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Quantocracy’s Daily Wrap for 01/23/2018

This is a summary of links featured on Quantocracy on Tuesday, 01/23/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Deep Learning for Trading Part 3: Feed Forward Networks [Robot Wealth]

    This is the third in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful signals from historical market data. If you
  • Can mutual fund investors beat the market? [Mathematical Investor]

    Many individual investors employ mutual funds as an alternative to direct ownership of stocks or bonds. Indeed, mutual funds have some advantages: Diversity: Even a single fund can encapsulate a large sector of the market. Peace of mind: One is less likely to stress out about sudden bad news regarding a particular firm if one owns shares in it only indirectly as part of a large mutual funds
  • When New Years Begin With A Steady Stream Of Up Days [Quantifiable Edges]

    The start to 2018 has been fairly remarkable. The SPX has only closed down 3 days so far, while closing up 11 days. That is a substantial hot streak, and one might think that such a strong run to start the year would almost certainly have to pullback soon. So I checked. 2018-01-23 The imminent pullback theory certainly does not seem to work here. All 6 previous instances were higher 2, 7, 8, and 9

Quantocracy’s Daily Wrap for 01/22/2018

This is a summary of links featured on Quantocracy on Monday, 01/22/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantifying Timing Luck [Flirting with Models]

    When two managers implement identical strategies, but merely choose to rebalance on different days, we call variance between their returns timing luck. Timing luck can easily be overcome by using a method of overlapping portfolios, but few firms do this in practice. We believe the magnitude of timing luck impact is much larger than most believe, particularly in tactical strategies. We derive
  • Gold Price Prediction Using Machine Learning In Python [Quant Insti]

    Is it possible to predict where the Gold price is headed? Yes, lets use machine learning regression techniques to predict the price of one of the most important precious metal, the Gold. We will create a machine learning linear regression model that takes information from the past Gold ETF (GLD) prices and returns a prediction of the Gold ETF price the next day. GLD is the largest ETF to invest
  • Can You Short The S&P Successfully? (with @DBurgh) [System Trader Success]

    A short signal for the S&P500? Believe it or not, some do exist. Although these can often be hard to find they can also contribute greatly to your success so tons of traders search endlessly for a complimentary short system or two for their portfolios. I want to talk about a simple short edge that I have recently been tweeting about for the past year or so as it has been an interesting
  • A Historical Look At Market Reaction To New Fed Chairmen [Quantifiable Edges]

    Jerome Powell is expected to take over for Janet Yellen as the new Fed chairman on Feb 3rd. A few days ago in the letter I looked at SPX performance after a new chairman takes over. I used the SPX and looked back to 1970. Tonight I decided to take the analysis back to 1923 using my Dow data. Like with the SPX, I found the first few weeks to be the most consistent and interesting data. Once we look
  • The Government Shutdown [Highly Evolved Vol]

    Over the last ten years, a number of congress members have been elected on a fairly nihilistic platform, voting against practically any spending bill (unless it buys tanks). This is a good way to get elected but it makes it hard to govern. The government has to spend money. While the Republicans have majorities in both houses, there is a huge difference in political philosophies between members of

Quantocracy’s Daily Wrap for 01/19/2018

This is a summary of links featured on Quantocracy on Friday, 01/19/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mixture Model Trading (Part 4 – Strategy Implementation) [Black Arbs]

    This notebook will walkthrough the algorithm implementation process on the quantconnect platform. Please be advised that this notebook will not actually run the algorithm as I have not installed the quantconnect backtesting engine locally. This is a demonstration of the process. The script is available to copy and paste into the quantconnect environment within the ./scripts/ directory of the
  • Value and Momentum Factor Portfolio Construction: Combine, Intersect or Sequence? [Alpha Architect]

    Wes asked that I contribute to the ongoing debates regarding the construction of value and momentum portfolios. There are three key research pieces on the topic, all with different viewpoints: Alpha Architects take AQRs take Newfound Researchs Take I encourage everyone to dig into the three articles above and then tackle my article below. And if you are interesting in learning more about
  • Most popular posts 2017 [Eran Raviv]

    Writing this, I cant believe how quickly the year 2017 has gone by. Also weird, we are already three weeks into 2018, unreal. Time flies when youre having fun I guess. The analytics report shows that the three most popular posts for 2017 are: Understanding False Discovery Rate (4 minutes average time on page) R vs MATLAB round 4 Understanding K-Means Clustering Own personal
  • Research Review | 19 January 2018 | The Business Cycle [Capital Spectator]

    Fama-French Factors and Business Cycles Arnav Sheth and Tee Lim (Saint Marys College of California) December 4, 2017 We examine the behavior of Fama-French factors across business cycles measured in various ways. We first split up the business cycles into four stages and examine the cumulative returns of factors in each of those stages. We then look at the behavior of the factors after a yield

Quantocracy’s Daily Wrap for 01/18/2018

This is a summary of links featured on Quantocracy on Thursday, 01/18/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mixture Model Trading (Part 3 – Strategy Research) [Black Arbs]

    This is the beginning of a three part series that I completed towards the end of 2017 as a learning module for Quantinsti.com. The purpose of the series is to demonstrate a research workflow focused around the theory and application of mixture models as the core framework behind a algorithmic trading strategy. Below is a quote taken from the README of the github repo: The primary goal of this
  • The Mother of All Momentum Research Reports. A Must Read! [Alpha Architect]

    J.P. Morgan researchers, Marko Kolanovic and Zhen Wei, produced an incredibly detailed report on all aspects of momentum (one of our favorite topics!) Here is a link to the report 188 pages of pure effort and information. Here is a summary of what is examined in the research: As the virtually unlimited number of possible implementations may confound an investor, we first provide a framework for
  • Crash Sensitivity Explains the Momentum Effect in Stocks [Quantpedia]

    This paper proposes a risk-based explanation of the momentum anomaly on equity markets. Regressing the momentum strategy return on the return of a self-financing portfolio going long (short) in stocks with high (low) crash sensitivity in the USA from 1963 to 2012 reduces the momentum effect from a highly statistically significant 11.94% to an insignificant 1.84%. We find additional supportive
  • Highly Unusual Behavior Between SPX and VIX [Quantifiable Edges]

    Wednesday saw both SPX and VIX close at 40-day highs (about 2 months). Since they commonly trade opposite each other, to have them both be extended up like this is very rare. In fact, it has only happened 4 other times. Below is a list of those instances along with their 4-day results. 2018-01-18 The takeaway here is not that they all lost money over the next few days. Though that is notable, it

Quantocracy’s Daily Wrap for 01/17/2018

This is a summary of links featured on Quantocracy on Wednesday, 01/17/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mixture Model Trading (Part 2 – Gaussian Mixtures) [Black Arbs]

    This is the beginning of a three part series that I completed towards the end of 2017 as a learning module for Quantinsti.com. The purpose of the series is to demonstrate a research workflow focused around the theory and application of mixture models as the core framework behind a algorithmic trading strategy. Below is a quote taken from the README of the github repo: The primary goal of this
  • Covered Call Options Strategy using Machine Learning [Quant Insti]

    A covered call is used by an investor to make some small profit while holding the stock. Mostly the reason why a trader would want to create a covered call is because the trader is bullish on the underlying stock and wants to hold for long-term, but the stock doesnt pay any dividend.The stock is expected to go up over a period of next 6 months, and in the meantime, you would want to use this
  • Cointegration in Economy: a long-term relationship [Quant Dare]

    The relationship between series can be measured by different methods. The most common is to check if both series move in the same way. Wed like to go further, and see if the difference between them is always the same. We call it cointegration. In many cases, we are interested in expressing one series according to another, or looking for common characteristics from which we can draw conclusions

Quantocracy’s Daily Wrap for 01/14/2018

This is a summary of links featured on Quantocracy on Sunday, 01/14/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Investing: Gross to Net Returns [Factor Research]

    Long-short multi-factor portfolios generate attractive returns before fees Returns are much less attractive post fees charged historically However, some fees in the long-short space are likely justified given higher complexity INTRODUCTION Reality is the murder of a beautiful theory by a gang of ugly facts (Robert Glass, 2002). Factor investing can be considered one of the beautiful theories of

Quantocracy’s Daily Wrap for 01/12/2018

This is a summary of links featured on Quantocracy on Friday, 01/12/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Replicating Volatiltiy ETN Returns From CBOE Futures [QuantStrat TradeR]

    This post will demonstrate how to replicate the volatility ETNs (XIV, VXX, ZIV, VXZ) from CBOE futures, thereby allowing any individual to create synthetic ETF returns from before their inception, free of cost. So, before I get to the actual algorithm, it depends on an update to the term structure algorithm I shared some months back. In that algorithm, mistakenly (or for the purpose of

Quantocracy’s Daily Wrap for 01/11/2018

This is a summary of links featured on Quantocracy on Thursday, 01/11/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Long-Short Equity Strategy using Ranking: Simple Trading Strategies Part 4 [Auquan]

    In the last post, we covered Pairs trading strategy and demonstrated how to leverage data and mathematical analysis to create and automate a trading strategy. Long-Short Equity Strategy is a natural extension of Pairs Trading applied to a basket of stocks. Download Ipython Notebook here. Underlying Principle Long-Short equity strategy is both long and short stocks simultaneously in the market.
  • A Down Day After A Persistent Upmove To New Highs [Quantifiable Edges]

    One compelling study from last nights Quantifinder suggested the recent persistent upmove is unlikely to abruptly end. (This is a theme we have seen many times over the years.) It considers what happens after the market moves up at least 5 days in a row to a 50-day high, and then pulls back. I have updated the stats in the table below. 2018-01-11 We see here a decent edge that becomes stronger

Quantocracy’s Daily Wrap for 01/10/2018

This is a summary of links featured on Quantocracy on Wednesday, 01/10/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Plotting Volatility Surface for Options [AAA Quants]

    This blog post is a revised edition of Toms original blog post with a newer data set. More information, source code & inspiration can be found here. Code for this blog post is in our Github repository. Options are complex instruments with many moving parts. Specifically, options are contracts that grant the right, but not the obligation to buy or sell an underlying asset at a set price on
  • How to turn a losing strategy to a winning strategy with commissions [Alvarez Quant Trading]

    A mean reversion strategy I trade was developed with another researcher. This strategy enters on a further intraday weakness with a limit order and typically exits a few days later when the stock bounces. Recently this researcher sent me and email saying Try the strategy as a day trade. Enter at the open and exit at the close. Surprisingly good results. Of course, this peaked my interest and
  • Why You Need Independent Verification of Strategy Results [Allocate Smartly]

    Our site serves a lot of purposes for tactical asset allocation (TAA) investors: curating the best published strategies, testing those strategies with superior historical data, providing the ability to combine strategies into custom portfolios, and tracking even the most complex strategies in near real-time. But maybe the most important function we serve is simply independent verification of
  • How Bad Are False Positives, Really? [Alex Chinco]

    Imagine youre looking for variables that predict the cross-section of expected returns. No search process is perfect. So, as you work, you will inevitably uncover both tradable anomalies as well as spurious correlations. To figure out which are which, you regress returns on each variables that you come across: \begin{equation*} r_n = \hat{\alpha} + \hat{\beta} \cdot x_n + \hat{\epsilon}_n

Quantocracy’s Daily Wrap for 01/09/2018

This is a summary of links featured on Quantocracy on Tuesday, 01/09/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Big Data and Machine Learning Conference in London [Raven Pack]

    On the back of our recent event in New York, we are bringing the big data & machine learning revolution to London this April 24th. Register to receive updates on the agenda! Register Now The London Revolution More than 750 finance professionals registered to attend the New York Revolution but we could only accommodate one third at the conference venue. Now in London, you have the opportunity
  • R/Finance 2018: Call for Papers [Foss Trading]

    The tenth annual R/Finance conference for applied finance using R will be held June 1 and 2, 2018 in Chicago, IL, USA at the University of Illinois at Chicago. The conference will cover topics including portfolio management, time series analysis, advanced risk tools, high-performance computing, market microstructure, and econometrics. All will be discussed within the context of using R as a
  • The Value Effect and Macroeconomic Risk [Alpha Architect]

    It has been well-documented that value stocks have provided higher expected returns than growth stocks. However, there is a great debate about the source of that premium: Is it risk-based or is it related to behavioral errors that create persistent mispricings? There are many papers presenting arguments on both sides. Hence the debate. Cathy Xuying Cao, Chongyang Chen and Vinay Datar contribute to
  • State of Trend Following in December [Au Tra Sy]

    Near-perfect neutral month for the State of Trend Following index to close the year just in negative double-digit territory. 2017 was not the best year for the strategy. Lets see what 2018 has in store. Happy new year to all readers and best wishes for profitable trading. Please check below for more details. Detailed Results The figures for the month are: December return: 0.04% YTD return:
  • Yes, Departing Outside Directors Are Aware of Fraud Before They Resign [Alpha Architect]

    What are the research questions? Is the rate of turnover for outside directors unusually high either before fraud is discovered by the firm, or during its commission? Are there regularities in the characteristics of outside directors who depart during the period in which the financial fraud is committed? Are there regularities in board governance variables related to the turnover of outside