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Best Links of the Last Two Weeks

The best quant mashup links for the two weeks ending Saturday, 03/19 as voted by our readers:

We also welcome one blog making its first ever appearance on the mashup this week:

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Votes by Clickthroughs

[click graph to enlarge]

Your votes matter to the quant community.

The graph to the right shows the average number of clickthroughs a link receives from our website (excluding RSS, Twitter and Stocktwits), broken out by the number of votes cast by our readers.

A core goal of Quantocracy is to have a positive impact on our corner of the financial world by rewarding the best work, and encouraging the best minds to keep writing.

As the graph makes clear, the citizens of Quantocracy are doing just that (way to go guys). Links with 11 or more votes receive nearly 6-times as many clickthroughs as a link with no votes (wow).

If you haven’t done so already, we invite you to register to vote and be a part of the effort. Your votes matter to the quant community.

Read on Readers!
Mike @ Quantocracy

Quantocracy’s Daily Wrap for 03/20/2016

This is a summary of links featured on Quantocracy on Sunday, 03/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Predicting Stock Market Returns Lose the Normal and Switch to Laplace [Six Figure Investing]

    Everyone agrees the normal distribution isnt a great statistical model for stock market returns, but no generally accepted alternative has emerged. A bottom-up simulation points to the Laplace distribution as a much better choice. A well-known problem in financial risk assessment is the failure of the normal distribution (also known as the Gaussian distribution) to correctly predict big up or

Quantocracy’s Daily Wrap for 03/19/2016

This is a summary of links featured on Quantocracy on Saturday, 03/19/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Reflections on Careers in Quantitative Finance [Jonathan Kinlay]

    Carnegie Mellon's Steve Shreve is out with an interesting post on careers in quantitative finance, with his commentary on the changing landscape in quantitative research and the implications for financial education. I taught at Carnegie Mellon in the late 1990's, including its excellent Master's program in quantitative finance that Steve co-founded, with Sanjay Srivastava. The
  • Price Breakout with NR7 | Trading Strategy (Setup & Entry) [Oxford Capital]

    I. Trading Strategy Developer: Toby Crabel (Setup: NR7 Pattern); Laurence A. Connors, Linda B. Raschke (Entry: Price Breakout with NR7). Source: (i) Crabel, T. (1990). Day Trading with Short Term Price Patterns and Opening Range Breakout. Greenville: Traders Press, Inc; (ii) Laurence A. Connors, Linda B. Raschke (1995). Street Smarts | High Probability Short Term Trading Strategies. M. Gordon

Quantocracy’s Daily Wrap for 03/17/2016

This is a summary of links featured on Quantocracy on Thursday, 03/17/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Meet the DIY Quants Who Ditched Wall Street for the Desert (h/t @AbnormalReturns)

    In the high desert plain of New Mexico, Roger Hunter monitors automated trades on hog futures and currency pairs. Roger Hunter in his home office. Roger Hunter in his home office. Photographer: David Paul Morris/Bloomberg Four computer screens display a dizzying array of price charts and program codes in the office of his single-story, thatched adobe home in the town of Las Cruces. Out back, where
  • Covered Calls Uncovered [Quantpedia]

    Equity index covered calls have historically provided attractive risk-adjusted returns largely because they collect equity and volatility risk premia from their long equity and short volatility exposures. However, they also embed exposure to an uncompensated risk, a nave equity market reversal strategy. This paper presents a novel performance attribution methodology, which deconstructs the
  • EP 064: The casino edge, mean reversion strategies, and how to develop robust trading systems w/ Nick Radge [Chat With Traders]

    For this episode I spoke with returning guest Nick Radge, who was originally on episode number 4. But in case you missed it; Nick is a systematic trend follower and momentum trader, most active in Australian and US equity markets. This time around, we discussed mean reversion strategies and why they may appeal to certain traders, the importance of trade frequency when developing a system, which
  • Let s make a deal : from TV shows to identifying trends [Quant Dare]

    How about trying to find any use of the famous Monty Hall problem in a stock index context? Let your imagination run First of all, some of you may be confused because neither Monty Hall problem nor Lets make a deal are familiar to you so I will refresh you what these names are concerned to. Monty Hall was a TV presenter for Lets make a deal, a famous American show in the
  • Justin’s Take: Building a Portfolio for Resolve’s March Madness Challenge [Flirting with Models]

    A Newfound, we try to embrace March Madness as an opportunity to foster some good-natured competition within the company. This year we decided to mix things up and go with our own version of ReSolve's unique March Madness Challenge. When Corey originally suggested the idea, my initial reaction is probably best conveyed with this Puff Daddy lyric: "Put your money on the table and get your

Quantocracy’s Daily Wrap for 03/16/2016

This is a summary of links featured on Quantocracy on Wednesday, 03/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Corey’s Take: Building a Portfolio for ReSolve s March Madness Challenge [Flirting with Models]

    The team over at ReSolve recently posted about their very unique March Madness Challenge. The crux of their idea is that the rules governing a more traditional bracket system is fundamentally flawed since it inherently reduces the sample size upon which skill is measured. For example, nearly everyone in the bracket will choose the #1 seed to beat the #16 seed in each region, eliminating the
  • Never Book a Loss (And Why That s Bad For You) [Throwing Good Money]

    I have got a great trading system for you. I mean, look at that equity curve! Its very straight, no drawdowns, and $30,000, compounded, became almost $120,000 over time. Whats the catch? They say (and Im not sure who they are) that the average retail investor hates to book a loss, and takes profits too soon. The reverse of the cut your losses early and let your profits run
  • Adding Stops and Scaling Out to a Mean Reversion Strategy [Alvarez Quant Trading]

    I came on an idea recently that I had tested. I have tested adding max loss stops to a mean reversion strategy, with no success. See this post for more on that. About eight years ago, I tested scaling out of trades. But this person claimed that adding the two together was how to improve a mean reversion strategy. Interesting idea I had not tested. I have a one question poll below about what to do
  • The Seven Deadly Sins of Quantitative Data Analysts [Quandl]

    Sooner or later, every quant is tempted by forbidden fruit. These all-too-human traits can permeate even the most sophisticated analysis. Keep these tips in mind as you develop strategies, and you just may turn vice into virtue. Quandl_7_sins_v04 (1) Download the printable version here.
  • Price Breakout with NR7 | Trading Strategy (Filter & Exit) [Oxford Capital]

    I. Trading Strategy Developer: Toby Crabel (Setup: NR7 Pattern); Laurence A. Connors, Linda B. Raschke (Entry: Price Breakout with NR7). Source: (i) Crabel, T. (1990). Day Trading with Short Term Price Patterns and Opening Range Breakout. Greenville: Traders Press, Inc; (ii) Laurence A. Connors, Linda B. Raschke (1995). Street Smarts | High Probability Short Term Trading Strategies. M. Gordon
  • Testing The Beta Premise [Larry Swedroe]

    One of the most important issues in finance concerns the relationship between risk and expected return. John Lintner, William Sharpe and Jack Treynor are generally given most of the credit for introducing the first formal asset pricing model, the capital asset pricing model (CAPM), which was developed in the early 1960s. The CAPM provided the first precise definition of risk and how it drives
  • Limits of Machine Learning Part 2 [MKTSTK]

    Last weeks podcast was pretty negative on the value of machine learning in trading, so this week I wanted to provide my own counterpoint and explore life within the limits I identified earlier. Specifically, I wanted to begin mapping the things machine learning algorithms might really be great at doing in the world of trading and finance. This podcast is presented in a slightly different manner

Quantocracy’s Daily Wrap for 03/13/2016

This is a summary of links featured on Quantocracy on Sunday, 03/13/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Understanding Modern Portfolio Construction (h/t @AbnormalReturns) [Pragmatic Capitalism]

    My newest research paper, Understanding Modern Portfolio Construction, is available on SSRN. This paper is the culmination of years of work and I consider it to be the most important piece of research Ive published. I wrote this paper in much the same way that I wrote my paper, Understanding the Modern Monetary System, however, since Im not an academic economist, this paper is more along the

Quantocracy’s Daily Wrap for 03/12/2016

This is a summary of links featured on Quantocracy on Saturday, 03/12/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Careers in Quantitative Finance [Quant Start]

    "I'm actually really optimistic about the future of quants. The industry is more technical than ever, and there is as much need to understand the risks in the system as ever." – Robert C. Merton, quoted in "Risk," August 2012 In 1997, when Robert Merton won the Nobel Prize in Economics for his work on the Black-Scholes option pricing formula, the demand in the finance
  • Yes, Virginia, the Markets are Mean-Reverting [Throwing Good Money]

    Heres a stupid system. In fact, I call it the Stupid 10 Days system. You look at the last ten days of trading. If the market at the closing bell is up from the market close of 10 days ago, you buy at the next open. You hold for 10 days, then sell at the next open. A classic momentum play. Wash, rinse, repeat. And remember, you have that uncle who owns a brokerage, so you dont have to

Quantocracy’s Daily Wrap for 03/10/2016

This is a summary of links featured on Quantocracy on Thursday, 03/10/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Some Limits of Machine Learning in Trading [MKTSTK]

    Aka my first podcast. Sorry Ive been AWOL for so long; Ive been deep in the lab working on my creation. As my work-life balance has careened towards work, I have found it difficult to sit down and write like I used to, so I am giving this whole podcast thing a try So without further ado, I present a thought which occurred to me whilst gazing at the bay, why is machine learning so hard in
  • What You Don t Want to Hear About Dividend Stocks [Meb Faber]

    When was the last time you had an idea that resulted in the threat of your torture? In the early 1600s, Galileo was expanding upon Copernicus idea that the earth revolves around the sun. The easy-going church was slightly less than enthused. In short, things escalated The Pope got involved There was a trial The threat of torture Eventually, Galileo was found vehemently suspect of
  • Quant Nerds Can Be Fun Too: We’re Hosting a March Madness Challenge [GestaltU]

    This is cross-posted from SkewU, our sister blog. We dont normally do this sort of thing, but this is important because: 1. You should check out SkewU, as its quite a bit different than GestaltU. Our posts over there are more diverse and whimsical if thats something that interests you. 2. And more importantly, standard March Madness bracket rules are the most awful, terrible, nonsensical,
  • Trading the Presidential Election [Jonathan Kinlay]

    There is a great deal of market lore related to the US presidential elections. It is generally held that elections are good for the market, regardless of whether the incoming president is Democrat or Republican. To examine this thesis, I gathered data on presidential elections since 1950, considering only the first term of each newly elected president. My reason for considering first terms only

Quantocracy’s Daily Wrap for 03/09/2016

This is a summary of links featured on Quantocracy on Wednesday, 03/09/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Diversification and small account size [Investment Idiocy]

    I get occasional emails asking me to cover subjects in my blog (keep them coming! I will eventually get round to them). A pretty common one runs something like this: "I understand that diversification over instruments is the best way to improve returns- you trade almost 40 futures markets, and the likes of AHL and Winton trade hundreds. But how can someone with a small account trade enough
  • Why Python Algorithmic Trading is Preferred Choice Among Traders [Quant Insti]

    To survive in the age of robots-it is necessary to learn a programming language that makes your trading algorithms smarter and not just faster. Having knowledge of a popular programming language is the building block to becoming a professional algorithmic trader. It is not just enough if a person has love for numbers. Professionals need to put the logic using numbers into a software program to
  • A Closer Look At Ben Graham s “Net Current Asset Value” (NCAV) Rule [Quantpedia]

    Following Ben Grahams net current asset value (NCAV) rule for stock selection (net net strategy), we provide evidence that buying stocks in companies with per share NCAV greater than the current share price produced superior risk-adjusted returns over the 1975- 2010 period. The risk factors that explain the returns associated with these firms include market risk, market liquidity, a
  • Trend Following in February [Wisdom Trading]

    February 2016 Trend Following: UP +4.24% / YTD: +9.99% Another strong month for the trend following index, with a similar pattern to last month: a near-double-digit spike mid-month to finish close to +5% for the month. The YTD performance is already just 0.01% shy from the double-digit barrier. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart
  • Sh** Happens (on Tuesday and Thursday Nights) [Throwing Good Money]

    This all started over the weekend, when I started wondering about a trade I had going. The trade hadnt hit my profit target on Friday and so carried over through the weekend. I started wondering about day-of-week seasonality and thought Id bust out the old charts and see whats up. Lets take a look at SPY from 2000 through the current date. If we buy on a given weekday at the open
  • Has the Value Investing Pain Train Ended? [Alpha Architect]

    Last year we highlighted what we deemed the value investing pain train. In 2015, cheap high-quality stocks started getting crushed by expensive junk stocks. Here is a recap of the carnage. In many respects, value investing is a lot like Terry Tate the huge office linebacker that would crush employees who made mistakes. His nickname: the pain train. SAT answer: Terry Tate is
  • State of Trend Following in February [Au Tra Sy]

    The strong January start carries on into February for the State of TF Index. The performance is already in double-digit territory for the Year-To-Date. Please check below for more details. Detailed Results The figures for the month are: February return: 3.59% YTD return: 12.51% Below is the chart displaying individual system results throughout February: StateTF February And in tabular format:

Quantocracy’s Daily Wrap for 03/08/2016

This is a summary of links featured on Quantocracy on Tuesday, 03/08/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • When Measures Become Targets: How Index Investing Changes Indexes [Investor’s Field Guide]

    In Vietnam, under French colonial rule, there was a rat problem. To solve the rat infestation, the French offered a bounty on rats, which could be collected by delivering a rats tail as proof of murder. Many bounties were paid out, but the rat problem didnt improve. Officials soon noticed rats running around without tailspeople were cutting off the tails and releasing the rats to breed,
  • NR7 Pattern | Trading Strategy (Setup & Exit) [Oxford Capital]

    I. Trading Strategy Developer: Toby Crabel (NR7 Pattern). Source: Crabel, T. (1990). Day Trading with Short Term Price Patterns and Opening Range Breakout. Greenville: Traders Press, Inc. Concept: Volatility cycles. Research Goal: Performance verification of the NR7 pattern. Specification: Table 1. Results: Figure 1-2. Trade Setup: The current daily range is narrower than the previous six days
  • Selected Interesting Papers from MFA Conference [MathFinance.cn]

    I just returned Beijing from the Midwest Finance Association 2016 Annual Meeting in Atlanta, it is my first time in America, and the life there is quite different from that in the British cities… few people in downtown, hard to go out without a car, people are less friendly (at least look like)… MFA annual conference provides a forum for the interaction of finance academics and practitioners
  • Estimating Return-Shortfall Risk For Portfolios [Capital Spectator]

    Failure isnt an option, but it happens. Modeling the possibility that a portfolio strategy will stumble isnt exactly cheery work, but its a productive and necessary exercise for stress testing what the future can do to the best-laid plans for investing. The good news is that theres a rainbow of options for estimating the potential for trouble. But its usually best to start with a