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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

Quantocracy’s Daily Wrap for 08/02/2016

This is a summary of links featured on Quantocracy on Tuesday, 08/02/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mailbag: How Do You Move From Quant Developer To Quant Trader? [Quant Start]

    I was emailed recently with a career-related question about jumping from one quant role to another. The question posed was "How can I make the jump from being a quant/software developer to a quant trader/researcher in a fund or investment bank?". This is certainly possible and does happen occasionally. However, it will require some extra-curricular work, and some initiative, in order to
  • Fine Wine is a Fine Addition to Your Investment Portfolio [Alpha Architect]

    Here we are in August, a great time to drinkand thinkabout wine. Of course, as a research-focused finance blog, our angle on wine is a bit different than that of Dr. Vino. A summary of the discussion: we estimate a real financial return to wine investment (net of storage costs) of 4.1%, which exceeds bonds, art, and stamps. Get your attention? Lets dive right in, but grab a glass of
  • Cassandra as a Historical Finance DB [Ryan Kennedy]

    While the explosion of noSQL database offerings of late can be daunting, each of them is typically suited for a particular purpose. Most CRUD web-applications can be comfortably done with either noSQL or a RDBMS, however for true high performance applications, the choice of database is of make-or-break importance. In this post I'll explain what we typically want from a database to store

Quantocracy’s Daily Wrap for 08/01/2016

This is a summary of links featured on Quantocracy on Monday, 08/01/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Using System Parameter Randomization To Estimate Future Returns [System Trader Success]

    You just spent a ton of time creating a trading system and being very careful not to over-optimize. You then tested it on the out-of-sample data segment and the performance looks good. What's next? Jump right into the live market? Maybe. But instead, you would like to perform one more test called System Parameter Randomization. The article, System Parameter Permutation a better
  • Paper: Stock Portfolio Design and Backtest Overfitting (h/t Abnormal Returns)

    We demonstrate a computer program that designs a portfolio consisting of common securities, such as the constituents of the S&P 500 index, that achieves any desired profile via in-sample backtest optimization. Unfortunately, the program also shows that these portfolios typically perform erratically on more recent, out-of-sample data, which is symptomatic of selection bias. One implication of
  • Empirical Analysis of Limit Order Books [Quant Insti]

    What is an Order book? With the growing popularity of Algorithmic and High Frequency Trading, study of order books has grown manifolds. Order book is essentially an electronic list of all Buy and Sell orders, arranged as per price time priority. This means that a person having higher price on the buy side or lower price on the sell side will get priority over others to execute the trade. If
  • Can Dividend (Swaps) Replace Bonds? [Flirting with Models]

    Summary As a stand-alone asset class, dividends may make an interesting alternative to fixed income: they offer low volatility, are generally robust to market crises, and may serve as an inflation hedge. Accessing dividend strips was previously restricted to institutional investors, using over-the-counter swaps or exchange traded futures. For retail investors today, the ETF DIVY enables access to