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Quantocracy’s Daily Wrap for 12/12/2016

This is a summary of links featured on Quantocracy on Monday, 12/12/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Dynamic Approach to Factor Allocation [EconomPic]

    ETF Trends (hat tip Josh) showed the following "quilt" of large cap factor calendar year returns in the post Low Volatility is Not a Buy and Hold Strategy. Author John Lunt's takeaway (bold mine): It is reasonable to conclude that low volatility is not a buy and hold strategy. This is not because it is unlikely to outperform over the long term, but rather because few investors are
  • New Book Added (Fin Math): Quantitative Risk Management: A Practical Guide to Financial Risk

    State of the art risk management techniques and practicessupplemented with interactive analytics All too often risk management books focus on risk measurement details without taking a broader view. Quantitative Risk Management delivers a synthesis of common sense management together with the cutting-edge tools of modern theory. This book presents a road map for tactical and strategic decision
  • The Ghost of GDP Past [Flirting with Models]

    Summary Economic growth is a key driver of long-term stock and bond returns. Economic growth comes from two main sources: demographic changes (i.e. increases in the number of workers) and productivity growth (i.e. each worker producing more output). Historically, approximately 55% of growth has come from productivity growth and 45% has come from demographic changes. Slowing population growth
  • Interest Rates and Value Investing [Alpha Architect]

    There is still no value in bonds today. Many readers just had a knee-jerk reaction and theyve determined that I fall into one of two categories: A total idiot A total genius But lets dig a bit deeper into the claim that bonds lack value, even with this quarters 85 basis point back-up in 10 year treasury note yields. One way to view value within non-credit fixed income assets is to
  • Hacking True Random Numbers in Python: Blockchain Miners [Quant at Risk]

    The magnitude and importance of random numbers in finance does not have to be explained. We need them. Either it is an option pricing or a Monte Carlo simulation, random numbers are with us. However, we make a trade-off: the speed in their generation versus uniqueness. That is why a widely accepted use of, inter alia, Mersenne Twister algorithm as a source of pseudo-random numbers has established
  • The Most Wonderful Weeeeek Of The Yeeeaaaarrrrr!! [Quantifiable Edges]

    Over several time horizons op-ex week in December has been the most bullish week of the year for the SPX. The positive seasonality actually has persisted for up to 3 weeks. Ive shown the study below in the blog many times since 2008. It looks back to 1984, which was the first year that SPX options traded. The table is updated again this year.

Quantocracy’s Daily Wrap for 12/11/2016

This is a summary of links featured on Quantocracy on Sunday, 12/11/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cryptocurrencies and Machine Learning with @BMouler [Better System Trader]

    As markets become more mature and more efficient, it can be become increasingly difficult to find sustainable edges. Many traders are looking at the same data and using the same techniques, so what are our options here? 2 of the obvious options we have are: Try to find a unique approach to the markets or at least something that isnt so popular, Explore alternative markets where inefficiencies
  • Sources of Return for CTAs – A Brief Survey of Relevant Research [Quantpedia]

    This survey paper will discuss the (potential) structural sources of return for both CTAs and commodity indices based on a review of empirical research articles from both academics and practitioners. The paper specifically covers (a) the long-term return sources for both managed futures programs and for commodity indices; (b) the investor expectations and the portfolio context for futures
  • Reading Fundamental Data from Yahoo Finance [Copula.de]

    Recently I read a blogpost and someone was recommending the book "DIY Financial Advisor "by Wesley R. Gray, Jack Vogel and David Foulke. I believe it was the QuantStrat blog but I might be wrong. The book is a good read and also suggest a couple of simple systems any investor can implement and follow. One system requires fundamental data like P/E or EBITDA/TEV ratios and I could trace

Quantocracy’s Daily Wrap for 12/09/2016

This is a summary of links featured on Quantocracy on Friday, 12/09/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research Review | 8 Dec 2016 | Volatility & Risk Management [Capital Spectator]

    How Should Investors Respond to Increases in Volatility? Alan Moreira (Yale University) andn Tyler Muir (UCLA) December 2, 2016 They should reduce their equity position. We study the portfolio problem of a long-horizon investor that allocates between a risk-less and a risky asset in an environment where both volatility and expected returns are time-varying. We find that investors, regardless of
  • You Probably Can’t Lose [Cantab Capital]

    What can an interesting and surprising experiment with finance students and finance professionals tell us about financial decisions and how to maximise extracting returns from low information content systems? Introduction It is well known that humans are bad at estimating probabilities. We overestimate how likely very low probability events are(1) and we get confused estimating the relative
  • Pairs Trading on ETF – EPAT Project Work [Quant Insti]

    This article is the final project submitted by the author as part of his coursework in Executive Programme in Algorithmic Trading (EPAT) at QuantInsti. You can check out our Projects page and have a look at what our students are building after reading this article. About the AuthorEPAT student Edmund Ho did his Bachelors in commerce from University of British Columbia, He completed his Masters

Quantocracy’s Daily Wrap for 12/08/2016

This is a summary of links featured on Quantocracy on Thursday, 12/08/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added (Machine Learning): Probabilistic Graphical Models

    Most tasks require a person or an automated system to reason — to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned
  • Placing your first Forex trade with Python [Jon.IO]

    Update: I updated the code so it works with Oanda's new API. Get it here Time to talk about brokers, how to place a trade programmatically and most importantly how not to get scammed. This is the third part of the series: How to build your own algotrading platform. A broker is nothing more than a company that lets you trade (buy or sell) assets on a market through their platform. What is very
  • Conditional Value-at-Risk in the Normal and Student t Linear VaR Model [Quant at Risk]

    Conditional Value-at-Risk (CVaR), also referred to as the Expected Shortfall (ES) or the Expected Tail Loss (ETL), has an interpretation of the expected loss (in present value terms) given that the loss exceeds the VaR (e.g. Alexander 2008). For many risk analysts, CVaR makes more sense: if VaR is a magical threshold, the CVaR provides us with more intuitive expectation of how much we will

Quantocracy’s Daily Wrap for 12/07/2016

This is a summary of links featured on Quantocracy on Wednesday, 12/07/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Replicating CRSP Volatility Decile Portfolios in R [Propfolio Management]

    In this post, I provide R code that enables the replication of the Center for Research in Security Prices (CRSP) Volatiliy Deciles using Yahoo! Finance data. This post is related to my last blog post in that it will generate the CRSP low volatility decile portfolio, thereby facilitating the replication of the associated EMA trading strategy. There are a few caveats to this replication: There will
  • Using recent returns for Mean Reversion [Alvarez Quant Trading]

    In most of my mean reversion posts, I use RSI(2) to determine if a stock has sold off. In this post, I will explore how to use a stocks recent return to determine if it has sold off. This will be done in way to normalize the return between low and high volatile stocks. This basic strategy has only two setup rules. Rate of Change We will be using Rate of Change (ROC) of the closing price. The
  • Ranking the top and bottom TAA strategies [Investing For A Living]

    Following up on my last post, Id like to take a deeper dive into the performance of TAA strategies. In particular, Ill take a look at the differences between the top performing TAA strategies and the bottom performing ones. There are some important points that come out of this analysis which I think are quite useful when deciding which TAA strategies are right for you. As in my last post
  • State of Trend Following Drawdown Levels Comparison [Wisdom Trading]

    A couple of months ago, we published a study on the performance of trend following after drawdowns, as the State of Trend Following index was hitting high levels of drawdown (about 2/3 of the historical maximum). We showed that in 80% of cases, the post-drawdown performance is positive, showing that investing in trend following strategies during periods of under-performance can be beneficial.

Quantocracy’s Daily Wrap for 12/06/2016

This is a summary of links featured on Quantocracy on Tuesday, 12/06/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Testing Popular Portfolio Optimization Techniques [Allocate Smartly]

    This is a test of a number of popular approaches to portfolio optimization. Each seeks to answer the question: given a universe of assets, how much should we allocate to each? Weve intentionally made these tests as simple and fair (read: unoptimized) as possible in order to best represent each technique. Here we focus on the US market, and in a future post well extend these tests to global
  • TRINdicators [Throwing Good Money]

    When I start to write a blog post, usually my process is this: Come up with a really bad pun for the title. Write the rest of it. Bad puns are an important part of finance, and life in general. A blog reader contacted me recently to chat about various technical analysis indicators, and one he mentioned was TRIN, aka the Arms Index. If youve been reading my blog awhile, you know that
  • The Look of a Winner is a Loser (h/t SystematicRelativeStrength.com) [Basis Pointing]

    Investors tend to have some pretty engrained misconceptions of what winning funds look like. For instance, winning funds lay waste to the index and category peers; they do so over the short- and long-term; they corner really well, deftly avoiding big drawdowns and rocking during rallies; they dont rattle around much; they succeed like clockwork. Theyre Tom Brady. For those who have

Quantocracy’s Daily Wrap for 12/02/2016

This is a summary of links featured on Quantocracy on Friday, 12/02/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sentiment Analysis on News Articles using Python for traders [Quant Insti]

    In our previous post on sentiment analysis we briefly explained sentiment analysis within the context of trading, and also provided a model code in R. The R model was applied on an earnings call conference transcript of an NSE listed company, and the output of the model was compared with the quarterly earnings numbers, and by charting the one-month stock price movement post the earnings call date.
  • You Would Have Missed 780% In Gains Using The CAPE Ratio, And That’s A Good Thing [Meb Faber]

    780%. Thats the amount of gains you would have missed had you followed the market timing strategy Im going to describe in the following article that utilizes the CAPE ratio. Yes, thats significant. But theres far more to this story, and I suspect that had you acted on this strategy, youd have actually been quite happy to miss out on those gains. Lets start by rewinding a few
  • November Fall for Trend Following [Wisdom Trading]

    Every month of this second half of the year seems to have a recurring theme and/or unilateral direction, rendering the YTD performance quite clearly negative. November was no different and produced a variation on the same theme, as you can see below. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for November: Wisdom State of Trend Following

Quantocracy’s Daily Wrap for 11/30/2016

This is a summary of links featured on Quantocracy on Wednesday, 11/30/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Predicting Forward 60/40 Returns [EconomPic]

    In a recent post, Long-Term Bonds Behave More Like Stocks Than You Might Think, Lawrence via Fortune Financial fame outlined: It shouldn't be surprising that long-term Treasurys exhibit almost the same degree of volatility as equities. After all, as we discussed in A Better Way to Think of Cash, Bonds, and Stocks, stocks are essentially high-duration instruments, or perpetuities. The further
  • BERT: a newcomer in the R Excel connection [R Trader]

    A few months ago a reader point me out this new way of connecting R and Excel. I dont know for how long this has been around, but I never came across it and Ive never seen any blog post or article about it. So I decided to write a post as the tool is really worth it and before anyone asks, Im not related to the company in any way. BERT stands for Basic Excel R Toolkit. Its free
  • Is the Low Volatility Anomaly driven by Lottery Demand? [Alpha Architect]

    A few years ago I wrote a summary on a working paper titled A Lottery Demand-Based Explanation of the Beta Anomaly. The paper is still a working paper, and has been updated (unfortunately they took out a neat picture from the original paper!). Here is a link to the new version of the paper, and the updated abstract is listed below. The low (high) abnormal returns of stocks with high (low)

Quantocracy’s Daily Wrap for 11/28/2016

This is a summary of links featured on Quantocracy on Monday, 11/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Very Different Kind of Trend Model [Following the Trend]

    Trend following is all about following the price. Typically the only input we need for a trend following model is the price. But what if I told that we could make a kind of trend following model which does not use the price direction as an input at all? It also has no stops and no targets. In this article, Id like to introduce you to a different way of thinking about futures trading models. It
  • Should we celebrate rising rates? [Flirting with Models]

    With 10-year rates jumping over 40bp in November, investors are beginning to talk about rising rates again. While rising rates may cause short-term volatility, coupon yield is a much more significant contributor to portfolio return over the long run. Increasing rates actually allow us to reinvest at higher coupon rates, helping offset the short-term losses. The bigger risk for asset allocators may
  • Trading Market Sentiment [Jonathan Kinlay]

    Text and sentiment analysis has become a very popular topic in quantitative research over the last decade, with applications ranging from market research and political science, to e-commerce. In this post I am going to outline an approach to the subject, together with some core techniques, that have applications in investment strategy. In the early days of the developing field of market sentiment
  • Bootstrap Aggregation, Random Forests and Boosted Trees [Quant Start]

    In a previous article the decision tree (DT) was introduced as a supervised learning method. In the article it was mentioned that the real power of DTs lies in their ability to perform extremely well as predictors when utilised in a statistical ensemble. In this article it will be shown how combining multiple DTs in a statistical ensemble will vastly improve the predictive performance on the
  • FX Market Pairs Trading Strategy [Quant Insti]

    This article is the final project submitted by the author as a part of his coursework in Executive Programme in Algorithmic Trading (EPAT) at QuantInsti. Do check our Projects page and have a look at what our students are building. About the Author Harish Maranani did his Bachelors in Technology from Acharya Nagarjuna University Electronics and Communication Engineering, and Master of Science from

Quantocracy’s Daily Wrap for 11/27/2016

This is a summary of links featured on Quantocracy on Sunday, 11/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Market Leverage as an Explanation of Low Volatility Anomaly [Quantpedia]

    The 'low-beta' or 'low-volatility anomaly' is one of the most researched in the field of 'alternative beta'. Despite strong published evidence going back to the 1970s that high beta/volatility stocks underperform relative to expectations generated by the Capital Asset Pricing Model (CAPM), the anomaly still persists. The explanations given for this are all
  • Podcast: Market Regimes with @HelixTrader [Better System Trader]

    Most trading strategies have an optimal type of market condition where they work at their absolute best, so having an understanding of market conditions and being able to detect and adapt to them can really have a huge impact on trading performance. But how can we measure market regimes properly? What techniques can we use to find that delicate balance between stability and reactivity so that it