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Quantocracy’s Daily Wrap for 06/21/2016

This is a summary of links featured on Quantocracy on Tuesday, 06/21/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Recommended Reading [Robot Wealth]

    If theres one thing Ive done a lot of over the last few years, reading would be it. Ive devoted a great deal of time to devouring any material that I thought might give me an edge in my trading textbooks, academic papers, blog articles, training courses, lecture notes, conference presentationsanything and everything I could get my hands on. I was browsing the folder called
  • Manage Your Luck [Systematic Relative Strength]

    There is a lot more luck involved in investing than people think. Im not saying there isnt skill involved in investing or that there arent ways to outperform the market over time. Even if you have a process that can be shown to outperform the market over long time periods, there can be a great deal of variation in returns from year to year. A well designed investment model can certainly

Quantocracy’s Daily Wrap for 06/20/2016

This is a summary of links featured on Quantocracy on Monday, 06/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Digging Deeper into Adaptive Asset Allocation [Alpha Architect]

    In some ways, investing is simple. After all, we all want the same things. High returns. Low volatility. Small max drawdowns. Unfortunately, its very difficultif not impossibleto have your cake and eat it too. There are always tradeoffs among these desires that have to be managed by investors. Want low volatility? Be willing to accept lower returns. Want to maximize returns? You may have
  • Johansen Test for Cointegrating Time Series Analysis in R [Quant Start]

    In the previous article on the Cointegrated Augmented Dickey Fuller (CADF) test we noted that one of the biggest drawbacks of the test was that it was only capable of being applied to two separate time series. However, we can clearly imagine a set of three or more financial assets that might share an underlying cointegrated relationship. A trivial example would be three separate share classes on
  • Mini-Meucci : Appplying The Checklist – Steps 8-9 [Return and Risk]

    "Predicting rain doesn't count. Building arks does." Warren Buffett, The Oracle of Omaha (born 1930) In this penultimate leg of the tour we'll be visiting 2 more attractions along Via Meucci, Construction and Execution. Construction Portfolio Construction is another yuge! topic. In a nutshell, the overall goal is to find optimal holdings that maximize Satisfaction, subject to
  • Beginner’s Guide to Automated Trading with Python [Quant Insti]

    Python has emerged as one of the most popular language to code in Algorithmic Trading, owing to its ease of installation, free usage, easy structure, and availability of variety of modules. Globally, Algo Traders and researchers in Quant are extensively using Python for prototyping, backtesting, building their proprietary risk and order management system as well as in optimisation of testing
  • Is Internal Bar Strength A Random Walk? The Case of Exxon-Mobil [Jonathan Kinlay]

    For those who prefer a little more rigor in their quantitative research, I can offer more a somewhat more substantive statistical argument in favor of the IBS indication discussed in my previous post. Specifically, we can show quite convincingly that the IBS process is stationary, a highly desirable property much sought-after in, for example, the construction of statistical arbitrage strategies.

Quantocracy’s Daily Wrap for 06/18/2016

This is a summary of links featured on Quantocracy on Saturday, 06/18/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Monte Carlo and Arima for stock selection [Tulip Quant]

    A few days ago, in this post, I talked about how ARIMA models could be used to forecast the S&P 500 index, and use this information in order to buy or sell the index every day, if the algorithm predicts an increase or decrease in the price, respectively. In this post, I will go a step further. The idea of the trading algorithm will be the following: Given a day, for each stock of a certain
  • Binary Options: Scam or Opportunity? [Financial Hacker]

    Were recently getting more and more contracts of developing systems for trading binary options. This calls for a closer look. Binary options resemble financial instruments, but are widely understood as a scheme to separate naive traders from their money. And indeed, binary options brokers make no good impression at first look. Some are regulated in Cyprus under a fake address, others are not

Quantocracy’s Daily Wrap for 06/17/2016

This is a summary of links featured on Quantocracy on Friday, 06/17/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Invert, Always Invert: Will Stocks Diversify Bonds in the Future? [Alpha Architect]

    My last post, Will bonds deliver crisis alpha in the next crisis?, created quite a stir on the blogosphere. The underlying assumption of the analysis is that stocks are a core component of a portfolio and bonds are included to diversify the portfolio. The key takeaway from my analysis is that the crisis alpha associated with bond exposures seems to be driven by the income component of bond
  • Mean Reversion and the Broken Rubber Band [Alvarez Quant Trading]

    A common way to describe a mean reversion trade is a rubber band that stretches away and then snaps back. Something that Steve, my trading buddy, and I discuss when a trade keeps going against us is that the rubber band has broken. I have never tested that concept. Meaning after N day sell-off, are we now more likely to continue to sell off than bounce? Doing research is not always about trying to
  • A Return.Portfolio Wrapper to Automate Harry Long Backtests [QuantStrat TradeR]

    This post will cover a function to simplify creating Harry Long type rebalancing strategies from SeekingAlpha for interested readers. As Harry Long has stated, most, if not all of his strategies are more for demonstrative purposes rather than actual recommended investments. So, since Harry Long has been posting some more articles on Seeknig Alpha, Ive had a reader or two ask me to analyze his
  • Rough Net Worth Growth Benchmarks [CXO Advisory]

    How fast should individuals plan to grow net worth as they age? To investigate, we examine median levels of household (1) total net worth and (2) net worth excluding home equity from several vintages of U.S. Census Bureau data. We make the following head-of-household age cohort assumptions: Less than 35 years means about age 30. 35 to 44 years means about age 39. 45 to 54 years
  • Information Ratio Analysis of Time-Series Momentum Strategy [Quantpedia]

    In the past 20 years, momentum or trend following strategies have become an established part of the investor toolbox. We introduce a new way of analyzing momentum strategies by looking at the information ratio (IR, average return divided by standard deviation). We calculate the theoretical IR of a momentum strategy, and show that if momentum is mainly due to the positive autocorrelation in

Quantocracy’s Daily Wrap for 06/16/2016

This is a summary of links featured on Quantocracy on Thursday, 06/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Some harmless data-mining: Testing individual words in EDGAR filings [Greg Harris]

    Everyone knows about the perils of data-mining and multiple testing. So, dont take this post too seriously. I recently made an inverted index into all 11 million regulatory filings disseminated online by the SEC. This means that for each string of three or more letters I have a list of all documents that contain it. I did this to facilitate full text search. But, now that I have it, I decided

Quantocracy’s Daily Wrap for 06/15/2016

This is a summary of links featured on Quantocracy on Wednesday, 06/15/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Lasso applied in Portfolio Management [Quant Dare]

    There are a wide variety of Machine Learning techniques that help us to solve Big Data problems. In this post we talk about how to apply Lasso Regression in Portfolio Management. You may have heard of this technique in the past, for that reason Ill try to explain it in a brief introduction. Lasso definition Least Absolute Shrinkage and Selection Operator or Lasso is a regression analysis method
  • Write Covered Call Strategy in Python [Quant Insti]

    Traders in the derivative market often exercise one of the following: Call option or Put Option. Call option is a financial contract between a buyer and seller, whereby the buyer has the right, but not the obligation, to buy an agreed quantity of a financial instrument from the seller of the option at a certain time for a certain price (the strike price). The Pull Option serves the

Quantocracy’s Daily Wrap for 06/14/2016

This is a summary of links featured on Quantocracy on Tuesday, 06/14/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Differential equation that models a support and resistance strategy [Tulip Quant]

    Support and resistance indicators are widely used in technical analysis. What I tried to do is to model this strategy using a suitable differential equation, in order to test it with historical data. Support-resistance_binary_options A simple model could be the following: S'(t)=\gamma_t \displaystyle{\min_{s\in[0, N]} }\left ( |S'(t-s)|^\alpha + |S(t-s)-S(t)|^\beta \right ) Where S(t)
  • Cointegrated Augmented Dickey Fuller Test for Pairs Trading Evaluation in R [Quant Start]

    In the previous article on cointegration in R we simulated two non-stationary time series that formed a cointegrated pair under a specific linear combination. We made use of the statistical Augmented Dickey-Fuller, Phillips-Perron and Phillips-Ouliaris tests for the presence of unit roots and cointegration. A problem with the ADF test is that it does not provide us with the necessary ? regression
  • The Brutal Math of a 60/40 Portfolio [EconomPic]

    Think only a bear market can keep returns of a 60/40 near 0%… think again. Given the huge opportunity cost of allocating to cash or bonds at current yield levels, even generally optimistic return assumptions for stocks are enough to keep portfolio level returns near 0% real. The goal of this post is to set the stage for a future post where I hope to share potential solutions that may improve
  • Trading strategy for the S&P 500 index based on ARIMA models [Tulip Quant]

    ARIMA models are a family of models for time series that are used to forecast future behaviour. It can be (and it is) used in finance, and in particular in trading. In this post I will try to show a specific use for a trading strategy based on these models, and it will be applied to the S&P 500 index. ARIMA models are denoted by ARIMA(p,d,q), where: p is the order of the autorregresive factor.
  • Mini-Meucci : Applying The Checklist – Steps 6-7 [Return and Risk]

    Today we'll be visiting 2 sites along Via Meucci, Evaluation and Attribution. Evaluation We need some way to measure the goodness of the ex-ante portfolio across the scenarios from the Aggregation step, and for this Meucci introduces the concept of a Satisfaction index. Given the distribution of the ex-ante performance we can compute the satisfaction index (or its opposite, the risk index)

Quantocracy’s Daily Wrap for 06/13/2016

This is a summary of links featured on Quantocracy on Monday, 06/13/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Smart Beta Is Still Beta [Dual Momentum]

    Some say that bull markets climb a wall of worry. This is good news for those already in the market. Worriers will help the market go higher later when they finally decide to jump on the bandwagon. Herding, representativeness, and regret aversion (fear of losing out on future profits) can eventually overtake loss aversion. Investors Skeptical The iconic investor and money manager, Sir John
  • Forecast combinations in R [Eran Raviv]

    Few weeks back I gave a talk in the R/Finance 2016 conference, about forecast combinations in R. Here are the slides.
  • Want to Know the Secret to Inefficient Prices? Lazy Prices. [Alpha Architect]

    How do you handle repetitive tasks? If youre like most people, you work through a task in a variety of ways, find the most efficient approach, and then stick to that workflow. Consider email address autofill, automatic payment plans, or automatic renewal of magazine subscriptions. Because of behavior bias and the power of inertia (also known as the, Yeah, whatever, heuristic), it takes
  • Diversification opportunities in fixed income [Flirting with Models]

    Summary Many investors look at fixed income as the diversifying sleeve of their portfolio, helping to safeguard capital against losses in more volatile equity positions. Traditional fixed income indices are very heavily weighted towards U.S. Treasuries and agency mortgage-backed securities, offering very little internal diversification. There are numerous extended sectors now available as ETFs
  • Sharp Drops From Intermediate-Term Highs Short Term Bullish [Quantifiable Edges]

    Thursday and Friday saw relatively large selloffs in SPX. After closing at a 50-day high on Wednesday it closed at a 10-day low on Friday. This triggered an interesting study from the Quantifinder that looked at relatively sharp selloffs that made at least 8-day lows. I have updated that study below. 2016-06-13 image1 The stats all suggest an upside edge over the next 1-5 days. Traders may want to
  • FTSE 100 around FOMC announcements [UK Stock Market Almanac]

    The Federal Open Market Committee (FOMC) is the monetary policy-making body of the U.S. Federal Reserve System. Since 1981 the FOMC has had eight scheduled meetings per year, the timing of which is quite irregular, The schedule of meetings for a particular year is announced ahead of time [calendar here]. Starting in 1994, the FOMC began to issue a policy statement (FOMC statement) after the