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Quantocracy’s Daily Wrap for 10/21/2019

This is a summary of links featured on Quantocracy on Monday, 10/21/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Skew and expected returns [Investment Idiocy]

    Some bloke* once said "The most overlooked characteristic of a strategy is the expected skew of it's returns, i.e. how symmetrical they are" * It was me. "Systematic Trading" page 40 Skew then is an important concept, and one which I find myself thinking about a lot. So I've decided to write a series of posts about skew, of which is the first. In fact I've
  • The Case Against Equity Income Funds [Factor Research]

    Equity income mutual funds have underperformed the S&P 500 since 1988 Especially on a post-tax basis Investors can create tax-efficient equity portfolios, but it does not represent a free lunch INTRODUCTION Warren Buffett is probably the most well-known and well-liked investor, which is easily explained by the immense wealth he achieved through extraordinary investment acumen and his charming
  • Risk-Adjusted Momentum: A Momentum and Low-Volatility Barbell? [Flirting with Models]

    After the Great Financial Crisis, the Momentum factor has exhibited positive returns, but those returns have been largely driven by the short side of the portfolio. One research note suggests that this is driven by increased risk aversion among investors, using the correlation of high volatility and low momentum baskets as evidence. In contradiction to this point, the iShares Momentum ETF (MTUM)
  • Dynamic Asset Allocation Papers [Two Centuries Investments]

    Most asset allocation approaches are more or less static. From 60/40 to Risk Parity, such allocations can be easily replicated with a couple ETFs, and so the outcomes of static asset allocation portfolios, especially the risks such as drawdowns, are 90%+ pre-determined. There is a strand of academic research that focuses on an alternative approach called Dynamic Asset Allocation. Unlike static

Quantocracy’s Daily Wrap for 10/20/2019

This is a summary of links featured on Quantocracy on Sunday, 10/20/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • CUR matrix decomposition for improved data analysis [Eran Raviv]

    I have recently been reading about more modern ways to decompose a matrix. Singular value decomposition is a popular way, but there are more. I went down the rabbit whole. After a couple of see references therein I found something which looks to justify spending time on this. An excellent paper titled CUR matrix decomposition for improved data analysis. This post describes how to
  • Research Review | 18 October 2019 | Portfolio Design And Analysis [Capital Spectator]

    Explaining the Demise of Value Investing Baruch Lev (NY University) and Anup Srivastava (U. of Calgary) August 25, 2019 The business press claims that the long-standing and highly popular value investing strategyinvesting in low-valued stocks and selling short high-valued equitieslost its edge since 2007. The reasons for this putative sudden demise of value investing elude investors and

Quantocracy’s Daily Wrap for 10/17/2019

This is a summary of links featured on Quantocracy on Thursday, 10/17/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Scikit Learn Tutorial: Installation, Requirements And Building Classification Model [Quant Insti]

    Scikit-learn is one of the most versatile and efficient Machine Learning libraries available across the board. Built on top of other popular libraries such as NumPy, SciPy and Matplotlib, scikit learn contains a lot of powerful tools for machine learning and statistical modelling. No wonder scikit learn is widely used by data scientists, researchers and students alike. Big organizations are using
  • Active Share: Predictor of Future Performance or Urban Legend? [Alpha Architect]

    The crowning achievement for investors is the ability to identify which of the few active mutual funds will outperform in the future. Despite an overwhelming body of academic research which has demonstrated that past performance doesnt guarantee future performance and (as the annual SPIVA Persistence Scorecards regularly report) there is less persistence of outperformance than randomly

Quantocracy’s Daily Wrap for 10/16/2019

This is a summary of links featured on Quantocracy on Wednesday, 10/16/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Exiting using limit orders [Alvarez Quant Trading]

    Most of us focus our research time looking to find better entries. We dont spend enough time thinking about our exits. I am definitely guilty of this. A popular way to enter a mean reversion trade is by using a limit order. I use that on the strategy on RSI2 Strategy: Double returns with a simple rule change post. The exit on that strategy is on the open. Many people dont like exiting on the
  • Kalman Filter Pairs Trading with Zorro and R [Robot Wealth]

    In the first three posts of this mini-series on pairs trading with Zorro and R, we: Implemented a Kalman filter in R Implemented a simple pairs trading algorithm in Zorro Connected Zorro and R and exchanged data between the two platforms In this fourth and final post, were going to put it all together and develop a pairs trading script that uses Zorro for all the simulation aspects (data
  • Mitigating over tting on Financial Datasets with Generative Adversarial Networks [Quant Dare]

    What good is synthetic data for in a financial setting? This is a very valid question, given that data augmentation techniques can be hard to evaluate and the time series they produce are very complex. As we will see in this post however, it turns out that synthetic series can be very useful! Specially mitigating overfitting in financial settings. Following up on our latest posts about GANs (see
  • Crowded trades, asset centrality and predicting equity bubbles [Alpha Architect]

    What is a crowded trade? What is asset centrality? Does asset centrality predict bubbles? Can it be exploited? What are the Academic Insights? In the normal course of events, investors perceive and act upon changes in fundamentals that will persist indefinitely (or until the next change in fundamentals) resulting in price movements that are difficult to anticipate or otherwise exploit. In contrast
  • State of Trend Following in September [Au Tra Sy]

    Big swing down. The State of Trend Following report erased most of the gains for the year in one sharp down month. Please check below for more details. Detailed Results The figures for the month are: September return: -8.85% YTD return: 0.96% Below is the chart displaying individual system results throughout September: StateTF September And in tabular format: System September Return YTD Return

Quantocracy’s Daily Wrap for 10/14/2019

This is a summary of links featured on Quantocracy on Monday, 10/14/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Yield Curve Trades with Trend and Momentum [Flirting with Models]

    Yield curve changes over time can be decomposed into Level, Slope, and Curvature changes, and these changes can be used to construct portfolios. Market shocks, monetary policy, and preferences of different segments of investors (e,g. pensions) may create trends within these portfolios that can be exploited with absolute and relative momentum. In this commentary, we investigate these two factors in
  • 17 Self-Help Books for Quant Investors [Two Centuries Investments]

    Took a look at my book shelf this morning and decided to share some of my non-finance books that help me grow as a quant investor. Its an eclectic list, not in the order of importance. A More Beautiful Question by Warren Berger Rework by Jason Fried and David Heinemeier Hansson Measure What Matters by John Doerr The Business of Creativity by Keith Granet Alchemy by
  • A Columbus Day Edge Revisited [Quantifiable Edges]

    While the stock market is open on Monday, banks, schools, government offices, and the bond market are closed. In past years with the bond market closed, the stock market has done quite well on Columbus Day. Of course the most famous Columbus Day rally was in 2008 when the market gained over 11% after having crashed the week before. A few times here on the blog (most recently 10/12/15) I showed
  • AI, What Have You Done For Me Lately? [Factor Research]

    AI-focused companies have underperformed markets AI-powered ETFs have generated unimpressive returns In contrast, AI-powered hedge funds easily beat their benchmark, but the performance can be challenged INVESTING IN AI VERSUS AI INVESTING AI will probably most likely lead to the end of the world, but in the meantime, therell be great companies. Sam Altman If investors are looking for

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