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Quantocracy’s Daily Wrap for 11/03/2021

This is a summary of links featured on Quantocracy on Wednesday, 11/03/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The brave new world of probability and statistics [Mathematical Investor]

    Today, arguably more than ever before, the world is governed by the science of probability and statistics. Big data is now the norm in scientific research, with terabytes of data streaming into research centers from satellites and experimental facilities, analyzed by supercomputers. Data mining is now an essential part of mathematical finance and business management. Numerous public
  • One-N against the world! [OSM]

    Were taking a short break from neural networks to return to portfolio optimization. Our last posts in the portfolio series discussed risk-constrained optimization. Before that we examined satisificing vs. mean-variance optimization (MVO). In our last post on that topic, we simulated 1,000 60-month (5-year) return series using the 1987-1991 period for our four assets: stocks, bonds, commodities

Quantocracy’s Daily Wrap for 11/02/2021

This is a summary of links featured on Quantocracy on Tuesday, 11/02/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Reddit for Fun and Profit [part 1] [Alpha Scientist]

    The news story in 2021 that captured the complete attention of the financial press was the Gamestop / WallStreetBets / RoaringKitty episode of late January. A group of presumably small, retail traders banded together on Reddit's r/wallstreetbets forum to drive the price of $GME, $AMC and other "meme stocks" to unimaginable heights, wreaking havoc with the crowd of hedge funds who
  • What Is Machine Learning? [Enjine]

    Im South Korean by birth, but I spent most of my highschool years in Ireland. I wanted to remain in an English speaking country after I graduated, so I chose to go to the University of Waterloo, located in Canada. During the first lecture I attended, I needed to edit something I wrote. I rummaged through my pencil case, but failed to find what I was looking for, so I turned to a couple of
  • Hong Kong Machine Learning Meetup [Gautier Marti]

    When? Wednesday, October 27, 2021 from 7:00 PM to 9:00 PM (Hong Kong Time) Where? At your home, on zoom. All meetups will be online as long as this COVID-19 crisis is not over. The page of the event on Meetup: HKML S4E2 Programme: Talk 1: Systematic Pricing and Trading of Municipal Bonds Petter N. Kolm Professor at New York University (NYU) – Courant Institute of Mathematical Sciences Sudar
  • A Complete System for New Traders: Adding Entry Signals [Raposa Trade]

    If youre new to trading, it may be challenging to know how to get started. There are so many new terms, maths, and concepts, it can seem overwhelming! Now you have to take all that stuff and figure out how to make a profitable system out of it? Most people give up at this point. To address this, were building on a system thats built for newbies, Rob Carvers Starter System as outlined
  • Short-Term Momentum in Stocks, Commodities, and Cryptos [Factor Research]

    Developed markets have evolved from momentum to mean-reversion markets Other markets like EM or cryptos are momentum-dominated Likely explained by the distribution of retail vs institutional investors INTRODUCTION Markets evolve constantly, but they rarely change structurally. When they do, it is usually either due to new policies or products. Regulations tend to have a more immediate effect, e.g.

Quantocracy’s Daily Wrap for 10/29/2021

This is a summary of links featured on Quantocracy on Friday, 10/29/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • An Introduction to Value at Risk Methodologies [Quantpedia]

    Understanding the risks of any quantitative trading strategy is one of the pillars of successful portfolio management. Of course, we can hope for good future performance, but to survive market whipsaws, we must have tools for sound risk management. The Value at Risk measure is such a standard tool used to assess the riskiness of trading and investment strategies over time. We plan to unveil

Quantocracy’s Daily Wrap for 10/27/2021

This is a summary of links featured on Quantocracy on Wednesday, 10/27/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Czekanowski Index-Based Similarity as Alternative Correlation Measure [Quant at Risk]

    In quantitative finance we are used to measuring direct linear correlations or non-linear cross-bicorrelations among various time-series. For the former, by default, one adopts the calculation of Pearson product-moment correlation coefficients to quantify a linear relationship between two vectors. This is true if the the data follow Gaussian distribution. In other case, the rank correlation
  • Realized Volatility In Bitcoin Index [Lucas Miranda]

    One of the most relevant characteristics of digital assets is the high volatility observed in their prices. In this context, it is necessary that we have an adequate estimate of this parameter. In addition, there is great value in models that seek to predict future asset volatility values, which can be seen in the extensive literature on this topic. Here we will manipulate a high frequency
  • Will the Fed ruin my S&P500 investments? [Quant Dare]

    It is widely known that each time the Fed gives an announcement, the whole investing world is watching. So, one may wonder if those events can ruin their investments. Recently in this blog, we have studied a set of variables which might move the market. From this post one can extract that Fed statements are a powerful variable that moves the world economy. Why not to take advantage of it in an
  • Do factors have a role in asset allocation? [Alpha Architect]

    What is the role of factors in asset allocation? Should investors substitute factor exposures for asset classes in constructing strategic portfolios? Or should factors be used as an instrument to enhance the performance of asset class-based allocation schemes? There are still quite a few questions surrounding the implementation of asset allocation strategies even though they have permeated

Quantocracy’s Daily Wrap for 10/25/2021

This is a summary of links featured on Quantocracy on Monday, 10/25/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pairs Trading Based on Renko and Kagi Models [Hudson and Thames]

    A group of strategies, named statistical arbitrage or pairs trading strategies are well-known for being market-neutral gained their popularity among institutional and individual investors. In general, to develop a pairs trading strategy, one needs to figure out two aspects, the first is how to select assets to form a process with mean-reverting properties, and the second is how to decide when and
  • Does the Equity Market Lead the Currency Market? [Factor Research]

    Past equity market returns seem to predict currency returns Such a currency timing strategy may be interesting as a diversifier However, it is difficult to rationalize the results INTRODUCTION Bloomberg TV at 08:30 am EST: The S&P 500 futures are trading lower as the US Dollar depreciated against G10 currencies overnight. CNBC at 9:45 am EST: The USD appreciated given a strong opening
  • A Complete Starter System for New Traders [Raposa Trade]

    Your biggest investment just took another move higher. It has gotten to the point that you start thinking about taking some profit off the table: its looking more and more enticing by the day! Do you do it? If youre like most investors, you cant resist taking some money today, even if it winds up costing you in the long run. Or, perhaps your brilliant investment thesis hasnt panned

Quantocracy’s Daily Wrap for 10/24/2021

This is a summary of links featured on Quantocracy on Sunday, 10/24/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Site: Is the diversification ratio time-varying? [Lucas Miranda]

    Today we are going to check whether the diversification index proposed by Choueifaty and Coignard (2008) varies over time and some characteristics of this index. The construction of this analysis will be done using python. The Bovespa Index is the main stock index in the Brazilian market and is composed of around 90 stocks. You can check the daily composition of the index here. Remember the
  • A History of Wealth Creation in the U.S. Equity Markets [Alpha Architect]

    Hendrik Bessembinder contributes to the literature on the returns to public equity investment diversification benefits with his study Wealth Creation in the US Public Stock Markets 1926-2019, published in the April 2021 issue of The Journal of Investing. The study updated his 2018 paper, Do Stocks Outperform Treasury Bills?, (Summary and More) adding three more years of data. He
  • A New parameterization of Correlation Matrices [Eran Raviv]

    In volatility modelling, a typical challenge is to keep the covariance matrix estimate valid, meaning (1) symmetric and (2) positive semi definite*. A new paper published in Econometrica (citing from the paper) introduces a novel parametrization of the correlation matrix. The reparametrization facilitates modeling of correlation and covariance matrices by an unrestricted vector, where positive

Quantocracy’s Daily Wrap for 10/22/2021

This is a summary of links featured on Quantocracy on Friday, 10/22/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building a Raspberry Pi Cluster for QSTrader using SLURM – Part 1 [Quant Start]

    When carrying out systematic trading strategy research one of the main steps is to optimise a collection of strategy parameters to maximise or minimise some objective function. A simple example would be optimising the lookback parameters of the 'fast' and 'slow' moving averages in a trend following system to maximise the strategy's historical Sharpe ratio. Each variation

Quantocracy’s Daily Wrap for 10/20/2021

This is a summary of links featured on Quantocracy on Wednesday, 10/20/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Break into Finance: New Podcast from Quant at Risk [Quant at Risk]

    Let me kick off the series of QuantAtRisks podcasts Break into Finance. I address it to all of you who wish to join the financial industry but have no clue how to do it as well as to those of you who would like to make a change, improve your career, get better, and succeed within the industry. This podcast will be full of practical examples based on my experience across Asia, Australia, and
  • Markov chain as market predictor [Quant Dare]

    Markov chains are well-known in the world of both mathematics and finance. It is common to describe the market as a group of states, for instance bull and bear. From these two there are different ways to create a great deal of other states. If you want to establish the transition relationship between states, Markov chains are really useful. There are some articles in this blog that introduce de

Quantocracy’s Daily Wrap for 10/19/2021

This is a summary of links featured on Quantocracy on Tuesday, 10/19/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on S&P 500 [Enjine]

    Although Harry Potters world of magic exists on the same earth as our magic-less Muggle world, the worlds might as well be on different planets. Each world is governed by its own sets of rules and values, and their residents hardly ever cross each others paths. Academia and industry similarly exist as parallel worlds. In academia, a persons work is judged by its logical rigour. In
  • New Site: Options Derived Analytics [Newmark Risk]

    The Put-Call ratio is often the most commonly used Options-Implied Indicator due to it's simplicity in calculation. However there exists several variations in methodology to calculate it. In this blogpost we give an overview of these different methods and their relevance. The Put-Call ratio can be calculated using volume or open interest and it can be filtered by moneyness (ATM, ITM or OTM).
  • Beyond Hierarchical Risk Parity: Hierarchical Clustering-Based Risk Parity [Portfolio Optimizer]

    In a previous post, I introduced the Hierarchical Risk Parity portfolio optimization algorithm1. In this post, I will present one of its variations, called Hierarchical Clustering-Based Risk Parity, first described in Papenbrock2 and then generalized in Raffinot34 and in Lohre et al.5, from which the implementation in Portfolio Optimizer is inspired. Hierarchical clustering-based risk parity

Quantocracy’s Daily Wrap for 10/18/2021

This is a summary of links featured on Quantocracy on Monday, 10/18/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pairs Trading with Markov Regime-Switching Model [Hudson and Thames]

    Traditional pairs trading strategies are prone to failures when fundamental or economic reasons cause a structural break and the pair of assets that were expected to move together are no longer having a strong relationship. Such a break may result in asset price spread having abnormally high deviations failing to revert to its historical mean values. Under these circumstances, betting on the
  • Long Volatility Strategies: Hedge Funds vs DIY [Factor Research]

    Long volatility exposure is typically achieved via hedge funds A simple DIY strategy would have generated similar attractive diversification benefits Most of the returns are explained by risk-off currencies, government bonds, and gold INTRODUCTION Do-it-yourself is the best and worst financial advice for retail investors. On the one hand, retail investors are not particularly good investors and
  • Measuring the value-added of algorithmic trading strategies [SR SV]

    Standard performance statistics are insufficient and potentially misleading for evaluating algorithmic trading strategies. Metrics based on prediction errors mistakenly assume that all errors matter equally. Metrics based on classification accuracy disregard the magnitudes of errors. And traditional performance ratios, such as Sharpe, Sortino and Calmar are affected by factors outside the