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Quantocracy’s Daily Wrap for 07/29/2016

This is a summary of links featured on Quantocracy on Friday, 07/29/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Case for Hedge Funds / Creating an Ideal Liquid Alt [EconomPic]

    A hedge fund is simply a go anywhere investment vehicle that attempts to provide excess returns to cash with a low correlation to traditional asset classes (i.e. vehicles that provide alpha). Hedge funds and liquid alternatives have taken a lot of heat recently, much of it deserved, but in this post I'll outline the bull case. Specifically, this post will outline the benefit of a hedge fund
  • Clustering: “Two’s company, three’s a crowd” [Quant Dare]

    Its hard enough deciding which Machine Learning technique to use, but after selecting an appropriate clustering algorithm the next challenges begin: how good is the separation and into how many groups should you divide the data? Maybe three is not always a crowd First, lets set the scene We want to create a diversified investment strategy using a set of predictions and making use of

Quantocracy’s Daily Wrap for 07/28/2016

This is a summary of links featured on Quantocracy on Thursday, 07/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Asset Class Risk Premiums Explained by Skewness [Quantpedia]

    We present extensive evidence that "risk premium" is strongly correlated with tail-risk skewness but very little with volatility. We introduce a new, intuitive definition of skewness and elicit an approximately linear relation between the Sharpe ratio of various risk premium strategies (Equity, Fama-French, FX Carry, Short Vol, Bonds, Credit) and their negative skewness.
  • Momentum on Individual Stocks vs Asset Classes [Sharpe Returns]

    I had the pleasure of finally meeting Gary Antonacci earlier this year. Gary is the creator of the momentum strategy that I follow and have been discussing on this blog. I first came across his work in 2011 on the blog Abnormal Returns (which should be a daily read for investors). Gary and I have been e-mailing each other ever since. After over 4 years, it was nice to finally see him in person.
  • Beginner’s Guide to Unsupervised Learning [Quant Start]

    The majority of machine learning posts to date on QuantStart have all been about supervised learning. In this post we are going to take a look at unsupervised learning, which is a far more challenging area of machine learning. Supervised learning involves taking a number of data observations, each of which contains a feature, or predictor, vector as well as an associated output, or response. The
  • Look at Data with a Discerning Eye [Flirting with Models]

    I recently came across a graph similar to the following while doing some market research. 1 Source: Yahoo! Finance. Analysis by Newfound Research. Data from January 1951 December 2015. The argument was that the markets are getting more volatile. While this certainly looks to be the case based on the upward trend of the histogram, let's investigate the data more thoroughly and ask
  • Trading Ethereum: Making 10% every 20 minutes [Jon.IO]

    This is more of a "How to build your own algotrading strategy – the Ethereum edition" and not a "make money fast" blog post. It is also a real example with real returns (and real production errors that cost me money) where you can see how to identify opportunities, why algotrading is awesome and why risk management can save your ass. This is the another post of the series: How
  • Tight Consolidations After New Highs [Quantifiable Edges]

    The range over the last week has been extremely tight. On 7/20/16 SPY closed at a 50-day high. Every SPY close in the 5 days since 7/20 has been within the intraday range of that 7/20/16 bar. (And it wasnt even that big of a range.) It is said that consolidations are often resolved in the direction of the trend. This guideline suggests that were more likely to see another leg up from here

Quantocracy’s Daily Wrap for 07/27/2016

This is a summary of links featured on Quantocracy on Wednesday, 07/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Unbearable Transience of Alpha [Quandl]

    In 2004 I enjoyed my 15 minutes of fame for an article I wrote called The Tao of Alpha, in which I explained the concept of alpha as a zero-sum game. Sources of alpha in 2004 were much different than those available in the mid-1990s when I started my career and they are also different from todays. Alpha is highly transient and has been coming and going for as long as capital markets have
  • Pair Trading Strategy and Backtesting using Quantstrat [Quant Insti]

    One of my favorite classes during EPAT was the one on statistical arbitrage, so the pair trading strategy seemed a nice idea for me. My strategy triggers new orders when the pair ratio of the prices of the stocks diverge from the mean. But in order to work, we first have to test for the pair to be cointegrated. If the pair ratio is cointegrated, the ratio is mean-reverting and the greater the
  • Evidence-Based Investing Requires Less Religion and More Reason [Alpha Architect]

    During the 1600s, the Dutch had a large merchant fleet and the port city of Amsterdam was a dominant commercial hub for trade from around the world. Based on the growing influence of the Dutch Republic, in 1602 the Dutch East India Company was founded, and its evolution into the first publicly traded global corporation drove a number of financial innovations to the Amsterdam Stock Exchange,

Quantocracy’s Daily Wrap for 07/26/2016

This is a summary of links featured on Quantocracy on Tuesday, 07/26/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Paper: The Trinity Portfolio [Meb Faber]

    Lets say one sets out to design a portfolio, knowing everything we know today about investing. Where would a logical, evidence-based investor even start? Investors today have access to more market data and strategic information than at any other time in history. While beneficial in some ways, this huge volume of fragmented information presents a challenge how should one actually implement

Quantocracy’s Daily Wrap for 07/25/2016

This is a summary of links featured on Quantocracy on Monday, 07/25/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What is the Proper Benchmark for Momentum or Trend-Following Strategies? [Blue Sky AM]

    Most academics and practitioners tend to compare momentum or trend-following strategies to a buy and hold investment strategy. They do so by comparing the results of the strategies to a benchmark that is a proxy for buy and hold. From this type of analysis many experts justify why the strategies are either superior to buy and hold, or on the contrary side why they arent worth pursuing.

Quantocracy’s Daily Wrap for 07/24/2016

This is a summary of links featured on Quantocracy on Sunday, 07/24/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio selection, Laplace equation and Random Walk [Tulip Quant]

    Suppose you would like to invest 1000$ in two stocks, and you have to decide how much money you should put into these two stocks. You could use some Modern Portfolio Theory for that, for example. However, I would like to talk about a second approach, which is quite curious in my opinion. This post is based on this text by Julio Rossi, from the University of Buenos Aires. In this text, the
  • From trading ideas to robust strategies [Better System Trader]

    To prepare for the previous episode on system trading through the Brexit, I had to dig through some of the past podcast episodes for background information. As I was going through them I realized there was so much great information there, some that I had already forgotten about. Such a shame, all that valuable trading information just sitting there, waiting for our attention, so I thought it must

Quantocracy’s Daily Wrap for 07/22/2016

This is a summary of links featured on Quantocracy on Friday, 07/22/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What Drives Momentum Performance? [EconomPic]

    Mar Vista Investment Partners has a really interesting research piece out The Price You Pay which has a great table outlining the benefit of an asymmetric return profile (i.e. having more market exposure during up markets than down markets). It is a mathematical truism that superior down capture in negative periods provides more capital for compounding in the ensuing positive periods. Using
  • The Arbitrage of Price-to-Book [Portfolio Perfection]

    The trending value strategy buys the top 25 stocks by their 6 month price momentum among the top decile of stocks ranked by value composite 2 (VC2), a combination of price-to-earnings ratio, price-to-sales ratio, price-to-book ratio, earnings before interest tax depreciation and amortization to enterprise value ratio (EBITDA/EV), price-to-cash flow ratio, and shareholder yield. Price-to-book was

Quantocracy’s Daily Wrap for 07/21/2016

This is a summary of links featured on Quantocracy on Thursday, 07/21/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantitative Strategy Development Overview Brian Peterson [Open Source Quant]

    I have had the pleasure of getting to know and work with Brian Peterson of late building out the blotter::mcsim function in the blotter package. I will be writing about this function soon and where it is headed, but in this post i wanted to share a presentation Brian gave the CapeR User Group last week on Developing and Backtesting Systematic Trading Strategies with an application in R, and in
  • Risk Managing Risk Management [Flirting with Models]

    Well, despite some recent market turmoil from the Brexit, the S&P 500 is still hovering near its high from last year on a price basis. If we include the reinvestment of dividends, then we have already seen new highs in April, May, and June of this year. As we wrote about previously, a bear market might be the only way to boost the expected returns on U.S. equities. Investor who thought they
  • Stale Performance Chasing: Beware of Horizon Effects [Alpha Architect]

    Investors talk a big game when describing how they evaluate mutual funds. They say they consider things like the objectives of the fund, its size, and the longevity of its managers. But theres one factor that looms larger than all the others: Performance. We wrote here about how investors tend to chase the performance of funds, allocating to funds that have done well, and redeeming from

Quantocracy’s Daily Wrap for 07/20/2016

This is a summary of links featured on Quantocracy on Wednesday, 07/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Machine Learning in Algorithmic Trading Systems Presentation [Robot Wealth]

    Last night it was my pleasure to present at the Tyro Fintech Hub in Sydney on the topic of using machine learning in algorithmic trading systems. Here you can download the presentation Many thanks to all who attended and particularly for the engaging questions. I thoroughly enjoyed myself! In particular, thanks to Andrien Juric for oraganising the event and Sharon Lu from Tyro for making available
  • Unbalanced Classes in Machine Learning and the Stock Market [MKTSTK]

    Many assets exhibit bull or bear trends which persist for long periods of time. This presents an interesting problem for anyone trying to predict the future return of an asset: a lack of diversity in your training set. This problem is known as unbalanced classes in the machine learning field. The basic issue is that many classification methods work best when your training data is roughly uniform
  • Hull Moving Average Filter | Trading Strategy (Entry & Exit) [Oxford Capital]

    Developer: Alan Hull. Source: Kaufman, P. J. (2013). Trading Systems and Methods. New Jersey: John Wiley & Sons, Inc. Concept: Trend following trading strategy based on low lag moving averages. Research Goal: To verify performance of the Hull Moving Average (HMA). Specification: Table 1. Results: Figure 1-2. Trade Filter: Long Trades: Two Hull Moving Averages turn upwards. Short Trades: Two
  • Impact of 1987 Black Monday on Trading Behavior of Stock Investors [Quantpedia]

    Using a simple sign test, we report new empirical evidence, taken from both the US and the German stock markets, showing that trading behavior substantially changed around Black Monday in 1987. It turned out that before Black Monday investors behaved more as in the momentum strategy; and after Black Monday more as in the contrarian strategy. We argue that crashes, in general, themselves are merely