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Quantocracy’s Daily Wrap for 04/18/2022

This is a summary of links featured on Quantocracy on Monday, 04/18/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Government Bonds Have Failed to Deliver When Needed [Allocate Smartly]

    Most government bond funds have suffered major losses this year. What is worse is that those major losses have come when theyre needed most, when stocks and other risk assets are also falling. During times of market stress, gov bonds tend to act as a counterbalance to risk assets, but so far this year theyve failed to deliver when needed. Data dump: It has been 71 trading days since the
  • The Turbulence Index: Measuring Financial Risk [Portfolio Optimizer]

    One of the challenges in portfolio management is the timely detection of financial market stress periods, typically characterized by an increase in volatility and a breakdown in asset correlations1. Chow and al.2 propose to detect such periods through the usage of the caste distance, a measure initially introduced by Mahalanobis34 to classify human skulls in India and now commonly called the
  • How to use FX carry in trading strategies [SR SV]

    FX forward-implied carry is a valid basis for trading strategies because it is related to divergences in monetary and financial conditions. However, nominal carry is a cheap and rough indicator: related PnLs are highly seasonal, sensitive to global equity markets, and prone to large drawdowns. Simple alternative concepts such as real carry, interest rate differentials, and volatility-adjusted
  • Research Review | 15 April 2022 | Risk Factor Premia [Capital Spectator]

    A Look Under the Hood of Momentum Funds Ayelen Banegas and Carlo Rosa (Federal Reserve) February 2022 Momentum investing has surged over the past few years, with assets growing at three times the rate of conventional funds. Using a comprehensive dataset of US equity funds, this paper examines the economic value of momentum funds. Overall, we find that risk-adjusted returns of momentum funds are,

Quantocracy’s Daily Wrap for 04/14/2022

This is a summary of links featured on Quantocracy on Thursday, 04/14/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading and investing performance: year eight [Investment Idiocy]

    Eight years! Wow. In late 2013 I walked out of an office for the last time where I had been working for AHL, a large systematic futures trading fund. A few months later, in April 2014, I had my own very small systematic futures trading account, and I started doing these performance reviews. And this is my eighth review. Double wow! As usual these cover the UK tax year, in this case from April 6th
  • Bond Investing in Inflationary Times [Alpha Architect]

    As the chief research officer of Buckingham Strategic Partners, the issue I am being asked to address most often is about fixed income strategies when yields are at historically low levels and inflation risk is heightened due to the unprecedented increase in money creation (through quantitative easing), the extraordinary expansionary fiscal spending around the globe, and the war in Ukraine driving
  • Never Sell in May! [Financial Hacker]

    Sell in May and go away is an old stock traders wisdom. But in his TASC May 2022 article, Markos Katsanos examined that rule in detail and found that it should rather be Sell in August and buy back in October. Can trading be really this easy? Lets have a look at the simple seasonal trading rule and a far more complex application of it. The trading algorithm Sell in August and

Quantocracy’s Daily Wrap for 04/13/2022

This is a summary of links featured on Quantocracy on Wednesday, 04/13/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What’s the Best Factor for High Inflation Periods? – Part II [Quantpedia]

    This second article offers a different look at high inflation periods, which we already analyzed in Whats the Best Factor for High Inflation Periods? Part I. In this second part, we look at factor performance during 10-year periods of high inflation. High Inflation Periods As we already outlined in the first part, inflation measures an increase in prices over time. Most typically, we

Quantocracy’s Daily Wrap for 04/12/2022

This is a summary of links featured on Quantocracy on Tuesday, 04/12/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • An Introduction to Stooq Pricing Data [Quant Start]

    In the previous article we learnt how to setup a prototyping environment for algorithmic trading using Jupyter Notebooks. We used Yahoo data with Pandas DataReader. In this article we will be looking at another free market data provider Stooq. If you would like to follow along with the tutorial and do not have the protoyping environment set up you will need: Jupyter v1.0 Pandas v1.4

Quantocracy’s Daily Wrap for 04/11/2022

This is a summary of links featured on Quantocracy on Monday, 04/11/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What’s the Best Factor for High Inflation Periods? – Part I [Quantpedia]

    In the past couple of weeks, we have done a few event studies, analyzing events that in one way or another resemble what is happening in the world today. At the beginning of March, we examined Factor Performance in Cold War Crises, and at the end of March, we brought you an article analyzing Nuclear Threats and Factor Performance. Today we are going to look into factor performance during high
  • Trend Following & Factor Investing – Unexpected Cousins? [Factor Research]

    Trend following and beta-neutral factor investing are considered diversifying strategies However, since 2009 their correlations to stocks moved in tandem Both strategies had related performance drivers and risk exposures INTRODUCTION Asset classes seem easy to distinguish at first. For example, stocks and corporate bonds provide different exposure to the capital structure of companies. However,
  • Find Your Best Market to Trade With the Hurst Exponent [Raposa Trade]

    After five consecutive years of drought, Northern Californians welcomed the heavy rainfall in the winter of 2016-2017. By February, however, the rain had led many to worry about the integrity of the Lake Oroville Dam. Officials evacuated over 200,000 residents who lived downstream of the dam along the Feather River and engineers opened the emergency spillway. Soon, however, a small crack in the
  • Shorting ETFs: A look into the ETF Loan Market [Alpha Architect]

    The growth of ETFs has been explosive (and we arent helping the matter via ETF Architect which facilitates low-cost high quality ETF white label services). At the end of 2020, there was roughly $5.4 trillion invested in ETFs in the United States, representing more than 25% of US market trading by daily volume in the recent decade (ICI Factbook, 2021). One of the many benefits of ETFs relative

Quantocracy’s Daily Wrap for 04/08/2022

This is a summary of links featured on Quantocracy on Friday, 04/08/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Guide to Obtaining Time Series Datasets in Python (h/t @PyQuantNews) [Machine Learning Mastery]

    Datasets from real-world scenarios are important for building and testing machine learning models. You may just want to have some data to experiment with an algorithm. You may also want to evaluate your model by setting up a benchmark or determining its weaknesses using different sets of data. Sometimes, you may also want to create synthetic datasets, where you can test your algorithms under
  • Is Sector-neutrality in Factor Investing a Mistake? [Alpha Architect]

    Firm characteristics such as size, book-to-market ratio, profitability, and momentum have been found to be correlated with expected returns. The predictive power of these characteristics may stem from their industry component, their firm-specific component, or both. For example, while the study Do Industries Explain Momentum, found that momentum in stocks stems from the industry component,
  • Simple Machine Learning Models on OrderBook/PositionBook Features [Dekalog Blog]

    This post is about using OrderBook/PositionBook features as input to simple machine learning models after previous investigation into the relevance of such features. Due to the amount of training data available I decided to look only at a linear model and small neural networks (NN) with a single hidden layer with up to 6 hidden neurons. This choice was motivated by an academic paper I read online

Quantocracy’s Daily Wrap for 04/06/2022

This is a summary of links featured on Quantocracy on Wednesday, 04/06/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Benford s Law and Strategy Selection [Alvarez Quant Trading]

    While talking to a trader, he mentioned an article in the December 2021 issue of Technical Analysis of Stocks & Commodities about Benfords Law. I had read the same article and was wondering how it could be applied to my trading. Benfords Law is often used to look for fraud. I am sure I am not committing fraud on myself. As we talked, we wondered whether this could help in selecting which
  • Transformers: is attention all we need in finance? Part II [Quant Dare]

    Using PyTorch to test the attention mechanism applied to time series forecasting. Introduction In the previous post we saw what Transformers are and how they work in its basic form. In this post we will develop one possible way to adapt the original design, which was created [1] to target NLP tasks, for time series applications. We will use PyTorch to implement some models and test the results

Quantocracy’s Daily Wrap for 04/04/2022

This is a summary of links featured on Quantocracy on Monday, 04/04/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why 90% of Backtests Fail [Financial Hacker]

    About 9 out of 10 backtests produce wrong or misleading results. This is the number one reason why carefully developed algorithmic trading systems often fail in live trading. Even with out-of-sample data and even with cross-validation or walk-forward analysis, backtest results are often way off to the optimistic side. The majority of trading systems with a positive backtest are in fact
  • Optimize Your Trading Strategy With Python And The Kelly Criterion [Raposa Trade]

    Retail traders almost always have small trading accounts. To get the returns they're after, traders frequently take on leverage – often times imprudent amounts in highly levered FOREX accounts that can be levered 50-100x! Retail equity brokers are a bit more conservative with maximum leverage ratios of 2-3x. Leverage is a dangerous thing to play with – and we don't recommend
  • Gaining an Edge via Textual Analysis of FOMC Meetings [Alpha Architect]

    How investors understand and use central bank communications, aka FEDSPEAK, is oftentimes cryptic and difficult to analyze. This study attempts to provide some clarity to this issue by applying textual analysis to both high-frequency price and communication data, to focus on episodes whereby stock price movements are identifiable and on investors reactions to specific sentences communicated by
  • Factor Olympics Q1 2022 [Factor Research]

    Factor volatility was low despite the significant geopolitical and economic turmoil in Q1 2022 Value is the clear winner with double-digit gains Quality stocks underperformed, but only moderately INTRODUCTION We present the performance of five well-known factors on an annual basis for the last 10 years. Specifically, we only present factors where academic research supports the existence of
  • Equity convexity and gamma strategies [SR SV]

    Equity convexity means that a stock outperforms in times of large upward or downward movements of the broad market: its elasticity to the market return is curved upward. Gamma is a measure of that convexity. All else equal, positive gamma is attractive, as a stock would outperform in market rallies and diversify in market stress. However, gamma is not observable, changeable, and needs to be

Quantocracy’s Daily Wrap for 04/01/2022

This is a summary of links featured on Quantocracy on Friday, 04/01/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean-Variance Optimization: Well Diversified (Near) Efficient Portfolios [Portfolio Optimizer]

    One well-known stylized fact of the Markowitzs mean-variance framework is that, irrespective of the quality of the estimates of asset returns and (co)variances, efficient portfolios are concentrated in a very few assets1. From a practitioners perspective, this has always been a problem12. In this blog post, I will describe a solution proposed in Corvalan3, which is to replace efficient
  • Nuclear Threats and Factor Performance – Takeaway for Russia-Ukraine Conflict [Quantpedia]

    The Russian invasion of Ukraine and its repercussions continue to occupy front pages all around the world. The battle situation is very dynamic, but it seems that Ukraine holds ground very well and is even able to execute strong local counter-offensives against Russian forces. Thats definitely not a situation that president Putin had expected when he started his special operation.

Quantocracy’s Daily Wrap for 03/31/2022

This is a summary of links featured on Quantocracy on Thursday, 03/31/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Are Stock Market Bubbles Identifiable? [Alpha Architect]

    We can define an investment bubble as an irrational strong price increaseimplying a predictable strong decline. The efficient market hypothesis (EMH) implies both the absence of bubbles and that the future return is unpredictable. In his Nobel Prize lecture, the father of the EMH, Eugene Fama stated: The available research provides no reliable evidence that stock market price declines are
  • Yield Curve Inversions and SPX Returns [Quantifiable Edges]

    There has been a lot of talk recently about yield curve inversions and whether that means a recession is on the way, and how soon? And if there is a recession, will there also be a bear market? I decided to forget about economic forecast and just look at how the SPX did after a curve inversion. I looked at both the 2yr/10yr and the 3mo/10yr combinations. For the study I used Norgate Data, and
  • Mutual Fund Returns vs Investor Returns [Quant Dare]

    It is well known that we investors are full of biases when making investment decisions (loss-aversion, trend-chasing, ), but what is the real impact of these biases on our performance? In this post we will try to answer this question quantitatively, and we will also compare the average investor returns with the average mutual funds returns. Do you want to know how good is the investors