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Quantocracy’s Daily Wrap for 08/25/2020

This is a summary of links featured on Quantocracy on Tuesday, 08/25/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sigma Algebras and Probability Spaces [Quant Start]

    Our recent 2020 Content Survey highlighted the desire from many of you to study the more advanced mathematics necessary for carrying out applications in quantitative finance. Two of the highlighted areas were Linear Algebra for Deep Learning along with Stochastic Calculus. The latter is the underlying theoretical framework utilised for pricing derivatives contracts. Learning Stochastic Calculus

Quantocracy’s Daily Wrap for 08/24/2020

This is a summary of links featured on Quantocracy on Monday, 08/24/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Risk-Neutral Probability Distributions: CLK2020 [Quantoisseur]

    Risk-neutral probability distributions (RND) are used to compute the fair value of an asset as a discounted conditional expectation of its future payoff. In 1978, Breeden and Litzenberger presented a method to derive this distribution for an underlying asset from observable option prices [1]. The derivation of the relationship is well presented in A Simple and Reliable Way to Compute Option-Based
  • Does Gold do What it is Supposed to do? [Alpha Architect]

    The world has unquestionably be sent on a wild ride in 2020. We entered the year full of optimism and hope. Markets were at or near all-time highs, unemployment was low, living on easy street was good. Then the impact of COVID-19 ripped through the market and the economy with enough force to make the winds of even a double hurricane green with envy. This massive and rapid readjustment of the
  • Training the Perceptron with Scikit-Learn and TensorFlow [Quant Start]

    In the previous article on the topic of artificial neural networks we introduced the concept of the perceptron. We demonstrated that the perceptron was capable of classifying input data via a linear decision boundary. However we postponed a discussion on how to calculate the parameters that govern this linear decision boundary. Determining these parameters by means of 'training' the
  • How Risky Are Value Stocks? [Factor Research]

    The Value factor is often explained as representing a risk premium or a behavioral bias However, financial analysts regard cheap stocks as less risky than expensive ones Data shows that expensive stocks were riskier than cheap ones, which challenges the risk premium theory INTRODUCTION Which of the following two portfolios comprised of US stocks would you consider riskier? Portfolio A: Amazon,
  • Market-implied macro shocks [SR SV]

    Combinations of equity returns and yield-curve changes can be used to classify market-implied underlying macro news. The methodology is structural vector autoregression. Theoretical restrictions on unexpected changes to this multivariate linear model allow identifying economically interpretable shocks. In particular, one can distinguish news on growth, monetary policy, common risk premia and

Quantocracy’s Daily Wrap for 08/21/2020

This is a summary of links featured on Quantocracy on Friday, 08/21/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Petra on Programming: Four Dimensions of Strength [Financial Hacker]

    In the S&C September 2020 article Tracking Relative Strength In Four Dimensions, James Garofallou presents a metric for evaluating a securitys strength relative to 11 major market sectors and over several time periods. All this information is squeezed into a single value. Maybe at cost of losing other important information? In this article well look into how to program such a
  • Even Great Investments Experience Massive Drawdowns [Alpha Architect]

    Editors Note: The ability of value investors to adhere to their investment strategy has been put to the greatest test ever. From January 2017 through March 2020, in terms of total returns, the Russell 3000 Growth Index outperformed the Russell 3000 Value Index by 51.7 percentage points (46.7% vs. -5.0%). That drawdown was much greater than previous ones and has lasted longer. For frequent

Quantocracy’s Daily Wrap for 08/19/2020

This is a summary of links featured on Quantocracy on Wednesday, 08/19/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Multi-Asset Skewness Trading Strategy [Quantpedia]

    Our main goal in Quantpedia is to broaden the horizons of our readers in the field of systematic investing and quantitative trading. We do not aim to sell trading signals but to inspire and give fresh ideas, of how to invest limited time and resources on quantitative research. Clients can adopt trading strategy ideas derived out of academic research or further adapt them to their needs and

Quantocracy’s Daily Wrap for 08/18/2020

This is a summary of links featured on Quantocracy on Tuesday, 08/18/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Neural Network based trading strategy [Philipp Kahler]

    I always dreamed about the machine which tells me to enter long right before the market starts to go up. Might a neural network be this machine? Using Tradesignal and the free Python Neural Net library Pyrenn it is easy to find out Part one: Classification of data The first step in the process is to tell the Neural Network when it should give me a go. Therefore I designed me small indicator
  • Value Investing: An Examination of the 1,000 Largest Firms [Alpha Architect]

    Among stock investors, a common strategy/belief held is Value investing buying stocks that are relative cheaper on price/fundamental ratios. The idea behind why value investing works is that Value stocks are either (1) riskier and/or (2) have been mispriced by the market. In theory, these elements of risk/mispricing lead to expected above-market returns. However, this strategy has failed over
  • Bank Risk Premia Indices: Unbankable? [Factor Research]

    Factor investing can be pursued across asset classes Risk premia products sold by investment banks have generated mostly unattractive returns since 2006 The idea of risk premia indices is great, but the implementation has been poor INTRODUCTION Monoculture can be considered the biggest threat to our food supply and therefore our livelihood. Although our diet may seem varied, about 20 species of

Quantocracy’s Daily Wrap for 08/17/2020

This is a summary of links featured on Quantocracy on Monday, 08/17/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • EDGAR timestamps [Regressionist]

    I need precise timestamp in order to study the market reaction to news. Sadly, the SEC has not joined the exchanges in providing nanosecond timestamps from GPS-synced rubidium atomic clocks. Rather, it looks like the best EDGAR timestamps I can get from the SEC are only accurate within a couple of minutes. Here are three ways to get the timestamps: Filing header XML Oldloads archives

Quantocracy’s Daily Wrap for 08/16/2020

This is a summary of links featured on Quantocracy on Sunday, 08/16/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Feature Selection (3 / 3) [Tr8dr]

    In the prior two posts, investigated: Subspace Projections: feature selection (1/3) Information Geometric: feature selection (2/3) In this post will evaluate feature importance as implemented by Random Forest and compare to Information Geometric approaches. Here is an outline of what would like to discuss: similarities between Decision Trees and Information Geometric approaches for feature
  • Candlestick Pattern Scanner Functions [Dekalog Blog]

    Since my last currency strength candlestick chart post it seemed to make sense to be able to scan said charts for signals, so below is the code for two Octave functions which act as candlestick pattern scanners. The code is fully vectorised and self-contained, and on my machine they can scan more than 300,000 OHLC bars for 27/29 separate patterns in less than 0.5 seconds. Both functions have a

Quantocracy’s Daily Wrap for 08/14/2020

This is a summary of links featured on Quantocracy on Friday, 08/14/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Feature Selection (2 / 3) [Tr8dr]

    As mentioned in the prior discussion feature selection (1/3), of primary interest is understanding the contribution of each feature in x to the outcome or class labeling function f(x ) . One way to examine this is to understand how the distributions: p(xf) , the probability distribution of feature f (without regard to label) p(xf|f(x)=y) , the feature distribution conditional on class label
  • Rebalance Timing Luck: The (Dumb) Luck of Smart Beta [Flirting with Models]

    We are proud to announce the release of our newest paper, Rebalance Timing Luck: The (Dumb) Luck of Smart Beta. Abstract Prior research and empirical investment results have shown that portfolio construction choices related to rebalance schedules may have non-trivial impacts on realized performance. We construct long-only indices that provide exposures to popular U.S. equity factors (value, size,

Quantocracy’s Daily Wrap for 08/13/2020

This is a summary of links featured on Quantocracy on Thursday, 08/13/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Feature Selection (1 / 3) [Tr8dr]

    I am often confronted with the problem of trying to reduce a high dimensional feature set to a, smaller, more effective one. Reducing dimension is important for machine learning models as: the volume of the search space grows exponentially at a minimum rate of 2d for binary categorical variables to a much higher exponential for continuous or n-ary categoricals. the joint-distribution of high
  • An Introduction to Digital Signal Processing for Trend Following [Alpha Architect]

    Digital signal processing (DSP), specifically the use of digital filters, is embedded in many indicators used by technical analysts to study and make trading decisions using time series of stock, bond, currency, commodity, and other financial asset prices. This analysis takes a look at several of the most commonly-used indicators from a DSP perspective to illustrate their properties and

Quantocracy’s Daily Wrap for 08/11/2020

This is a summary of links featured on Quantocracy on Tuesday, 08/11/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Best Machine Learning Algos for Landing a Top Hedge Fund Job in the 2020s [Auquan]

    Hedge Funds analyst roles represent some of the most fiercely contested roles in all of Finance (if not any industry). The work is incredibly varied and challenging, making the jobs the long term aim of many ambitious juniors from diverse backgrounds like Computer Science, finance, economics, Physics and so much more. This creates an intense competition for roles. In turn, the challenge becomes
  • Looking at 7-day Win Streaks [Quantifiable Edges]

    The recent rally has left the market short-term overbought by most measures. Short-term overbought often triggers some studies that suggest a downside edge, but when the overbought condition gets very strongly overbought, then those downside edges often disappear. And at some point, rather than strength leading to weakness, the strength will beget more strength. The strong move higher over the
  • What is Sequence Risk and Can Trend Following Help Reduce It? [Alpha Architect]

    What exactly is sequence risk? Well get more into the weeds of it, but for now, consider it the risk of loss when you can least afford it. Think of a client leaving their retirement party with their shiny new set of steak knives and then learning via the news that their enormous position in their employers stock has just dropped 50%. The authors of this paper look deeply into sequence