Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 12/23/2021

This is a summary of links featured on Quantocracy on Thursday, 12/23/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • January Effect on Stocks [Alvarez Quant Trading]

    A member of The Crew recently asked me about the January Effect and if had I done any research on it. I had not. I have tested the December effect, which is buying the worst stocks of the year on December 1st, Should You Buy the Best or Worst YTD Stocks. From Investopedia, The January Effect is a perceived seasonal increase in stock prices during the month of January. Analysts generally
  • Our Top 5 Geeky Finance Posts for 2021 [Alpha Architect]

    We are calling it quits for the holidays. Most of us have kids and Santa is coming to town! Well talk about research and educate investors next week. Here are the Top 5 content pieces this year (Based on traffic): Even God would get fired as an Active Investor Does Gamma Hedging Actually Affect Stock Returns? Market Timing Using Aggregate Equity Allocation Signals How to Predict Stock Returns
  • Research Review | 23 December 2021 | ETFs [Capital Spectator]

    Trading Down: The Effects of Active Trading on One-Month ETF Returns Ian Gray (Loyola Marymount University) December 15, 2021 Ark Investment Management (ARK), led by CIO Cathie Wood, has risen to prominence over the past few years because of its remarkable performance. Because of requirements for active ETFs to publish daily holdings, market participants have gained unprecedented access to

Quantocracy’s Daily Wrap for 12/22/2021

This is a summary of links featured on Quantocracy on Wednesday, 12/22/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Value investing: What history says about five-year periods after valuation peaks [Alpha Architect]

    No matter how you slice it, Value stocks are historically cheap compared to the past. There have been numerous articles on this topic, such as Ryans post here, Larry Swedroes post here, and more recently, Cliff Asness post here. Cliffs post is one picture, shown below. 1 Source: https://www.aqr.com/Insights/Perspectives/Thats-it-Thats-the-Blog The image above shows the relative
  • Twas 3 Nights Before Christmas: Updated NASDAQ Version [Quantifiable Edges]

    Ive posted and updated the Twas 3 Nights Before Christmas study on the blog here several times since 2008. The study will kick in at the close today (12/21). This year I will again show the Nasdaq version of the study. While all the major indices have performed well during this period, the Nasdaq Composite has some of the best stats. NASDAQ Rally Around Christmas The stats in this table

Quantocracy’s Daily Wrap for 12/18/2021

This is a summary of links featured on Quantocracy on Saturday, 12/18/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • QuantMinds 2021 in Barcelona [Cuemacro]

    The above photo is of Montjuc Castle which has one of the best views over the city. The view from there kind of explains why the location was chosen from a strategic perspective. As a city Barcelona has many places which punctuate the skyline, whether its that castle, or the (still unfinished) Sagrada Familla, Gaudis masterpiece. One of the newer additions to the skyline is the Hotel Arts,
  • When the Close Is Not Really the Close (A Geeky Discussion) [Allocate Smartly]

    This post covers an issue rarely discussed in backtesting: the days last real-time price shown at 4pm ET often differs slightly from the days official closing price determined shortly after 4pm. This is not an Allocate Smartly issue; its an oddity of the exchanges. Every so often this difference can cause discrepancies between backtests based on the close and investors real-world
  • The Relationship Between the Value Premium and Interest Rates [Alpha Architect]

    Value stocks sharply underperformed growth stocks from 2017 to 2020, exacerbating a longer period of lackluster performance dating back to the Global Financial Crisis. The Death of Systemic Value Investing is not new news for frequent readers of the blog nor are the possible pathways to Resurrecting the Value Premium. From 2007 through August 2020, this drawdown was the deepest and longest in
  • Causal inference as a tool for publishing robust results [Alex Chinco]

    Imagine youre an asset-pricing researcher. Youve just thought up a new variable, X, that might predict the cross-section of returns. And youve regressed returns on X in a market environment e of your choosing (i.e., using data on some specific time period, country, asset class, set of test assets, etc): (1) begin{equation*} R(i) = alpha_e + beta_e cdot X(i) + epsilon_e(i) qquad

Quantocracy’s Daily Wrap for 12/15/2021

This is a summary of links featured on Quantocracy on Wednesday, 12/15/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading a Complete Starter System Live with @AlpacaHQ [Raposa Trade]

    Weve spent the past few posts building up the Starter System laid out in Rob Carvers book, Leveraged Trading. Weve gone from a simple moving average cross-over model, to a volatility targeting system with multiple instruments and time frames that dynamically sizes and re-positions your portfolio as market conditions change. Youve done all this work, now its time to make it pay off
  • Yet Another Improved RSI [Financial Hacker]

    John Ehlers strikes again. The TASC January 2022 issue features another indicator supposedly improved with Hann windowing the RSIH, a RSI with Hann flavour. Can it beat the standard RSI? The RSI is basically the normalized difference of price up/down movements. And its here presented Hann variant filters the price differences with a Hann window that was described in a previous article on this
  • Self-organizing maps for clustering [Quant Dare]

    We can use self-organizing maps for clustering data, trained in an unsupervised way. Lets see how. This week we are going back to basics, as we will see one of the first successfully deployed machine learning algorithms: self-organizing maps (SOM, sometimes also called Kohonen maps). This is an unsupervised technique, so we will not need any labeled data for training, just raw inputs.
  • A Stab at Fiction (Unrelated to Quant, but we support our friends) [Following the Trend]

    When I wrote my first book a decade ago, I didnt expect it to get much attention, or sales. I was in the wrong country, of the wrong nationality, I had shunned social media and was nearly invisible on the internet. On top of these obstacles, I tried out a whole new style of writing trading books. It was quite shocking when I saw that book take off and hit the number one slot on the

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