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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

Quantocracy’s Daily Wrap for 01/08/2018

This is a summary of links featured on Quantocracy on Monday, 01/08/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Levered ETFs for the Long Run? [Flirting with Models]

    We believe that capital efficiency should remain a paramount objective for investors. The prudent use of leverage can help investors employ more risk efficient portfolios without necessarily sacrificing potential returns. Many investors, however, do not have access to leverage (be it via borrowing or derivatives). They may, however, have access to leverage via Levered ETFs. Levered ETFs are often
  • Multi-Factor Models 101 [Factor Research]

    FactorResearch publishes a white paper on building multi-factor models. SUMMARY Three common approaches for creating multi-factor portfolios are the Combination, the Intersectional and the Sequential models The results from the Combination and Intersectional models are comparable in terms of trend Each model has its own advantages and disadvantages, the selection will depend on investor
  • Historical Results Following 4 Up Days To Begin A New Year [Quantifiable Edges]

    The simple fact that the SPX posted a gain on the first 4 days of the year is a pretty rare occurrence, with 2018 only being the 9th instance since 1961. While instances have been low, the intermediate-term performance following such strong starts to the year has been impressive. And looking at most timeframes from 50 days to 250 (or more) days, the returns have been strong. Below is the list of

Quantocracy’s Daily Wrap for 01/07/2018

This is a summary of links featured on Quantocracy on Sunday, 01/07/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Deep Learning for Trading Part 2: Configuring TensorFlow and Keras to run on GPU [Robot Wealth]

    This is the second 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
  • Academic Research Papers and Presentations Galore! [Alpha Architect]

    It is that time of year again, the American Finance Association Annual Meeting is underway. The conference is in Philadelphia, starting today (January 5) and running through Sunday (January 7). This 3-day conference has 73 sessions, 246 papers and 12 presentations with no papers (general discussions) a lot of information to cover! Here is the preliminary conference program, as well as the

Quantocracy’s Daily Wrap for 01/05/2018

This is a summary of links featured on Quantocracy on Friday, 01/05/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Beyond Excess Returns: How to Enhance Sentiment Strategies using MSCI Barra Risk Models [Raven Pack]

    We have just published a white paper showcasing the benefits of hedging a sentiment signal using risk factors from several MSCI Barra Risk Models. In this post, I provide some details on the methodology used for the strategy and on the achieved results. Excess returns: Ignores several risk factors For simplicity, researchers often use excess returns when evaluating the efficacy of a trading
  • Predicting Stock Returns Using Firm Characteristics [Alpha Architect]

    A few weeks ago, we did a deep dive into the factors versus characteristics debate. One of the reasons weve brought up this debate is due to the fact that factor loadings (from regressions) are arguably not as helpful as portfolio characteristics. In other words, knowing a portfolio P/E ratio is more informative for forecasting expected returns than knowing the HML factor loading is .6.

Quantocracy’s Daily Wrap for 01/04/2018

This is a summary of links featured on Quantocracy on Thursday, 01/04/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A novel capital booster: Sports Arbitrage [EP Chan]

    As traders, we of course need money to make money, but not everyone has 10-50k of capital lying around to start one's trading journey. Perhaps the starting capital is only 1k or less. This article describes how one can take a small amount of capital and multiply it as much as 10 fold in one year by taking advantage of large market inefficiencies (leading to arbitrage opportunities) in the
  • All About the Exits Revisited [Throwing Good Money]

    Back in June of 2016, I wrote this post about random entries and trailing exits. It turns out (on average) that you can beat buy-and-hold of the S&P 500 by simply buying members of the S&P 100 randomly, as long as you a) have a market-timing filter, and 2) have a trailing stop of 20%. Yes thats right, just pick them at random! Here are the details of that original system (its
  • Can the January effect be exploited in the market? [Mathematical Investor]

    The January effect, in common with the Halloween indicator and sell in May and go away, is a catchy, get-rich-quick investment idea adored by financial commentators because it is so easy to explain to unsophisticated readers. It rests on the claim that the U.S. stock market performs better in January, compared to the other months in the year. Unfortunately, financial reports
  • When A New Year Starts On A Positive Note [Quantifiable Edges]

    Last nights subscriber letter featured (an expanded version of) the following study, which looks at performance in the 1st couple of days following a positive 1st day of a new year. 2018-01-03 The stats and curve all suggest some immediate follow-through has been typical. There have now been 9 winners in a row, with the last loser occurring in 1998. Also notable is that 24 of the 26 instances
  • Deep Learning Insights for Factor Investing [Quantpedia]

    Deep learning is an active area of research in machine learning. I train deep feedforward neural networks (DFN) based on a set of 68 firm characteristics (FC) to predict the US cross-section of stock returns. After applying a network optimization strategy, I find that DFN long-short portfolios can generate attractive risk-adjusted returns compared to a linear benchmark. These findings underscore