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Quantocracy’s Daily Wrap for 10/13/2019

This is a summary of links featured on Quantocracy on Sunday, 10/13/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Modelling Bid/Offer Spread In Equities Trading Strategy Backtest [Python For Finance]

    In this blog post I wanted to run a couple of quick experiments to see how clearly I was able to highlight the importance of incorporating various elements and components into a backtest that I admittedly often overlook in most of my posts that is I make the assumption that they will be dealt with by the reader at some point down the line, but choose not to include them for sake of simplicity.
  • Another Method of Creating Synthetic Data [Dekalog Blog]

    Over the years I have posted about several different methodologies for creating synthetic data and I have recently come across yet another one which readers may find useful. One of my first posts was Creation of Synthetic Data, which essentially is a random scrambling of historic data for a single time series with an attempt to preserve some of the bar to bar dependencies based upon a bar's
  • Crowded trades: measure and effect [SR SV]

    One measure of the crowdedness of trades in a portfolio is centrality. Centrality is a concept of network analysis that measures how similar one institutions portfolio is to its peers by assessing its importance as a network node. Empirical analysis suggests that [1] the centrality of individual portfolios is negatively related to future returns, [2] mutual fund holdings become more similar

Quantocracy’s Daily Wrap for 10/11/2019

This is a summary of links featured on Quantocracy on Friday, 10/11/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Using Firm Characteristics to Enhance Momentum Strategies [Alpha Architect]

    Research into the momentum factor continues to demonstrate its persistence and pervasiveness, including across factors. Recent papers have focused on trying to identify ways to improve the explanatory power and performance of momentum strategies. Prior research on Momentum The study Momentum Has Its Moments found that momentum strategies can be improved by scaling for volatilitytargeting
  • Momentum Explains a Bunch Of Equity Factors [Quantpedia]

    Financial academics have described so many equity factors that the whole universe of them is sometimes called factor zoo. Therefore, it is no surprise that there is a quest within an academic community to bring some order into this chaos. An interesting research paper written by Favilukis and Zhang suggests explaining a lot of equity factors with momentum anomaly. They show that very often,

Quantocracy’s Daily Wrap for 10/09/2019

This is a summary of links featured on Quantocracy on Wednesday, 10/09/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Master of the Robots on machine learning in finance [Mathematical Investor]

    Marcos Lopez de Prado, who was named Quant of the Year for 2019 by the Journal of Portfolio Management, is widely regarded as one of the leading quantitative mathematicians in todays financial world. He currently ranks #1 among authors in the economics field on the SSRN research network, as measured by downloaded articles within the past 12 months. In a Bloomberg article titled The Master of
  • An age prediction solution applied to rank returns [Quant Dare]

    Image processing is one of the hot topics in AI research, alongside with reinforcement learning, ethics in AI and many others. A recent solution to perform ordinal regression on age of people has been published, and in this post we apply that technique to financial data. Ranking classification is an usual challenge in companies and research hasnt stopped looking for better ways to solve this

Quantocracy’s Daily Wrap for 10/08/2019

This is a summary of links featured on Quantocracy on Tuesday, 10/08/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building a Basic Cross-Sectional Momentum Strategy Python Tutorial [Quantoisseur]

    In this tutorial we utilize the free Alpha Vantage API to pull price data and build a basic momentum strategy that is rebalanced weekly. This approach can be adapted for any feature youd like to explore. Let me know what youd like to see in the next video!
  • An Analysis of Graham s Net-Nets: Outdated or Outstanding? [Alpha Architect]

    In an earlier post we analyzed the prominent and often-cited study on net-nets conducted by Henry R. Oppenheimer from the Financial Analysts Journal (1986). In this post, we analyze the article Grahams Net-Nets: Outdated or Outstanding? by James Montier. The objective of the article was to examine the performance of securities that were trading at no more than two-thirds of their

Quantocracy’s Daily Wrap for 10/07/2019

This is a summary of links featured on Quantocracy on Monday, 10/07/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Concurrent Scalping Algo Using Async Python [Alpaca]

    One of the advantages of running automatic trading strategies is that you can quickly and consistently act on price action. Even if you have enough time to trade the same idea manually, you need to watch the market movement very closely and keep paying attention to multiple monitors. With algorithmic trading, you can automate this. Also, please keep in mind that this is only an example to help get
  • The Hidden Truths About Stop loss In Trading [Quant Insti]

    A stop-loss order, or stops as is generally said, is an order placed with the broker to sell (or buy) if the stock of a company which you hold, reaches a pre-determined price in order to avoid large losses. In the trading world, the use of stops is seen as an essential part of risk control and money management. And usually, they take the utility of stops to be self-evident. How can you go broke
  • A Framework for Creating Model Portfolios [Alpha Architect]

    Asset allocation is a very important decision for investors. Model portfolios are constructed with an optimized asset allocation process to help meet investor needs and preferences. The authors investigate the following research question: How does one construct a model portfolio? What are the Academic Insights? This article lays out a framework for how to construct an optimal portfolio. This
  • 9 Things That Get Me Fired Up About Being a Quant Investor Today [Two Centuries Investments]

    As trading costs have just hit zero, and passive investing overtook active in August, the investment industry is braced for further pressure to deliver alpha after fees. In my view, the potential to build great models today is huge, but constrained by the research cultures of most firms. Here is what gives me hope. Data. a) availability of amazingly unique data that 20 years ago you couldnt
  • Macro Timing with Trend Following [Flirting with Models]

    While it may be tempting to time allocations to active strategies, it is generally best to hold them as long-term allocations. Despite this, some research has shown that there may be certain economic environments where trend following equity strategies are better suited. In this commentary, we replicate this data and find that a broad filter of recessionary periods does indeed show this for
  • AI and Data Science in Trading Conference London [Cuemacro]

    AIDST has quickly established itself as one of the most important finance based data science conferences in the calendar. I recently attended and presented at the recent AIDST London event in September. The conference featured a mixture of both high-level talks and also more technical sessions. The conference began with a presentation from Manoj Saxena, currently at AI Global and formerly the head
  • Low Volatility vs Option-Based Strategies [Factor Research]

    Option-based strategies have similar characteristics to Low Volatility portfolios Combining these reduces idiosyncratic strategy risks The combinations feature higher risk-adjusted returns and lower drawdowns than the S&P 500 INTRODUCTION Some investment products and strategies can be considered toxic given their history on Wall Street. Portfolio insurance is rarely used in marketing

Quantocracy’s Daily Wrap for 10/03/2019

This is a summary of links featured on Quantocracy on Thursday, 10/03/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Integrating R with the Zorro Backtesting and Execution Platform [Robot Wealth]

    n the last two posts, we implemented a Kalman filter in R for calculating a dynamic hedge ratio, and presented a Zorro script for backtesting and trading price-based spreads using a static hedge ratio. The goal is to get the best of both worlds and use our dynamic hedge ratio within the Zorro script. Rather than implement the Kalman filter in Lite-C, its much easier to make use of Zorros R
  • Alternative Investments – A Field Manual [Alpha Architect]

    Its not a perfect world out there and often times alternative funds are mischaracterized, misused, and not put through a rigorous enough portfolio construction process. Its my hope that I can forewarn you of the proverbial landmines and better prepare you to invest (or not invest) in the alternative space.(1)(2) Identifying a Unique Return Stream The first step most analysts make with
  • Continuous Futures Contracts Methodology for Backtesting [Quantpedia]

    The problem with spliced futures No doubt, the correct datasets are the key when one does some analysis in the financial markets. For some financial instruments, the data can be found for free and ready for the upcoming process, but on the other hand, some instruments are more complicated. Nowadays, futures contracts are widely spread and popular among practitioners. However, each delivery month
  • New book: Leveraged Trading [Investment Idiocy]

    This month* marks the release of my third book, with the snappy title "Leveraged Trading", and the slightly less snappy subtitle "A professional approach to trading FX, stocks on margin, CFDs, spread bets and futures for all traders". Photo courtesy of Harriman House. As you can see, the book makes an excellent books-stand for itself * Official publication date is 29th October.

Quantocracy’s Daily Wrap for 10/01/2019

This is a summary of links featured on Quantocracy on Tuesday, 10/01/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Ideal Cyclic Tau Embedding as Times Series Features [Dekalog Blog]

    Continuing on from my Ideal Tau for Time Series Embedding post, I have now written an Octave function based on these ideas to produce features for time series modelling. The function outputs are two slightly different versions of features, examples of which are shown in the following two plots, which show up and down trends in black, following a sinusoidal sideways market partially visible to the
  • Tactical Asset Allocation in September [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent Tactical Asset Allocation (TAA) strategies, net of transaction costs. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Learn more about what we do or let AllocateSmartly help
  • Factor Olympics Q3 2019 [Factor Research]

    Most factors generated positive returns in Q1-3 2019 Low Volatility produced the best and Value the worst performance year-to-date The factor rotation from Momentum into Value in Q3 was short-lived INTRODUCTION We present the performance of five well-known factors on an annual basis for the last 10 years. We only present factors where academic research highlights positive excess returns across
  • Short-Duration Stock Anomaly: Risk or Mispricing [Alpha Architect]

    Some background on Bond duration: Duration measures bonds price sensitivity to interest rates changes. Its estimated based on the discounted expectations of the bond future cash flows and expressed in the number of years. The longer the duration, the higher the bond interest rate risk. (read more: Bond Performance when Interest Rates Spike) Dechow, Sloan, and Soliman (2004) employs this

Quantocracy’s Daily Wrap for 09/30/2019

This is a summary of links featured on Quantocracy on Monday, 09/30/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building a garden “trading” office (off topic but fun) [Investment Idiocy]

    (Both) regular followers of this blog will have been on tenterhooks for many months now, waiting for my next post. I have been busy! First of all, I've been finishing my third book. More detail on that later, in the next post. I've also had a fair bit of holiday time. But mainly over the summer I've been building my own garden office. Now a post about constructing a garden office
  • Quality: Independent attributes or a real factor? [Alpha Architect]

    The authors do a very nice survey on measures of quality found in the academic literature and in commercially available quality indexes. They examine seven quality categories including: profitability, earnings stability, capital structure, growth, accounting quality, payout/dilution and investment. In order to mitigate datamining biases the authors employ 3 criteria (Hsu, Kalesnik and Viswanathan,
  • Macro and Momentum Factor Rotation [Flirting with Models]

    While many investors have adopted a multi-factor approach to style investing, some have pushed these boundaries by advocating for an active, rotational approach to factor allocation. In a recent white paper, MSCI suggests several methods that might be conducive for performing style rotation, including macro-, momentum-, and value-based signals. In this commentary, we attempt to test the macro- and

Quantocracy’s Daily Wrap for 09/26/2019

This is a summary of links featured on Quantocracy on Thursday, 09/26/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • An Approach to Time Series Data when Data is Limited (ARIMA / VAR) [Auquan]

    Investors are slowly becoming more and more interested in ethical investing. Part of the reason is the industry is starting to care more, but the other reason is that there is a lot of evidence to show that it can produce better or at least equivalent returns. One subset of this type of investing is known as ESG investing. In short, this uses company filings about their environmental, social and
  • The Short Duration Premium [Alpha Architect]

    In my June 4, 2019 article The Re-Death of Value, or Dj Vu All Over? I noted that one possible explanation for at least part of the poor performance of value stocks over the past decade has been the sharp fall in both the real interest rate (due to weak global growth) and unexpected inflation. As supporting evidence, I cited a study which found that those two outcomes favor longer
  • Trading Using Machine Learning In Python [Quant Insti]

    In recent years, machine learning, more specifically machine learning in Python has become the buzz-word for many quant firms. In their quest to seek the elusive alpha, a number of funds and trading firms have adopted to machine learning. While the algorithms deployed by quant hedge funds are never made public, we know that top funds employ machine learning algorithms to a large extent. Take, for