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Quantocracy’s Daily Wrap for 11/11/2019

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

  • Robot Wealth 6 Week Bootcamp – Trading with Machine Learning – Enrollment Ends Friday [Robot Wealth]

    Use machine learning to find profitable trades in big data sets, boost the performance of existing strategies, and build a winning trading portfolio Use machine learning and distributed computing to solve the universe selection problem in an equity pairs trading operation Build a framework for trade filtering that can be applied to any strategy to improve performance Work with a team of retail
  • The Limit of Factor Timing [Flirting with Models]

    We have shown previously that it is possible to time factors using value and momentum but that the benefit is not large. By constructing a simple model for factor timing, we examine what accuracy would be required to do better than a momentum-based timing strategy. While the accuracy required is not high, finding the system that achieves that accuracy may be difficult. For investors focused on
  • Two Centuries of Creativity Quantified [Two Centuries Investments]

    In the past, I shared thoughts about the need to combine creative and organized thinking in order to generate investment alpha. A robust investment process and a strict disciplined application are concepts that one often hears in typical quant and fundamental investment teams. These are important but not sufficient to generate out-performance. In addition, readers know that I believe
  • The Case Against REITs [Factor Research]

    Real estate stocks featured moderate correlations to stock markets over the last 30 years However, diversification benefits for equity portfolios were only marginal Other strategies provide similar yield and downside protection characteristics INTRODUCTION Surveys often reveal investor behaviour that is challenging to understand. For example, Preqins Alternative Investor Outlook for H2 2019
  • Combine Market Trend and Economic Trend Signals? [CXO Advisory]

    A subscriber requested review of an analysis concluding that combining economic trend and market trend signals enhances market timing performance. Specifically, per the example in the referenced analysis, we look at combining: The 10-month simple moving average (SMA10) for the broad U.S. stock market. The trend is positive (negative) when the market is above (below) its SMA10. The 12-month simple

Quantocracy’s Daily Wrap for 11/08/2019

This is a summary of links featured on Quantocracy on Friday, 11/08/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What s The Best Methodology For Measuring Drawdown Risk? [Capital Spectator]

    The possibilities for quantifying risk in portfolio analytics seems to be limited only by the imagination of researchers. Indeed, you can find dictionaries that wade through an ever-lengthening list of indicators. But any short list of robust metrics surely deserves to include drawdown, which offers a powerful combination of relevance and simplicity. A new research paper reminds, however, that

Quantocracy’s Daily Wrap for 11/05/2019

This is a summary of links featured on Quantocracy on Tuesday, 11/05/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Buying Global Stocks at All-Time Highs [Allocate Smartly]

    This analysis was inspired by EconomPic and Meb Faber. Here we test a simple strategy that goes long Global Stocks (ACWI) when they make a new all-time month-end high, otherwise US bonds. The takeaway: Dont fear buying stock (indices) when they close at all-time highs. A new high shouldnt be your only reason for buying stocks, but its not in and of itself a reason to shy away. Test
  • Combinatorial Purged Cross-Validation Explained [Quantoisseur]

    In this tutorial I explain how to adapt the traditional k-fold CV to financial applications with purging, embargoing, and combinatorial backtest paths.
  • Engineering To Quant Finance – How To Make The Transition [Quant Start]

    At QuantStart we often receive email queries about the possibility of making a career transition to quantitative finance, particularly for individuals who currently consider themselves mid-career. In a more general sense we have previously discussed whether it is possible to become a quant during your thirties. However, for those with more specific technical expertiseespecially those with a
  • Is Buying Stocks at an All-Time High a Good Idea? [Meb Faber]

    No, its not a good idea, which should surprise no one. The fact that it is a GREAT idea, well, that should surprise everyone. Most investors fret when markets hit new highs, but should they? The below is inspired by our friend Jake @ Econompic, who examined the following query: What if you bought stocks at all-time highs, otherwise you sat in the safety of government bonds? Jake found
  • Introduction to Support Vector Machines [Quant Insti]

    Support Vector Machines were widely used a decade back, but now they have fallen out of favour. The below data from google trends can establish this more clearly. (Source: Google Trends) Why did this happen? As more and more advanced models were developed, support vector machines fell out of favour. It takes a lot of time to train a non-linear kernel, say RBF (Radial Basis Function), of a support
  • 2 Unfilled Up Gaps And A 50-Day High [Quantifiable Edges]

    Monday not only saw SPY make a 50-day high, but it was also the 2nd day in a row with an unfilled gap up. The study below is from last nights letter and was previously discussed several other times in the subscriber letter (click here for free trial). It examined other times SPY left at least 2 unfilled up gaps and closed at a 50-day high. 2019-11-05-1 The size of the follow-through isnt

Quantocracy’s Daily Wrap for 11/04/2019

This is a summary of links featured on Quantocracy on Monday, 11/04/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 16 Articles on Quantamental Investing [Two Centuries Investments]

    As the book about the most successful quant, Jim Simons, comes out tomorrow (The Man Who Solved the Market), I felt inspired to review the recent press on quant investing, but with a focus on the quantamental theme – where quantitative and qualitative ideas can collaborate well to produce alpha. Sorted by date: 1. Human Insight, Computer Power: What is Quantamental Investing?
  • Tactical Asset Allocation in October [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
  • Global Growth-Trend Timing [Flirting with Models]

    While trend following may help investors avoid prolonged drawdowns, it is susceptible to whipsaw where false signals cause investors to either buy high and sell low (realizing losses) or sell low and buy high (a missed opportunity). Empirical evidence suggests that using economic data in the United States as a filter of when to employ trend-following a growth-trend timing model has
  • Factor Investing in Emerging Markets [Factor Research]

    The trends in factor performance are similar in emerging and developed markets Factor returns were higher in emerging than in developed markets However, higher transaction costs need to be considered carefully INTRODUCTION Capital markets of developed countries like the US are highly efficient and mutual fund managers have struggled to generate any alpha, at least after fees. Theoretically, fund
  • A method for de-trending asset prices [SR SV]

    Financial market prices and return indices are non-stationary time series, even in logarithmic form. This means not only that they are drifting, but also that their distribution changes overtime. The main purpose of de-trending is to mitigate the effects of non-stationarity on estimated price or return distribution. De-trending can also support the design of trading strategies. The simplest basis
  • Preliminary Results from Weight Agnostic Training [Dekalog Blog]

    Following on from my last post, below is a selection of the typical resultant output from the Bayesopt Library minimisation 3 3 2 2 2 8 99 22 30 1 3 3 2 3 2 39 9 25 25 1 2 2 3 2 2 60 43 83 54 3 2 1 2 2 2 2 0 90 96 43 3 2 3 2 2 2 2 43 33 1 2 3 2 3 2 2 0 62 98 21 2 2 2 2 2 18 43 49 2 2 2 3 2 4 1 2 0 23 0 0 2 2 1 2 3 2 0 24 63 65 3 2 2 2 3 5 92 49 1 0 2 3 2 1 1 7 84 22 17 1 3 2 4 1 1 46 1 0 99 7 2 2

Quantocracy’s Daily Wrap for 11/01/2019

This is a summary of links featured on Quantocracy on Friday, 11/01/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Jim Cramer Using the S&P Oscillator [CXO Advisory]

    A reader asked about the usefulness of the S&P Short-range Oscillator as sometimes used by Jim Cramer to forecast U.S. stock market returns. The self-reported Performance of the oscillator, relying on in-sample visual inspection with snooped thresholds, is of small use. Since continuous historical values of the indicator are not publicly available, we conduct an out-of-sample test by:

Quantocracy’s Daily Wrap for 10/30/2019

This is a summary of links featured on Quantocracy on Wednesday, 10/30/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Ubiquitous “Sell in May” [Allocate Smartly]

    As a site that tracks all things asset allocation, it seems like a miss not to include the most well known of asset allocation strategies: Sell in May and go away (aka the Halloween Indicator). This is a fitting time to add it to the lineup: The strategy is killing it this year and a change in allocation is upon us. The Sell in May strategy advocates holding stocks during the wintery
  • Weight Agnostic Neural Net Training [Dekalog Blog]

    I have recently come across the idea of weight agnostic neural net training and have implemented a crude version of this combined with the recent work I have been doing on Taken's Theorem ( see my posts here, here and here ) and using the statistical mechanics approach to creating synthetic data. Using the simple Octave function below with the Akaike Information Criterion as the minimisation
  • Can Anomalies Survive Insider Disagreements [Alpha Architect]

    Anomalies such as Value and Momentum have been exploited for years, yet the source of these premiums emerged as a major unresolved puzzle. Potential explanations can be grouped into two broad categories: compensation for risk or mispricing. This paper studies this puzzle by investigating the relationship between insider trades and stock anomalies. Heres a post about Insider trading
  • Towards the Risk-Free Curve: Logarithmic vs. Arithmetic Returns [Quant Dare]

    As Nassim Taleb states, ideas come and go, stories stay. So today Maximiliano and myself are going to build for you a story which hopefully will carve in your mind the importance of doing things right; or put differently, of using logarithmic returns instead of arithmetic returns when you should. To do so, we will use one again a common process carried out in Finance: return annualization. We will
  • Tracking Macro Factors In Portfolio Strategies [Capital Spectator]

    Earlier this month I briefly reviewed a recent BlackRock report that highlighted that macroeconomic factors are typically driving investment strategy results. As a follow-up, lets take a quick look at a basic real-world example of analyzing portfolios through a macroeconomic lens. First, lets recap why macroeconomic analytics are useful. The basic takeaway: macro influences (economic growth,

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