Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 05/15/2020

This is a summary of links featured on Quantocracy on Friday, 05/15/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Run Python from R Studio [Robot Wealth]

    Modern data science is fundamentally multi-lingual. At a minimum, most data scientists are comfortable working in R, Python and SQL; many add Java and/or Scala to their toolkit, and its not uncommon to also know ones way around JavaScript. Personally, I prefer to use R for data analysis. But, until recently, Id tend to reach for Python for anything more general, like scraping web data or
  • Is this the pullback you ve been waiting for? [Quantifiable Edges]

    It has been 47 trading days since SPX posted its last 3-day pullback. That is a long time. And it is especially long considering SPX is still below its 200ma. Should SPX fail to rally out of this early hole this morning, we will finally see the 1st 3-day pullback since March 9th. Bulls could look at it and exclaim Finally, the 3-day pullback I have been waiting for to load up!. Bears might
  • YTD Performance of Equity Factors – Update After Two Months [Quantpedia]

    Nearly two months ago, in a time of the highest turmoil during the current pandemic crisis, we performed a quick assessment of the status of performance of equity factor strategies. The world has still not been able to ward-off health-care crisis completely, but a lot of countries have made significant progress (on the other hand, there are still a lot of countries in a worse state than a few
  • Discussion: Managing the Costs of Passively Investing in Active Strategies [Alpha Architect]

    We recently covered a paper by David Blitz that highlighted the potential problems with passively investing in active strategies. The research piece is great and surfaces a lot of great concepts. Like a lot of research we publish/summarize this article appears to shoot Alpha Architect in the foot. To summarize, the piece was essentially a smack-down on implementing factors via ETFs
  • Tracking Bitcoin Gains since its 3rd Halving in May 2020 using Python [Quant at Risk]

    The Bitcoins 3rd halving was the most anticipated event this year. This a moment when a reward for all Bitcoin block miners is cut by half. It happens every 4 years or every 210,000 blocks on the Bitcoin blockchain. The previous two halving events took place in 2012 and 2016, respectively. Before the 1st halving, miners were rewarded with 50 BTC and after that only with 25 BTC per block. After

Quantocracy’s Daily Wrap for 05/14/2020

This is a summary of links featured on Quantocracy on Thursday, 05/14/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Financial Data Manipulation in dplyr for Quant Traders [Robot Wealth]

    In this post, were going to show how a quant trader can manipulate stock price data using the dplyr R package. Getting set up and loading data Load the dplyr package via the tidyverse package. if (!require('tidyverse')) install.packages('tidyverse') library(tidyverse) First, load some price data. energystockprices.RDS contains a data frame of daily price observations for 3
  • Academic Finance Research Galore. WFA Sessions Announced [Alpha Architect]

    Attention all finance geeks. The latest and greatest from academic researchers is available for all to review. The WFA recently released their sessions. WFA is one of the more prestigious academic conferences and papers presented at the conference often find their way into top-tier academic journals. Highly recommend you check it out! A few papers that caught my eye as we actively contemplate our

Quantocracy’s Daily Wrap for 05/13/2020

This is a summary of links featured on Quantocracy on Wednesday, 05/13/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Machine Learning and Investing: Forecasting Fundamentals w/ Ensembles [Alpha Architect]

    Quantitative factor portfolios generally use historical company fundamental data in portfolio construction. The key assumption behind this approach is that past fundamentals proxy for elements of risk and/or systematic mispricing. However, what if we could forecast fundamentals, with a small margin of error, and compare that with market expectations? Intuitively, this seems like a more promising
  • Get Rich Quick Trading Strategies (and why they don’t work) [Robot Wealth]

    Every aspiring millionaire who comes to the markets armed with some programming ability has implemented a systematic Get Rich Quick (GRQ) trading strategy. Of course, they dont work. Deep down even the greenest of newbies knows this. Yet, still, we are compelled to give them a try, just once, just for fun (or so we tell ourselves). In this series, well explore three of the Get Rich Quick
  • Designing an energy arbitrage strategy (h/t @PyQuantNews) [Steve Klosterman]

    The price of energy changes hourly, which opens up the possibility of temporal arbitrage: buying energy at a low price, storing it, and selling it later at a higher price. To successfully execute any temporal arbitrage strategy, some amount of confidence in future prices is required, to be able to expect to make a profit. In the case of energy arbitrage, the constraints of the energy storage

Quantocracy’s Daily Wrap for 05/12/2020

This is a summary of links featured on Quantocracy on Tuesday, 05/12/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How To Get Historical S&P 500 Constituents Data For Free [Robot Wealth]

    In this post, we are going to construct snapshots of historic S&P 500 index constituents, from freely available data on the internet. Why? Well, one of the biggest challenges in looking for opportunities amongst a broad universe of stocks is choosing what stock universe to look at. One approach to dealing with this is to pick the stocks that are currently in the S&P 500 index.
  • Skulls, Financial Turbulence and Risk Management [Alpha Architect]

    When hunting for diversity, the typical investor considers only average correlations. However, when measuring an assets diversification benefits utilizing average correlations tend to mislead investors. For example, when both U.S. and non-U.S. equities produce returns greater than one standard deviation above their means (ie when times are good), their correlation equals 17 percent; but when

Quantocracy’s Daily Wrap for 05/11/2020

This is a summary of links featured on Quantocracy on Monday, 05/11/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Straddles and Trend Following [Flirting with Models]

    The convex payoff profile of trend following strategies naturally lends itself to comparative analysis with option strategies. Unlike options, however, the payout of trend following is not guaranteed. To compare and contrast the two approaches, we replicate simple trend following strategies with corresponding option straddle strategies. While trend-following has no explicit up-front cost, it also
  • How to Find Cheap Options to Buy and Expensive Options to Sell [Robot Wealth]

    If you want to make money trading, youre going to need a way to identify when an asset is likely to be cheap and when it is likely to be expensive. You want to be a net buyer of the cheap stuff and a net seller of the expensive stuff. Thanks, Capitain Obvious. Youre welcome. How does this relate to equity options? If we take the (liquid) US Equity options market as an example then there are
  • Value Investing: Even Deeper History [Two Centuries Investments]

    In last weeks post we extended the systematic value factor (or at least a pretty good proxy of it) back to 1871. The response from readers was encouraging, perhaps because of the pain that value investing has been causing lately. Long-run history gives some relief. This week we dig deeper, reconstructing another 46 years of unseen history. As a result, we now have an extra 100 years of data for
  • The Case Against Factor Investing [Factor Research]

    Factor investing is likely the best option for investors seeking to outperform the market However, the cyclicality of factors makes factor investing challenging when it underperforms Investors that do not understand this cyclicality are likely better served by plain, rather than smart beta FREE AINT EASY Free and easy are concepts that often go hand-in-hand. However, there are also many

Quantocracy’s Daily Wrap for 05/10/2020

This is a summary of links featured on Quantocracy on Sunday, 05/10/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Online Portfolio Selection: Mean Reversion [Hudson and Thames]

    Mean Reversion is an effective quantitative strategy based on the theory that prices will revert back to its historical mean. A basic example of mean reversion follows the benchmark of Constant Rebalanced Portfolio. By setting a predetermined allocation of weight to each asset, the portfolio shifts its weights from increasing to decreasing ones. This module will implement four types of mean

Quantocracy’s Daily Wrap for 05/08/2020

This is a summary of links featured on Quantocracy on Friday, 05/08/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Hedge a Portfolio with Put Options [Robot Wealth]

    There are 2 good reasons to buy put options: because you think they are cheap because you want downside protection. In the latter case, you are looking to use the skewed payoff profile of the put option to protect a portfolio against large downside moves without capping your upside too much. The first requires a pricing model. Or, at the least, an understanding of when and under what conditions
  • Machined risk premia [OSM]

    Over the last few posts, weve discussed methods to set return expectations to construct a satisfactory portfolio. These methods are historical averages, discounted cash flow models, and risk premia. our last post, focused on the third method: risk premia. Using the Capital Asset Pricing Model (CAPM) one can derive the required return for a particular asset based on the market price of risk, the

Quantocracy’s Daily Wrap for 05/07/2020

This is a summary of links featured on Quantocracy on Thursday, 05/07/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • When endogenous risk management isn’t enough: a simple risk overlay [Investment Idiocy]

    "How does your risk management work?" … is a question I'm frequently asked. In fact this is actually a difficult question, if you were to look at my open source python backtesting project pysystemtrade, you would struggle to point at a piece of code and say "Behold! Right there, that's the risk management part alright!". The reason is that the risk management in my
  • Backtesting ESG Factor Investing Strategies [Quantpedia]

    Socially Responsible Investing (also called ESG Factor Investing) grows in popularity. More and more investors enter the stock market not just to invest their savings, but they are also want to support companies that bring positive social or environmental change. ESG factor investing can bring satisfaction to those investors. But does it also brings a real outperformance in a financial sense? Is
  • The Size Effect in Multifactor Portfolios [Alpha Architect]

    The lack of a statistically significant size premium in the U.S. since the publication of Rolf Banzs 1981 paper, The Relationship Between Return and Market Value of Common Stocks, published in the Journal of Financial Economics, led many investors to question its use in building portfolios. This conclusion is typically arrived at by considering the standalone performance of the size

Quantocracy’s Daily Wrap for 05/06/2020

This is a summary of links featured on Quantocracy on Wednesday, 05/06/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sentiment analysis: ifo business climate data [Grzegorz Link]

    Sentiment analysis is one of the investing tools I'm most fond of. There are multiple ways of measuring sentiment: from basic investor surveys to advanced text mining techniques, but one of the most robust and long-term datasets is ifo Institute's business climate sentiment polls.[4] Sentiment analysis: ifo business climate data The data is available from 1991 (Germany's
  • Can neural networks predict the stock market just by reading newspapers? [Quant Dare]

    Markets are said to be driven by randomness, but this does not imply that they are 100% random and thus, completely unpredictable. In the end, there are always people behind investments and many of them are making decisions based on what they read in newspapers. We will be trying to estimate the returns of a time series, namely Bitcoin, only using text data from relevant articles. BERT, an NLP
  • How to download fundamentals data with Python (h/t @PyQuantNews) [TheAutomatic.net]

    In this post we will explore how to download fundamentals data with Python. Well be extracting fundamentals data from Yahoo Finance using the yahoo_fin package. For more on yahoo_fin, including installation instructions, check out its full documentation here. Getting started Now, lets import the stock_info module from yahoo_fin. This will provide us with the functionality we need to scrape
  • Pairs Trading Literature Review [Robot Wealth]

    This post summarises the key lessons of the academic literature that has been published on pairs trading. The key themes are highlighted at the end of the page. Pair Trading Literature Review Gatev, Goetzmann, Rouwenhorst Pairs Trading: Performance of a Relative Value Arbitrage Strategy https://papers.ssrn.com/sol3/papers.cfm?abstract_id=141615 This is the first meaningful academic paper

Quantocracy’s Daily Wrap for 05/05/2020

This is a summary of links featured on Quantocracy on Tuesday, 05/05/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Using Aggregate TAA Allocation as a Tool for Timing the Market [Allocate Smartly]

    We track 50+ public Tactical Asset Allocation (TAA) strategies. A unique feature of our platform is that we show the aggregate allocation across all of those strategies each day (member link). For example, the graph below shows the aggregate allocation year to date by category of asset. Note the increase in defensive allocation (ex. bonds) and decrease in risk allocation (ex. stocks) as the most
  • Cheap vs. Expensive Factors: Does Valuation Matter for Future Returns? [Alpha Architect]

    Tesla (TSLA) breached the $100 billion market capitalization in January 2020 and became the most valuable car manufacturer globally. However, valuing the company is challenging given the growth profile, complexity of the business, and erratic CEO. It is not yet profitable and cash flow is negative, which means that traditional valuation metrics based on historical data are currently less
  • Overnight and Intraday SPX returns [Robot Wealth]

    One of the things Ive noticed from staring at the screen all day for the last few months is that most of the large negative returns in US stock indexes have come overnight. What do you mean by overnight? The core stock trading session for US stocks is between 9:30 am and 4 pm Eastern Time. Thats when most stock market transactions take place. When we look at daily OHLC (Open High Low
  • LSTM Networks: Can They Predict Equity Index Prices? [Quant Insti]

    In this article, we will study a deep learning framework based on recurrent neural networks to predict daily equity index price movements. Specifically, the focus will be on long short-term memory (LSTM) networks – which are a type of recurrent neural network. Different types of inputs and network architectures will be studied to determine their effect on predictability. We will see that with a
  • Why Passively Investing in Active Methods May Not Work [Alpha Architect]

    In this piece, David Blitz provides an interesting perspective on using the passive framework as a blueprint for constructing active (ETF-like) products. The article is not an empirical (no charts!) nor a theoretical (no analytics) analysis, but is focused on just one question: Is it efficient to implement an active strategy by using passive investing techniques, that is, to first turn the active