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Quantocracy’s Daily Wrap for 07/26/2021

This is a summary of links featured on Quantocracy on Monday, 07/26/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Test and Trade RSI Divergence in Python [Raposa Trade]

    Divergences occur when price and your indicator move in opposite directions. For example, youre trading with the RSI and it last had a peak at 80, now it peaks at 70. The underlying security youre trading was at $14 when RSI hit 80, and now hits a new peak at $18. This is a divergence. Traders will refer to the price as reaching a higher high and the RSI as a lower high because
  • Digital Asset ETFs: Not Crypto Enough? [Factor Research]

    Digital asset ETFs have outperformed tech stocks in recent years However, they provide no exposure to cryptocurrencies Their returns are explained by market beta and equity factors INTRODUCTION Cathie Wood, the founder and CEO of Ark Invest, an ETF manager, is the latest entrant to launching a Bitcoin ETF in the US. The Winklevoss twins of Facebook fame have been trying this for years, but have
  • The Role of Book-to-Market in Bond Returns [Alpha Architect]

    My August 17, 2020, article for Advisor Perspectives, Factor-Based Investing Beats Active Management for Bonds, provided the evidence from a series of academic papers on the ability of common factors to explain the variation of returns of bond funds, results which have important implications for investors in terms of portfolio construction, risk monitoring, and manager selection. Following
  • Accounting data as investment factors [SR SV]

    Corporate balance sheet data are important building blocks of quantitative-fundamental (quantamental) investment factors. However, accounting terms are easily misunderstood and confused with economic concepts. Accounting is as much driven by assessment of risk as it is by economic value. For example, earnings are recognized only when receipt of cash is highly certain. Investment spending is

Quantocracy’s Daily Wrap for 07/22/2021

This is a summary of links featured on Quantocracy on Thursday, 07/22/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Growth Trend Timing With US Stocks [Decoding Markets]

    Im always on the lookout for interesting ways to time the market. Using a market timing model can help to avoid painful bear markets and indicate when is a good time to buy stocks. Recently, I was looking at some of the strategies on Allocate Smartly and came across one called Growth Trend Timing. Growth Trend Timing was originally created by Jesse Livermore from Philosophical Economics. (Not
  • 4 Simple Strategies to Trade Bollinger Bands [Raposa Trade]

    Bollinger Bands have been a popular indicator by traders since they were invented in the early 1980s. Theyre calculated in four, easy steps and are intended to provide traders an idea of the price range of a security. We can use these to develop a number of different algorithmic strategies to test. Below, we walk through 4 different trading strategies relying on the bands for mean reversion

Quantocracy’s Daily Wrap for 07/21/2021

This is a summary of links featured on Quantocracy on Wednesday, 07/21/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Beyond linear: the Extended Kalman Filter [Quant Dare]

    Although linear systems are pretty convenient at many levels, many real world applications cannot rely in this assumption. The Extended Kalman Filter can deal with these nonlinearities in a simple way. Learn how in this post. Introduction In the 1960s, Rufold E. Kalman codeveloped one of the most important and used algorithms of the 20th century: the Kalman Filter [6][7]. The Kalman Filter is an

Quantocracy’s Daily Wrap for 07/20/2021

This is a summary of links featured on Quantocracy on Tuesday, 07/20/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stock Market Data And Analysis In Python [Quant Insti]

    Are you looking to get stock market data and analyse the historical data in Python? You have come to right place. After reading this, you will be able to: Get historical data for stocks Plot the stock market data and analyse the performance Get the fundamental, futures and options data For easy navigation, this article is divided as below. How to get Stock Market Data in Python? How to get Stock
  • Factor Investing in Sovereign Bond Markets: 221 years of evidence! [Alpha Architect]

    Despite government bonds being one of the major asset classes invested in global portfolios, 30% of overall market capitalization according to Doeswijk, et al. (2020), little work has been done to investigate whether factors are present in the sovereign bond market. (Here is a deep dive into fixed income factors by Ward). If factors are indeed present in the sovereign bond market do they add value

Quantocracy’s Daily Wrap for 07/19/2021

This is a summary of links featured on Quantocracy on Monday, 07/19/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Statistical Distributions and the Costliness of Hidden Assumptions [Enjine]

    By the third year of my PhD program, I was impatient. I had endured 8 years of lectures, exams, and keeping close watch over my bank accounts balance. Meanwhile, my colleagues from undergrad had embarked on interesting projects with big potential, and were getting paid well to do so. Their lives were going somewhere. Mine felt at a standstill. I wanted a taste of what they had, so I took a few
  • Myth Busting: Equities are an Inflation Hedge [Factor Research]

    Equities generated attractive nominal returns across all inflation regimes However, real returns were zero when inflation was above 10% Energy and materials performed best, consumer-facing sectors worst INTRODUCTION I came of age and studied economics in the 1970s and I remember what that terrible period was like, U.S. Treasury Secretary Janet Yellen told a U.S. House subcommittee in May

Quantocracy’s Daily Wrap for 07/18/2021

This is a summary of links featured on Quantocracy on Sunday, 07/18/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Create a Personal Portfolio/Wealth Simulation in Python (Part 2) [Python For Finance]

    Welcome to Part 2 of the series of posts dealing with how to build your own python based personal portfolio /wealth simulation model. At the end of the first post (which can be found here), we got to the point where we had modelled some inflows, some outflows, we had applied an annual salary raise to our future income flows, along with applying various tax rates to both our active income (salary)
  • Man vs. Machine: Stock Analysis [Quantpedia]

    Nowadays, we see an increasing number of machine learning based strategies and other related financial analyses. But can the machines replace us? Undoubtedly, AI algorithms have greater capacities to digest big data, but as always in the markets, everything is not rational. Cao et al. (2021) dives deeper into this topic and examines the stock analysts. Target prices and earnings forecasts
  • The Misery Index and Future Equity Returns [Alpha Architect]

    Prospect theory was developed by Daniel Kahneman and Amos Tversky in 1979. The theory starts with the concept of loss aversionthe observation that people react differently between potential losses and potential gains. Thus, people make decisions based on the potential gain or loss relative to their specific situation rather than in absolute terms. Faced with a risky choice leading to gains,
  • Research Review | 16 July 2021 | Forecasting [Capital Spectator]

    Forecasting the Long-Term Equity Premium for Asset Allocation Athanasios Sakkas (U. of Nottingham) and Nikolaos Tessaromatis (EDHEC) July 12, 2021 Long-term country equity premium forecasts based on a cross-sectional global factor model (CS-GFM), where factors represent compensation for risks proxied by valuation and financial variables, are superior, statistically and economically, from forecasts

Quantocracy’s Daily Wrap for 07/15/2021

This is a summary of links featured on Quantocracy on Thursday, 07/15/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Everything About Faber: A Critical Look at Market Timing [Light Finance]

    In 2006, Meb Faber wrote a highly influential paper on tactical asset allocation and market timing. The strategy was particularly attractive in part because of its simplicity: Buy when monthly price > 10-month SMA Sell and move to cash when monthly price By applying this simple, mechanical strategy to the S&P 500 going back to 1900, Faber concluded that market timing can be used to enhance
  • Modeling US Stock Market Expected Returns, Part I [Capital Spectator]

    In recent posts I reviewed several basic applications for generating fair-value estimates for the 10-year Treasury yield, which can be used as a proxy for projecting return. Lets expand this effort by forecasting performance for the US equity market over a 10-year window. The goal is developing a baseline outlook for a 60/40 US stock/bond portfolio over a 10-year horizon. In recent updates I
  • The risk of investing: An exploration on SPDR Sector ETFs [Quant Dare]

    We will examine the relationship between annual returns and largest annual drop. Lets use some well known Select Sector SPDRs and the SPDR S&P 500 Trust (SPY). Using prices from 1999-01-01 to 2021-06-30 we calculate the annual returns and the biggest drop for each year. For example, if we compare the figures between SPY (S&P500) and XLK (Technology), we see how the dispersion in returns

Quantocracy’s Daily Wrap for 07/14/2021

This is a summary of links featured on Quantocracy on Wednesday, 07/14/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Volume Positive Negative Indicator for Breakouts [Alvarez Quant Trading]

    Probably like a lot of you, I am an indicator junkie. Whenever I read about an indicator I have not tested and makes some sense, I got to try it out. Now, most of the time they turn out to not be useful for my strategies. While reading the April 2021 Technical Analysis of Stocks & Commodities, I came across an article about Volume Positive Negative (VPN) Indicator for detecting high-volume
  • Metalabeling and the duality between cross-sectional and time-series factors [EP Chan]

    Features are inputs to supervised machine learning (ML) models. In traditional finance, they are typically called factors, and they are used in linear regression models to either explain or predict returns. In the former usage, the factors are contemporaneous with the target returns, while in the latter the factors must be from a prior period. There are generally two types of factors:
  • Moving Average Bands [Financial Hacker]

    Compared to plain indicators, bands have the advantage that they look more colorful on charts. And they offer more lines to trigger trade signals. In this way, bands beat any old single-line indicator hands down. This was also noticed by Vitali Apirine, who invented in the Stocks&Commodities August 2021 issue a new sort of bands. Vitalis Moving Average Bands are like Bollinger Bands without

Quantocracy’s Daily Wrap for 07/13/2021

This is a summary of links featured on Quantocracy on Tuesday, 07/13/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Size Really Does Matter: Position Sizing and Controlling your Risk [Raposa Trade]

    Social media is replete with examples of people showing how much money they made going all-in on Bitcoin, Tesla, Gamestop, Dogecoin or whatever new fad is out there. These are the lucky ones. Most people bet too large and eventually blow up their account, losing years of hard work and wealth in the process. If youre going to make an investment, how much should you put into it? Putting

Quantocracy’s Daily Wrap for 07/12/2021

This is a summary of links featured on Quantocracy on Monday, 07/12/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Unconventional Guide To The Best Websites For Quants [Quant Insti]

    Generally, a quant is a professional in the financial technology industry who designs complex algorithms with the help of quantitative analysis. Quants are skilled in mathematics, finance and computer skills – a blend which is rare. In the trading domain, quants design and implement the algorithms to predict the price. To find the information that has quality is most important here because it is
  • Building a Long Volatility Strategy without Using Options [Factor Research]

    Long volatility strategies can be built without using options Our systematic approach has used exclusively currencies and bonds Investors can achieve attractive diversification benefits with such strategies INTRODUCTION The insurance policy is one of the game-changing products of our civilization as the individual is protected against great financial harm by the rest of society. Everyone
  • Diversified reward-risk parity [SR SV]

    Risk parity is a portfolio construction technique that seeks to equalize risk contributions from the different components of the portfolio. Risk parity with respect to uncorrelated risk sources maximizes diversification. Simple risk parity rules are based on the inverses of market beta, price standard deviation, or price variance. These methods can be combined with common reward risk metrics, such
  • Maximize ESG exposure or screen out sin stocks? [Alpha Architect]

    In a 2020 paper, the authors explore the side effects of applying ESG screens to a passive portfolio. While the ESG scores or tilt was improved and Sharpe ratios increased, significant regional, sector, and conventional risk factor exposures were magnified. While investors may be attracted and willing to adopt ESG strategies, they may be unwilling to accept exposures to these additional sources of