Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 10/09/2015

This is a summary of links featured on Quantocracy on Friday, 10/09/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is Scalping Irrational? [Financial Hacker]

    Clients often ask for strategies that trade on very short time frames. Some are possibly inspired by I just made $2000 in 5 minutes stories on trader forums. Others have heard of High Frequency Trading and concluded that the higher the frequency, the better must be the trading. Zorro developers had been pestered for years until they finally implemented tick-based price histories and
  • Volatility Stat-Arb Shenanigans [QuantStrat TradeR]

    This post deals with an impossible-to-implement statistical arbitrage strategy using VXX and XIV. The strategy is simple: if the average daily return of VXX and XIV was positive, short both of them at the close. This strategy makes two assumptions of varying dubiousness: that one can observe the close and act on the close, and that one can short VXX and XIV. So, recently, I decided to play
  • Is research in finance and economics reproducible? [Mathematical Investor]

    Reproducibility in scientific research In the past year or two, the reproducibility of research results in finance and economics has come under serious question. If it is any comfort, similar difficulties have emerged in numerous other scientific fields. In 2011, a team of Bayer researchers attempted to reproduce a set of key published pharmaceutical studies. They were only able to validate 11 out
  • Cumulative market gains are zero across even years [RRSP Strategy]

    Mkt-RF returns in even years sum to zero over the last 50+ years (data from Ken Frenchs library). This could be a spurious result although the stats suggest otherwise. odd-even-years Is this result statistically significant? Applying Students t-test gives a statistic of 2.3, i.e. mean returns of even versus odd years are different at the 5% significance level.
  • Simple Tests of Sy Harding s Seasonal Timing Strategy [CXO Advisory]

    Several readers have inquired over the years about the performance of Sy Hardings Street Smart Report Online (now unavailable due to Mr. Hardings death), which included the Seasonal Timing Strategy. This strategy combines the markets best average calendar entry [October 16] and exit [April 20] days with a technical indicator, the Moving Average Convergence Divergence (MACD).

Quantocracy’s Daily Wrap for 10/07/2015

This is a summary of links featured on Quantocracy on Wednesday, 10/07/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • ARIMA+GARCH Trading Strategy on the S&P500 Stock Market Index Using R [Quant Start]

    In this article I want to show you how to apply all of the knowledge gained in the previous time series analysis posts to a trading strategy on the S&P500 US stock market index. We will see that by combining the ARIMA and GARCH models we can significantly outperform a "Buy-and-Hold" approach over the long term. Strategy Overview The idea of the strategy is relatively simple but if
  • How to look up a Stock s Short Interest with Python [MKTSTK]

    Today I was trying to investigate short interest in the Energy sector: the group as a whole has rallied hard over the last few days and I suspect a short covering rally is at play, so some testing is in order. Much to my dismay, my searches didnt return an easy way to do this in Python. Lots of websites offer the data but quants want to consume the data programmatically Luckily it was super
  • Optimization of Equity Momentum [Quantpedia]

    Standard mean-variance optimized momentum outperforms the traditional equally weighted momentum strategy if the expected return vector used reflects momentum's top and bottom only characteristic. This top and bottom only characteristic is the phenomenon that only the stocks in the top decile of momentum's ranking outperform and that only stocks in the bottom decile underperform, while

Quantocracy’s Daily Wrap for 10/06/2015

This is a summary of links featured on Quantocracy on Tuesday, 10/06/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • VIX Trading Strategies in September [Volatility Made Simple]

    Weve tested 24 simple strategies for trading VIX ETPs on this blog (separate and unrelated to our own strategy). And while I cant speak for all traders, based on all of my readings both academic and in the blogosphere, the strategies weve tested are broadly representative of how the vast majority of traders are timing these products. Below Ive shown the September results of all 24
  • How to be a Quant [Turing Finance]

    Since writing about my experience writing the CFA Level I exam in June, I have received many emails from people interested in finding out how to become a quant. To some extent, this post will answer that question. That said, this post is actually not about how to become a quant, it is about how to be a quant in whatever sector of the financial services industry you already work in. Being is quant
  • An Example of a Trading Strategy Coded in C++ [Quant Insti]

    Any trading strategy can be broken down into a set of events and the reaction to those events. The reactions can get infinitely complex and varying but essentially strategy writing is quite simply put exactly that. The kind of events and their frequency would depend on the markets and the instruments on which this strategy would be working on however, broadly speaking most markets would have
  • A Review of DIY Financial Advisor from @AlphaArchitect [QuantStrat TradeR]

    This post will review the DIY Financial Advisor book, which I thought was a very solid read, and especially pertinent to those who are more beginners at investing (especially systematic investing). While it isnt exactly perfect, its about as excellent a primer on investing as one will find out there that is accessible to the lay-person, in my opinion. Okay, so, official announcement: I am
  • An Example of a Trading Strategy Coded in R [Quant Insti]

    Back-testing of a trading strategy can be implemented in four stages. Getting the historical data Formulate the trading strategy and specify the rules Execute the strategy on the historical data Evaluate performance metrics In this post, we will back-test our trading strategy in R. The quantmod package has made it really easy to pull historical data from Yahoo Finance. The one line code below
  • A little demonstration of portfolio optimisation [Investment Idiocy]

    I've had a request for the code used to do the optimisations in chapter 4 of my book "Systematic Trading" (the 'one-period' and 'bootstrapping' methods; there isn't much point in including code to the 'handcrafted' method as it's supposed to avoid programming). Although this post will make more sense if you've read the book, it can also
  • Test for Jumps using Neural Networks [Top of the Bell Curve]

    Modelling of financial markets is usually undertaken using stochastic process. Stochastic processes are collection of random variables indexed, for our purposes, by time. Examples of stochastic processes used in finance include GBM, OU, Heston Model and Jump Diffusion processes. For a more mathematically detailed explanation of Stochastic processes, diffusion and jump diffusion models, read this
  • Using Random Portfolios To Test Asset Allocation Strategies [Capital Spectator]

    Last month I tested random rebalancing strategies based on dates and found that searching for optimal points through time to reset asset allocation may not be terribly productive after all. Lets continue to probe this line of analysis by reviewing the results of randomly changing asset weights for testing rebalancing strategies. Ill use the same 11-fund portfolio thats globally
  • State of Trend Following in September: Still on the Rise [Wisdom Trading]

    September 2015: Trend Following UP +4.64% YTD: +13.98% Third month in a row of the index producing a positive return. The YTD and 12-month figures well in the black (over 10% and 35% respectively) show that trend following has been a good strategy to invest or trade in these past market conditions. Below is the full State of Trend Following report as of last month. Performance is
  • Research Review | 6 Oct 2015 | Portfolio Risk Management [Capital Spectator]

    How Do Investors Measure Risk? Jonathan Berk and Jules H. Van Binsbergen October 1, 2015 We infer which risk model investors use by looking at their capital allocation decisions. We find that investors adjust for risk using the beta of the Capital Asset Pricing Model (CAPM). Extensions to the CAPM perform poorly, implying that they do not help explain how investors measure risk. A Smart
  • Using Machine Learning to Select Your Indicators for a Trading Strategy [Inovance]

    Selecting the indicators to use is one of the most important and difficult aspects of building a successful strategy. Not only are there thousands of different indicators, but most indicators have numerous settings which amounts to virtually limitless indicator combinations. Clearly testing every combination is not possible, so many traders are left on their own selecting somewhat random inputs to
  • State of Trend Following in September: It was a Good Summer [Au Tra Sy]

    The index had a strong September, continuing the Summer uptrend started in mid-July. This has now resulted in the YTD performance returning to positive territory, after the Spring slump took the index from the Winter highs to negative performance. Lets see what Autumn (or Fall depending on where you live) has in store Please check below for more details. Detailed Results
  • Book Review: DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth [Scott’s Investments]

    Readers of Scotts Investments know I am a proponent of do-it-yourself investing solutions. I am also a big fan of Alpha Architect, so I was excited when I was asked to review a book which combines the best of both worlds, DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth (DIY). Authors Wes Gray, Jack Vogel, and David Foulke, all of Alpha Architect, split DIY into two

Quantocracy’s Daily Wrap for 10/02/2015

This is a summary of links featured on Quantocracy on Friday, 10/02/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Using Normal Drawdowns as a Timing Signal [EconomPic]

    The below analysis was purely an accident. I was actually looking into periods the U.S. stock market "suffered" a 10% drawdown for the absolute opposite reason; to show that a buy and hold investor should likely ignore these regularly occurring events. How regular? The always interesting Ryan Detrick points out: I looked at every calendar year since 1960 and looked at various correction
  • Volatility Reconcialiation [John Orford]

    Yesterday I wrote up a post, and immediately after, was sure I got something wrong. This is the offending chart. Volatility increases linearly as we add more positions to the equally weighted portfolio. What I failed to mention is that each position was weighted by 100% of the portfolio. So two positions meant the portfolio was leveraged 2x and so on. Stupid right? Well, not necessarily. What was
  • Relative Strength and Dividend Investing [Systematic Relative Strength]

    The portfolio manager of a large, active dividend fund was recently interviewed by Morningstar. (What Active Management Can Bring To Dividend Investing http://www.morningstar.com/cover/videocenter.aspx?id=716392). The portfolio manager argues that simply looking for stocks with high dividend yields is insufficient because so many of those very high yielding stocks go through dividend cuts.

Quantocracy’s Daily Wrap for 09/30/2015

This is a summary of links featured on Quantocracy on Wednesday, 09/30/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Forecasting with HoltWinters Exponential Smoothing [Quant Insti]

    I recently enrolled in the QuantInsti Executive Program in Algorithmic Trading and one of the areas in quantitative finance that interests me greatly is the analysis of financial time series. During the course we will take on a massive project to build our own trading strategy with the help of a mentor, and in attempt to familiarize myself with the work, I built this simple strategy using the
  • Stop Losses and Profit Targets. Plus Happy Birthday Excel! [Alvarez Quant Trading]

    In the post, Maximum Loss Stops: Do you really need them?, we looked at how maximum loss stops changed the results of a mean reversion strategy. At the end of the post I asked the readers to vote for what to try next. Let us see how these are ideas turn out. The rules from the original post Setup Close greater than 100-day moving average Close less than the 5-day moving average 3 lower lows Member
  • Timing / Stock Pick Duality Strategy [John Orford]

    Months ago I came up with an idea. How about building a portfolio where choosing positions and a holding period is interchangeable? Weird right? Let me explain. Academics assume day over day returns have little to do with each other and that markets are more or less efficient. So increasing your holding period results in more risk but it tails off exponentially (at the rate of the square root of
  • Risk is Still Not a Mathematical Concept [Factor Wave]

    I wrote last week that many different measures of risk can be used to demonstrate that low risk stocks outperform high risk stocks, and illustrated this by sorting stocks according to the Hurst exponent. Today we are going to offer another example of this by using entropy as the measure of risk. The original concept of entropy was the amount of disorder in a thermodynamic system. However this was
  • A Few Notes on Systematic Trading [CXO Advisory]

    Robert Carver introduces his 2015 book, Systematic Trading: A Unique New Method for Designing Trading and Investing Systems, by stating that: I dont believe there is any magic system that will automatically make you huge profits, and you should be wary of anyone who says otherwise, especially if they want to sell it to you. Instead, success in systematic trading is mostly down to avoiding
  • Python code for the two trading rules in “Systematic Trading” [Investment Idiocy]

    This is a brief post aimed at those who have already bought a copy of "Systematic Trading" (by the way thanks!) As you've probably noticed I've included excel sheets here explaining the two trading rules I describe in the book – exponentially weighted moving average crossover ("EWMAC") and Carry ("Carry"). Since I also have the python code for these rules, I

Quantocracy’s Daily Wrap for 09/29/2015

This is a summary of links featured on Quantocracy on Tuesday, 09/29/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Combining volatility, momentum, and trend in asset allocation [Alpha Architect]

    The Efficient Market Hypothesis (EMH) has been widely called into question in the investment literature, through two main anomalies: timing and low-volatility anomalies. In this paper, we aim to combine the predictive power of timing and low-volatility strategies to deliver better risk-adjusted portfolio performance. We adopt a two-step approach for a constant dataset composed of 18 country MSCI

Quantocracy’s Daily Wrap for 09/28/2015

This is a summary of links featured on Quantocracy on Monday, 09/28/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 09/26/2015

This is a summary of links featured on Quantocracy on Saturday, 09/26/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Empirical Finance: Meeting Fiduciary Standards Through Skepticism, Not Cynicism [GestaltU]

    Michael Edesses is out with a scathing article lambasting the field of empirical finance. He draws inspiration from Harvey, Liu and Zhus (HLZ) recent article, entitled and the Cross Section of Expected Returns, but extends HLZs conclusions to an absurd limit. In this article, we discuss why we embrace the framework of healthy skepticism described by HLZ, but in the context of a more

Quantocracy’s Daily Wrap for 09/25/2015

This is a summary of links featured on Quantocracy on Friday, 09/25/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A story of poor statistical intuition [Investment Idiocy]

    In my last post I had a bit of a controversial pop at a brilliant and successful billionaire hedge fund manager; Jim Simons. In continuing my futile quest to raise the level of debate in the quantitative investment community I thought I'd have a go at another clever and very wealthy guy, Cliff Asness, founder of giant fund AQR. Cliff Asness. There is nothing wrong with his statistical
  • The S&P 500 Death Cross Time to Panic? [iMarketSignals]

    At the end of August 2015 the 50-day moving average of the S&P500 crossed its 200-day moving average to the downside the 33rd occurrence of a Death Cross since 1950. The performance of the S&P500 was investigated for periods ranging from one year before to two years after a Death Cross. During the last 65 years there were ten recessions. A Death Cross preceded six recessions and
  • Runge-Kutta Methods [Dekalog Blog]

    As stated in my previous post I have been focusing on getting some meaningful features as possible inputs to my machine learning based trading system, and one of the possible ideas that has caught my attention is using Runge-Kutta methods to project ( otherwise known as "guessing" ) future price evolution. I have used this sort of approach before in the construction of my perfect
  • Correlation and Cointegration [Quant Dare]

    I want a strategy that is able to choose the assets that makes it look like an index Yt. -Then take the ones most correlated to it. -Ok, but look: CC1 The Xt and the Xt+c series have exactly the same correlation with Yt -I prefer Xt+c!! -Yes, but I am trying to be very similar to Yt and Xt+c have a large deviation. -Cointegration? -If two or more series are individually integrated (in the time

Quantocracy’s Daily Wrap for 09/23/2015

This is a summary of links featured on Quantocracy on Wednesday, 09/23/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sorry Jim, trend following probably still works (though not the fast stuff) [Investment Idiocy]

    Sorry Jim, trend following probably still works (though not the fast stuff) Sometimes really smart people still get things wrong. Even really smart people, who have been very successful, and are thus very rich. Jim Simons. Very Rich. Very Smart (www.pbs.org) During a recent TED talk Jim Simons said: Trend-following would have been great in the 60s, and it was sort of OK in the 70s. By the
  • ‘Javascript for Financial Analysts’ Chapter 3 – First Draft [John Orford]

    The first chapter ended with code which included a map and a filter which we will dive back into now with a less applied more intuitive example. Open up the JavaScript console and paste or type in the following code, [0,1,2,3,4,5,6,7,8,9] .filter( function(j){ return j%2===1; } ); This code filters the array of numbers and returns an array of odd numbers. > [1,3,5,7,9] One feature of filter and
  • Out-of-sample testing of Sell in May market timing rule [Quantitative Investor]

    In one of the previous posts I considered market timing with moving averages on 9 quite different equity indices that were chosen in other post, and came to the conclusion that the rule is the viable alternative to the standard B&H, allowing to avoid large drawdowns in one cases and even improve performance in others (e.g. it is bad idea to passively invest in Nikkei index, but even with
  • Momentum trends with Andreas @Clenow [Automated Trader]

    Andreas Clenow is CIO of Zurich-based ACIES Asset Management ($300+ million AuM), and author of 'Stocks on the Move: Beating the Market with Hedge Fund Momentum Strategies'. Why would he give up the super secret sauce in a tell-all? Automated Trader finds out. CTAsHedge Funds Andreas Clenow, CIO, ACIES Asset Management Andreas Clenow, CIO, ACIES Asset Management "People think that
  • Using a Random Forest and Hidden Markov Model to Improve Trade Performance [Inovance]

    Machine learning is a powerful tool for not only coming up with new strategies (like we do in TRAIDE) but also for improving your existing strategies. In this article, well cover adjusting your position size using a random forest algorithm and turning your strategy on an off using a Hidden Markov Model. You can copy and paste the R code to try it yourself on your own strategies. This article