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Quantocracy’s Daily Wrap for 10/29/2019

This is a summary of links featured on Quantocracy on Tuesday, 10/29/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Yield Curves: Common Patterns in Prices of Fixed-Income Securities [Scalable Capital]

    Interest rates and yield curves are not observable, but need to be estimated from prices of fixed-income securities. Common patterns in prices of fixed-income securities can be expressed in three ways: yield curves, forward rate curves or discount functions. When working with interest rates, we need to know the exact unit of measurement in order to know the correct formula to compute present
  • Podcast with Jack Vogel (@jvogs02): Market anomalies and quantitative approach to investing [System Trader Show]

    Investing is simple, but not easy, as Warren Buffett says. And yes technically the investment process should be as simple as possible. But does it mean that an average investor should not even think about active investment strategies and entirely rely on a passive portfolio? Which strategy is best to follow? And will it work tomorrow? In this interview, my guest, Jack Vogel from Alpha
  • Calendar / Seasonal Trading and Momentum Factor [Quantpedia]

    We are continuing in our short series of articles about calendar / seasonal trading. In our previous work, we have examined various calendar / seasonal equity trading strategies. In this study, we aim to take this composite calendar strategy as a building block and add another block to enhance the resulting performance. This article can be another example, how to work with anomalies that are
  • Liquidity might be a better proxy for Size in equity markets [Alpha Architect]

    The size premium is one of the factors that we have researched and dug into several times on the blog. You can find just a few here, here, and here. This paper though took a fresh look at the size premium and adds a new perspective that we havent previously covered. What are the research questions? Given various approaches to measuring the size of a company, is the total amount of daily
  • Volatility Clustering: Alternative Methods of Filtering [Oxford Capital]

    Concept: Large price moves tend to be followed by large price moves, and small price moves tend to be followed by small price moves (Volatility Clustering). Research Question: Can we improve performance of the original volatility clustering model via standard deviation filtering of large price moves? Specification: Table 1. Results: Figure 1-4. Trade Setup: We identify large price moves via

Quantocracy’s Daily Wrap for 10/28/2019

This is a summary of links featured on Quantocracy on Monday, 10/28/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Edge of an Entry Signal [Philipp Kahler]

    When developing a new trading strategy you are usually confronted with multiple tasks: Design the entry, design the exit and design position sizing and overall risk control. This article is about how you can test the edge of your entry signal before thinking about your exit strategy. The results of these tests will guide you to the perfect exit for the tested entry signal (entry-exit combination)
  • Factor Orphans [Flirting with Models]

    To generate returns that are different than the market, we must adopt a positioning that is different than the market. With the increasing adoption of systematic factor portfolios, we explore whether an anti-factor stance can generate contrarian-based profits. Specifically, we explore the idea of factor orphans: stocks that are not included in any factor portfolio at a given time. To identify
  • Tradable economics [SR SV]

    Tradable economics is a technology for building systematic trading strategies based on economic data. Economic data are statistics that unlike market prices directly inform on economic activity. Tradable economics is not a zero-sum game. Trading profits are ultimately paid out of the economic gains from a faster and smoother alignment of market prices with economic conditions. Hence,
  • The Complexity of Factor Exposure Analysis [Factor Research]

    Factor exposure analysis is essential for performance and risk contribution However, the results vary depending on methodologies, factor definitions, and other assumptions A holdings-based approach is preferable over regression analysis INTRODUCTION A large part of a capital allocators job is to be a detective and solve puzzles. A never-ending puzzle is explaining past performance and risk

Quantocracy’s Daily Wrap for 10/24/2019

This is a summary of links featured on Quantocracy on Thursday, 10/24/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Predictable End-of-Month Treasury ETF Returns [Allocate Smartly]

    The inspiration for this post comes from a new paper titled Predictable End-of-Month Treasury Returns (h/t Capital Spectator). A description from the authors: We document a distinct pattern in the timing of excess returns on coupon Treasury securities. Average returns are positive and highly significant in the last few days of the month and are not significantly different from zero at other times.
  • Core Earnings: New Data and Evidence [Alpha Architect]

    Researchers love novel datasetsit gives them a new set of information to conduct studies and test theories. That brings us to this paper, titled Core Earnings: New Data and Evidence by Ethan Rouen, Eric So, and Charles C.Y. Wang. The paper uses a novel database created by our friends at NewConstructs. What is new in this database? Essentially, the database attempts to adjust a firms

Quantocracy’s Daily Wrap for 10/23/2019

This is a summary of links featured on Quantocracy on Wednesday, 10/23/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Equities Market Intraday Momentum Strategy in Python Part 1 [Python For Finance]

    For this post, I want to take a look at the concept of intra-day momentum and investigate whether we are able to identify any positive signs of such a phenomenon occurring across (quite a large) universe of NYSE stocks. It has been suggested that, for the wider market in general at least, there is a statistically significant intra-day momentum effect resulting in a positive relationship between
  • Trick or treat. It s Halloween! [Quant Dare]

    Lets start with an experiment. We divide people into two groups, A and B. Then, we ask group A to guess how old Mahatma Gandhi was when he died, taking into account it was after age 9. And we ask group B the same question but taking into account that it was before age 140. Of course, the extra information is useless in both cases. However, it influences the answers in some way. Group As
  • Pairs Trading Basics: Correlation, Cointegration And Strategy [Quant Insti]

    Pairs trading is supposedly one of the most popular types of trading strategy. In this strategy, usually a pair of stocks are traded in a market-neutral strategy, i.e. it doesnt matter whether the market is trending upwards or downwards, the two open positions for each stock hedge against each other. The key challenges in pairs trading are to: Choose a pair which will give you good statistical
  • Superstar Investors [Alpha Architect]

    Many famous investors are outspoken about their investment philosophies, and carefully apply them to a select number of securities. Who among us hasnt thought if they could at least capture some of the talents of our favorite investors in a bottle, we too could be super investors? Turns out you might just be able to capture some of the magic, but you have to be patient and take the pain to get
  • The Quality Factor What Exactly Is It? [Alpha Architect]

    While the quality factor has been identified in the literature (including papers such as Buffetts Alpha, Global Return Premiums on Earnings Quality, Value, and Size, and The Excess Returns of Quality Stocks: A Behavioral Anomaly), and there are now a number of investment vehicles with quality strategies (such as the iShares Edge MSCI USA Quality Factor ETF, or QUAL, and

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