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Quantocracy’s Daily Wrap for 08/07/2023

This is a summary of links featured on Quantocracy on Monday, 08/07/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quant_rv part 8: a multi-vol approach [Babbage9010]

    Sum up: by combining all the vols into one strategy and randomizing key parameters, we can generate useful signals that yield a decent return with some consistency. Were not meeting all the quant_rv goals yet, but were making progress on all the fronts. ~ Links to earlier parts ~ Part 1: jumping in, Part 2: cleanup, Part 3: new goals, Part 4: heatmaps, Part 5: param exploration, Part 6:
  • Quant And Machine Learning Links: 20230806 [Machine Learning Applied]

    Portfolio Management: A Deep Distributional RL Approach David Pacheco Aznar This thesis presents the development and implementation of a novel Deep Distributional Reinforcement Learning (DDRL) approach in the field of quantitative finance: the Distributional Soft Actor-Critic (DSAC) with an LSTM embedding. The model is built to further stabilize the performance of the widely used deep
  • Statistical Shrinkage (2) [Eran Raviv]

    During 2017 I blogged about Statistical Shrinkage. At the end of that post I mentioned the important role signal-to-noise ratio (SNR) plays when it comes to the need for shrinkage. This post shares some recent related empirical results published in the Journal of Machine Learning Research from the paper Randomization as Regularization. While mainly for tree-based algorithms, the intuition
  • Investor demand: can it explain returns? [Alpha Architect]

    The traditional financial theory attributes security returns to market- or factor-based risk, with no role ascribed to other influences. In this research, the authors argue for including investor demand as an additional variable in explaining returns. Can changes in investor demand generate systematic changes in security returns? Overall, our analysis shows that demand effects caused by

Quantocracy’s Daily Wrap for 08/05/2023

This is a summary of links featured on Quantocracy on Saturday, 08/05/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Integrating the No-Code Quant Backtester into the Russian Doll Engine [Hanguk Quant]

    We started off with the conceptualisation of trading alpha in different abstract representations, such as mathematical formulas, graphs and visual representations: Alpha-Encoding Data Structures Alpha-Encoding Data Structures HangukQuant Jun 30 Read full story For machine trading this would require a convenient translation between the different representations onto computer bits, and we
  • Why Backtests Run Fast or Slow: A Comparison of Zipline, Moonshot, and Lean [Quant Rocket]

    Backtest speed can significantly affect research friction. The ability to form a hypothesis and quickly get an answer from a backtest allows you to investigate more hypotheses. In this article, I explore several factors that affect backtest speed and compare the performance of 3 open-source backtesters. The backtesters I compare are: Moonshot, a vectorized backtester written in Python Zipline, an
  • The Low-Beta Anomaly: are its returns justified? [Alpha Architect]

    The low-beta anomaly for the capital asset pricing model (CAPM)low-beta stocks outperform high-beta stockswas first documented more than 50 years ago by Fischer Black, Michael Jensen, and Myron Scholes in their 1972 paper, The Capital Asset Pricing Model: Some Empirical Tests. In our 2016 book, Your Complete Guide to Factor-Based Investing, Andrew Berkin and I presented evidence

Quantocracy’s Daily Wrap for 07/30/2023

This is a summary of links featured on Quantocracy on Sunday, 07/30/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Realistic Backtester for Perpetual Futures (Part 1/2) (With Code) [Taiwan Quant]

    Introduction Simulator/backtester architecture Preparing the data Simulating a single market Simulating market orders Part 2: Simulating trading costs Simulating funding Simulating many markets Finish Subscriber materials (source code) Introduction In the last article, we looked at how markets work and at simulating them in theory. Today we will write a complete event-driven simulator/backtester
  • Pure macro FX strategies: the benefits of double diversification [SR SV]

    Pure macro(economic) strategies are trading rules that are informed by macroeconomic indicators alone. They are rarer and require greater analytical resources than standard price-based strategies. However, they are also more suitable for pure alpha generation. This post investigates a pure macro strategy for FX forward trading across developed and emerging countries based on an external
  • Quant And Machine Learning Links: 20230730 [Machine Learning Applied]

    Quantocracy: This is a curated mashup of quantitative trading links. Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling Masanori Hirano, Kentaro Minami, Kentaro Imajo Deep hedging is a deep-learning-based framework for derivative hedging in incomplete markets. The advantage of deep hedging lies in its ability to handle various realistic market conditions, such as market

Quantocracy’s Daily Wrap for 07/28/2023

This is a summary of links featured on Quantocracy on Friday, 07/28/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Square root of a portfolio covariance matrix [OS Quant]

    The square root of your portfolios covariance matrix gives you a powerful way of understanding where your portfolio variance is coming from. Here I show how to calculate the square root and provide an interactive example to explore how it works. Author Adrian Letchford Published 27 July 2023 Length 4 minutes Like what you see? Follow Adrian on Twitter to be notified of new content. Follow
  • Retail attention metrics: do they produce differences in returns? [Alpha Architect]

    Abstract: We find that by using a novel measure of investor attention, generated from InvestingChannels clickstream data on online financial news consumption, we can identify broad groups of stocks which are less efficiently priced and therefore where anomalies such as Value and Momentum are likely to produce greater cross-sectional differentiation in returns. We also apply these groupings to

Quantocracy’s Daily Wrap for 07/27/2023

This is a summary of links featured on Quantocracy on Thursday, 07/27/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • XRP-based Crypto Investment Portfolio Inspired by Ripple vs SEC Lawsuit [Quant at Risk]

    Crypto-market price actions often revolves around the news. Good or bad? It does not matter. However, the recent long-term battle between the SEC and Ripple seemed to reignite the markets. On July 13, 2023, XRP/USDT suddenly shoot up, dragging a number of not so obvious cryptos up along. This was the implication of the courts verdict in the lawsuit. This observation led me to formulate a
  • Top Models for Natural Language Understanding (NLU) Usage [Quantpedia]

    In recent years, the Transformer architecture has experienced extensive adoption in the fields of Natural Language Processing (NLP) and Natural Language Understanding (NLU). Google AI Researchs introduction of Bidirectional Encoder Representations from Transformers (BERT) in 2018 set remarkable new standards in NLP. Since then, BERT has paved the way for even more advanced and improved models.
  • Building a No Code Quantitative Backtest Engine for Machine Trading [Hanguk Quant]

    We started off with the conceptualisation of trading alpha in different abstract representations, such as mathematical formulas, graphs and visual representations: Alpha-Encoding Data Structures Alpha-Encoding Data Structures HangukQuant Jun 30 Read full story For machine trading this would require a convenient translation between the different representations onto computer bits, and we
  • Regression is a tool that can turn you into a fool [Alpha Architect]

    Running regressions on past returns is a great tool for academic researchers who understand this approachs nuance, assumptions, pitfalls, and limitations. However, when factor regressions become part of a sales effort and/or are put in the hands of investors/advisors/DIYers, the tool can quickly turn you into a fool. Dont get me wrong, running regressions on return series is useful for

Quantocracy’s Daily Wrap for 07/26/2023

This is a summary of links featured on Quantocracy on Wednesday, 07/26/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 07/25/2023

This is a summary of links featured on Quantocracy on Tuesday, 07/25/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • All the vols, for quant_rv [Babbage9010]

    Its just too easy to do all the volatility measures, with quantmod (well, with TTR actually). Lets skip all the preliminaries and have a look. And, a Pearson pairs table: C2C Parkinson Rogers-Satchell Garman-Klass,Yang-Zhang C2C 1.0000000 0.4395541 0.2619220 0.3573710 Parkinson 0.4395541 1.0000000 0.8322215 0.8214485 Rogers-Satchell 0.2619220 0.8322215 1.0000000 0.8649953

Quantocracy’s Daily Wrap for 07/23/2023

This is a summary of links featured on Quantocracy on Sunday, 07/23/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Recursive least-squares linear regression [OS Quant]

    I first learned about this algorithm in the book Kernel Adaptive Filter: A Comprehensive Introduction1 sometime in 2012 or 2013. This book goes in depth into how to build kernel filters and does a fantastic job of easing you into the mathematics. I highly recommend having a read if you can. In my trading algorithms, at each time period, I use a linear regression to predict future returns of each
  • Quant And Machine Learning Links: 20230723 [Machine Learning Applied]

    Reinforcement Learning for Credit Index Option Hedging Francesco Mandelli, Marco Pinciroli, Michele Trapletti, Edoardo Vittori In this paper, we focus on finding the optimal hedging strategy of a credit index option using reinforcement learning. We take a practical approach, where the focus is on realism i.e. discrete time, transaction costs; even testing our policy on real market data. We
  • Research Review | 21 July 2023 | Forecasting Markets [Capital Spectator]

    Betting on War? Oil Prices, Stock Returns and Extreme Geopolitical Events Knut Nygaard (Oslo Metropolitan U.) and L.Q. Srensen (Storebrand Asset Mgt.) July 2023 We show that the ability of oil price changes to predict stock returns is largely limited to five extreme geopolitical events: the 2022 invasion of Ukraine, the 2003 invasion of Iraq, the 1990/91 Persian gulf war, the 1986 OPEC collapse,
  • Risk of Momentum Crashes: can it be reduced? [Alpha Architect]

    My August 4, 2022, Alpha Architect article examined the research demonstrating that cross-sectional momentum has provided a premium that has been found to be persistent across time and economic regimes, pervasive around the globe and across sectors and asset classes (stocks, bonds, commodities and currencies), robust to various definitions, and survives transactions costs. And within equities,

Quantocracy’s Daily Wrap for 07/20/2023

This is a summary of links featured on Quantocracy on Thursday, 07/20/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A different measure of volatility for quant_rv [Babbage9010]

    Everybody knows what volatility is. But theres more than one way to measure it. The last couple of posts Ive been trying to document a little more about the plain vanilla standard way to measure vol in the context of my efforts toward finding an ETF switching strategy to use realized volatility as the primary signal, especially looking at being out of the market during higher volatility
  • Fund Concentration: Does it impact return? [Alpha Architect]

    This study explores the degree to which fund concentration as measured by high tracking error or active share, affects the magnitude of excess returns and whether or not the likelihood of outperformance or underperformance are distributed similarly. Three methods of analysis were used to examine the relationship between dispersion of fund excess returns and the degree of portfolio concentration

Quantocracy’s Daily Wrap for 07/16/2023

This is a summary of links featured on Quantocracy on Sunday, 07/16/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quant_rv: more exploration of strategy parameters [Babbage9010]

    There is grave danger in tying your strategy to one selected set of parameters, particularly if those parameters are cherry picked to give more exciting results than other possible choices. Im trying to working to avoid that in quant_rv. So far, quant_rv has two main parameters that can vary: the lookback_period for calculating realized volatility, and the volatility threshold used as a cutoff
  • Quant And Machine Learning Links: 20230716 [Machine Learning Applied]

    Financial Machine Learning Bryan T. Kelly, Dacheng Xiu We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for
  • Are Sustainable Investors Compensated Adequately? [Alpha Architect]

    While sustainable investing continues to gain in popularity, economic theory suggests that if a large enough proportion of investors choose to favor companies with high sustainability ratings and avoid those with low sustainability ratings (sin businesses), the favored companys share prices will be elevated and the sin stock shares will be depressed. In equilibrium, the screening out of certain
  • A model for bond risk premia and the macroeconomy [SR SV]

    An empirical analysis of the U.S. bond market since the 1960s emphasizes occasional abrupt regime changes, as defined by yield levels, curve slopes, and related volatility metrics. An arbitrage-free bond pricing model illustrates that bond risk premia can be decomposed into two types. One is related to continuous risk factors, traditionally summarized as the level, slope, and curvature of the