Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 03/04/2019

This is a summary of links featured on Quantocracy on Monday, 03/04/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tiingo.com – My Go-To Database For Historical Market Prices [Capital Spectator]

    In the spring of 2017, Yahoo pulled a fast one on the crowd by suddenly changing the technical coding rules for accessing its financial data, leaving countless R users high and dry, including yours truly. Numerous R files that had been meticulously written, revised and maintained over months and years were suddenly broken. Not fun. As frustrating as that day was (not to mention the weeks that
  • Day of the Week Matters for Some Anomalies [Alpha Architect]

    According to psychology literature, mood increases from Thursday to Friday and decreases on Monday. In general, people tend to evaluate future prospects more optimistically when they are in a good mood than when they are in a bad mood. In equity markets, the presence of optimism or pessimism that is unrelated to fundamentals, usually called sentiment, delivers clear, testable cross-sectional
  • The Quant Conference – April 12th in New York City – Learn About The Industry From Those Who Built It

    The rise of big data, application of machine learning and other technological developments in recent years have transformed the quantitative finance industry, and The Quant Conference has been conceived as a unique event to provide an educational forum for delegates to hear about current trends in the cutting-edge field of quantitative finance. The Quant Conferences speaker roster of industry
  • Value, Momentum and Basis in Commodity Futures: 1877-2017 [Two Centuries Investments]

    Commodity Futures contracts were established in 1865, but commercially available data starts in 1959, leaving an 80+ year period of unstudied history. In our latest academic paper Two Centuries of Commodity Futures Premia Chris Geczy and I use hand-collected futures data to extend the well-known cross-sectional Value, Momentum and Basis factors in commodity futures back to 1877. This paper
  • How Much Accuracy Is Enough? [Flirting with Models]

    It can be difficult to disentangle the difference between luck and skill by examining performance on its own. We simulate the returns of investors with different prediction accuracy levels and find that an investor with the skill of a fair coin (i.e. 50%) would likely under-perform a simple buy-and-hold investor, even before costs are considered. It is not until an investor exhibits accuracy in
  • Tactical Asset Allocation in February [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent Tactical Asset Allocation (TAA) strategies, net of transaction costs. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Learn more about what we do or let AllocateSmartly help
  • Benchmarking Smart Beta ETFs [Factor Research]

    Long-only factor portfolios can be used for benchmarking smart beta ETFs Results highlight minor tracking errors Likely explained by relatively homogenous factor definitions by ETF issuers INTRODUCTION Investment professionals are not known for their creativity, but that is perhaps only because people outside of the finance industry do not understand the intricacies of finance. Significant amounts

Quantocracy’s Daily Wrap for 03/02/2019

This is a summary of links featured on Quantocracy on Saturday, 03/02/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Open Source Hedge Fund Project from Jacques Joubert (@JacquesQuant) [Quants Portal]

    Dear Hedge Fund Enthusiasts, Its been long since we sent out a newsletter but we would like to report that the Open Source Hedge Fund Project is alive and kicking again! My Msc in Financial Engineering has provided me with the unique opportunity to build an open source python package, like pandas, for my final research project. I am hunting for a unique contribution to the literature in the
  • How salience theory explains the mispricing of risk [SR SV]

    Salience theory suggests that decision makers exaggerate the probability of extreme events if they are aware of their possibility. This gives rise to subjective probability distributions and undermines conventional rationality. In particular, salience theory explains skewness preference, i.e. the overpricing of assets with a positive skew and the under-pricing of contracts with a negative skew.

Quantocracy’s Daily Wrap for 02/28/2019

This is a summary of links featured on Quantocracy on Thursday, 02/28/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Skew and Trend Following [Investment Idiocy]

    In this post I discuss a well known stylised fact of the investment industry: "Trend following is a positively skewed strategy". Spoiler alert: yes it is (sort of), but it's much more complicated (and interesting!) than you might think. A quick primer on positive skew So what actually is positive skew? Essentially it's an asset, or trading strategy, whose returns have the

Quantocracy’s Daily Wrap for 02/27/2019

This is a summary of links featured on Quantocracy on Wednesday, 02/27/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • KDA – Robustness Results [QuantStrat TradeR]

    This post will display some robustness results for KDA asset allocation. Ultimately, the two canary instruments fare much better using the original filter weights in Defensive Asset Allocation than in other variants of the weights for the filter. While this isnt as worrying (the filter most likely was created that way and paired with those instruments by design), what *is* somewhat more
  • Rebalancing…Not so Fast [Alpha Architect]

    My last article used Warren Buffetts pre-crisis sale of put options to highlight the risk of getting over our financial skis. In both temperament and negotiation, Warren can outlast most bear markets. Many of us cannot. Proponents of rebalancing should acknowledge the real risk that downturns can continue, and that rebalancing increases the risk of crying uncle, giving into the pain and

Quantocracy’s Daily Wrap for 02/26/2019

This is a summary of links featured on Quantocracy on Tuesday, 02/26/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Ilya Kipnis’ Defensive Adaptive Asset Allocation [Allocate Smartly]

    This is a test of Ilya Kipnis Defensive Adaptive Asset Allocation (KDA). KDA is a Meta model of sorts, combining successful elements of multiple other tactical asset allocation strategies that we track. Results from 1989 to the present, net of transaction costs, follow. Read more about our backtests or let AllocateSmartly help you follow this strategy in near real-time.
  • The Extreme Persistence Of The Current SPX Rally [Quantifiable Edges]

    The last time the SPX closed below its 10-day moving average was January 3rd. That means it has now been 35 straight trading days that SPX has closed above the 10ma. That is a very long streak. Below is a list of all streaks since 1928 of 35 days or longer. (Note: prior to 1957 S&P 90 Index data is used. This is the predecessor to the S&P 500.) 2019-02-26 I have highlighted the

Quantocracy’s Daily Wrap for 02/25/2019

This is a summary of links featured on Quantocracy on Monday, 02/25/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Developing a Trading Strategy using Volume Data [Quant News]

    Traders and market analysts use volume data, which is the amount of buying and selling of an instrument over a given time period, to gauge the strength of an existing trend or identify a reversal. The back-and-forth movement between buyers and sellers for the best available price allows us to analyze volume to confirm trends and predict reversals. Generally, volume tends to increase as a trend
  • Low Volatility Can Be Low Turnover [Alpha Architect]

    Low volatility strategies have garnered a fair amount of popularity and a growing body of supporting research. Studies have shown risk reduction levels of 25%, while turnover has varied from 20% to 120%. However, higher turnover produces higher costs of trading, such that the excess return obtained with low volatility products may actually be subsumed by those same trading costs. The authors of
  • Three Applications of Trend Equity [Flirting with Models]

    Trend equity strategies seek to meaningfully participate with equity market growth while side-stepping significant and prolonged drawdowns. These strategies aim to achieve this goal by dynamically adjusting market exposure based upon trend-following signals. A nave example of such a strategy would be a portfolio that invests in U.S. equities when the prior 1-year return for U.S. equities is
  • Minimum Variance Versus Low Volatility [Factor Research]

    The largest smart beta Low Volatility ETF is technically a Minimum Variance strategy Low Volatility and Minimum Variance have comparable and attractive characteristics However, both currently feature a high sensitivity to interest rates INTRODUCTION The Low Volatility factor was the best performing factor in 2018, which few investors expected at the beginning of the year. Central banks across the

Quantocracy’s Daily Wrap for 02/21/2019

This is a summary of links featured on Quantocracy on Thursday, 02/21/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pairs Trading – Part 2: Practical Considerations [Jonathan Kinlay]

    One of the first things you quickly come to understand in equity pairs trading is how important it is to spread your risk. The reason is obvious: stocks are subject to a multitude of risk factors amongst them earning shocks and corporate actions -that can blow up an otherwise profitable pairs trade. Instead of the pair re-converging, they continue to diverge until you are stopped out of the
  • Factor Decay [Talton Capital]

    Recently John Cotter and Niall McGeever posted an interesting paper to ssrn.com. They studied the persistence of nine anomalies in the U.K. equity market: Accruals Asset growth Book to market ratio Profitability Stock issuance Return reversal Momentum Equity turnover Size

Quantocracy’s Daily Wrap for 02/20/2019

This is a summary of links featured on Quantocracy on Wednesday, 02/20/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend-Following: A Decade of Underperformance [Alpha Architect]

    Everyone in finance remembers 2008the Global Financial Crisis. Yes, I know, the final downward movement in the stock market was in early 2009. However, many remember 2008 as the year of the crisis. So now we are 10 years removed from the crisis. Why do I mention this? After the crisis, some began to question the logic/benefits of B&H investing. After all, a ~50% cut in the value of stocks
  • ETF Bond Rotation [Alvarez Quant Trading]

    In my last post I discussed SPY/TLT rotation strategies. Today, I will be using the same ideas from the post but on a basket of bond ETFs. The Basket The first difficult decision one must make is what ETFs will be in the basket. What we choose here, can have a big impact on the results. I wanted to focus primarily on the US bond market. One factor I considered is picking ETFs with long histories.
  • If you had to do a trend following strategy, what would it be and why? [Alpha Architect]

    The topic of this blog post was inspired by Wes Gray from Alpha Architect. In the text below I limit my attention to following the trends in stock markets. To follow the trend or not? Marry, and you will regret it; dont marry, you will also regret it; marry or dont marry, you will regret it either way wise men quote Trend following is not a magical system that makes money without any

Quantocracy’s Daily Wrap for 02/19/2019

This is a summary of links featured on Quantocracy on Tuesday, 02/19/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Build a BitCoin(tegration) Backtester [Patrick David]

    This tutorial is in 2 parts(you can run the backtester as a separate standalone module) : Learn the Statistical technique of Cointegration. Build a Bitcoin Backtesting engine using Python to analyze the performance of a Cointegration based trading strategy. Just want the code? click here. What are we building We are going to build a python based event-driven backtester that pulls 2 crypto
  • Glitch [Flirting with Models]

    Trend followings simple, systematic, and transparent approach does not make it any less frustrating to allocate to during periods of rapid market reversals. With most trend equity strategies exhibiting whipsaws in 2010, 2011, 2015-2016, and early 2018, it is tempting to ask, is this something we can fix? We argue that there are three historically-salient features that make trend following

Quantocracy’s Daily Wrap for 02/18/2019

This is a summary of links featured on Quantocracy on Monday, 02/18/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quant Minds International – May, 2019 – Vienna, Austria (Use VIP Code FKN2595QCYMU to Save 10%)

    QuantMinds International heads to Vienna on 13-17 May! Now in its 26th year, QuantMinds International brings together 400+ global quant finance experts from banks, buy-side, academia and beyond, to cover every hot topic in quant finance over the course of 5 days. Quote VIP code FKN2595QCYMU for a 10% discount.
  • Exploiting Business Day Patterns in Forex Markets [Quant Rocket]

    Do businesses exchange currencies in predictable ways that forex traders can exploit? This post explores an intraday EUR.USD strategy based on the hypothesis that businesses cause currencies to depreciate during local business hours and appreciate during foreign business hours. Business patterns in foreign exchange Source paper: Breedon, Francis and Ranaldo, Angelo, Intraday Patterns in FX Returns
  • What is Worse: Data-Mining or Not Innovating? [Two Centuries Investments]

    In most decisions including investing, there are two ways to be wrong: Doing something that doesnt work (false positive, type 1 error) Not doing something that would have worked (false negative, type 2 error) Investors and quants in particular worry more about the type 1 error – accepting a fake result thinking it is real. However there is another type of error that lurks behind – the type 2
  • Factor Investing in Financials, Real Estate & MLPs [Factor Research]

    Beating benchmarks is challenging for fund managers, even in unique sectors Factor performance in financials, REITs, and MLPs is comparable to the cross-sector factor returns Classic factor investing strategies are likely more attractive than industry expertise INTRODUCTION Stating that active managers have a performance problem would be a slight understatement, especially if the benchmark is a