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Quantocracy’s Daily Wrap for 09/30/2016

This is a summary of links featured on Quantocracy on Friday, 09/30/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Really, Beware of Low Frequency Data [EP Chan]

    I wrote in a previous article about why we should backtest even end-of-day (daily) strategies with intraday quote data. Otherwise, the performance of such strategies can be inflated. Here is another brilliant example that I came across recently. Consider the oil futures ETF USO and its evil twin, the inverse oil futures ETF DNO*. In theory, if USO has a daily return of x%, DNO will have a daily
  • Buyback Bulls and Bears [Investing Research]

    A lot of attention has been paid to share repurchases recently, which makes sense given the amount of money involved. As of June 30th, 2016, there had been almost $450 billion net transferred from companies to shareholders over the trailing twelve months through repurchase programs, very close to the all-time high in March of 2008. Transferring that much wealth between stakeholders will garner
  • Research Review | 30 Sep 2016 | Managing Volatility Risk [Capital Spectator]

    Managed portfolios that take less risk when volatility is high produce large alphas, substantially increase factor Sharpe ratios, and produce large utility gains for mean-variance investors. We document this for the market, value, momentum, profitability, return on equity, and investment factors in equities, as well as the currency carry trade. Volatility timing increases Sharpe ratios because
  • Option Pricing Methods in the Late 19th Century [Quantpedia]

    This paper examines option pricing methods used by investors in the late 19th century. Based on the book called PUT-AND-CALL written by Leonard R. Higgins in 1896 and published in 1906 it is shown that investors in that period used routinely the put-call parity for option conversion and static replication of option positions, and had developed no-arbitrage pricing formulas for determining
  • Sell Rosh Hashanah, Buy Yom Kippur [UK Stock Market Almanac]

    In 1935, the Pennsylvania Mirror referred to a Wall Street adage, Sell before Rosh Hashanah; buy before Yom Kippur. Recently an academic paper quoted this article and set out to establish if the adage was true and still valid today. The theory is that the market is weak during the approximately seven trading-days gap between the Jewish New Year (Rosh Hashanah ) and the Day of Atonement (Yom

Quantocracy’s Daily Wrap for 09/29/2016

This is a summary of links featured on Quantocracy on Thursday, 09/29/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Introducing the Global Earnings Announcement Premium [Alpha Architect]

    How do stock prices react to earnings announcements? Sometimes prices go up, and sometimes they go down. But here is a potentially more interesting question: What is the average performance across all stocks that have an announcement? The question of whether stocks earn excess returns in announcement months was first investigated by Prof. William Beaver back in 1968. He found that the magnitude of
  • Trading with different time frames [Milton FMR]

    Time frames are used in order to forecast future price trends. Many traders are missing out on this important aspect of trading by only looking at one time frame when trying to define a trend. Therefore its important to categorize trends as primary, intermediate and short-term trends. As a rule of thumb the primary trend is filtering out much of the market noise and is giving us more reliable
  • Profitable Market Timing with the Unemployment Rate [iMarketSignals]

    If the unemployment rate is higher than three months ago the model exits the stock market and enters the bond market, and re-enters the market when the unemployment rate is equal or lower than where it was three months ago. From 2001 to 2016 switching between bonds and stocks provided significant benefits. This strategy would have produced an average annual return of 13.0% versus only 5.2% for

Quantocracy’s Daily Wrap for 09/28/2016

This is a summary of links featured on Quantocracy on Wednesday, 09/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Optimal ETFs For Each Market Segment [Signal Plot]

    In one of my previous posts, I wrote about my framework for choosing ETFs since I wanted a method of choosing the optimal ETFs for expressing my market views. Generally, the framework is to choose an ETF with a low expense ratio, low trading costs, low tracking error, and a tax-advantaged structure. One of the key insights I got from writing that post was that investors should weigh each of those
  • Momentum Rotation System AmiBroker Code [DTR Trading]

    I've received several requests for details on the AmiBroker (AB) code and settings used for the backtest shown in my April post: Momentum Rotation 60 Day ROC System Results. That post used the AmiBroker Formula Language (AFL) code from my article in March 2015. That was a long time ago, so here is the 60 day momentum rotation system AFL again:
  • Taming High Return and High Risk [Alvarez Quant Trading]

    I was at a recent talk of the Northwest Traders and Technical Analysts group where they presented a VXX strategy with some huge return and drawdown numbers. Trading this would be very difficult. This got me thinking. If I had a strategy like this, how could I tame the numbers? Through the years, I have seen various ideas about how to do this but never looked into it. Searching the web one can find
  • Learning with kernels: an introductory approach [Quant Dare]

    Time series pervade financial markets and, although some embrace the so-called efficient market hypothesis, stating that current market prices reflect all available information about a security into its price, I am more inclined to think they provide us with a lot of information that we rarely know how to exploit it for our own benefit. I agree that financial time series may be damn difficult to
  • QuantStart Events in October and November 2016 [Quant Start]

    This is a short post to let QuantStart readers know that I'll be speaking at some events in New York and Singapore over the next couple of months: Wednesday, October 5th 2016, NYC – I'll be talking about "The Quest for Profitability", along with Seong Lee who will be talking about "Data Driven Strategies" Thursday, October 6th 2016, NYC – I'll be moderating a

Quantocracy’s Daily Wrap for 09/27/2016

This is a summary of links featured on Quantocracy on Tuesday, 09/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • TAA Alpha and The Greatest Trick the Devil Ever Pulled [GestaltU]

    The investment industry has investors convinced that the only path to better performance is through stock selection. As a result, most investors approach the challenge of portfolio construction exactly backward, and miss out on the most important opportunities to produce differentiated performance. The purpose of this series is to challenge the conventions that lead to misguided asset allocation
  • The Cost of Contango It s Not the Daily Roll [Six Figure Investing]

    Its well known that long volatility Exchange Traded Products (ETPs) like VXX, UVXY, and TVIX often experience devastating losses during market quiet spellseven when the value of the VIX is staying relatively stable. These heavy losses occur when the VIX futures that underlie these funds are in a price/time arrangement called contango. The chart below shows an example of VIX futures in a
  • How to Scrape Data for Over 1,900 ETFs [Signal Plot]

    You can download the ETF metadata I scraped here and the price history here. You can also take a look at the code I used to scrape this data at my Github repository. Why Scrape ETFs? At the investment management firm I worked at, we had teams of people that devoted lots of time just trying to accurately calculate the total return of assets. Why? Having accurate return data is a prerequisite to
  • Predicting Booms and Busts in Low Volatility Strategies [Alpha Architect]

    Low volatility funds are some of the best performers in the market these days. As such, they have attracted renewed attention in addition to significant asset flows. (note: a refresher on low volatility investing is here, h.t. Eric Falkenstein). But a question remains What does the future hold for low volatility performance? Predictions are really difficult, especially about the future.

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