Quantocracy

Quant Blog Mashup

ST

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

Quantocracy’s Daily Wrap for 01/02/2018

This is a summary of links featured on Quantocracy on Tuesday, 01/02/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in December [Allocate Smartly]

    Blogging was light in December. We spent the month working on the launch of a new fintech project that many of our readers will be excited about. Well be sharing details in the coming month and getting back to our regular blogging and site development schedule. Allocate Smartly This is a summary of the recent performance of a wide range of excellent tactical asset allocation strategies.
  • A Null Hypothesis for the New Year [Flirting with Models]

    In statistics, the null hypothesis is the default statement that you test with data. From this test, you can either reject the null hypothesis in support of an alternative or assert that there is not enough evidence to believe anything other than the null hypothesis with a certain degree of confidence. In an industry driven by speculation and talking heads pushing the next hot investments, an
  • Factor Olympics 2017 [Factor Research]

    2017 was a positive year for most factors Quality, Growth and Momentum showed the strongest performance Value, Dividend Yield and Size generated negative returns INTRODUCTION We present the performance of seven well-known factors on an annual basis for the last 10 years and the full-year 2017. It is worth mentioning that not all factors have strong academic support, e.g. Growth lacks a long-term

Quantocracy’s Daily Wrap for 01/01/2018

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

  • Deep Learning for Trading: Part 1 [Robot Wealth]

    In the last few years, deep learning has gone from being an interesting but impractical academic pursuit to an ubiquitous technology that touches many aspects of our lives on a daily basis including in the world of trading. This meteoric rise has been fuelled by a perfect storm of: Frequent breakthroughs in deep learning research which regularly provide better tools for training deep neural

Quantocracy’s Daily Wrap for 12/29/2017

This is a summary of links featured on Quantocracy on Friday, 12/29/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean Reverting and Trending Properties of SPX and VIX [Relative Value Arbitrage]

    In the previous post, we looked at some statistical properties of the empirical distributions of spot SPX and VIX. In this post, we are going to investigate the mean reverting and trending properties of these indices. To do so, we are going to calculate their Hurst exponents. There exist a variety of techniques for calculating the Hurst exponent, see e.g. the Wikipedia page. We prefer the method
  • Best of Research Review 2017 [Capital Spectator]

    So many research papers, so little time. How do you separate the wheat from the chaff? You might start with the following five economic and financial papers that appeared in The Capital Spectators Research Review column in 2017. In a sea of newly minted studies over the past 12 months, these titles stand out as worthy of a second read. Time-Varying Risk Premiums and Economic Cycles Thomas
  • The Tax Efficiency of Long-Short Strategies [Alpha Architect]

    Conventional wisdom can be defined as ideas that are so accepted that they go unquestioned. Unfortunately, conventional wisdom is often wrong. Two great examples are that millions of people once believed the conventional wisdom that the Earth is flat, and millions also believed that the Earth is the center of the universe. Much of todays conventional wisdom about investing is also wrong. The
  • Persistance in Cryptocurrencies [Quantpedia]

    This paper examines persistence in the cryptocurrency market. Two different longmemory methods (R/S analysis and fractional integration) are used to analyse it in the case of the four main cryptocurrencies (BitCoin, LiteCoin, Ripple, Dash) over the sample period 2013-2017. The findings indicate that this market exhibits persistence (there is a positive correlation between its past and future

Quantocracy’s Daily Wrap for 12/27/2017

This is a summary of links featured on Quantocracy on Wednesday, 12/27/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Deep Learning Systems for Bitcoins Part 1 [Financial Hacker]

    Since December, bitcoins can not only be traded at more or less dubious exchanges, but also as futures at the CME and CBOE. And already several trading systems popped up for bitcoins and other cryptocurrencies. None of them can claim big success, with one exception. There is a strategy that easily surpasses all bitcoin systems and probably all other known historical trading systems. Its name:
  • Predicting Long Run Stock Returns? It’s All About the Payouts and the Real Economy [Alpha Architect]

    What are the research questions? Given the prevalence of buybacks as a form of corporate payouts, should they be explicitly included in supply-side models such as the dividend discount model (DDM) used to forecast of stock returns? Does the same superior performance extend to the prediction of short-term changes in expected returns? What are the Academic Insights? YES. Dividends, as a payout
  • A Not-so Merry VIX-mas Part 2 [Quantifiable Edges]

    Yesterday I decided to examine performance of XIV during the last few days of the year. The thought was that we are now in a time period that is generally regarded as seasonally bullish. Additionally, volume and volatility are often light this week with many traders on vacation. So I thought with low volatility and bullish seasonality, it could be a bullish time for XIV (the inverse-VIX etf). I