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Quantocracy’s Daily Wrap for 12/16/2022

This is a summary of links featured on Quantocracy on Friday, 12/16/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 100 Years of Historical Market Cycles [Quantpedia]

    Which assets perform best when rates are rising, and inflation is high? And what happens if rates are still rising but inflation is already falling? And whats the impact of the business cycle? These are the questions that everyone is currently trying to answer. Today, we will start a longer series of articles with the goal of giving an exact quantitative answer to all questions related to
  • The Informativeness: Measuring the Homogeneity of a Universe of Assets [Portfolio Optimizer]

    In this post, I will describe a measure of the homogeneity of a universe of assets, called the informativeness, introduced by Brockmeier et al.1 in their paper Quantifying the Informativeness of Similarity Measurements. After quickly going through the associated mathematics, I will present two examples of usage of this measure – one as potential indicator of systemic risk and the other as as a
  • Machine Learning and Emerging Market Stock Returns [Alpha Architect]

    More specifically, the paper differentiates between: Traditional linear models (ordinary least squares regression and elastic net) and Machine learning methods that allow for non-linearities and interactions (tree-based models such gradient boosted regression trees and random forest and neural networks with one to five layers) What are the main results? #1 Return forecasts based on machine

Quantocracy’s Daily Wrap for 12/13/2022

This is a summary of links featured on Quantocracy on Tuesday, 12/13/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Experimental Design and Common Pitfalls of Machine Learning in Finance [Hudson and Thames]

    The first lecture from the Experimental Design and Common Pitfalls of Machine Learning in Finance series addresses the four horsemen that present a barrier to adopting the scientific approach to machine learning in finance. The second lecture focuses on a protocol for backtesting and how to avoid the seven sins of backtesting. By implementing the research protocol outlined in these articles, an
  • CoinGecko API Python Tutorial [Analyzing Alpha]

    This article will show you how to access the CoinGecko API endpoints in Python to retrieve live cryptocurrency information. You will use the pycoingecko and the Python requests library to fetch data from CoinGecko API. The official CoinGecko API and pycoingecko libraries documentations lack concrete examples and explanations. Despite having over a decade of Python programming experience, It
  • Beware of Spurious Factors [Eran Raviv]

    The word spurious refers to outwardly similar or corresponding to something without having its genuine qualities. Fake. While the meanings of spurious correlation and spurious regression are common knowledge nowadays, much less is understood about spurious factors. This post draws your attention to recent, top-shelf, research flagging the risks around spurious factor analysis. While formal
  • Myth Busting: Alts’ Uncorrelated Returns Diversify Portfolios [Finominal]

    Alternatives with lower correlations to equities & bonds did not lead to greater diversification benefits Correlations often break when markets crash Better metrics are required to measure the diversification potential of alternatives INTRODUCTION Alternative investments accounted for $13 trillion in assets under management (AUM) in 2021, nearly twice what it was 2015. By 2026, that figure is
  • Volatility scaling: is it useful for factor timing? [Alpha Architect]

    The research summarized here is built upon a documented risk management strategy applied to factor investing (Barroso and Santa-Clara, 2015; Moreira and Muir, 2017). The idea was to overlay a scaled volatility measure designed to change risk exposures and hopefully produce higher Sharpe ratios. That basic research is tweaked in this article by analyzing the effect of scaling on portfolios

Quantocracy’s Daily Wrap for 12/11/2022

This is a summary of links featured on Quantocracy on Sunday, 12/11/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Managed Futures and Trend Following – Inside the Black Box [Light Finance]

    It goes without saying that 2022 has been a difficult year across markets. Investors have had to contend with an inflationary bear market for which the traditional playbook has proven woefully inadequate. NASDAQ and high yield debt, the darlings of yesteryear, have fallen from grace with few exceptions. Treasuries, the most common hedge against stock volatility, have suffered their worst drawdown
  • The Size Effect: Does it vary in accordance with monetary policy? [Alpha Architect]

    The size effect was first documented by Rolf Banz in his 1981 paper The Relationship Between Return and Market Value of Common Stocks, which was published in the Journal of Financial Economics. After the 1992 publication of Eugene Fama and Kenneth Frenchs paper The Cross-Section of Expected Stock Returns, the size effect was incorporated into what became finances new workhorse
  • Research Review | 9 Dec 2022 | Valuation Analysis [Capital Spectator]

    Preference for dividends and stock returns around the world Allaudeen Hameed (National University of Singapore), et al. November 2022 We find strong international evidence favoring dividend payout as a salient stock characteristic affecting expected stock returns. We find that dividend-paying stocks outperform non-payers by 0.54% per month in 44 countries, adjusting for exposures to global and

Quantocracy’s Daily Wrap for 12/07/2022

This is a summary of links featured on Quantocracy on Wednesday, 12/07/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building a Raspberry Pi Cluster for QSTrader Using SLURM – Part 5 [Quant Start]

    In the previous article we created a virtual environment and installed QSTrader on all our secondary nodes. We then carried out a test of the sixty forty strategy across all secondary nodes to make sure our installation had been successful. Now that we have successfully paralellised QSTrader we can start to carry out parameter sweeps for strategies. In this article we are going to carry out just
  • Volume and Mean Reversion [Alvarez Quant Trading]

    Overall, I have had very little success integrating volume into any of my strategies. Either volume would have no predictive value or if it did, using it reduced the number of trades too much to be worthwhile. It has been a long while since I have looked into this and I had some new ideas. The Rules Test date range 1/1/2007 to 10/31/2022. I wanted to keep the rules simple. Buy Rules Stock is a
  • Why I prefer probabilistic forecasts – hitting time probabilities [Sarem Seitz]

    Probabilistic forecasts are a more comprehensive way to predict future events compared to point forecasts. Probabilistic forecasts involve creating a model that predicts the entire probability distribution for a given future period, providing insight into all likely outcomes. This allows for the derivation of both point and interval forecasts. Point forecasts are easier to communicate to
  • Doubling Down: Double Deep Q-networks for trading [Quant Dare]

    In previous posts, we have seen the basic RL algorithm, Deep Q learning (DQN). We have also seen it applied, using Neural Networks as the Agent, to an investment strategy. We finally even used it for a cryptocurrency investment strategy. This time, we will implement a slightly more advanced technique, Double Deep Q-networks (DDQN), and create a trading strategy using this algorithm. DQN revisited

Quantocracy’s Daily Wrap for 12/06/2022

This is a summary of links featured on Quantocracy on Tuesday, 12/06/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Investing in Deflation, Inflation, and Stagflation Regimes [Alpha Architect]

    Spikes in inflation and fear of stagflation prompted this study which answers the following question: 1. How do risk premiums and investment strategies behave across inflationary regimes like periods of high inflation, deflation, or stagflation? What are the Academic Insights? By utilizing the deepest sample available for a relatively broad cross-section of asset class and factor portfolio
  • Are Alternative ETFs Good Diversifiers? [Finominal]

    Alternative products with uncorrelated returns do not necessarily provide diversification benefits Out of 10 alternative ETFs, only one product improved the Sharpe ratio of a 60/40 portfolio Correlations should be regarded carefully in fund selection INTRODUCTION Given the demise of the traditional 60/40 portfolio comprised of equities and bonds in 2022, investors are desperate for diversifying

Quantocracy’s Daily Wrap for 12/03/2022

This is a summary of links featured on Quantocracy on Saturday, 12/03/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Vol-of-Vol for Crypto-Derivative Products [Quant at Risk]

    In quantitative finance, the Volatility of Volatility (also referred to as Vol-of-Vol or VoV) is an important parameter for pricing various derivative products (e.g. Volatility Dispersion Swaps) and its correct estimation is frequently desired. VoV is usually a single number treated an an input parameter. One can make a couple of assumptions to start working within VoV framework. Namely, (1) there
  • How Much Are Bitcoin Returns Driven by News? [Quantpedia]

    The main theme of these days in the crypto world is unmistakenly clear, its the mayhem connected with the collapse of the FTX empire, insolvencies of various lenders, and questions about underlying holdings in GBTC OTC ETF and reserves of exchanges and Tether (or other stablecoins as well). With new information, nothing does paint a bright picture of this industry in the financial world now and
  • Trend Following and Relative Sentiment: Complementary Factors [Alpha Architect]

    Trend following (time series momentum) is one of the most well-documented and well-known factors in investing, demonstrating persistence, pervasiveness, robustness, and implementability (survives transaction costs). Lesser well-known is relative sentimentan indicator that measures the positions, flows, and attitudes of institutional investors compared to those of individual investors. The

Quantocracy’s Daily Wrap for 11/29/2022

This is a summary of links featured on Quantocracy on Tuesday, 11/29/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Weekly rebalancing Sector ETFs using Structural Entropy [Pravin Bezwada]

    Based on previous article Structural Entropy, this article attempts to test the performance of a long short strategy on US sectors using structural entropy. Ideally it should use liquid US sector futures but since data is unavailable (not for free), it uses ETFs as proxy. The assumption in previous article (referenced above), was that when entropy is low there is contagion in the market and we

Quantocracy’s Daily Wrap for 11/28/2022

This is a summary of links featured on Quantocracy on Monday, 11/28/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Slava Ukraini! Latest from Quantocracy contributor in Ukraine: VIX and Expected Range [Only VIX]

    Continuing on trying to fight disinformation about VIX. Everyone knows that VIX index the square root of the expected 30-day variance, and if we drop mathematical precision – 30 day expected volatility of S&P index. Scott Bauer, on CBO's website – "the VIX Index tells us the level of expected volatility of the S&P 500 Index for the next 30 days, with a 68% confidence level",
  • Binance API Python Tutorial [Analyzing Alpha]

    This tutorial explains how to call Binance API endpoints in Python using the python-binance library and the Python requests function. The Binance API provides extensive documentation on how to call its various endpoints. Furthermore, multiple online blogs explain how to call the Binance API using a Python client. However, I find that existing resources merely explain how to call a particular
  • Replicating a CTA via Factor Exposures [Finominal]

    Strategies can be copied by recreating them from scratch or using factor exposures CTAs can be replicated via factor exposure analysis by utilizing only 4 asset classes Not a perfect replication, but surprisingly good given the limited input INTRODUCTION Our two most recent research articles focused on creating a CTA, also known as managed futures or trend following strategies, from scratch (read
  • Identifying the drivers of the commodity market [SR SV]

    Commodity futures returns are correlated across many different raw materials and products. Research has identified various types of factors behind this commonality: [i] macroeconomic changes, [ii] financial market trends, and [iii] shifts in general uncertainty. A new paper proposes to estimate the strength and time horizon of these influences through mixed-frequency vector autoregression.

Quantocracy’s Daily Wrap for 11/25/2022

This is a summary of links featured on Quantocracy on Friday, 11/25/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Creating a CTA from Scratch – II [Finominal]

    CTAs pursue trends across asset classes, regardless if long or short 2022 is exceptional as CTAs have more short positions than during the GFC No single long bond index position remains, fixed income is one big short INTRODUCTION In our last research article (read Creating a CTA from Scratch I) we demonstrated that building a CTA strategy is not particularly difficult. All it takes is a broad
  • Option Momentum: does it work? [Alpha Architect]

    Several studies show momentum works in global equities, corporate bonds, currencies, and commodities. This paper asks the following research question: Does momentum work within option markets? What are the Academic Insights? By focusing on the returns of delta-neutral straddles (from 1996 to 2019) on individual equities to form a strategy whose returns are approximately invariant to the
  • Why bother with unbiasedness? [Quant Dare]

    For every quantity to be estimated (estimand), theres a plethora of ways to estimate it (estimators). This raises the question of what properties we should be looking for so as to make a sensible choice. Often highlighted as one of such properties is unbiasedness, which we will discuss below with a particular focus on its shortcomings. Introducing unbiasedness In estimation, we want to obtain

Quantocracy’s Daily Wrap for 11/20/2022

This is a summary of links featured on Quantocracy on Sunday, 11/20/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend-Following Rules in Two-State Regime-Switching Models [Alpha Architect]

    Academic research on trend-following investing has almost exclusively been focused on testing the profitability of various trading rules. Most of these rules are based on moving averages of past prices. The most popular is the Simple Moving Average (SMA). Less commonly used types of moving averages are the Linear Moving Average (LMA) and Exponential Moving Average (EMA). Each moving average is
  • Active manager bias is it the same as for individual investors? [Alpha Architect]

    The empirical research on the ability of actively managed funds, including such studies as the 2002 paper Mutual Fund Performance: An Empirical Decomposition into Stock-Picking Talent, Style, Transactions Costs, and Expenses by Russ Wermers, has found that they do have the ability to identify stocks that go on to outperform appropriate benchmarksthey are skilled. But are active managers