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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

Quantocracy’s Daily Wrap for 02/17/2019

This is a summary of links featured on Quantocracy on Sunday, 02/17/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Aggregator for Academic Quant Research: Academic-Quant-News.com

    Academic Quant News is, at heart, an aggregator of academic research articles and journals related to quantitative finance. A question? A suggestion? Drop me an email! Interested in quantitative portfolio allocation? You can find on my GitHub account an open source JavaScript library with algorithms to solve portfolio allocation problems (Mean-Variance optimization, Risk Budgeting
  • Algorithmic strategies: managing the overfitting bias [SR SV]

    The business of algorithmic trading strategies creates incentives for model overfitting and backtest embellishment: researchers must pass Sharpe ratio thresholds for their strategies to be considered, while managers lack interest in realistic simulations of ideas. Overfitting leads to bad investment decisions and underestimated risk. Sound ethical principles are the best method for containing this

Quantocracy’s Daily Wrap for 02/15/2019

This is a summary of links featured on Quantocracy on Friday, 02/15/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Asset Allocation Roundup [Allocate Smartly]

    Six recent asset allocation articles (tactical or otherwise) that you might have missed: 1. Right Now Its KDAAsset Allocation (QuantStrat TradeR) Here Ilya shares a TAA strategy that combines elements of two popular strategies that we track: Keller & Keunings Defensive Asset Allocation and ReSolves Adaptive Asset Allocation. Expect to see a test of Ilyas KDA coming to our site

Quantocracy’s Daily Wrap for 02/14/2019

This is a summary of links featured on Quantocracy on Thursday, 02/14/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stock Prediction with ML: Ensemble Modeling [Alpha Scientist]

    Markets are, in my view, mostly random. However, they're not completely random. Many small inefficiencies and patterns exist in markets which can be identified and used to gain slight edge on the market. These edges are rarely large enough to trade in isolation – transaction costs and overhead can easily exceed the expected profits offered. But when we are able to combine many such small
  • Is There a Size Effect in the Stock Market? [Alpha Architect]

    One of the oldest and most persuasive arguments in the stock market is that small stocks outperform large stocks.(1) Warren Buffett, speaking at the 2013 Berkshire Hathaway Annual Meeting, summarized the sentiment when discussing the disadvantages of managing a huge amount of capital: Theres no question size is an anchor to performance. The implication is that managing a huge asset base
  • MACD: Moving Average Convergence Divergence (Part 2) [Oxford Capital]

    Developer: Gerald Appel. Source: Appel, G. (2005). Technical Analysis. NJ: Pearson Education, Inc. Concept: Trend following trading strategy based on the MACD (Moving Average Convergence Divergence) signal line. Research Goal: Performance verification of momentum signals. Specification: Table 1. Results: Figure 1-2. Trade Setup: Long Setup: MACD[i] > 0 and MACD[i] > Signal_Line[i]. Short
  • Top 10 Machine Learning Algorithms For Beginners [Quant Insti]

    Alan Turing, an English mathematician, computer scientist, logician, and cryptanalyst, surmised about machines that, It would be like a pupil who had learnt much from his master but had added much more by his own work. When this happens I feel that one is obliged to regard the machine as showing intelligence. To give you an example of the impact of machine learning, Man groups AHL

Quantocracy’s Daily Wrap for 02/12/2019

This is a summary of links featured on Quantocracy on Tuesday, 02/12/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fractional Differencing Derivation Walkthrough (FD Part 2) [Kid Quant]

    Just a quick warning before I start, this post is going to be math heavy. Those who are not brave enough to traverse these waters, be forewarned! Let's get right to it: To recap, last time I talked about a few basic statistical concepts regarding time series. Stationarity, Memory and reconciling them both using an idea called fractional differencing. This post walks through how we do this