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Quantocracy’s Daily Wrap for 02/14/2019

This is a summary of links featured on Quantocracy on Thursday, 02/14/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stock Prediction with ML: Ensemble Modeling [Alpha Scientist]

    Markets are, in my view, mostly random. However, they're not completely random. Many small inefficiencies and patterns exist in markets which can be identified and used to gain slight edge on the market. These edges are rarely large enough to trade in isolation – transaction costs and overhead can easily exceed the expected profits offered. But when we are able to combine many such small
  • Is There a Size Effect in the Stock Market? [Alpha Architect]

    One of the oldest and most persuasive arguments in the stock market is that small stocks outperform large stocks.(1) Warren Buffett, speaking at the 2013 Berkshire Hathaway Annual Meeting, summarized the sentiment when discussing the disadvantages of managing a huge amount of capital: Theres no question size is an anchor to performance. The implication is that managing a huge asset base
  • MACD: Moving Average Convergence Divergence (Part 2) [Oxford Capital]

    Developer: Gerald Appel. Source: Appel, G. (2005). Technical Analysis. NJ: Pearson Education, Inc. Concept: Trend following trading strategy based on the MACD (Moving Average Convergence Divergence) signal line. Research Goal: Performance verification of momentum signals. Specification: Table 1. Results: Figure 1-2. Trade Setup: Long Setup: MACD[i] > 0 and MACD[i] > Signal_Line[i]. Short
  • Top 10 Machine Learning Algorithms For Beginners [Quant Insti]

    Alan Turing, an English mathematician, computer scientist, logician, and cryptanalyst, surmised about machines that, It would be like a pupil who had learnt much from his master but had added much more by his own work. When this happens I feel that one is obliged to regard the machine as showing intelligence. To give you an example of the impact of machine learning, Man groups AHL

Quantocracy’s Daily Wrap for 02/12/2019

This is a summary of links featured on Quantocracy on Tuesday, 02/12/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fractional Differencing Derivation Walkthrough (FD Part 2) [Kid Quant]

    Just a quick warning before I start, this post is going to be math heavy. Those who are not brave enough to traverse these waters, be forewarned! Let's get right to it: To recap, last time I talked about a few basic statistical concepts regarding time series. Stationarity, Memory and reconciling them both using an idea called fractional differencing. This post walks through how we do this

Quantocracy’s Daily Wrap for 02/11/2019

This is a summary of links featured on Quantocracy on Monday, 02/11/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research Symposition – May 23rd, London – Big Data is the New Currency [Raven Pack]

    Cryptocurrencies have been a huge distraction when in fact we should be focusing on the currency of the future – Big Data Join Industry Leaders For almost a decade, RavenPack Symposiums have consistently provided data-driven finance professionals with riveting forward-looking content, new research and insights, and practical use cases from industry leaders and top scholars. This year's
  • Trend: Convexity & Premium [Flirting with Models]

    Trend following is unique among style premia in that it has historically exhibited a convex payoff profile with positive skew. While the historical premium is anomalous, the convexity makes sense when we use options to replicate trend following strategies. We explore reasons why frequent rebalancing in trend following strategies is necessary and decompose the return contributions from different
  • Smart Beta: Broken By Design? [Factor Research]

    SUMMARY Smart beta excess returns are different from factor returns The Low Volatility factor shows the highest discrepancy between theoretical and realized returns Investors might be better served by embracing long-short factor products REALITY DYSFUNCTION Steve Jobss reality distortion field warped Apple employees perception of what was technically possible. Though it led to many
  • Fed Days – How to profit from FOMC Meetings [We Love Algos]

    The US Federal Reserves monetary-policy decisions invariably are closely followed by market participants worldwide since they are of great importance to the development of the capital markets. David O Lucca and Emanuel Moench have examined which patterns occur in the stock market and what these could be attributed to (download The Pre-FOMC Announcement Drift here). Their conclusions:
  • How Risky are the Value and Size Premiums? Part 2/2 of Volatility Lessons [Alpha Architect]

    What are the research questions? The main purpose of this study was to examine the changes in the distribution of the US equity risk premium as the return horizon varies over the short term, medium and long term (see here for a piece that covers those topics). In this recap, we look at ancillary analysis from the Volatility Lessons paper, with a specific focus on the risk premiums associated

Quantocracy’s Daily Wrap for 02/10/2019

This is a summary of links featured on Quantocracy on Sunday, 02/10/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Welcome and Introduction to Fractional Differencing (FD Part 1) [Kid Quant]

    So somehow you've wandered into this hazy corner of the internet and found my blog. Not sure how…or why you're exactly here but I hope you'll stay. Let me introduce myself, I'm just your run of the mill budding algorithmic trader. I studied Mathematical Economics and Computer Science at the University of Richmond and was looking for an outlet to apply these tools to finance.
  • Most popular machine learning R packages [Eran Raviv]

    In a previous post: Most popular machine learning R packages, trying to hash out what are the most frequently used machine learning packages, I simply chose few names from my own memory. However, there is a CRAN task views web page which aims to provide some guidance which packages on CRAN are relevant for tasks related to a certain topic. So instead of relying on my own experience, in this

Quantocracy’s Daily Wrap for 02/09/2019

This is a summary of links featured on Quantocracy on Saturday, 02/09/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Towards Better Keras Modeling [Alpha Scientist]

    The field of deep learning is frequently described as a mix of art and science. One of the most "art-sy" parts of the field, in my experience, is the subject of network topology design – i.e., choosing the right geometry, size, depth, and type of the network. Machine learning practitioners develop rules of thumb and instincts for reasonable starting points, and heuristics exist for how
  • Portfolio construction through handcrafting: Empirical tests [Investment Idiocy]

    This post is all about handcrafting; a method for doing portfolio construction which human beings can do without computing power, or at least with a spreadsheet. The method aims to achieve the following goals: Humans can trust it: intuitive and transparent method which produces robust weights Can be easily implemented by a human in a spreadsheet Can be back tested Grounded in solid theoretical
  • Understanding dollar cross-currency basis [SR SV]

    Covered interest parity is an arbitrage condition that equalizes costs of direct USD funding and of synthetic USD funding through FX swaps. Deviations are called dollar cross-currency basis and have become a common occurrence since the great financial crisis. A negative dollar basis means direct funding in USD if accessible is cheaper than synthetic funding via swaps. An apparent

Quantocracy’s Daily Wrap for 02/08/2019

This is a summary of links featured on Quantocracy on Friday, 02/08/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Smart Money Indicator: A New Risk Management Tool [Alpha Architect]

    We have all heard the mantra, You cant time the market! But in reality, investors attempt to do just that every day as part of their tactical asset allocation strategies, which are less extreme variants of the classic trend-following risk-on/risk-off approach, which many associate with market timing.(1) Moreover, numerous studies have shown that institutional investors routinely
  • Portfolio weightlifting (II) [Quant Dare]

    In a previous post, we took a look at the computation of a portfolios exposure to its allocations. Then, to show the effects of active management, we compared the return made by two portfolios. But there is so much more to look inside the financial time series. Since we left a couple of cliffhangers, lets jump into them now. Risk metrics First of all, lets begin with those hidden dangers

Quantocracy’s Daily Wrap for 02/04/2019

This is a summary of links featured on Quantocracy on Monday, 02/04/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Two Risks That Ruin Long-Run Investing [Two Centuries Investments]

    The first risk of investing is the Drawdown Risk – the loss from the peak. The second risk of investing is the Low Return Risk – the under-performance vs. expectations over a stretched period of time. First, a few words about drawdown. Quants measure risk in many ways like Volatility, Skew, Variance, Beta, Tracking Error etc – but, in my experience, clients care the most about the drawdown. Not
  • What Caused the Volatility Tsunami on 5-Feb-2018? [Six Figure Investing]

    In the afternoon of February 5th, 2018, what looked like a bad day for a group of high flying volatility-based products turned into a devastating decline. Four factors combined to ruin their day: A Flawed Architecture Relying on the Past to Predict the Future Billions Under Management A Record-Breaking VIX spike Twenty-five minutes before the close of the New York Stock exchange on February 5th,
  • Manager Sentiment and Stock Returns [Alpha Architect]

    What are the Research Questions? The authors investigate the asset pricing implications of corporate manager sentiment, focusing on its predictability for future U.S. stock market returns. Specifically, they ask the following research questions: Does high corporate manager sentiment lead to speculative market overvaluation? Is the predictive power of such an indicator stronger compared to other
  • No Pain, No Premium [Flirting with Models]

    In this commentary, we discuss what we mean by the phrase, no pain, no premium. We re-frame the discussion of portfolio construction from one about returns to one about risk and argue that without risk, there should be no expectation of return. With a risk-based framework, we argue that investors inherently act as insurance companies, earning a premium for bearing risk. This risk often
  • Over Two Centuries of Global Factor Premiums [Invest ReSolve]

    Hot off the press, a new paper by Guido Baltussen, Laurens Swinkels and Pim van Vliet at Dutch quant powerhouse, Robeco, covers global multi-asset factor premiums over an unprecedented sample of 217 years. We thought the topics and findings were important and timely enough to warrant a summary. The new paper, titled Global Factor Premiums examines global equity indexes, 10-year government
  • Storing time series data [Cuemacro]

    As regular readers of my posts may have realised, I kind of like burgers. Its a simple meal, but somehow very satisfying. Theres obviously vast differences between the burgers on sale, in terms of quality. It isnt necessarily the case that the most expensive burger will be best. In practice, excessively expensive burgers, end up having too many ingredients, which result in a burger which
  • The Basic Recipe For Rationalizing Errors In Belief [Alex Chinco]

    Behavioral-finance models are often written down so that, although each individual trader holds incorrect beliefs, market events nevertheless unfold in such a way that traders can rationalize their own errors. e.g., consider the model in Scheinkman and Xiong (2003). In this model, each individual trader knows that every other trader is over-confident, and he knows that every other trader thinks

Quantocracy’s Daily Wrap for 02/02/2019

This is a summary of links featured on Quantocracy on Saturday, 02/02/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in January [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
  • Why herding is the death of momentum [SR SV]

    Momentum trading, buying winning assets and selling losing assets, is a most popular trading strategy. It relies on sluggish market adjustment, allowing the trader to follow best-informed investors before the more inert part of the market does. Herding simply means that market participants imitate each others actions. Herding accelerates and potentially exaggerates market adjustments. The more

Quantocracy’s Daily Wrap for 02/01/2019

This is a summary of links featured on Quantocracy on Friday, 02/01/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Size and Value in China [Alpha Architect]

    What are the research questions? China represents the worlds second largest stock market and a growing component of the worlds GDP. China also operates under peculiar political and economic environments relative to the market economies of the Western world. Because China is so unique, a plausible research hypothesis is that traditional asset pricing models, such as the Fama-French 3-factor
  • Classic Cars as an Alternative Investment [CXO Advisory]

    Are some types of cars attractive alternative investments? In their September 2018 paper entitled My Kingdom for a Horse (or a Classic Car), Dries Laurs and Luc Renneboog investigate price determinants and investment performance of classic cars from veteran cars (built 1888-1907) through modern classics (1975-1990). They estimate returns and risks for several classic car price indexes via a

Quantocracy’s Daily Wrap for 01/30/2019

This is a summary of links featured on Quantocracy on Wednesday, 01/30/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • SPY TLT Rotation [Alvarez Quant Trading]

    For my retirement accounts, I like to trade ETF strategies that require little work. One strategy we have all seen is the SPY/TLT strategy. There are many flavors of this concept. Some pick the best one over the last N months. Then there are different ways of allocating a portion of the portfolio to each. I currently dont trade any SPY/TLT strategy and wanted to see if there was something
  • Can a Machine Learning Model Predict the SP500 by Looking at Candlesticks? [Mario Filho]

    Candlestick chart patterns are one of the most widely known techniques that claim to predict the market direction inside technical analysis circles. The development of this technique goes back to 18th century Japan, and its attributed to a Japanese rice trader. It consists of finding patterns based on charts made of the above figure with prices over a period of time. There are many
  • Marcos Lopez de Prado named 2019 Quant of the Year by Journal of Portfolio Management [Mathematical Investor]

    Marcos Lopez de Prado, a member of Mathematicians Against Fraudulent Financial and Investment Advice (MAFFIA), has been named 2019 Quant of the Year by Journal of Portfolio Management. Here are some excerpts from their press release: The Journal of Portfolio Management (JPM) has named Marcos Lopez de Prado 2019 Quant of the Year. JPM has instituted the annual Quant of the Year
  • Where is the Value? [Factor Investor]

    Investors always want to know whats cheapcheap relative to the opportunity set and relative to history. Cheapness could refer to any number of thingsprice relative to trailing twelve months earnings, to trailing earnings over multiple years, to analyst earnings estimates, to long-run projections, or a dozen other variations based on sales, cash flows, book value, etc. Because analyst