Quantocracy

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Quantocracy’s Daily Wrap for 06/27/2017

This is a summary of links featured on Quantocracy on Tuesday, 06/27/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Tell-Tale Sign of Short-Run Trading [Alex Chinco]

    Motivation Trading has gotten a lot faster over the last two decades. The term short-run trader used to refer to people who traded multiple times a day. Now, it refers to algorithms that trade multiple times a second. Some people are worried about this new breed of short-run trader making stock prices less informative about firm fundamentals by trading so often on information thats

Quantocracy’s Daily Wrap for 06/26/2017

This is a summary of links featured on Quantocracy on Monday, 06/26/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Struggling Quant Episode 1: How I lost USD 500,000 [Quant Journey]

    STRUGGLING QUANT episode 1: How I lost USD 500.000 while figuring out the link between questions, math, stats, coding and trading Say that you are 30 years old and you have a good 25 years to work hard. Instead of going down the easy way of working for someone else during the day and killing time in the evenings and weekends, you have chosen the hard path of quantitative trading and started the
  • Trading Decisions of Your Stone Age Grandpa can Make You Money in Forex [Quant Journey]

    Why Ferrari or Rolex does not price their products at 149.999 or 12.999 but most of the items you see in your supermarket is priced like 4.99? Because Ferrari never likes to position itself as a bargain. Did you know that we tend to chose the price with less syllables even if the two prices have the same written lenght? These are some of the pricing strategies used by marketers. This is a very
  • Density Estimation Using Regression [Eran Raviv]

    Density estimation using regression? Yes we can! I like regression. It is one of those simple yet powerful statistical methods. You always know exactly what you are doing. This post is about density estimation, and how to get an estimate of the density using (Poisson) regression. The go-to estimator for density is currently a nonparametric (or semiparametric) kernel. This is the estimator
  • Visualizing Time Series Data in R [R Trader]

    Im very pleased to announce my DataCamp course on Visualizing Time Series Data in R. This course is also part of the Time Series with R skills track. Feel free to have a look, the first chapter is free! Course Description As the saying goes, A chart is worth a thousand words. This is why visualization is the most used and powerful way to get a better understanding of your data. After this
  • Should You Buy or Rent a GPU-Based Deep Learning Machine for Quant Trading Research? [Quant Start]

    We've recently been considering the field of deep learning as a modelling methodology for forming new quantitative trading models. Such models have been shown to be 'unreasonably effective' in the fields of computer vision, natural language processing and games of strategy. This motivates us to see if these models can be applied to quant trading strategies. We've so far looked
  • The birth of a strategy a common effort [Quant Bear]

    Lets start an experiment! This post will be the first in a series on going through the process of creating a trading strategy. It will not only detail the steps that I myself curently follow when I am building a strategy, what Im hoping for is that others contribute to the process by adding their ideas, criticism, point out logical flaws etc. Maybe someone also wants to share their process.
  • Duration Timing with Style Premia [Flirting with Models]

    In a rising rate environment, conventional wisdom says to shorten duration in bond portfolios. Even as rates rise in general, the influence of central banks and expectations for inflation can create short term movements in the yield curve that can be exploited using systematic style premia. Value, momentum, carry, and an explicit measure of the bond risk premium all produce strong absolute and
  • Academic Research Insight: The Value of Crowsourced Earnings Forecasts [Alpha Architect]

    What are the research questions? Are crowdsourced earnings forecasts from a source such as Estimize, useful in the capital markets by capturing new information about future earnings? Does a site such as Estimize add incremental accuracy when combined with the conventional, sell-side earnings forecasts such as the IBES consensus as well as a statistical model of forecasts? Is the crowdsourced

Quantocracy’s Daily Wrap for 06/22/2017

This is a summary of links featured on Quantocracy on Thursday, 06/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Rough Path Theory and Signatures Applied To Quantitative Finance – Part 4 [Quant Start]

    This is the fourth in a new advanced series of posts written by Imanol Prez, a PhD researcher in Mathematics at Oxford University and an expert guest contributor to QuantStart. In this post Imanol applies the Theory of Rough Paths to the task of predicting which country a company belongs to based on the evolution of its stock price and traded volume. – Mike. As we saw in the last article, the
  • Taming Mean Reversion s Left Tail Don t use Stop Losses! [Sutherland Research]

    Mean reversion strategies rely on the premise that extremes in price eventually revert to the mean price over time. They are effective during established markets bull, bear or sideways but unfortunately do not perform well during market regime changes or tail events. Tail events are outcomes that have a low probability of occurring, but may inflict significant damage to a portfolio when
  • Some more trading rules [Investment Idiocy]

    It is a common misconception that the most important thing to have when you're trading, or investing, systematically is good trading rules. In fact it is much, much, much more important to have a good position management framework (as discussed in my first book) and to trade a diversified set of instruments. Combine those with a couple of simple trading rules, and you'll have a pretty
  • Factor Investing: Evidence Based Insights [Alpha Architect]

    I will be talking on the Factor Investing panel at the upcoming Evidence-Based Investing Conference in Dana Point, CA next Sunday Tuesday. I am excited for the opportunity to chat, and figured I would highlight a few thoughts we have on the topic going into the event. First, what is evidence based investing? What does that even mean? If we read a index factsheet, review a long-term live
  • Iron Condor Results Summary – Part 2 – Loss Levels [DTR Trading]

    In the last article we looked at the backtest results from 600,912 iron condor trades entered between January 2007 and September 2016. The focus in that article was on win rate and normalized P&L per day for each of the 3024 variations tested. Recall that we looked at combinations of: Trade entry dates based on days to expiration (DTE) Iron condor wing widths Iron condor short strike position
  • Matrix Algebra – Linear Algebra for Deep Learning (Part 2) [Quant Start]

    Last week I posted an article, which formed the first part in a series on Linear Algebra For Deep Learning. The response to the article was extremely positive, both in terms of feedback, article views and also more broadly on social media. Many of you commented that there was "an appetite" for introductory mathematical content and this only confirms the results of the QuantStart 2017

Quantocracy’s Daily Wrap for 06/21/2017

This is a summary of links featured on Quantocracy on Wednesday, 06/21/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • In-Sample and Out-Of-Sample Testing [Alvarez Quant Trading]

    I am frequently asked if I do out-of-sample testing. The short answer is not always and when I do, it is not how most people do the test. There are lots of considerations and pitfalls to avoid when doing out-of-sample testing. Out-of-sample testing is not the panacea it is made out to be. There are lots of grey areas which I will discuss below. Definition To do in-sample (IS) and out-of-sample
  • Importing and Managing Financial Data [Foss Trading]

    I'm excited to announce my DataCamp course on importing and managing financial data in R! I'm also honored that it is included in DataCamp's Quantitative Analyst with R Career Track! You can explore the first chapter for free, so be sure to check it out! Course Description Financial and economic time series data come in various shapes, sizes, and periodicities. Getting the data into
  • Survey of Quality Investing [Quantpedia]

    Factor investing has experienced a resurgence in popularity under the moniker smart beta. Several traditional factors, such as value, size, momentum, and low beta, are well defined and have been heavily researched in academia as return anomalies for many decades. These factors have also been exploited by practitioners as quantitative strategies for enhancing returns. Today, these factors

Quantocracy’s Daily Wrap for 06/20/2017

This is a summary of links featured on Quantocracy on Tuesday, 06/20/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • An Out of Sample Update on DDN s Volatility Momentum Trading Strategy and Beta Convexity [QuantStrat TradeR]

    The first part of this post is a quick update on Tony Coopers of Double Digit Numericss volatility ETN momentum strategy from the volatility made simple blog (which has stopped updating as of a year and a half ago). The second part will cover Dr. Jonathan Kinlays Beta Convexity concept. So, now that I have the ability to generate a term structure and constant expiry contracts, I decided
  • Dynamic Asset Allocation for Practitioners, Part 2: The Many Faces of Price Momentum [Invest Resolve]

    In our last post, we covered the importance of a well-designed investment universe as a precondition for thoughtful diversification. In this second article on Dynamic Asset Allocation for Practitioners we will explore several methods for measuring price momentum to compare and contrast their utility under different portfolio concentration and asset universe specifications. What is momentum?
  • You Don’t Want to Buy Vol, You Want to Sell Vol! [Meb Faber]

    That headline was a response I received from a handful of friends regarding my last post on buying puts as tail risk insurance. And I agree. Well, sort of. Its been long known that there exists a premium for selling insurancehey, otherwise why would anyone do it? Now what if you could combine the best of both? Selling vol to capture the premium but buying vol to protect against big down
  • Isolating the Monkey Effect [Markov Processes]

    Continuing our exploration into the smart beta segment (Part 1, Part 2), in this third post we introduce a simple IQ Test that can help investors and managers measure the smartness of the increasing number of non-cap-weight rules-based products on the market. There are numerous arguments in circulation saying that smart beta in general isnt particularly smart. A prominent one,

Quantocracy’s Daily Wrap for 06/19/2017

This is a summary of links featured on Quantocracy on Monday, 06/19/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to come up with quant trading ideas? [Cuemacro]

    I used to play the violin. I really enjoyed it. However, there was something that was very clear to anyone listening to me playing, who perhaps would not have enjoyed it quite as much. The sound which came out of the violin, might have been technically similar to the music on a sheet in front of me, but it didnt sound perhaps quite like the composer intended. The tuning wasnt that
  • Is Your Multi-Asset Strategy Really Multi-Asset? [Flirting with Models]

    The term multi-asset appears in many investment strategies and applies to both balanced funds and target date retirement funds. However, multi-asset strategies may be concentrated in a limited set of asset classes, and the performance of these asset classes may be driven by an even more limited set of risk factors. By looking through the lenses of performance, asset classes, risk factors,
  • Academic Research Insight: The Strategic Timing of Earnings News [Alpha Architect]

    Title: FURTHER EVIDENCE ON THE STRATEGIC TIMING OF EARNINGS NEWS: JOINT ANALYSIS OF WEEKDAYS AND TIMES OF DAY Authors: RONI MICHAEY, AMIR RUBIN, ALEXANDER VEDRASHKO Publication: JOURNAL OF ACCOUNTING AND ECONOMICS, 2016 (version here) What are the research questions? Do managers act to strategically time negative earnings announcements? Is there a strategic weekday (Monday through Friday) and/or
  • Machine Learning In Python for Trading [Quant Insti]

    At the end of my last blog, I had asked a few questions. Now, I will answer them all at the same time. I will also discuss a way to detect the regime/trend in the market without training the algorithm for trends. But before we go ahead, please use a fix to fetch the data from Google to run the code below. data from Google to run the code Trading Using Machine Learning In Python Part-2Click To

Quantocracy’s Daily Wrap for 06/18/2017

This is a summary of links featured on Quantocracy on Sunday, 06/18/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Algorithmic Options Trading, Part 2 [Financial Hacker]

    In this second part of the Algorithmic Options trading series well look more closely into option returns. Especially into the methods of combining different option types for getting user-tailored profit and risk curves, which gives options an interesting advantage over other financial instruments. Options traders know combinations with funny names like Iron Condor or Butterfly, but

Quantocracy’s Daily Wrap for 06/16/2017

This is a summary of links featured on Quantocracy on Friday, 06/16/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Nervous About The Market? It Might Be Time for This Strategy [Meb Faber]

    When the tech bubble collapsed back in 2000, the Nasdaq fell from 5,132 to just 1,470 a few months later. Many popular stocks found their market prices gutted. For example, Cisco lost 86% of its market cap, while Amazon fell over 90% from $107 to $7. Losses such as these decimated investor portfolios. In 2008, it happened again. The average diversified U.S. stock fund fell a whopping 38 percent.
  • Research Review | 16 June 2017 | Yield Curve Analysis [Capital Spectator]

    Monetary Policy Uncertainty and Bond Risk Premium Fuwei Jiang (Central University of Finance and Economics) and Guoshi Tong (Renmin University) October 1, 2016 We show that uncertainty of monetary policy (MPU) commands a risk premium in the US Treasury bond market. Using the news based MPU measure in Baker, Bloom, and Davis (2016) to capture monetary policy uncertainty, we find that MPU forecasts
  • Active Share: Does it Predict Fund Performance? [Alpha Architect]

    The Holy Grail for mutual fund investors is the ability to identify in advance, which of the active mutual funds (or ETFs nowadays) will outperform in the future. The evidence suggests this task is almost impossible. To date, the overwhelming body of academic research has demonstrated that past performance not only doesnt guarantee future performance (as the required SEC disclaimer states), but

Quantocracy’s Daily Wrap for 06/15/2017

This is a summary of links featured on Quantocracy on Thursday, 06/15/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Scalars, Vectors, Matrices and Tensors – Linear Algebra for Deep Learning (Part 1) [Quant Start]

    Back in March we ran a content survey and found that many of you were interested in a refresher course for the key mathematical topics needed to understand deep learning and quant finance in general. Since deep learning is going to be a big part of this year's content we thought it would be worthwhile to write some beginner tutorials on the key mathematical topicslinear algebra, calculus

Quantocracy’s Daily Wrap for 06/14/2017

This is a summary of links featured on Quantocracy on Wednesday, 06/14/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fractal Adaptive Moving Average | Trading Strategy (Setup) [Oxford Capital]

    I. Trading Strategy Developer: John Ehlers. Source: Ehlers, J., FRAMA: Fractal Adaptive Moving Average. Concept: Trend following trading strategy based on adaptive price filters. Research Goal: To verify performance of the Fractal Adaptive Moving Average (FRAMA). Specification: Table 1. Results: Figure 1-2. Trade Setup: Long Trades: Close[i 1] > Entry_Upper_Band[i 1]. Short Trades:
  • “Passive” Investing: Theory and Practice in a Global Market [Alpha Architect]

    Purely passive investing is theoretically plausible, but practically impossible. That said, the practical implementations can often be good enough. As a theoretical index investor, you deploy capital, take a long snooze, and wake up some day to consume your portfolio. Unfortunately, the world doesnt work like. Allocations change, life happens, and as we cover in this blog post, there are
  • Portfolio Weighting Schemes for Commodity Futures Risk Premia [Quantpedia]

    We examine whether and to what extent successful equities investment strategies are transferrable to the commodities futures market. We investigate a total of 7 investment strategies that involve optimization and mean-variance timing techniques. To account for the unique characteristics of the commodity futures market, we propose a novel method of classification based on momentum or term structure
  • Podcast: Optimal bet sizing – lessons from biased coin flip experiment w/ Victor Haghani [Chat With Traders]

    Victor Haghani began his career at Salomon Brothers in 1984, starting out in a research role before joining their prop trading desk. In 1992, Victor left Salomon to become one of the founding partners of Long Term Capital Management LTCM was an incredibly successful hedge fund, up until 1998, when it failed in a spectacular fashion. Causing the Federal Reserve to step in and organize a bailout,