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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

Quantocracy’s Daily Wrap for 02/19/2020

This is a summary of links featured on Quantocracy on Wednesday, 02/19/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Investing Update: An Analysis of 2019 International Factor Returns [Alpha Architect]

    Last week I summarized the 2019 factor performance for U.S. stocks. A natural follow-up question was the followingwhat about International stocks? A great question. So below I dig into the 2019 performance for International Factor Portfolios. 1 Lets dig into the results. Factor Investing Data and Results One needs to start with a universe. I defined the universe as the top 1,500 stocks
  • Factor Exposure: The Turn of The Screw [Quant Dare]

    You may have seen in different papers or websites, analysis of how a specific active portfolio is exposed to different financial factors (Value, Growth, Size, Quality, etc). This insight is very interesting in order to know what to expect from a strategy and to explain and understand its behaviour, compared with a benchmark (which defines the field of play). The analysis could be done by several

Quantocracy’s Daily Wrap for 02/18/2020

This is a summary of links featured on Quantocracy on Tuesday, 02/18/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Diversification with Portable Beta [Flirting with Models]

    A long/flat tactical equity strategy with a portable beta bond overlay a tactical 90/60 portfolio has many moving parts that can make attribution and analysis difficult. By decomposing the strategy into its passive holdings (a 50/50 stock/bond portfolio and U.S. Treasury futures) and active long/short overlays (trend equity, bond carry, bond momentum, and bond value), we can explore the
  • Essential Mathematical Concepts for Algorithmic Trading [Quant Insti]

    If you have landed on this article, one thing is for sure, that you aim to learn algorithmic trading from the mathematical point of view. Before starting the mathematical concepts of algorithmic trading, let us understand how imperative is maths in trading. And before that, let us take a look at two important components of the same, which is a Trader and a Quant/Quantitative Analyst. Who is a
  • Venture Capital: Worth Venturing Into? [Factor Research]

    Venture capital returns are likely to be overstated Top-performing VC funds generated attractive returns, but are difficult to access Average venture capital returns can be replicated efficiently with public equities WINNERS & LOSERS The further the global financial crisis retreats into history, the clearer the winners and losers become. Insurance companies, banks, pension funds, savers, and

Quantocracy’s Daily Wrap for 02/14/2020

This is a summary of links featured on Quantocracy on Friday, 02/14/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Benchmarking the portfolio [OSM]

    In our last post, we looked at one measure of risk-adjusted returns, the Sharpe ratio, to help our hero decide whether he wanted to alter his portfolio allocations. Then, as opposed to finding the maximum return for our heros initial level of risk, we broadened the risk parameters and searched for portfolios that would at least offer the same return or better as his current portfolio and would
  • Research Review | 14 February 2020 | Business Cycle Risk [Capital Spectator]

    A New Index of the Business Cycle William B. Kinlaw (State Street Global Markets), et al. January 2020 The authors introduce a new index of the business cycle that uses the Mahalanobis distance to measure the statistical similarity of current economic conditions to past episodes of recession and robust growth. Their index has several important features that distinguish it from the Conference

Quantocracy’s Daily Wrap for 02/13/2020

This is a summary of links featured on Quantocracy on Thursday, 02/13/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Have you tried to calculate derivatives using TensorFlow 2? [Quant Dare]

    We will learn how to implement a simple function using TensorFlow 2 and how to obtain the derivatives from it. We will implement a Black-Scholes model for pricing a call option and then we are going to obtain the greeks. Matthias Groncki wrote a very interesting post about how to obtain the greeks of a pricing option using TensorFlow which inspired me to write this post. So, I took the same
  • Introduction to XGBoost in Python [Quant Insti]

    Ah! XGBoost! The supposed miracle worker which is the weapon of choice for machine learning enthusiasts and competition winners alike. It is said that XGBoost was developed to increase computational speed and optimize model performance. As we were tinkering with the features and parameters of XGBoost, we decided to build a portfolio of five companies and applied XGBoost model on it to create a

Quantocracy’s Daily Wrap for 02/12/2020

This is a summary of links featured on Quantocracy on Wednesday, 02/12/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Simple Vol Estimators [Falkenblog]

    While short-term asset returns are unpredictable, volatility is highly predictable theoretically and practically. The VIX index is a forward-looking estimate of volatility based on index option prices. Though introduced in 1992 it has been calculated back to 1986, because when released they wanted people to understand how it behaved. Given the conditional volatility varies significantly over time

Quantocracy’s Daily Wrap for 02/11/2020

This is a summary of links featured on Quantocracy on Tuesday, 02/11/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Investing Update: An Analysis of 2019 U.S. Factor Returns [Alpha Architect]

    In case you missed it, 2019 was a good year to be an equity investor. Examining market-cap-weighted indices, the U.S. stock market was up ~ 30%, Developed International Markets were up ~ 22%, and Emerging Markets were up ~ 18%. But how did factors do in 2019? Below I update my post from last year, examining the performance of simple factor portfolios. This year, I make some minor tweaks to

Quantocracy’s Daily Wrap for 02/10/2020

This is a summary of links featured on Quantocracy on Monday, 02/10/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Investing in “Distressed” TAA Strategies [Allocate Smartly]

    In response to a member question: Have you ever looked at a systematic approach that invested only in tactical asset allocation strategies that were experiencing a significant drawdown? We know that performance chasing is a flawed behavioral bias, so an approach like this could exploit it. We always appreciate thoughtful questions like this from members. First off, we can confirm that chasing
  • Payoff Diversification [Flirting with Models]

    At Newfound, we adopt a holistic view of diversification that encompasses not only what we invest in, but also how and when we make those investment decisions. In this three-dimensional perspective, what is correlation-based, how is payoff-based, and when is opportunity-based. In this piece, we provide an example of what we mean by payoff-based diversification, using a simple strategically
  • Timing Low Volatility with Factor Valuations [Factor Research]

    Factors can be valued like stocks or markets The Low Volatility factor in the US had the best subsequent returns when cheapest and worst when most expensive However, the perspective is less clear when analyzing European and Japanese stock markets INTRODUCTION Funds flows are frequently analyzed by investors to gauge the demand for investment strategies, but it represents a challenging exercise.
  • Python Regression Analysis: Drivers of German Power Prices [Philipp Kahler]

    German Power prices can be explained by supply and demand, but also by causal correlations to underlying energy future prices. A properly weighted basket of gas, coal and emissions should therefore be able to resemble the moves of the power price. This article will introduce multivariate regression analysis to calculate the influence of the underlying markets on a given benchmark. It is an example
  • Robot Wealth 6 Week Bootcamp – Build A Fully Automated FX Strategy – Enrollment Ends Friday

    Beginner to live FX algo trading in 6 weeks. Team up, research and trade fully-automated systematic FX strategies and rapidly build your capital-growing portfolio. Gain the experience and support needed to trade a fully-automated systematic FX strategy live by March 2020. Copy the beginner-friendly approach we use to trade for a living so one day you can too. Share the workload with a community of