Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 06/25/2021

This is a summary of links featured on Quantocracy on Friday, 06/25/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Optimising portfolios for small accounts: Dynamic optimisation testing -> EPIC FAIL [Investment Idiocy]

    This is part two in a series of posts about using optimisation to get the best possible portfolio given a relatively small amount of capital. Part one is here (where I discussed the idea). You should read that now, if you haven't already done so. In this post I show you and explain the code and methodology used for the backtesting of this idea, and look at the results. The code is in my open
  • Research Review | 25 June 2021 | Tail Risk [Capital Spectator]

    Equity Tail Risk in the Treasury Bond Market Mirco Rubin (EDHEC) and Dario Ruzzi (Bank of Italy) December 23, 2020 This paper quantifies the effects of equity tail risk on the US government bond market. We estimate equity tail risk as the option-implied stock market volatility that stems from large negative jumps as in Bollerslev, Todorov and Xu (2015), and assess its value in reduced-form

Quantocracy’s Daily Wrap for 06/24/2021

This is a summary of links featured on Quantocracy on Thursday, 06/24/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Introducing: Arbitragelab Tear Sheets [Hudson and Thames]

    Pairs selection is the first crucial step to building a pairs trading strategy. And it is no surprise, to perform it correctly, one must diligently examine, compare and contrast numerous test results, graphs and characteristics. For example, cointegration analysis alone can be performed in one of two methods utilizing the Engle-Granger approach or the Johansen approach. To truly have the
  • Return based quality factor on Warsaw Stock Exchange [Mateusz Dadej]

    Recently I ran across an interesting paper published by National Bureau of Economic Research entitled Return Based Measue of Firm Quality. I happen to have a suitable data and thought why not reproduce it on data from polish stock exchange in the free time. It turned out not so bad and thanks to being not filled with boring mathematical formulae I guess its also pretty accessible. At the
  • An Introduction to Unsupervised Learning for Trading [Quant Insti]

    In the previous blogs, we examined supervised learning algorithms like linear regression in detail. In this blog, we look at what unsupervised learning is and how it differs from supervised learning. Then, we move on to discuss some use cases of unsupervised learning in investment and trading. We explore two unsupervised techniques in particular- k-means clustering and PCA with examples in Python.
  • A Sensible Approach to Bitcoin [Dual Momentum]

    Last year when bitcoin had its fourth drawdown of 80% in the past ten years, I thought It might be a good time to reenter that market. Having traded digital assets in 2017, I was familiar with the reasons for owning bitcoin. I wont reiterate them here. You can find information on bitcoin, blockchain, and decentralized finance (DeFi) here, here, here, and here. Every disruptive technology, like

Quantocracy’s Daily Wrap for 06/23/2021

This is a summary of links featured on Quantocracy on Wednesday, 06/23/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Replicating the J.P. Morgan Efficiente Index [Portfolio Optimizer]

    The J.P. Morgan Efficiente 5 Index is a tactical asset allocation strategy designed by J.P. Morgan based on a broad universe of 13 ETFs. This post will illustrate how to replicate this strategy with Google Sheets. Notes: A fully functional spreadsheet corresponding to this post is available here. Credit were credits due: I first discovered this strategy on AllocateSmartly. Strategy
  • Improving crypto investing with Reinforcement Learning [Quant Dare]

    Cryptocurrencies are a hot topic in the investing world, but is it possible to create an investment methodology combining modern Reinforcement Learning with classical indicators? Along this blog we have covered topics such as how to automate cryptocurrencies investment or whether reinforcement learning is suitable for trading. In this post, we try to combine Reinforcement Learning with a

Quantocracy’s Daily Wrap for 06/22/2021

This is a summary of links featured on Quantocracy on Tuesday, 06/22/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pairs Trading with Stochastic Control and OU process [Hudson and Thames]

    The concept of pairs trading is pretty straightforward. As described in [Gatev et al. (2006)], we first find two stocks that have moved together historically and then monitor the spread between these stocks. If the prices of the two stocks diverge, we short the winner and go long on the loser, hoping that these prices converge in the future. If the spread is mean reverting, it will revert to its
  • Private Equity: Is There Anything Special There? [Alpha Architect]

    As the following table demonstrates, since its inception in the 1970s, the private equity industry has grown significantly. According to Preqin data, there are now more than 18,000 private equity funds, with assets under management exceeding $4 trillion. Source: NACUBO endowment studies, FY 1987-2019 When deciding on whether to allocate capital to private equity, investors should consider whether

Quantocracy’s Daily Wrap for 06/21/2021

This is a summary of links featured on Quantocracy on Monday, 06/21/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio Optimization: Replicate a corporate bond index via Mixed-Integer Programming [DileQuante]

    While portfolio optimization is well known in the Equity space, in the Fixed Income industry, the subject is less discussed although it has very specific needs and it can be more complex compared to its Equity counterparts. One key difference between the two of them is the trading lot size. In Equities, most of the time, you can generate a portfolio composition directly with weights (continous
  • Avoiding Disasters with Catastrophe Bonds? [Factor Research]

    Catastrophe bonds offered exceptionally high risk-adjusted returns since 2005 These were uncorrelated to equities, making cat bonds attractive for diversification However, cat bonds might have underpriced risk historically, raising concerns going forward INTRODUCTION The global pandemic continues to be a catastrophe for our civilization. Compounding its effect: Few were insured against it. Sure,
  • Factors Timing is a Difficult Practice [Alpha Architect]

    Last week Tommi looked into whether hedge funds could time factors. The conclusion? Probably. This week we're going to see if Mutual Fund managers have any skill at cracking the factor timing code. The conclusion? They aren't great factor timers! The authors of the paper study a large sample of US equity mutual funds from late 2000 through 2016 and ask the following research question: Do

Quantocracy’s Daily Wrap for 06/19/2021

This is a summary of links featured on Quantocracy on Saturday, 06/19/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Buy&Hold? No, Buy&Sell! [Financial Hacker]

    Theres no doubt that buying and holding index ETFs is a long-term profitable strategy. But it has two problems. It does not reinvest profits, so the capital grows only linearly, not exponentially. And it exposes the capital to the full rollercoaster market risk. A sure way to go out of the market in a downtrend, and invest the profits back in an uptrend would be (almost) priceless. Markos
  • Many explanations for the same fact [Alex Chinco]

    Asset-pricing research consistently produces many different explanations for the same empirical facts. As a rule of thumb, you should expect asset-pricing researchers to wildly overachieve. Behavioral researchers can typically point to several psychological biases which might explain the same anomaly. e.g., it is possible to argue that the excess trading puzzle is due to a preference for gambling,

Quantocracy’s Daily Wrap for 06/18/2021

This is a summary of links featured on Quantocracy on Friday, 06/18/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Can astrology predict financial markets? (Of course not) [Mathematical Investor]

    In a previous MathInvestor article, we mentioned how absurd it would be if someone offered predictions of stock or bond prices or cryptocurrency rates based on astrological signs. Consider for a moment that financial market prices are based on a confluence of many thousands of factors worldwide, including developments in science and technology, changes in consumer sentiment and preferences,
  • The Performance of Volatility-Managed Portfolios [Alpha Architect]

    As far back as 1976, with the publication of Fischer Blacks Studies of Stock Price Volatility Changes financial economists have known that volatility and returns are negatively correlated. This relationship results in the tendency to produce negative equity returns in times of high volatility. In addition, the research, including the 2017 study Tail Risk Mitigation with Managed

Quantocracy’s Daily Wrap for 06/17/2021

This is a summary of links featured on Quantocracy on Thursday, 06/17/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Serenity DevOps #1 – Motivation [Kyle Downey]

    Serenity's production server is a Linux box sitting next to my desk which runs Ubuntu's microk8s Kubernetes distribution. It runs 24×7 collecting tick data from several cryptocurrency exchanges and once a day uploads the tick data to Azure Blob Storage. This presents a problem: this highly valuable, impossible-to-reproduce data is not read that often, and I have already had several

Quantocracy’s Daily Wrap for 06/16/2021

This is a summary of links featured on Quantocracy on Wednesday, 06/16/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • More ideas for ranking methods on a monthly S&P500 Stock Rotation Strategy [Alvarez Quant Trading]

    My last post on Different ranking methods for a monthly S&P500 Stock Rotation Strategy generated lots of emails on other ideas to try. Below are the results of these ideas Base Rules Backtest from 1/1/2007-12/31/2020. Buy It is the last trading day of the month Stock is a member of the S&P500 index Stock is above the 200-day moving average (spreadsheet has results without this rule) The
  • Automating cryptocurrencies investment [Quant Dare]

    Who has never heard about cryptocurrencies: Bitcoin, Ethereum, Cardano, or even the latest ones, such as Shiba or Safemoon? The investors are rapidly increasing their positions in those assets, although investing in them is usually a pain in the neck. These assets have a high volatility and their movements dont follow any traditional market rule and cryptocurrencies markets are not efficient.
  • Podcast with Ernie Chan (@ChanEP): Predicting profitability using machine learning [Better System Trader]

    Quant trader Ernie Chan from PredictNow.ai joins us to discuss how to predict the profitability of trades using machine learning, including: Unconditional probability and the problem with win% in backtest reports, Why conditional probability is much more useful for a trader and how to apply conditional probabilities to capital allocation, Why you should use Machine Learning for risk

Quantocracy’s Daily Wrap for 06/14/2021

This is a summary of links featured on Quantocracy on Monday, 06/14/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Financial Mentor’s All-Weather Quad Momentum [Allocate Smartly]

    This is an independent test of the tactical strategy All-Weather Quad Momentum (AWQM) from Todd Tresidder of FinancialMentor.com. Many of our members came to us from Financial Mentor, so its fitting that we add a strategy to our platform that demonstrates his approach to asset allocation. Backtested results from 1970 follow. Results are net of transaction costs (see backtest assumptions).
  • Markowitz Model [Quantpedia]

    We again present a short article as an insight into the methodology of the Quantpedia Pro report this time for the Markowitz Portfolio Optimization. As usually, Quantpedia Pro allows the optimization of model portfolios built from the passive market factors (commodities, equities, fixed income, etc.), systematic trading strategies and uploaded users equity curves. The current report helps
  • Can Hedge Funds Successfully Time Factors? [Alpha Architect]

    This study pulls together several threads in the academic literature: (1) the persistence of hedge fund outperformance; (2) the apparent use of time-varying beta exposures by hedge funds, where betas are predicated on conditions such as leverage, carry trade, major events and conditions in the equity market; and (3) the timing of equity and market factors, as a strategy. The research summarized in
  • Create a Personal Portfolio/Wealth Simulation in Python [Python For Finance]

    This post will introduce the first part (of multiple) where we build up a personal finance model to help simulate future time periods based on certain chosen input variables. We will input variables such as our current investable asset base, our annual salary, expected monthly inflows and outflows and a range of other relevant values. Firstly, after our necessary imports, we look to start on
  • Mid-Caps The Hidden Champions? [Factor Research]

    Mid-cap stocks are less popular than small or large caps In the US, they only outperformed in one out of 10 decades Globally, they have done better, creating a conundrum for investors INTRODUCTION A few weeks ago, David Stevenson, a well-known journalist and entrepreneur, asked me about my view on mid-cap stocks. To my own surprise, I had no view. Although Ive published more than 150 research
  • Markets neglect of macro news [SR SV]

    Empirical evidence suggests that investors pay less attention to macroeconomic news when market sentiment is positive. Market responses to economic data surprises have historically been muted in high sentiment periods. Behavioral research supports the idea that investors prefer heuristic decision-making and neglect fundamental information in bullish markets, but pay more attention in turbulent