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Quantocracy’s Daily Wrap for 03/05/2020

This is a summary of links featured on Quantocracy on Thursday, 03/05/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How much risk should we take? [Investment Idiocy]

    This is the second of three posts aimed at answering three fundamental questions in trading: How should we control risk (previous post) How much risk should we take? (this post) How fast should we trade? (TBC) These questions are extremely important, IMHO much more important than the question of which funky indicator to use. I won't be able to discuss all the finer details of position scaling
  • Should You React To The Surge In Stock Market Volatility? [Capital Spectator]

    The coronavirus thats roiling world markets and raising questions about the economic outlook has triggered a familiar shock to stocks: higher volatility. Is this a reason to change your asset allocation, rebalance the portfolio or modify risk management decisions? Maybe, but maybe not. There is no generic answer for everyone because every investor is different due to risk tolerance, time

Quantocracy’s Daily Wrap for 03/04/2020

This is a summary of links featured on Quantocracy on Wednesday, 03/04/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Graphical Lasso and its Financial Applications [Robot Wealth]

    Way back in November 2007, literally weeks after SPX put in its pre-GFC all-time high, Friedman, Hastie and Tibshirani published their Graphical Lasso algorithm for estimation of the sparse inverse covariance matrix. Are you suggesting that Friedman and his titans of statistical learning somehow caused the GFC by publishing their Graphical Lasso algorithm? Not at all. Im just setting you up to

Quantocracy’s Daily Wrap for 03/03/2020

This is a summary of links featured on Quantocracy on Tuesday, 03/03/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Algo Trading in the Cloud [Ran Aroussi]

    The last few months got me busy like a bee… It all started in 2016 with the release of QTPyLib. I was trying to shorten the time it takes to go from an idea to live trading by abstracting all the techie stuff as much as possible (while still allowing flexibility for developers). Last year I shared my vision for the next version of QTPyLib in hopes of making the lives of programmatic traders ever
  • Drawdowns by the data [OSM]

    Were taking a break from our series on portfolio construction for two reasons: life and the recent market sell-off. Life got in the way of focusing on the next couple of posts on rebalancing. And given the market sell-off we were too busy gamma hedging our convexity exposure, looking for cheap tail risk plays, and trying to figure out when we should go long the inevitable vol crush. Joking.

Quantocracy’s Daily Wrap for 03/02/2020

This is a summary of links featured on Quantocracy on Monday, 03/02/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Last Week (Painfully) Illustrated the Importance of Non-Binary Portfolios [Allocate Smartly]

    This is one of my favorite takes of the last week. It was tweeted on Friday, which held two distinctions: (1) It capped off a helluva scary week, and (2) It just so happened to be month-end, when many TAA strategies trade by default. I couldnt agree more. Imagine making a single risk on/risk off decision with your entire portfolio in the face of Armageddon. Thats how a lot of strategies
  • How do Institutional Investors approach Climate Risks? [Alpha Architect]

    Private and public companies face direct costs related to three types of climate risks: physical (i.e. extreme weather), regulatory (i.e. policies and regulations implemented to combat climate change), and technological (i.e. electric or fuel-cell-powered vehicles could disrupt traditional car manufacturers). These risks to portfolio companies have the potential to adversely affect the returns
  • Volatility vs Risk – Revised [Two Centuries Investments]

    Given the increasing drawdown in the market, it seems prudent to revisit the notion of volatility vs risk. See original post here. 1) To recap, in case you just got back from a 10 day silent mediation retreat, S&P500 peaked on Feb 19th 2020, and has been in pretty much a free-fall since then, down almost 13%. Unless you were massively short the market, these ten days did not feel good. 2) That
  • Domestic Fixed Income Factor Implementations [Flirting with Models]

    Prior academic and practitioner research suggests that factor-based fixed income investing can create attractive return profiles and be useful when building fixed income portfolios. Using an investment universe of eight domestic fixed income asset classes, we build dollar-neutral long-short portfolios targeting Value, Momentum, and Carry factors using both single-metric definitions and
  • ESG vs Low Carbon Investing [Factor Research]

    ESG and Low Carbon portfolios feature significant, but different sector & country biases Investors should expect large tracking errors in some ETFs Some products contain stocks that are likely unexpected and undesired INTRODUCTION Investors seeking exposure to global equities with a low carbon footprint could consider the iShares MSCI ACWI Low Carbon ETF (CRBN) or SPDR MSCI ACWI Low Carbon ETF

Quantocracy’s Daily Wrap for 02/28/2020

This is a summary of links featured on Quantocracy on Friday, 02/28/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Smart Money Indicator Rebuttal [Alpha Architect]

    In February 2019, Wes asked that I share my research on what I call the Smart Money Indicator. I did a guest post on the subject that summarized the results of a paper introducing my research on the topic. The indicator measures the relative sentiment in equities between institutional investors and individual investors. The metric is formed using the information on how each class of
  • pandas for Quants: New Video Course from QaR [Quant at Risk]

    Hi Guys! Im happy to kick off a new series of free video lectures devoted to Pythons library of pandas. Every week, I will be uploading something between 2 to 4 new videos especially crafted around practicalities of pandas library applied to financial data and their analysis and processing. The official webpage of this course is here. We start from the level of an absolute beginner/newcomer
  • Global Low Volatility and Momentum Factor Investing Portfolios [Alpha Architect]

    A Springbok antelope can reach a top speed of 55 miles per hour in the African savanna, whereas the fastest human manages barely half that speed and only for a few meters. In a short race, we are left in the dust. However, we are built for endurance and can run for hours at an almost constant speed, ultimately even run a Springbok into exhaustion. Persistence hunting is one of the earliest human
  • Market will be up 9.7% in 3 months! [Alvarez Quant Trading]

    When this sell-off indicator triggers, it is correct 100% of the time! On average the market is up only 2.6% in 3 months. OR NOT! After big moves in the market, we often see research saying that when the market has done X it will move Y%. I had a reader send me such research asking for my thoughts on it. The indicator was that the market had closed down 3 days in a row with volume each

Quantocracy’s Daily Wrap for 02/26/2020

This is a summary of links featured on Quantocracy on Wednesday, 02/26/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 15% Off Tickets to The Quant Conference | April 3rd, 2020 in NYC | Promo Code: QUANTOCRACY15

    Get 15% off tickets to the next Quant Conference with the promo code QUANTOCRACY15. The Quant Conference is a forum that engages the brightest young minds and foremost thought leaders from the industry and academia to dive into the latest innovations in quant finance, foster collaboration and facilitate opportunities.
  • Create your own Deep Learning framework using Numpy [Quant Dare]

    I have always been curious about how deep learning frameworks are created. I use Keras, TensorFlow, and PyTorch and they all are really good, but sometimes I feel like I am playing with a black box (in some frameworks I feel it more than in others) that hides its secrets. If you feel the same way, this post is for you. We are going to create a deep learning framework using Numpy arrays while we

Quantocracy’s Daily Wrap for 02/25/2020

This is a summary of links featured on Quantocracy on Tuesday, 02/25/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Assessing The Damage After Monday s Sharp Decline In Stocks [Capital Spectator]

    Well, that was painful. The increasingly hazy risk outlook linked to the coronavirus outbreak inspired a 3.35% haircut in the US stock market (S&P 500). The tumble was certainly a bracing counterpoint to the idea that sunny optimism is the only game in town. But before we let recency bias flip to the opposite extreme, lets review where we stand after yesterdays smackdown. Yesterdays
  • Macroeconomic Risks in Equity Factor Investing: Part 2/2 [Alpha Architect]

    What are the research questions? Although not a new topic, the first half of the article explored and documented the dependent relationship between factor returns and time-varying macroeconomic environments. In the second half of this paper, the authors provide insightful commentary and a renewed perspective on the potential for diversification across factors through the lens of assessing

Quantocracy’s Daily Wrap for 02/24/2020

This is a summary of links featured on Quantocracy on Monday, 02/24/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Essential Books on Algorithmic Trading [Quant Insti]

    When you are completely immersed in wanting to learn something new, you start looking for everything that surrounds the learning process. And with the aspiration to learn Algorithmic Trading, there must be certain questions crowding your mind, like: How do I learn Algorithmic Trading? What are the steps to start Algo trading? Which are the essential books on Algorithmic trading? How do I start
  • Ensembles and Rebalancing [Flirting with Models]

    While rebalancing studies typically focus on the combination of different asset classes, we evaluate a combination of two nave trend-following strategies. As expected, we find that a rebalanced fixed-mix of the two strategies generates a concave payoff profile. More interestingly, deriving the optimal blend of the two strategies allows the rebalanced portfolio to out-perform either of the two
  • When The Market Gaps Down Huge During A Long-Term Uptrend [Quantifiable Edges]

    With corona virus news scaring the market pre-open today, I decided to look back at other time SPY has gapped down more than 2% when it had been in a long-term uptrend. As you might suspect, instances have been fairly rare. Looking ack to SPY inception, there were only 16 other instances. And upping the filter to 2.5%, the instances drop to just 5. (As I type, SPY is indicating 2.4% below

Quantocracy’s Daily Wrap for 02/23/2020

This is a summary of links featured on Quantocracy on Sunday, 02/23/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Model Interpretability: The Model Fingerprint Algorithm [Hudson and Thames]

    The complexity of machine learning models presents a substantial barrier to their adoption for many investors. The algorithms that generate machine learning predictions are sometimes regarded as a black box and demand interpretation. Yimou Li, David Turkington, and Alireza Yazdani present a framework for demystifying the behavior of machine learning models. They decompose model predictions into
  • All About Time Series: Analysis and Forecasting [Quant Insti]

    Since predicting the future stock prices in the stock market is crucial for the investors, Time Series and its related concepts help in organizing the data for accurate prediction. In this article, we are focusing on Time Series, its analysis and forecasting. In this article, we aim to cover the following on Time Series: What is Time Series and Time Series Analysis? Types of Time Series What are
  • Rebalancing! Really? [OSM]

    In our last post, we introduced benchmarking as a way to analyze our heros investment results apart from comparing it to alternate weightings or Sharpe ratios. In this case, the benchmark was meant to capture the returns available to a global aggregate of investable risk assets. If you could own almost every stock and bond globally and in the same proportion as their global contribution, what
  • Detecting market price distortions with neural networks [SR SV]

    Detecting price deviations from fundamental value is challenging because the fundamental value itself is uncertain. A shortcut for doing so is to look at return time series alone and to detect strict local martingales, i.e. episodes when the risk-neutral return temporarily follows a random walk while medium-term return expectations decline with the forward horizon length. There is a test

Quantocracy’s Daily Wrap for 02/21/2020

This is a summary of links featured on Quantocracy on Friday, 02/21/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Massive Performance Divergence Between Large Growth and Small Value Stocks [Alpha Architect]

    From 2017 through 2019, the Russell 1000 Growth Index returned 20.5 percent per annum, outperforming the Russell 1000 Value Index, which returned 9.7 percent, by 10.8 percentage points a year; and the Russell 2000 Growth Index returned 12.5 percent per year, outperforming the Russell 2000 Value Index, which returned 4.8 percent, by 7.7 percentage points per year. The annual average value premium