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Quantocracy’s Daily Wrap for 12/29/2020

This is a summary of links featured on Quantocracy on Tuesday, 12/29/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend-Following Filters: Part 1/2 [Alpha Architect]

    Many traders use strategies based on trends that occur in stock, bond, currency, commodity, and other financial asset price time series in order to buy low and sell high. A trend is considered to be the overall direction of prices over a period of time. If prices have generally increased the trend has a positive slope and is called bullish. If prices have generally decreased, the
  • P-Hacking Via Academic Finance Research Conferences [Alpha Architect]

    This research is an update to Documentation of the File Drawer Problem in Academic Finance Journals published by the same authors in the Journal of Investment Management in 2018. A summary of that article can be found here. The file drawer problem refers to the idea that journal editors are predisposed to accepting articles for publication, only if they contain statistically

Quantocracy’s Daily Wrap for 12/28/2020

This is a summary of links featured on Quantocracy on Monday, 12/28/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research Compendium 2020 [Factor Research]

    In 2020 we published more than 50 research notes on mostly factor investing and smart beta ETFs, but also on topics like ESG, tail risk hedge funds, long volatility strategies, and private equity. The Research Compendium 2020 contains all of our research published this year. We would like to thank you for reading and always appreciate feedback, especially if critical.

Quantocracy’s Daily Wrap for 12/27/2020

This is a summary of links featured on Quantocracy on Sunday, 12/27/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Equity Fundamentals: Part 2 [Kyle Downey]

    In Part 1 we looked at using TimescaleDB and SQLAlchemy to build a relational database model of the Sharadar equity dataset with a Python object model sitting on top. The initial cut of this project ran on my desktop and broke up each of the dataset loads into a simple script that I could run in PyCharm. In this next blog post we'll dive into Spotify's Luigi and combine it all together

Quantocracy’s Daily Wrap for 12/26/2020

This is a summary of links featured on Quantocracy on Saturday, 12/26/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Hundreds of quant papers from #QuantLinkADay in 2020 [Cuemacro]

    This has been a challenging year by pretty much any other standard. I remember when I was kid, and heard the year 2020, it sounded very exciting. Itd be like Back to the Future, with hoverboards and flying cars. Instead, 2020 has been all about the coronavirus. Whilst Ive been lucky enough to be safe during this year, many have suffered tremendously. It is somewhat cliched, but at least for
  • What traders should know about seasonal adjustment [SR SV]

    The purpose of seasonal adjustment is to remove seasonal and calendar effects from economic time series. It is a common procedure but also a complex one, with side effects. Seasonal adjustment has two essential stages. The first accounts for deterministic effects by means of regression and selects a general time series model. The second stage decomposes the original time series into trend-cycle,

Quantocracy’s Daily Wrap for 12/24/2020

This is a summary of links featured on Quantocracy on Thursday, 12/24/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Equities Strategy (p2) [Tr8dr]

    In the prior post I showed results for a new equities strategy which uses a combination of signals to create and risk manage a high-momentum portfolio. Further investigation revealed that I had neglected on a couple of fronts: failed to account for dividends (which are substantial) some data issues improper sharpe calculation The good news is that solving these issues substantially improved the
  • How Should Trend-Followers Adjust to the Modern Environment?: Enter Adaptive Momentum [CSS Analytics]

    The premise of using either time-series momentum or trend-following using moving averages is the same only the math differs very slightly (see Which Trend Is Your Friend? by AQR): using some fixed lookback you can time market cycles and capture more upside than downside and therefore improve performance vs buy and hold OR at the very least improve return versus downside risk. The problem
  • Correlation approaches for stock pairs you have not seen before [Trade With Science]

    As described in our other articles, stock pairs are a mean-reversion trading system widely used in the industry. In pairs, you invest in 2 stocks that are correlated somehow and go long in one and short in another (classical Coca-Cola and Pepsi example). Naturally, you are hedged in the market, so this is a market-neutral strategy. The idea is pretty simple you watch the stocks whose price

Quantocracy’s Daily Wrap for 12/23/2020

This is a summary of links featured on Quantocracy on Wednesday, 12/23/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Petra on Programming: Short-Term Candle Patterns [Financial Hacker]

    Japanese traders invented candle patterns in the 17th century. Some traders believe that those patterns are still valid. But alas, no one yet got rich with them. Still, trading book authors are all the time inventing new patterns, in hope to find one that is really superior to randomly entering positions. In the Stocks & Commodities January 2021 issue, Perry Kaufman presented several new
  • Twas 3 Nights Before Christmas: 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 (12/22). 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.

Quantocracy’s Daily Wrap for 12/21/2020

This is a summary of links featured on Quantocracy on Monday, 12/21/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Monte Carlo option pricing – comparison of R and Julia languages [Mateusz Dadej]

    This example investigates the performance of R in comparison to Julia language. Additionally shows how to easily call Julia inside R code. With that being said, we will load JuliaCall library that enables us to do so. Alternatively, there is also XRJulia library available. library(JuliaCall) It is necessery to tell R where is Julia.exe stored, so the loaded library can communicate with it
  • Visualizing Correlations Among Dow 30 Stocks Via NetworkX [Machine Learning Applied]

    NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Using daily adjusted close data from 20201118 to 20201218 for Dow 30 stocks, we compute correlation coefficients, apply a threshold of 0.8 to find similar stocks, and produce two types of graphs with NetworkX. To compute correlation coefficients, we read in daily
  • Fed Model Improvement? [CXO Advisory]

    Is there a better way than the Fed model to measure relative attractiveness of equities and bonds. In his October 2020 paper entitled Towards a Better Fed Model, Raymond Micaletti examines seven Fed Model alternatives, each comparing a 10-year forward annualized estimate of equity returns to the yield of 10-year constant maturity U.S. Treasury notes (T-note). The seven estimates of future
  • Another miserable year for market forecasters [Mathematical Investor]

    Suppose, during a nightly TV weather broadcast, that a reporter presented forecasts by persons, with no credentials in mathematical meteorology, who based their analysis on eyeballing a few charts and graphs. If anyone took such amateur forecasts seriously, when a severe storm was approaching, rather than relying on the consensus of qualified scientists assisted by state-of-the-art supercomputer

Quantocracy’s Daily Wrap for 12/19/2020

This is a summary of links featured on Quantocracy on Saturday, 12/19/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bitcoin Mempool & Momentum [Tr8dr]

    I have been thinking about the recent institutional buying that has propelled the price of bitcoin to stratospheric levels; in particular considering how one might detect some of this interest early. Bitcoin and crypto in general is quite interesting in that at some level, due to the decentralized ledger, there is more transparency in this market than any other financial market. The majority of
  • Is Size a Useful Investing Factor or Not? [Alpha Architect]

    In his famous 1981 paper, The Relationship Between Return and Market Value of Common Stocks, Rolf Banz found that small firms have higher risk-adjusted returns than large firms. This was one of the first major challenges to the capital asset pricing model (CAPM) and market efficiency in general. However, its failure to generate statistically significant premiums post-publication has called

Quantocracy’s Daily Wrap for 12/18/2020

This is a summary of links featured on Quantocracy on Friday, 12/18/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • QQQ:IWM for Risk-on and GLD:TLT for Risk-off? [CXO Advisory]

    A subscriber asked about a strategy that switches between an equal-weighted portfolio of Invesco QQQ Trust (QQQ) and iShares Russell 2000 ETF (IWM) when the S&P 500 Index is above its 200-day simple moving average (SMA200) and an equal-weighted portfolio of SPDR Gold Shares (GLD) and iShares 20+ Year Treasury Bond ETF (TLT) when below. Also, more generally, is an equal-weighted portfolio of
  • Reinforcement Learning for Trading [Quant Dare]

    One of the most appealing areas of Artificial Intelligence is Reinforcement Learning, for its applicability to a variety of areas. It can be applied to different kinds of problems, in the present article we will analyze an interesting one: Reinforcement Learning for trading strategies. Reinforcement Learning We introduced Reinforcement Learning and Q-Learning in a previous post. In order to

Quantocracy’s Daily Wrap for 12/15/2020

This is a summary of links featured on Quantocracy on Tuesday, 12/15/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Explaining variance [OSM]

    Were returning to our portfolio discussion after detours into topics on the put-write index and non-linear correlations. Well be investigating alternative methods to analyze, quantify, and mitigate risk, including risk-constrained optimization, a topic that figures large in factor research. The main idea is that there are certain risks one wants to bear and others one doesnt. Do you want