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Quantocracy’s Daily Wrap for 09/11/2023

This is a summary of links featured on Quantocracy on Monday, 09/11/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • There IS a low vol anomaly in SPY [Babbage9010]

    TL;DR There really is a low volatility anomaly in the SPY data; low volatility today predicts low volatility tomorrow and risk-adjusted returns are higher investing daily in the lower vol half of predicted market days. Same data, new analysis, better graphs and youll see it too. First up, a mea culpa. I misused my shallow understanding of stats::lag() in the last post and ended up
  • Momentum turning points and their impact on market cycles [Alpha Architect]

    The article investigates time-series (TS) momentum strategies and their performance in financial markets based on various speeds or lookback horizons. The study aims to understand the connections between different speeds of TS momentum, unobservable variables like trend, turning points, and noise levels in realized returns, and their impact on market cycles. Momentum Turning Points Goulding,
  • K-Nearest Neighbors Algorithm: Steps to Implement in Python [Quant Insti]

    Machine Learning (ML) has emerged as a powerful tool in the field of Artificial Intelligence, revolutionising various aspects of our lives. Whether it's recognising human handwriting or enabling self-driving cars, ML has become an integral part of our daily routines. With the exponential growth of data, the prevalence and importance of ML are only expected to increase in the coming years. ML
  • Fixed Income Factors II [Finominal]

    There are style factors like value and traditional fixed income factors like term premium The correlations of these factors has been low However, it is not clear which are better suited for a factor exposure analysis INTRODUCTION In our last research article, we compared fixed income factors from two asset managers, namely AQR Capital Management and Robeco, which highlighted different security

Quantocracy’s Daily Wrap for 09/09/2023

This is a summary of links featured on Quantocracy on Saturday, 09/09/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to backtest 2,000,000 simulations for the best exits [PyQuant News]

    If youve been a reader of this newsletter for a while, or a student of Getting Started With Python for Quant Finance, youll recognize this statement: Backtests are not a way to brute force optimize parameters to maximize a performance metric. Doing that leads to overfitting and losses. But optimization does play an important part in building trading strategies. Today, well see how. How to
  • Equity versus fixed income: the predictive power of bank surveys [SR SV]

    Bank lending surveys help predict the relative performance of equity and duration positions. Signals of strengthening credit demand and easing lending conditions favor a stronger economy and expanding leverage, benefiting equity positions. Signs of deteriorating credit demand and tightening credit supply bode for a weaker economy and more accommodative monetary policy, benefiting long-duration

Quantocracy’s Daily Wrap for 09/08/2023

This is a summary of links featured on Quantocracy on Friday, 09/08/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Price data from Yahoo Finance in R the Easy Way [Robot Wealth]

    Traders typically have many ideas for trading strategies more than they can ever implement in practice! Therefore its useful to be able to move quickly in the early research phase. You want to disprove things as quickly as possible so that you can move onto the next thing. Obviously there is immense value in reliable and easy data access. You dont want to be wrangling large data sets
  • Stock-bond correlation and its lessons for investors [Alpha Architect]

    The correlation between stocks and bonds should be a critical component of any asset allocation decision, as it impacts not only the overall risk of a diversified multi-asset class portfolio but also the risk premia one should expect to receive for taking risk in different asset classes. The problem for investors is that the correlation between stocks and bonds fluctuates extensively across time

Quantocracy’s Daily Wrap for 09/07/2023

This is a summary of links featured on Quantocracy on Thursday, 09/07/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Financial Distress Factors: Altman Z-Score and Interest Coverage Ratio [Quant Rocket]

    Are rising interest rates straining balance sheets and increasing the risk of bankruptcies? This article investigates two financial distress factors, the Altman Z-Score and interest coverage ratio, to see if distress is on the rise and how it impacts stock returns. This post is part of the fundamental factors series, which explores techniques for researching fundamental factors using Pipeline,
  • Code Walkthrough for the Alpha Simulator (for Programming Beginners) [Hanguk Quant]

    As we advance into our third year on this blog – its dawning upon me that many of the readers are getting left behindthe biggest concern by far is the complexity of the current Russian Doll model and not being sure how to proceed with using the statistical suite presented therein, together with the formulaic alphas. Although I was intending to further take the Russian Doll to have integrated

Quantocracy’s Daily Wrap for 09/05/2023

This is a summary of links featured on Quantocracy on Tuesday, 09/05/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Testing TrendYCMacro from Durian and Vojtko of @Quantpedia [Allocate Smartly]

    This is a test of the TrendYCMacro strategy from the paper Avoid Equity Bear Markets with a Market Timing Strategy from urian and Vojtko of Quantpedia. The strategy combines trends in price, the slope of the yield curve and key economic indicators to switch between US equities and cash. Backtested results from 1927 follow. Results are net of transaction costs see backtest assumptions.
  • Short Term Signals – can they produce meaningful alpha? [Alpha Architect]

    Short-term return anomalies are generally dismissed in the academic literature because they seemingly do not survive after accounting for market frictions. In this research, short-term factors are taken seriously, and the authors argue the standard parameters may not apply to short horizons. The authors ask: Do the standard assumptions regarding estimates for trading costs,

Quantocracy’s Daily Wrap for 09/02/2023

This is a summary of links featured on Quantocracy on Saturday, 09/02/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Investment Factor: does it impact returns? [Alpha Architect]

    Over the long term, low-investment firms have outperformed high-investment firms. This finding has led to the investment factor (CMA, or conservative minus aggressive) being incorporated into the leading asset pricing modelsthe four-factor q-theory model (market beta, size, investment, and profitability), the Fama-French five-factor model that adds value, and the Fama-French six-factor model
  • Autocorrelation in Trading: A Practical Python Approach to Analyzing Time Series Data [Quant Insti]

    Autocorrelation is a statistical concept that measures the correlation between observations of a time series and its lagged values. It is commonly used in various fields, including trading for technical analysis, to identify patterns, trends, and relationships within data. Autocorrelation helps analyse the dependence between past and present values and provides insights into the persistence or
  • How to use HDF5 for advanced, ultra fast market data storage [PyQuant News]

    If theres one thing algorithmic traders cannot get enough of, its data. The data that fuels our strategies is more than just numbersits the lifeblood of our decision-making processes. And having data available locallyor at least within your controlis a big part of that. In todays newsletter, well use the ultra-fast HDF5 file format to store data for research and analysis.
  • Research Review | 31 August 2023 | Financial Crises [Capital Spectator]

    Predicting Financial Crises: The Role of Asset Prices Tristan Hennig (International Monetary Fund), et al. August 2023 We explore the early warning properties of a composite indicator which summarizes signals from a range of asset price growth and asset price volatility indicators to capture mispricing of risk in asset markets. Using a quarterly panel of 108 advanced and emerging economies over

Quantocracy’s Daily Wrap for 08/28/2023

This is a summary of links featured on Quantocracy on Monday, 08/28/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 15 Ideas, Frameworks, and Lessons from 15 Years [Flirting with Models]

    Today, August 28th, 2023, my company Newfound Research turns 15. It feels kind of absurd saying that. I know Ive told this story before, but I never actually expected this company to turn into anything. I started the company while I was still in undergrad and I named it Newfound Research after a lake my family used to visit in New Hampshire. I fully expected the company to be shut down within a
  • The determinants of inflation [Alpha Architect]

    The research questions of the article are as follows: How can a Hidden Markov Model be applied to identify regimes of shifting inflation? What are the characteristics and descriptive information of the identified inflation regimes? Which economic variables are the determinants of inflation and how can their relative importance be measured? What implications do the findings have for policymakers in
  • Quant And Machine Learning Links: 20230827 [Machine Learning Applied]

    AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment Tianping Zhang, Yuanqi Li, Yifei Jin, Jian Li The multi-factor model is a widely used model in quantitative investment. The success of a multi-factor model is largely determined by the effectiveness of the alpha factors used in the model. This paper proposes a new evolutionary

Quantocracy’s Daily Wrap for 08/26/2023

This is a summary of links featured on Quantocracy on Saturday, 08/26/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Intro to Black-Scholes, implied volatility and hedging [OS Quant]

    Im a little embarrassed to admit this, I was recently in a quant interview and the interviewer quickly realised that I didnt know the Black-Scholes formula! That was definitely a moment when imposter syndrome became reality. To fix the situation, Ive written up an easy intro to the Black-Scholes model here. I hope this helps you as much as it helped me. Black-Scholes setup The
  • How to Launch Your Career as a Risk Quant in 2024? [Quant at Risk]

    Launching a career as a risk quant requires a well-thought-out strategy that combines a strong educational foundation, technical skills, and an understanding of the evolving landscape of risk management. To embark on this journey, aspiring risk quants should start by building a solid educational background. Pursuing a bachelors or Masters degree in mathematics, statistics, finance, economy,
  • Business sentiment and commodity future returns [SR SV]

    Business sentiment is a key driver of inventory dynamics in global industry and, therefore, a powerful indicator of aggregate demand for industrial commodities. Changes in manufacturing business confidence can be aggregated by industry size across all major economies to give a powerful directional signal of global demand for metals and energy. An empirical analysis based on information states of
  • Structured notes: Wall Street fairy tales that should be avoided! [Alpha Architect]

    As a general rule of thumb, the more complexity that exists in a Wall Street creation, the faster and farther investors should run. David Swensen, Unconventional Success Structured products are packages of synthetic investment instruments specifically designed to appeal to needs that investors perceive are not being met by available securities. They are often packaged as asset allocation tools

Quantocracy’s Daily Wrap for 08/23/2023

This is a summary of links featured on Quantocracy on Wednesday, 08/23/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Correlation Matrix Stress Testing: Random Perturbations of a Correlation Matrix [Portfolio Optimizer]

    In the previous posts of this series, I detailed a methodology to perform stress tests on a correlation matrix by linearly shrinking a baseline correlation matrix toward an equicorrelation matrix or, more generally, toward the lower and upper bounds of its coefficients. This methodology allows to easily model known unknowns when designing stress testing scenarios, but falls short with unknown
  • Design Crypto-Asset to Avoid Structural Failures Due to Random Vibrations [Quant at Risk]

    Although the relationship is not immediately clear and obvious, the structural engineering has a lot in common with financial assets. In both cases we deal with the objects under stress over their entire lifetimes. There are two possible outcomes: something can break or perform well. The engineers run lots of analyses and tests before a given element is ready for its use and service. It is
  • Sector Neutralization: Why It Matters and How to Use It [Quant Rocket]

    Sector neutralization is a technique to hedge out sector bets and reduce the impact of sector-specific risks on the portfolio by ranking factors within sectors rather than across sectors. This post uses the debt-to-equity ratio to show why sector neutralization is important and how to perform it in Pipeline. This post is part of the fundamental factors series, which explores techniques for
  • How to make amazing dashboards to easily power alpha analysis [PyQuant News]

    Principal component analysis (PCA) is used widely in data science. Its a way to reduce the number of dimensions in a data set. Its also used in quant finance to find alpha. In a stock portfolio, a dimension might be a column of returns for one of the stocks. Once you get the model built, you could spend your time tweaking the code. Or, you can do it in an interactive dashboard to power your

Quantocracy’s Daily Wrap for 08/21/2023

This is a summary of links featured on Quantocracy on Monday, 08/21/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Revisiting Link s Global Growth Cycle Strategy [Allocate Smartly]

    Weve previously covered Links Global Growth Cycle strategy, which uses OECD Composite Leading Indicator (CLI) data to time the market. The strategy has navigated the market gyrations over the last few years well, so naturally its gotten the attention of members. Recent strategy results follow. Learn about what we do and follow 70+ asset allocation strategies like this one in near
  • A Case Study in Finding Edge [Robot Wealth]

    In 2021, James, I, and a small team decided to set up a crypto trading venture. We faced several problems, but knowing almost nothing about crypto was the most significant. We sensed that the fractured, developing nature of the crypto market would likely be a good place to seek out inefficiencies, but beyond that, we were winging it. That turned out to be very true inefficiencies abounded in
  • Factor seasonality – an independent risk factor? [Alpha Architect]

    Factor seasonality always seemed to be an idea that was too close to factor timing to help build factor strategies. Surprisingly, the authors find a substantial factor seasonality effect across global markets, suggesting that the assumption is unwarranted. This is the first study I have encountered that tested the proposition that portfolios sorted twice on a specific factor first and high returns
  • Quant And Machine Learning Links: 20230820 [Machine Learning Applied]

    Portfolio Selection via Topological Data Analysis Petr Sokerin, Kristian Kuznetsov, Elizaveta Makhneva, Alexey Zaytsev Portfolio management is an essential part of investment decision-making. However, traditional methods often fail to deliver reasonable performance. This problem stems from the inability of these methods to account for the unique characteristics of multivariate time series data