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Quantocracy’s Daily Wrap for 12/13/2021

This is a summary of links featured on Quantocracy on Monday, 12/13/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Estimating Rebalancing Premium in Cryptocurrencies [Quantpedia]

    A long time ago, before elevators were a thing, a simple mechanism was used to get the miners in and out of the mines. This mechanism is called a Man Engine (or Fahrknst in German language) and works on a simple principle of two reciprocating ladders and stationary platforms. The two ladders move up and down, so if a miner stands on one platform, he is not going anywhere. However, if
  • An Important Test for the Global Growth Cycle [Grzegorz Link]

    Whatever kind of strategy you're employing as an investor, an invaluable tool for determining it's usefulness is testing. Not simply backtesting on historical data or stress testing on synthetic data that's the easy part. The fun and playful part. A much more important test comes with a strategy's performance on new, real-time data. A couple of months have passed since
  • Quantitative Analysis of a Sample Drawn from the Unknown Continuous Population [Quant at Risk]

    In quantitative finance, we very often deal with a sample mean and sample standard deviation being derived given a vector or a time-series or any other (1-dimensional) dataset. For many of us these calculations are so obvious that only a few understand the principles standing behind the scene. Lets give a second look at this trivial problem. Trust me, you wont regret! 1. Drawing a Random
  • ETFs for Rising Interest Rates [Factor Research]

    A wide range of strategies are marketed as beneficiaries of rising interest rates Portfolios are comprised of equities, bonds, options, long as well as short positions However, only financial services companies and short bonds offer a positive correlation to interest rates INTRODUCTION In this year, most developed markets have been experiencing inflation last seen decades ago, with the exception

Quantocracy’s Daily Wrap for 12/11/2021

This is a summary of links featured on Quantocracy on Saturday, 12/11/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Back to basics: PCA on stocks returns [Gautier Marti]

    A short code snippet to apply PCA on stocks returns. No secret sauce is used here to clean the empirical covariance matrix. This blog post will mostly serve as a basis for comparing several flavours of PCA and their impact on ex-ante volatility estimation. We may look in future blog posts into Sparse PCA, Nonlinear PCA, Kernel PCA, Robust PCA, revisit our previous implementation of Hierarchical
  • The risk-reversal premium [SR SV]

    The risk reversal premium manifests as an overpricing of out-of-the-money put options relative to out-of-the-money call options with equal expiration dates. The premium apparently arises from equity investors demand for downside protection, while most market participants are prohibited from selling put options. A typical risk reversal strategy is a delta-hedged long position in out-of-the-money

Quantocracy’s Daily Wrap for 12/09/2021

This is a summary of links featured on Quantocracy on Thursday, 12/09/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Synthetic Lending Rates Predict Subsequent Market Return [Quantpedia]

    It is indisputable that the data are changing financial markets computing power has increased, allowing to rise the trends of ML/AI and big data (number of possible predictors or granularity) or HFT strategies. Indeed, not all the datasets are worth the time of academics, investors or traders, but we are always keen to analyze the novel and unique datasets. Of course, if we believe that the

Quantocracy’s Daily Wrap for 12/08/2021

This is a summary of links featured on Quantocracy on Wednesday, 12/08/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Site: Financial market data analysis with pandas (h/t @PyQuantNews) [Wrighters.io]

    Pandas is a great tool for time series analysis of financial market data. Because pandas DataFrames and Series work well with a date/time based index, they can be used effectively to analyze historical data. By financial market data, I mean data like historical price information on a publicly traded financial instrument. However, any sort of historical financial information can be analyzed. Time

Quantocracy’s Daily Wrap for 12/07/2021

This is a summary of links featured on Quantocracy on Tuesday, 12/07/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • US Market Valuations: Looking down the Abyss! [Nava Capital]

    Value investing is at its core the marriage of a contrarian streak and a calculator. S. Klarman The first principle is that you must not fool yourself, and you are the easier person to fool. R. Feynman In this brief note, our goal is to show readers, as objectively as possible, the current discrepancy between the intrinsic and the current value of the S&P 500. Our conclusion is
  • Stock Market Returns and Volatility [Factor Research]

    Average stock market returns are similar regardless if volatility was high or low However, given skewed returns, it was not attractive investing when volatility was high Unfortunately implementing a strategy to avoid high volatility periods is emotionally challenging INTRODUCTION Active fund managers frequently complain about stock market volatility being too low for their taste and articulate a

Quantocracy’s Daily Wrap for 12/04/2021

This is a summary of links featured on Quantocracy on Saturday, 12/04/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • You Thought P-Hacking was Bad? Let’s talk about “Non-Standard Errors” [Alpha Architect]

    Most readers are familiar with p-hacking and the so-called replication crisis in financial research (see here, here, and here for differing views). Some claim that these research challenges are driven by a desire to find positive results in the data because these results get published, whereas negative results do not get published (the evidence backs these claims). But this research project
  • Book Review: Advanced Portfolio Mgmt – A Quant’s Guide for Fundamental Investors [Gautier Marti]

    Great book, I absolutely recommend. Precise and concise (less than 200 pages). This book will especially be useful to grads or analysts in the early stages of their career. A junior analyst/quant/data scientist who masters the content of this book will definitely be useful in a pod of fundamental discretionary portfolio managers and analysts. This book wont teach you anything about how to
  • Market data, investor surveys, and lab experiments [Alex Chinco]

    An asset-pricing model is a claim about which optimization problem people are solving when they choose their investment portfolios. One way to make such a claim testable is to derive a condition that should hold if people were actually solving this optimization problem. And the standard approach to testing whether an asset-pricing model is correct involves using market data to estimate the key
  • Size, Value, Profitability, and Investment Factors in International Stocks [Alpha Architect]

    The current workhorse asset pricing model is the Fama-French five-factor model (2015), which added the profitability and investment factors to their original (1992) three factors of market beta, size, and valueincreasing the models explanatory power. Nusret Cakici and Adam Zaremba contribute to the factor literature with their May 2021 study, Size, Value, Profitability, and Investment

Quantocracy’s Daily Wrap for 12/02/2021

This is a summary of links featured on Quantocracy on Thursday, 12/02/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • My trading system [Investment Idiocy]

    I realise that I've never actually sat down and described my fully automated futures trading system in all it's detail; despite having runit for around 7.5 years now. That isn't because I want to keep it a secret – far from it! I've blogged or written books about all the various components of the system. Since I've made a fair few changes to my over the last year or so, it

Quantocracy’s Daily Wrap for 12/01/2021

This is a summary of links featured on Quantocracy on Wednesday, 12/01/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Volatilities and Correlations of Cross Rates, a Geometrical Understanding [Quant Dare]

    In this post we will show how the properties of a triangle can be used to intuitively obtain insights about the volatilities and correlations of currency pairs. Once the dominant branch of mathematics, geometry plays now a secondary role. However, its graphical arguments still seem to be better suited for our brains, as they are often easier to understand and to remember. This is the path followed

Quantocracy’s Daily Wrap for 11/29/2021

This is a summary of links featured on Quantocracy on Monday, 11/29/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Complete Starter System for New Traders: Trading Multiple Instruments [Raposa Trade]

    Systematically trading a single instrument can be a bit dull. There are times when your chosen stock isnt trending or doing much. So your system just sits there and waitsand waits..and waits. Obviously we dont want to trade just to trade thats a good way to start losing money. But if youre bored, youre probably not going to stick to your system especially if youre
  • Can Prospect Theory Explain the Value and Momentum Factors? [Alpha Architect]

    Traditional finance academics lean towards risk-based models to explain why various return characteristics, such as value and momentum, predict returns. But there is another school of thought often referred to as behavioral finance. This field has some of its own ideas (see below) on why different stock factors might predict returns. Prospect Theory was first introduced by Kahneman and
  • Trading the Inflation Theme [Light Finance]

    While the holiday season has long been regarded as a time of excess, folks this year are bracing for another challenge besides annual waistline expansion: price inflation. As we gather with family and friends for the holidays in coming weeks many are predicting that this years turkey will be the most expensive in the history of the holiday with food prices having risen a remarkable 5.4% yoy.
  • Building a Long-Term Equity Portfolio [Factor Research]

    With a long-term time horizon, investors should consider alternatives to the market-cap weighted equity indices A valuation-based approach for creating an equities portfolio may seem more sensible Using EBITDA / EV yield seems to avoid some of the quality issues of other value metrics INTRODUCTION All the evidence points to active management providing negative alpha and most investors being best

Quantocracy’s Daily Wrap for 11/28/2021

This is a summary of links featured on Quantocracy on Sunday, 11/28/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Should We Never Invest in Individual Stocks? [Alpha Architect]

    Hendrik Bessembinder published a fascinating paper, which finds that nearly all publicly traded stocks in the U.S. if held as buy and hold investments underperform Treasury bills. This finding is incredibly surprising and interesting. Of course, when bold claims are made, they tend to attract a lot of attention. For example, Alpha Architect has covered the fascinating Bessembinder research
  • Action After Strong Friday Selloffs [Quantifiable Edges]

    Todays study is one of several that will be appearing in the Quantifiable Edges Subscriber Letter in a few hours. Quantifiable Edges Black Friday sale has been extended through Cyber-Monday. Act now to take advantage. After Monday its gone. Black Friday was a tough one for the market, with the major indices all closing down over 2%, and the VIX spiking over 10 points to close at 28.62. Big
  • Research Review | 26 November 2021 | Bitcoin and Crypto [Capital Spectator]

    We present a theoretical and empirical methodology that reflects the Cryptocurrency version of VIX, which we name it as CVIX (Crypto VIX), and captures the future 30 days forward Crypto risk (fear). Our framework is built on idiosyncratic and systematic Crypto risk, and is not based on the option implied volatility model, that developed by the CBOE for the S&P Volatility Index VIX. For back
  • How to construct a bond volatility index and extract market information [SR SV]

    Volatility indices, based upon the methodology of the Cboe volatility index (VIX), serve as measures of near-term market uncertainty across asset classes. They are constructed from out-of-the-money put and call premia using variance swap pricing. Volatility indices for fixed income markets are of particular importance, as they allow inferring market expectations about discount factors and credit