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Quantocracy’s Daily Wrap for 09/26/2016

This is a summary of links featured on Quantocracy on Monday, 09/26/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Kalman Filter-Based Pairs Trading Strategy In QSTrader [Quant Start]

    Previously on QuantStart we have considered the mathematical underpinnings of State Space Models and Kalman Filters, as well as the application of the pykalman library to a pair of ETFs to dynamically adjust a hedge ratio as a basis for a mean reverting trading strategy. In this article we will discuss a trading strategy originally due to Ernest Chan (2012)[1] and tested by Aidan O'Mahony
  • Alternate Trading Days: An Important Analytical Tool [Allocate Smartly]

    Many of the tactical asset allocation strategies that we track are designed to only trade at the end of the month. When tracking these strategies for members however, we show the results of trading on other days of the month as well. We dont do this to show off our backtesting prowess; its an important analytical tool for understanding more about a strategy and for sniffing out potential
  • Step One In Building An Intraday Trading System [System Trader Success]

    If you have been reading System Trader Success for a while youre probably familiar with how I develop trading systems. The very first step is to come up with a simple idea to act as the seed or core of your trading system. I call this your key concept. This key concept is a simple observation of market behavior. This observation does not need to be complex at all. In fact, they are often very
  • Is the Value Premium Disappearing (h/t @AbnormalReturns)? [A Wealth of Common Sense]

    The value premium has been talked about in investment circles going all the way back to the 1934 release of Benjamin Grahams Security Analysis. At its core value investing relies on buying undervalued securities, something every investor can or should be able to intuitively understand. You buy stocks for less than their fundamental value, wait until that value is recognized by the market,
  • High Yield Bond ETFs: Liquidity Time Bombs? [Flirting with Models]

    Many articles expounding upon the risks of ETFs that invest in illiquid assets (high yield bonds, bank loans, emerging markets, etc.) have been published in recent years. While there are additional risks inherent to these ETFs, the ETF structure provides an additional layer of liquidity that is not available when trading directly in the underlying securities. With the majority of trades in an ETF,
  • Better Model Selection for Evolving Models [Quintuitive]

    For quite some time now I have been using Rs caret package to choose the model for forecasting time series data. The approach is satisfactory as long as the model is not an evolving model (i.e. is not re-trained), or if it evolves rarely. If the model is re-trained often the approach has significant computational overhead. Interestingly enough, an alternative, more efficient approach allows
  • Webinar: Contemporary Portfolio Optimization Modeling with R [Interactive Brokers]

    In the first part of this webinar, we will review the most common ways to conduct the task of portfolio optimization with R. After this introduction, we will address some remarks on the modeling of portfolio problems. In the second part, we will demonstrate a revolutionary way to model and solve portfolio optimization problems using R. The basic idea of conceptualizing a new way to model portfolio

Quantocracy’s Daily Wrap for 09/25/2016

This is a summary of links featured on Quantocracy on Sunday, 09/25/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Does Interest Rate Exposure Explain the Low Volatility Anomaly? [Quantpedia]

    We show that part of the outperformance of low volatility stocks can be explained by a premium for interest rate exposure. Low volatile portfolios have a positive exposure to interest rates, whereas the more volatile stocks have a negative exposure. Incorporating an interest rate premium explains part of the anomaly. Depending on the methodology chosen the reduction of unexplained excess return is
  • Intuition Behind the Bayesian LASSO [Alex Chinco]

    Imagine youve just seen Apples most recent return, r, which is Apples long-run expected return, \mu^\star, plus some random noise, \epsilon \overset{\scriptscriptstyle \mathrm{iid}}{\sim} \mathrm{N}(0, \, 1): (1) \begin{align*} r &= \mu^\star + \epsilon. \end{align*} You want to use this realized return, r, to estimate Apples long-run expected return, \mu^\star. The LASSO is a

Quantocracy’s Daily Wrap for 09/23/2016

This is a summary of links featured on Quantocracy on Friday, 09/23/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Analyzing Risk-Managed Funds With R [Capital Spectator]

    Morningstar tells us that efforts at taming volatility in a multi-asset class framework generally turns up mixed results among publicly traded funds. Studying 60 products that are labeled multiasset volatility-protection funds, a recent Morningstar article reports that as a group, volatility-protection funds do generally offer refuge when equity markets turn negative. But its
  • Is Momentum Really Dead? [Larry Swedroe]

    Earlier this week, we examined a study that sought to determine whether the publication of academics findings on the momentum factor have led to a disappearing premium. To review, Steven Dolvin and Bryan Foltice, authors of the 2016 study Where Has the Trend Gone? An Update on Momentum Returns in the U.S. Stock Market, found that in two overlapping subperiods from their sample (both ended

Quantocracy’s Daily Wrap for 09/21/2016

This is a summary of links featured on Quantocracy on Wednesday, 09/21/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Adam Butler of @GestaltU: Adaptive Asset Allocation [Allocate Smartly]

    This is a test of a tactical asset allocation strategy from Adam Butler and the excellent team at GestaltU, as described in the paper: Adaptive Asset Allocation: A Primer. The model combines momentum with a minimum variance portfolio to trade a diverse array of global asset classes. The paper is a particularly accessible treatment of issues with traditional portfolio theory, and the effectiveness
  • Labeling Opportunities in Price Series with Python [Quintuitive]

    My plans are to use Python for the rest of this series. The main reasons are algorithm related, but irrelevant for the time being, however, I decided to re-write some of the code I posted recently and I found the experience rather surprising. The experience was quite positive, but let me explain: I have always liked Python for scripting, but this time, I enjoyed usiworking on a more data science

Quantocracy’s Daily Wrap for 09/20/2016

This is a summary of links featured on Quantocracy on Tuesday, 09/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Promise of Computing [Turing Finance]

    You would be forgiven for thinking that Moore's law is a law like Newton's laws. It really does seem that as surely as an apple will fall to the ground, so too shall our computers, phones, tablets, and (now) watches capacity increase year-after-year at an exponential rate … but Moore's law is not a law, it is the promise of computing. And it is the kind of promise that is hard to
  • Extreme Value Theory [Eran Raviv]

    Extreme Value Theory (EVT) is busy with understanding the behavior of the distribution, in the extremes. The extreme determine the average, not the reverse. If you understand the extreme, the average follows. But, getting the extreme right is extremely difficult. By construction, you have very few data points. By way of contradiction, if you have many data points then it is not the extreme you are
  • How Dumb Money and Smart Money Drive Stock Market Anomalies [Alpha Architect]

    Stock market anomalies behave in mysterious ways. Over long periods of time they can provide expected outperformance versus passive indexes, but in the short run they can experience bouts of gut-wrenching underperformance (e.g., value and momentum). What accounts for this sporadic performance and these tantrum-like swings? As with other difficult questions, academic research has provided some
  • Momentum & Value vs. Growth & Value [Systematic Relative Strength]

    At Dorsey Wright, we believe momentum can be used as a stand-alone investment strategy, however, combining it with other smart beta factors to which momentum is negatively correlated has its advantages. We have referenced this in previous blog posts, noting that it allows for a portfolio to capture alpha at different periods of the market cycle, which in turn can reduce both drawdowns and

Quantocracy’s Daily Wrap for 09/18/2016

This is a summary of links featured on Quantocracy on Sunday, 09/18/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Podcast: Adapting to market conditions with John Ehlers [Better System Trader]

    Building robust trading strategies that can detect and adapt to market conditions can be a real challenge, and failure to do so can often result in poor trading performance and drawdowns. How can we build more robust trading strategies that adapt to market conditions as they change? Our guest for this episode, John Ehlers, who was also a guest on episode 48, joins us to share some common problems

Quantocracy’s Daily Wrap for 09/16/2016

This is a summary of links featured on Quantocracy on Friday, 09/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Investing: Buyer Beware [Dual Momentum]

    A highlight of the 2016 Morningstar ETF Conference was the keynote address by the former leader of U.S. Navy Seal Team Six, Rob ONeill. Chief ONeill shared some stories about his training and operations as an elite Navy Seal. The take away lessons from his talk were the importance of preparation, discipline, and keeping the mission goal in mind. Overriding all this is the importance of
  • Average TAA Allocation by Month [Allocate Smartly]

    We delayed adding the latest strategy to our site (GestaltUs Adaptive Asset Allocation) for a week due to technical hurdles running the minimum variance component of the strategy in near real-time for members. Historical results on GestaltUs strategy are exceptional though, and we plan to have the kinks with real-time worked out shortly. In the meantime, I thought the following chart
  • Was the Financial Crisis Really a Valuation Crisis? [Alpha Architect]

    Most people look back at the dot-com bubble and acknowledge valuations were elevated far above historical norms. Investors ignored historically useful fundamentals, such as earnings and book value, and started relying on measures like eyeballs and clicks. Investors really started to believe, This time its different, the four most dangerous words in investing according to Sir John
  • A Persistent Kind Of Momentum [Larry Swedroe]

    Time-series momentum examines the trend of an asset with respect to its own past performance. This is very different than cross-sectional momentum (often referred to as Carhart momentum), which compares the performance of an asset with respect to the performance of another asset. Ian DSouza, Voraphat Srichanachaichok, George Jiaguo Wang and Chelsea Yaqiong Yao, who authored the 2016 study
  • Loading and Manipulating Historical Data From .csv Files [Dekalog Blog]

    In my last post I said I was going to look at data wrangling my data, and this post outlines what I have done since then. My problem was that I have numerous csv files containing historical data with different date formats and frequency, e.g. tick level and hourly and daily OHLC, and in the past I have always struggled with this. However, I have finally found a solution using the R quantmod

Quantocracy’s Daily Wrap for 09/15/2016

This is a summary of links featured on Quantocracy on Thursday, 09/15/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Podcast: This quants approach to designing algo strategies – Michael Halls-Moore [Chat With Traders]

    For this episode Im joined by Michael Halls-Moore, who runs QuantStart.coma site well-known by many algorithmic traders. Prior to trading, Michael studied computational fluid dynamics and was the co-founder of a tech startup, before getting involved a small equity fund as a quant developerwhere his key role was cleansing data. Now, independently, Michael trades his own short-term
  • Algorithmic Trading Strategies: Paradigms and Modelling Ideas [Quant Insti]

    Looks can be deceiving, a wise person once said. The phrase holds true for Algorithmic Trading Strategies. The term Algorithmic trading strategies might sound very fancy or too complicated. However, the concept is very simple to understand, once the basics are clear. In this article, I will be telling you about algorithmic trading strategies with some interesting examples. If you look at it
  • Value investing is quite possibly the worst idea…EVER [Alpha Architect]

    We believe deeply in the value philosophy as first described by Ben Graham: view stocks as ownership in a firm; buy with a margin of safety; avoid stories; think independently; and so forth. In fact, I was so intellectually stimulated by value investing I wrote my dissertation on the subject, co-authored an entire book on value investing, and weve written numerous blog posts highlighting why