linked CDs. With a monthly rebalance cycle, we find substantial timing luck risk. Using the same basic framework, we build a simple ensemble approach, diversifying both process and rebalance timing risk. We find that the virtual strategy-of-strategies is able to
  • The relation between value and momentum strategies [SR SV]

    Simple value and momentum strategies often end up with opposite market positions. One strategy succeeds when the other fails. There are two plausible reasons for this. First, value investors regularly bet against market trends that appear to have gone too far by standard valuation metrics. Second, value stocks carry particularly high market risk or bad beta and thus fare well when

Quantocracy’s Daily Wrap for 07/20/2019

This is a summary of links featured on Quantocracy on Saturday, 07/20/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Rebalancing Luck [Spring Valley]

    The date on which a portfolio is rebalanced can have a tremendous impact on realized performance. We demonstrate that a strategy rebalanced on different dates using the exact same investment process can exhibit return differentials of over 20% across short periods of time. These differences are entirely explained by path dependency, also known as rebalancing luck. In addition, we cannot

Quantocracy’s Daily Wrap for 07/19/2019

This is a summary of links featured on Quantocracy on Friday, 07/19/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading Evolved Taking it to the Next Level [Following the Trend]

    A year in the making, my third book is now complete. Trading Evolved is quite different from my previous books, and substantially more information packed. This book is a practical, in-depth guide on how to backtest and analyze strategies using powerful Python techniques. To my knowledge, no such book exist at the moment. Trading Evolved is a trading book. Not a programming book. This book will
  • Compound Your Knowledge Ep 18: Size, Mom, Sell-Offs, & R Code [Alpha Architect]

    In this weeks post, we discuss four articles. The size, written by the folks at AQR, is titled Fact, Fiction, and the Size Effect and is a deep dive into the Size effectI highly recommend everyone read the underlying paper as well. The second article examines the baseline historical facts of market sell-offs, both within the U.S. and other markets. The third paper, summarized by Larry

Quantocracy’s Daily Wrap for 07/18/2019

This is a summary of links featured on Quantocracy on Thursday, 07/18/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Value Investing & Concentration [Alpha Architect]

    As many investors have experienced, Value investing has underperformed for some time now. For the period following the Global Financial Crisis, Value investing (in general) has underperformed (1) the market and (2) Growth stocks. So while the past decade has been rough for Value investors, it can be a good time to examine the process and importantly determine how one forms their Value portfolio,
  • An intuition behind currency risk [Quant Dare]

    Although we find currency risk particularly interesting, it is not often the case with many investors for whom it is no more than a necessary inconvenience. As such, they tend to neglect it, accepting undesirable non-remunerated risks and missing potential opportunities. To prevent this, in this post we will try to provide an understanding of the mechanics of currency risk. For the sake of