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Quantocracy’s Daily Wrap for 02/14/2020

This is a summary of links featured on Quantocracy on Friday, 02/14/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Benchmarking the portfolio [OSM]

    In our last post, we looked at one measure of risk-adjusted returns, the Sharpe ratio, to help our hero decide whether he wanted to alter his portfolio allocations. Then, as opposed to finding the maximum return for our heros initial level of risk, we broadened the risk parameters and searched for portfolios that would at least offer the same return or better as his current portfolio and would
  • Research Review | 14 February 2020 | Business Cycle Risk [Capital Spectator]

    A New Index of the Business Cycle William B. Kinlaw (State Street Global Markets), et al. January 2020 The authors introduce a new index of the business cycle that uses the Mahalanobis distance to measure the statistical similarity of current economic conditions to past episodes of recession and robust growth. Their index has several important features that distinguish it from the Conference

Quantocracy’s Daily Wrap for 02/13/2020

This is a summary of links featured on Quantocracy on Thursday, 02/13/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Have you tried to calculate derivatives using TensorFlow 2? [Quant Dare]

    We will learn how to implement a simple function using TensorFlow 2 and how to obtain the derivatives from it. We will implement a Black-Scholes model for pricing a call option and then we are going to obtain the greeks. Matthias Groncki wrote a very interesting post about how to obtain the greeks of a pricing option using TensorFlow which inspired me to write this post. So, I took the same
  • Introduction to XGBoost in Python [Quant Insti]

    Ah! XGBoost! The supposed miracle worker which is the weapon of choice for machine learning enthusiasts and competition winners alike. It is said that XGBoost was developed to increase computational speed and optimize model performance. As we were tinkering with the features and parameters of XGBoost, we decided to build a portfolio of five companies and applied XGBoost model on it to create a

Quantocracy’s Daily Wrap for 02/12/2020

This is a summary of links featured on Quantocracy on Wednesday, 02/12/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Simple Vol Estimators [Falkenblog]

    While short-term asset returns are unpredictable, volatility is highly predictable theoretically and practically. The VIX index is a forward-looking estimate of volatility based on index option prices. Though introduced in 1992 it has been calculated back to 1986, because when released they wanted people to understand how it behaved. Given the conditional volatility varies significantly over time

Quantocracy’s Daily Wrap for 02/11/2020

This is a summary of links featured on Quantocracy on Tuesday, 02/11/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Investing Update: An Analysis of 2019 U.S. Factor Returns [Alpha Architect]

    In case you missed it, 2019 was a good year to be an equity investor. Examining market-cap-weighted indices, the U.S. stock market was up ~ 30%, Developed International Markets were up ~ 22%, and Emerging Markets were up ~ 18%. But how did factors do in 2019? Below I update my post from last year, examining the performance of simple factor portfolios. This year, I make some minor tweaks to

Quantocracy’s Daily Wrap for 02/10/2020

This is a summary of links featured on Quantocracy on Monday, 02/10/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Investing in “Distressed” TAA Strategies [Allocate Smartly]

    In response to a member question: Have you ever looked at a systematic approach that invested only in tactical asset allocation strategies that were experiencing a significant drawdown? We know that performance chasing is a flawed behavioral bias, so an approach like this could exploit it. We always appreciate thoughtful questions like this from members. First off, we can confirm that chasing
  • Payoff Diversification [Flirting with Models]

    At Newfound, we adopt a holistic view of diversification that encompasses not only what we invest in, but also how and when we make those investment decisions. In this three-dimensional perspective, what is correlation-based, how is payoff-based, and when is opportunity-based. In this piece, we provide an example of what we mean by payoff-based diversification, using a simple strategically
  • Timing Low Volatility with Factor Valuations [Factor Research]

    Factors can be valued like stocks or markets The Low Volatility factor in the US had the best subsequent returns when cheapest and worst when most expensive However, the perspective is less clear when analyzing European and Japanese stock markets INTRODUCTION Funds flows are frequently analyzed by investors to gauge the demand for investment strategies, but it represents a challenging exercise.
  • Python Regression Analysis: Drivers of German Power Prices [Philipp Kahler]

    German Power prices can be explained by supply and demand, but also by causal correlations to underlying energy future prices. A properly weighted basket of gas, coal and emissions should therefore be able to resemble the moves of the power price. This article will introduce multivariate regression analysis to calculate the influence of the underlying markets on a given benchmark. It is an example
  • Robot Wealth 6 Week Bootcamp – Build A Fully Automated FX Strategy – Enrollment Ends Friday

    Beginner to live FX algo trading in 6 weeks. Team up, research and trade fully-automated systematic FX strategies and rapidly build your capital-growing portfolio. Gain the experience and support needed to trade a fully-automated systematic FX strategy live by March 2020. Copy the beginner-friendly approach we use to trade for a living so one day you can too. Share the workload with a community of

Quantocracy’s Daily Wrap for 02/09/2020

This is a summary of links featured on Quantocracy on Sunday, 02/09/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 02/08/2020

This is a summary of links featured on Quantocracy on Saturday, 02/08/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • SHARPEn your portfolio [OSM]

    In our last post, we started building the intuition around constructing a reasonable portfolio to achieve an acceptable return. The hero of our story had built up a small nest egg and then decided to invest it equally across the three major asset classes: stocks, bonds, and real assets. For that we used three liquid ETFs (SPY, SHY, and GLD) as proxies. But our protagonist was faced with some
  • Tracking investor expectations with ETF data [SR SV]

    Retail investors return expectations affect market momentum and risk premia. The rise of ETFs with varying and inverse leverage offers an opportunity to estimate the distribution of such expectations based on actual transactions. A new paper shows how to do this through ETFs that track the S&P 500. The resulting estimates are correlated with investor sentiment surveys but more informative.

Quantocracy’s Daily Wrap for 02/07/2020

This is a summary of links featured on Quantocracy on Friday, 02/07/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What is the Bitcoin’s Risk-Free Interest Rate? [Quantpedia]

    Cryptocurrencies, and most notably Bitcoin, are recognized as decentralized currencies. While some see Bitcoin (BTC) as a payment method of the future, others see it as a speculative asset class. No doubt, many have gained on the skyrocketing prices of BTC, but note that many have lost. Despite the speculative activity connected with BTC, after all, it is a currency that is different from fiat

Quantocracy’s Daily Wrap for 02/06/2020

This is a summary of links featured on Quantocracy on Thursday, 02/06/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Case Against REIT’s [Alpha Architect]

    Surveys often reveal investor behavior that is challenging to understand. For example, Preqins Alternative Investor Outlook for H2 2019 highlighted the following: 65% of institutional investors believe that real estate is overvalued and a correction likely to occur in 2019, 2020, or beyond. However, 45% want to allocate the same amount of capital to real estate and 28% want to allocate even
  • What is the right way to set stop losses? [Investment Idiocy]

    Stop losses are the most common method used by traders to control risk. However, they're often used inappropriately. In this post I'll quickly bust some of the myths around them, and explain how to use them properly. This is the first of three posts aimed at answering three fundamental questions in trading: How should we control risk (this post) How much risk should we take? How fast

Quantocracy’s Daily Wrap for 02/05/2020

This is a summary of links featured on Quantocracy on Wednesday, 02/05/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Inverse Volatility Sizing Index [Alvarez Quant Trading]

    In my last post, Inverse Volatility Position Sizing, I tested inverse volatility sizing on a monthly rotation strategy. I saw very little difference in the rest results versus equal position sizing. I was talking to a trading friend about the research and how I was surprised at how there was not any difference in the results. He suggested that made creating an index using this method. Now, this
  • Factor Risk and Return [Falkenblog]

    Factor returns should reflect risk, in that they have traditionally been interpreted as proxies for some kind of risk not measured by beta. The idea is that perhaps what people really care about is whether there will be another oil shock, and nothing matters as much. Stocks that have a high dependence on cheap oil would have more risk than other stocks. In the early 1980s, this was a common
  • Visualising ETFs with UMAP [Quant Dare]

    In previous posts (Visualising Fixed Income ETFs with T-SNE) we have talked about dimensionality reduction algorithms to visualize financial assets and find recognizable patterns. The conclusions were that it didnt perform well compared to PCA, which is a more classical approach. Can we do any better? T-SNE was from 2008, but more dimensionality reduction algorithms have been released since