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Quantocracy’s Daily Wrap for 11/18/2016

This is a summary of links featured on Quantocracy on Friday, 11/18/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pre-earnings Annoucement Strategies [EP Chan]

    Much has been written about the Post-Earnings Announcement Drift (PEAD) strategy (see, for example, my book), but less was written about pre-earnings announcement strategies. That changed recently with the publication of two papers. Just as with PEAD, these pre-announcement strategies do not make use of any actual earnings numbers or even estimates. They are based entirely on announcement dates
  • Tradelib Developments [Quintuitive]

    It has been a while since I posted about my back-testing framework tradelib, nevertheless, I have been constantly improving it. Among the new features, the most prominent is the support for SQLite. SQLite is a standalone, single file database, thus, its much easier to get software up and running with it compared to MySQL. I liked the SQLite support so much, that I actually started using it
  • In Calm Markets Should We Buy “Cheap” Put Protection? [Alpha Architect]

    Time for a little myth busting. Recently, the Motley Fool posted an article that argued the following: when market volatility is low, protective put options are cheap. From the article: Smart investors know that the time to buy most investments is when most investors arent paying attention to them. The same is true of options. Typically, put options are cheapest during big bull markets,

Quantocracy’s Daily Wrap for 11/17/2016

This is a summary of links featured on Quantocracy on Thursday, 11/17/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: A Linear Algebra Primer for Financial Engineering [Amazon]

    This book covers linear algebra methods for financial engineering applications from a numerical point of view. The book contains many such applications, as well as pseudocodes, numerical examples, and questions often asked in interviews for quantitative positions. Financial Applications The ArrowDebreu one period market model One period index options arbitrage Covariance and correlation matrix
  • R-view: Backtesting Harvey & Liu (2015) [Open Source Quant]

    In this post i take an R-view of Backtesting Harvey & Liu (2015). The authors propose an alternative to the commonly practiced 50% discount that is applied to reported Sharpe ratios when evaluating backtests of trading strategies. The reason for the discount is due to the inevitable data mining inherent in research, both past and present. Harvey & Liu (HL) propose a multiple

Quantocracy’s Daily Wrap for 11/16/2016

This is a summary of links featured on Quantocracy on Wednesday, 11/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean Reversion Trading System [Milton FMR]

    Many traders who managed to design and implement a mean reversion system correctly made a fortune. Fact is that financial markets move in patterns and especially in cycles. In simple words everything that goes up must come down and everything that goes down must come up. Nothing moves in one direction forever. When it comes to the markets we basically have two possible outcomes its either
  • Levy flights. Foraging in a finance blog [Quant Dare]

    Does this graph look like a kids drawing? Maybe a piece of art from the monkey Jeff? No, of course Jeff draws better than this. Actually, it is a representation of what is known as a Lvy flight, a mathematical concept that shows up in nature, marketing, cryptography, astronomy, biology, physics and nowadays even in social media. Here are a couple of examples of Lvy flights for
  • Quant investing: making momentum tolerable [Investing For A Living]

    For today s post and the next few Ill be going back to my favorite topic, quant investing. In this post I want to explore pure momentum quant portfolios and in particular ways to make pure momentum investing tolerable and implementable to more investors. Note: for a refresher on momentum and its power (arguably the most powerful factor in investing) see this great paper from AQR. You may
  • Is synthetic XIV/VXX data safe to use? [Alvarez Quant Trading]

    I have done several posts about trading XIV & VXX. In these posts (here, here and here) I refer to using synthetic data before these ETFs started trading. I supported the use of the data due to the very high correlation of daily returns during the overlap period. With a correlation of .97, I thought great the data should be good to use for backtesting. Then the head slapping moment. Run the
  • An Evidence-Based Low Volatility Investing Discussion [Alpha Architect]

    Jack and I had the honor of attending the Evidence-Based Investing conference, hosted by the team at Ritholz Wealth Management. Wow. What a great event and a great group of inspiring investors and thinkers. Abe, Meb, John, Mike, and I had the opportunity to chat about systematic investing. Mr. Lincoln was a little lost during the conversation, but thats okay hes old.
  • What is the Capacity of Smart Beta Strategies? [Quantpedia]

    Using a transaction cost model, and an assumption for the smart beta premium observed in data, we estimate the capacity of momentum, quality, value, size, minimum volatility, and a multi-factor combination of the first four strategies. Flows into these factor strategies incur transaction costs. For a given trading horizon, we can find the fund size where the associated transaction costs negate the

Quantocracy’s Daily Wrap for 11/15/2016

This is a summary of links featured on Quantocracy on Tuesday, 11/15/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Long-Short Investing Might Shorten Your Investment Lifespan [Alpha Architect]

    Over the past several decades, academics have identified numerous variables that seem to predict future expected returns. This has led to a proliferation of so-called factors identified in the literature, and created what John Cochrane has labeled the factor zoo. Now we we have a zoo of new factors. The Journal of Finance 2010 Presidential Address Enter the zoo at your own risk
  • Momentum: Letting the Cheap Get Cheaper? [Flirting with Models]

    As an investment strategy, momentum focuses solely on prior returns. Being valuation agnostic, however, does not mean that a momentum strategy does not have first-order valuation effects on portfolio construction. Using historical US sector data, we find that both cross-sectional and time-series momentum strategies may serve as good diversifiers to the potential risks of large structural repricing
  • Does Risk Parity Maximize Risk-adjusted Returns? [Markov Processes]

    While it is well known that risk parity strategies typically allocate more weight or apply leverage to asset classes with lower risk, it is not well understood how higher volatility affects the Sharpe ratios exhibited by the assets that get over- or under- weighted. We find that in practice the strategy increases an assets weight in periods of lower risk, which ultimately produces higher

Quantocracy’s Daily Wrap for 11/14/2016

This is a summary of links featured on Quantocracy on Monday, 11/14/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Central Moments [Eran Raviv]

    Sometimes I read academic literature, and often times those papers contain some proofs. I usually gloss over some innocent-looking assumptions on moments existence, invariably popping before derivations of theorems or lemmas. Here is one among countless examples, actually taken from Making and Evaluating Point Forecasts: Example from Making and Evaluating Point Forecasts If the second moment
  • Testing A Euro Currency Futures Scalping Strategy, Part 6 [System Trader Success]

    In an attempt to make this system more tradable, Ive looked at many different stop methods and filters. My hunt for a stop value resulted in concluding that a stop value really hurts its performance. This is not so unusual for a mean reverting strategy, such as the Euro scalping strategy. In an effort to improve the trading metrics Im going to look at an area that I looked at very early on

Quantocracy’s Daily Wrap for 11/13/2016

This is a summary of links featured on Quantocracy on Sunday, 11/13/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: A Practical Guide To Quantitative Finance Interviews [Amazon]

    This book will prepare you for quantitative finance interviews by helping you zero in on the key concepts that are frequently tested in such interviews. In this book we analyze solutions to more than 200 real interview problems and provide valuable insights into how to ace quantitative interviews. The book covers a variety of topics that you are likely to encounter in quantitative interviews:
  • Podcast: Mean Reversion strategies with @QuantLabInfo [Better System Trader]

    The performance profile of Mean Reversion is extremely desirable to a lot of traders. Mean reversion trading strategies can produce high win rates and a smooth equity curve, however there are risks, which can result in giving back a large portion of profits, or of your trading account, some times in a very short period of time. So what can you do to build mean reversion strategies that produce
  • Diversification For The Long Term [Larry Swedroe]

    The table below, taken from the newly released book I co-authored with Andrew Berkin, Your Complete Guide to Factor-Based Investing, shows the annual premium and Sharpe ratio for the equity factors of market beta, size, value, momentum, profitability and quality. It also shows the odds that each premium will produce a negative return over various time horizons. There are two important

Quantocracy’s Daily Wrap for 11/11/2016

This is a summary of links featured on Quantocracy on Friday, 11/11/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pandas tutorial : Convert tick by tick data to OHLC data [Quant Insti]

    In this post, we will explore a feature of Python pandas package. We usually find queries about converting tick-by-tick data into OHLC (Open, High, Low and Close) frequently. This can be accomplished with minimal effort using pandas package. The OHLC data is used for performing technical analysis of price movement over a unit of time (1 day, 1 hour etc.). We have already seen How OHLC data is used

Quantocracy’s Daily Wrap for 11/10/2016

This is a summary of links featured on Quantocracy on Thursday, 11/10/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Algorithmic Trading (Part 2): Pairs Trading and Statistical Arbitrage [Keith Selover]

    This post will address what pairs trading is, how you can test for a pairs trading opportunity, and how to implement a pairs trading strategy. For information on the libraries Ive used and how I structured my trading methods, I recommend starting with my previous post on the subject. Pairs Trading is a Statistical Arbitrage strategy. In the strategy, a trader trades two stocks that tend to
  • TAA portfolios: Antonacci s Composite Dual Momentum [Investing For A Living]

    One of the TAA strategies that I have often been asked about is Antonaccis Composite Dual Momentum (ACDM from now on). I never got around to tracking or writing about it but now the the folks at Allocate Smartly have it covered. In this post Ill highlight the key details of the strategy and its results using the recent blog post from Allocate Smartly. The ACDM strategy basically applies
  • 100 Years of dow jones returns [Voodoo Markets]

    A quick look at annual returns over the 100+ years of daily percent change (close to close) data that we have on dow jones 1 2 3 4 5 6 7 import matplotlib.pyplot as plt import pandas as pd import numpy as np import datetime dj = local_csv("DjiaHist.csv", date_column = "Date", use_date_column_as_index = True) dia = get_pricing("DIA", start_date =
  • Five points of caution for dividend investors [Factor Investor]

    At a time when demand for income generating assets is at an all-time high, the yields on income generating assets are at, or near, all-time lows. While the headlines often speak to the number of Baby Boomers entering retirement, the more important statistic is actually the amount of wealth entering retirement. According to the U.S. Census Bureau, of the 125 million households in the U.S., 32% fall