Quantocracy

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Quantocracy’s Daily Wrap for 01/19/2021

This is a summary of links featured on Quantocracy on Tuesday, 01/19/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Keller’s Resilient Asset Allocation [Allocate Smartly]

    This is a test of the latest tactical strategy from Dr. Wouter Keller: Resilient Asset Allocation (RAA). RAA is intended to be a low turnover strategy, only shifting from a balanced risk portfolio to a defensive portfolio during the most potentially bearish of times. Backtested results from 1970 follow. Results are net of transaction costs (see backtest assumptions). Learn about what we do and
  • Extracting Interest Rate Bounds from Option Prices [Sitmo]

    In this post we describe a nice algorithm for computing implied interest rates upper- and lower-bounds from European option quotes. These bounds tell you what the highest and lowest effective interest rates are that you can get by depositing or borrowing risk-free money through combinations of option trades. Knowing these bounds allows you to do two things: 1. Compare implied interest rate levels

Quantocracy’s Daily Wrap for 01/18/2021

This is a summary of links featured on Quantocracy on Monday, 01/18/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Oh, Quality, Where Art Thou? [Factor Research]

    Quality and quality income ETFs have underperformed the S&P 500 since 2005 The most recent underperformance is explained by an underweight to technology stocks However, more importantly, quality ETFs have not reduced drawdowns during stock market crashes INTRODUCTION Investing is never easy, but it is sometimes easier. Buying US government bonds at 10%+ yields when inflation was steadily
  • Statistics of Point&Figure Charts [Philipp Kahler]

    Point&Figure charts have been around for more than a 100 years and they are still quite popular, especially with commodities and forex traders. This article will do some statistical analysis of the most basic Point&Figure signal. Point&Figure Charts price movements only Unless bar and candlestick charts, which draw a price marker every day, Point&Figure charts are only updated
  • Historical Returns for Newly Elected Presidents [Quantifiable Edges]

    Back in the 1/20/2009 blog I looked at inauguration day returns. I wondered at the time whether a new president brought about new hope and optimism for the market. I have decided to update that study today. I limited the instances to only those inaugurations where a new president was entering office. I dont think re-elections carry a sense of new hope the way a new president does. I also

Quantocracy’s Daily Wrap for 01/17/2021

This is a summary of links featured on Quantocracy on Sunday, 01/17/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research impact of delta hedging with Python [Cuemacro]

    One of the things which I found confusing at first with options, is the fact that lots of the folks trading them, dont really have a view about whether the spot price will go up and down. They are basically trading the volatility parameter from options pricing models (Emanuel Derman explains the point about trading parameter much better than me here and also here). One of the most common

Quantocracy’s Daily Wrap for 01/16/2021

This is a summary of links featured on Quantocracy on Saturday, 01/16/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • More factors, more variance…explained [OSM]

    Risk factor models are at the core of quantitative investing. Weve been exploring their application within our portfolio series to see if we could create such a model to quantify risk better than using a simplistic volatility measure. That is, given our four portfolios (Satisfactory, Naive, Max Sharpe, and Max Return) can we identify a set of factors that explain each portfolios variance

Quantocracy’s Daily Wrap for 01/15/2021

This is a summary of links featured on Quantocracy on Friday, 01/15/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How To Create A Fully Automated AI Based Trading System With Python (h/t @PyQuantNews)

    A couple of weeks ago I was casually chatting with a friend, masks on, social distance, the usual stuff. He was telling me how he was trying to, and I quote, detox from the broker app he was using. I asked him about the meaning of the word detox in this particular context, worrying that he might go broke, but nah: he told me that he was constantly trading. If a particular stock has been going
  • How to Get Historical Market Data Through Python Apis [Quant Insti]

    As a quant trader, you are always on the lookout to create and optimise your trading strategies. Backtesting forms a very important part of this process. And for backtesting, access to historical data is a necessity. But its a very daunting task to find decent historical price data for backtesting your trading strategies. While a simple google search can give you the end of day data for any
  • Research Review | 15 January 2021| Forecasting [Capital Spectator]

    Long-Term Stock Forecasting Magnus Pedersen (Hvass Laboratories) December 17, 2020 When plotting the relation between valuation ratios and long-term returns on individual stocks or entire stock-indices, we often see a particular pattern in the plot, where higher valuation ratios are strongly correlated with lower long-term stock-returns, and vice versa. Moreover the plots often show a particular

Quantocracy’s Daily Wrap for 01/13/2021

This is a summary of links featured on Quantocracy on Wednesday, 01/13/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bayesian Portfolio Optimisation: Introducing the Black-Litterman Model [Hudson and Thames]

    The Black-Litterman (BL) model is one of the many successfully used portfolio allocation models out there. Developed by Fischer Black and Robert Litterman at Goldman Sachs, it combines Capital Asset Pricing Theory (CAPM) with Bayesian statistics and Markowitzs modern portfolio theory (Mean-Variance Optimisation) to produce efficient estimates of the portfolio weights. Before getting into the

Quantocracy’s Daily Wrap for 01/12/2021

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

  • The Definitive Study on Long-Term Factor Investing Returns [Alpha Architect]

    Interest in factor investing was hot several years back but seems to have died on the back of poor relative performance and a move to hotter products in thematics and ESG. But, for better or worse, we havent moved on. We are boring and we trust the process. We still believe that markets do a decent job at pricing risks and rewards, but they arent perfect. There is a bunch of noise caused by
  • How Does ETF Liquidity Affect ETF Returns, Volatility, and Tracking Error? [Alpha Architect]

    Although the ETF market has grown exponentially over the recent 20 years, ETFs that are less popular are not always liquid. A majority of the dollars flowing into ETFs are concentrated in 3 products, accounting for 46.7% of total ETF trading volume (see Figure 3 below). If the next 8 ETFs are included that percentage increases to 61.5%. If that doesnt astound the reader, consider that the AUM$

Quantocracy’s Daily Wrap for 01/11/2021

This is a summary of links featured on Quantocracy on Monday, 01/11/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Musings about Factor Exposure Analysis [Factor Research]

    There are few alternatives to regression analysis when explaining investment performance Too few as well as too many independent variables can be problematic The results are often not intuitive, but also encourage asking further questions that may prove insightful INTRODUCTION The older I become, the less I feel I know anything with certainty. Almost every aspect of life seems to have various

Quantocracy’s Daily Wrap for 01/10/2021

This is a summary of links featured on Quantocracy on Sunday, 01/10/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Recovering Accurate Implied Dividend and Interest Rate Term-Structures from Option Prices [Sitmo]

    In this post we discuss the algorithms we use to accurately recover implied dividend and interest rates from option markets. Implied dividends and interest rates show up in a wide variety of applications: to link future-, call-, and put-prices together in a consistent market view de-noise market (closing) prices of options and futures and stabilize PnLs of option books give tighter true bid-ask
  • Calculating FX total returns in Python [Cuemacro]

    If you want a train, you have to build a train track. It doesnt matter, if its a steam train or bullet train, or any other train. Its a prerequisite. No track kind of implies the train cant run. Obviously, each train needs a different type of track, but ultimately the principle is the same in how the track works (admittedly, if its a maglev train then perhaps not). When it comes to
  • Classifying market states [SR SV]

    Typically, we cannot predict a meaningful portion of daily or higher-frequency market returns. A more realistic approach is classifying the state of the market for a particular day or hour. A powerful tool for this purpose is artificial neural networks. This is a popular machine learning method that consists of layers of data-processing units, connections between them and the application of

Quantocracy’s Daily Wrap for 01/07/2021

This is a summary of links featured on Quantocracy on Thursday, 01/07/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Value and Momentum and Investment Anomalies [Alpha Architect]

    The predictive abilities of value and momentum strategies are among the strongest and most pervasive empirical findings in the asset pricing literature. (here is a deep dive) For example, the study Value and Momentum Everywhere by Clifford Asness, Tobias Moskowitz and Lasse Pedersen, published in the June 2013 issue of The Journal of Finance, examined these two factors across eight different