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Quantocracy’s Daily Wrap for 08/11/2016

This is a summary of links featured on Quantocracy on Thursday, 08/11/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Shorting at High: Algo Trading Strategy in R [Quant Insti]

    Milind began his career in Gridstone Research, building earnings models and writing earnings notes for NYSE listed companies, covering Technology and REITs sectors. Milind has also worked at CRISIL and Deutsche Bank, where he was involved in modeling of Structured Finance deals covering Asset Backed Securities (ABS), and Collateralized Debt Obligations (CDOs) for the US and EMEA region. Milind
  • Low Vol Benefits Fading [Larry Swedroe]

    Low-volatility strategies have quickly become the darling of many investors, thanks largely to trauma caused by the bear market that arose from the 2008-2009 financial crisis combined with academic research showing that the low-volatility anomaly exists in equity markets around the globe. Earlier this week, we took a detailed look at a 2016 study from David Blitz, The Value of Low

Quantocracy’s Daily Wrap for 08/10/2016

This is a summary of links featured on Quantocracy on Wednesday, 08/10/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Taming the Momentum Investing Roller Coaster: Fact or Fiction? [Alpha Architect]

    Intermediate-Term Price momentum, originally researched by Jegadeesh and Titman in 1993, documented a how recent stock returns tended to continue in the future. Stocks that were past winners (on average) continue to do well, while stocks that were past losers (on average) continue to perform poorly. A natural inclination is to create a long-short portfolio to take advantage of this buy the

Quantocracy’s Daily Wrap for 08/09/2016

This is a summary of links featured on Quantocracy on Tuesday, 08/09/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Optimal Data Windows for Training a Machine Learning Model for Financial Prediction [Robot Wealth]

    It would be great if machine learning were as simple as just feeding data to an out-of-the box implementation of some learning algorithm, then standing back and admiring the predictive utility of the output. As anyone who has dabbled in this area will confirm, it is never that simple. We have features to engineer and transform (no trivial task see here and here for an exploration with
  • What if Factors Rarely Matter? [EconomPic]

    Back in December I wrote that It's Generally Smart to Avoid Credit Risk outlining that more than 100% of credit's excess performance over time has come when the level of credit spread was extreme. What if the same were true for well known investment factors? Taking a Look at the Small Cap Premium The chart below takes the average market cap of the 30% largest companies within Fama French
  • Can Investors Replicate the Dorsey Wright Focus 5 ETF Strategy? [Alpha Architect]

    A long-time reader asked that we examine the performance and process associated with the Dorsey Wright Focus Five ETF (ticker: FV). For those who are unfamiliar with the product, FV is a $3B+ sector rotation fund. The fund is designed to provide targeted exposure to five sector- and industry-based ETFs that Dorsey, Wright & Associates (DWA) believes offer the greatest potential to outperform
  • Low Vol Advantage Not What You d Expect [Larry Swedroe]

    One of the problems for the first formal asset pricing model developed by financial economists, the Capital Asset Pricing Model (CAPM), was that it predicted a positive relationship between risk and return. However, empirical studies have found the actual relationship to be flat, or even negative. Over the last 50 years, the most defensive stocks have delivered higher returns than the most

Quantocracy’s Daily Wrap for 08/08/2016

This is a summary of links featured on Quantocracy on Monday, 08/08/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Finding 7.5% Returns [Flirting with Models]

    This blog post is available as a PDF here. Summary Over the last year, weve written about how low interest rates and high equity valuations point to a low return rates for traditionally allocated portfolios. In a State Street survey of over 400 institutional investors, the expected return rate for stocks and bonds was 10.0% and 5.5% respectively: significantly higher than we would expect. The
  • When is a “Value” Company not a Value? (h/t Abnormal Returns) [Investing Research]

    Value has broadly been accepted as an investing style, and historically portfolios formed on cheap valuations outperformed expensive portfolios. But value comes in many flavors, and the factors(s) you choose to measure cheapness can determine your long-term success. In particular, several operating metrics of value, like Earnings and EBITDA, have outperformed the more traditional Price-to-Book
  • Backtests for VelocityShares’ BSWN, LSVX, and XIVH [Six Figure Investing]

    I have generated simulated end-of-day close indicative share values (4:15 PM ET) for VelocityShares' BSWN, LSVX, and XIVH Exchange Traded Notes (ETNs) from March 31st, 2004 through July 14th, 2016. BSWN VelocityShares VIX Tail Risk ETN LSVX VelocityShares VIX Variable Long/Short ETN XIVH VelocityShares VIX Short Volatility Hedged ETN These simulated ETN histories are useful if you want to
  • Machine Learning Trading Systems [Jonathan Kinlay]

    The SPDR S&P 500 ETF (SPY) is one of the widely traded ETF products on the market, with around $200Bn in assets and average turnover of just under 200M shares daily. So the likelihood of being able to develop a money-making trading system using publicly available information might appear to be slim-to-none. So, to give ourselves a fighting chance, we will focus on an attempt to predict the
  • Using Fundamentals to Improve Pairs Trading Strategy [Quantpedia]

    Pairs trading strategys return depends on the divergence/convergence movements of a selected pair of stocks prices. However, if the stable long term relationship of the stocks changes, price will not converge and the trade opened after divergence will close with losses. We propose a new model that, including companies fundamental variables that measure idiosyncratic factors, anticipates

Quantocracy’s Daily Wrap for 08/07/2016

This is a summary of links featured on Quantocracy on Sunday, 08/07/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Maximum Likelihood Estimation for Linear Regression [Quant Start]

    The purpose of this article series is to introduce a very familiar technique, Linear Regression, in a more rigourous mathematical setting under a probabilistic, supervised learning interpretation. This will allow us to understand the probability framework that will subsequently be used for more complex supervised learning models, in a more straightforward setting.

Quantocracy’s Daily Wrap for 08/05/2016

This is a summary of links featured on Quantocracy on Friday, 08/05/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Simple Moving Average Filter | Trading Strategy [Oxford Capital]

    I. Trading Strategy Source: Kaufman, P. J. (2013). Trading Systems and Methods. New Jersey: John Wiley & Sons, Inc. Concept: Trend following trading strategy based on Simple Moving Average (SMA) filters. Research Goal: To benchmark the Simple Moving Average (SMA) against the Hull Moving Average (HMA). Specification: Table 1. Results: Figure 1-2. Trade Filter: Long Trades: Fast_SMA[i ? 1]
  • SEBI Releases Paper on Algorithmic Trading & Co-Location [Quant Insti]

    SEBI issued a discussion paper today with inputs from all stakeholders such as investors, infrastructure institutions and intermediary to understand how Algorithmic Trading has led to fairness, concerns and changes in market quality in recent years. It states that more than 80% of the orders placed on most of the exchange traded products are generated by algorithms and such orders contribute to

Quantocracy’s Daily Wrap for 08/04/2016

This is a summary of links featured on Quantocracy on Thursday, 08/04/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Most Useful Investment Blogs [Dual Momentum]

    As with many people these days, most of my investment information comes from the internet. It has taken me years to compile a group of research-oriented blogs and websites that I have found most useful. Here is my list: Investment Blogs Quantocracy: This is an aggregator of quantitative trading links to blog posts and research articles. It covers a broad range of ideas from coding to theory. So
  • How Do VelocityShares’ BSWN, LSVX, & XIVH Work? [Six Figure Investing]

    The indexes that power VelocityShares new BSWN, LSVX, and XIVH funds have been live since 2011, but they havent been directly accessible via exchange traded products until July 2016. The goals of these new funds are pretty straightforward, on the long side BSWN & LSVX track upside volatility with some fidelity while minimizing decay costs, while XIVH captures the premium typically available
  • 50 Years Of Sharpe Ratio Analysis: Useful But Easily Abused [Capital Spectator]

    The Sharpe ratio was introduced half a century ago and its still going strong. Although the world is now awash with competitors, the granddaddy of quantitative risk metrics endures. Its longevity and widespread use drives some analysts batty, but for good or ill the SR is deeply embedded into the fabric of risk management discussions and analytics. Part of its appeal is its simplicity, but that
  • Flat and Slightly Down for Trend Following in July [Wisdom Trading]

    June 2016 Trend Following: UP -1.34% / YTD: -0.92% Not much volatility for the index in the month of July. It started around +1% and finished around -1% with little amplitude in the middle. The YTD just turned slightly negative. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for July: Wisdom State of Trend Following – July 2016 And the

Quantocracy’s Daily Wrap for 08/03/2016

This is a summary of links featured on Quantocracy on Wednesday, 08/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Practical Ethereum Arbitrage Experiments [Koppian Adventures]

    Introduction Inspired by another blogpost I decided to experiment with trading arbitrage between different exchanges. Not so long ago there was a hardfork in the blockchain-based cryptocurrency Ethereum. This means that we now have two Ethereums: Ethereum (ETH) and Ethereum Classic (ETC). The exchanges decided to also support the original cryptocurrency, i.e., ETC, and so we can now trade ETH
  • Podcast: Interview with Yves Hilpisch of @PythonQuants [Chat With Traders]

    This episode features Dr. Yves Hilpischthe founder of The Python Quants. TPQ do a lot of good for those involved in quantitative finance, they; frequently host meet-ups and workshops, have developed platforms and analytics libraries, and often contract to exchanges, banks and hedge funds for custom Python development. Yves is also a three-time published author, with his most notable title
  • Start Dates, Correlation and Random Strategy [Alvarez Quant Trading]

    In my last post I showed research on how optimization results can be mean reverting. Sometimes, my research keeps getting side tracked as I think of random ideas to look at. In this post, we look at the random walk my research took starting from my mean reverting optimization research. I will show how changing the start date can have a big change in the results, correlation of 1990s to now, and