Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 09/04/2018

This is a summary of links featured on Quantocracy on Tuesday, 09/04/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Timing Equity Returns Using Monetary Policy [Flirting with Models]

    Can the monetary policy environment be used to predict global equity market returns? Should we overweight/buy countries with expansionary monetary policy regimes and underweight/sell countries with contractionary monetary policy regimes? In twelve of the fourteen countries studied, both nominal and real equity returns are higher (lower) when the central banks most recent action was to cut (hike)
  • Chasing Mutual Fund Performance [Factor Research]

    Mutual funds exhibit momentum when measured by their one-year performance Momentum disappears when more reasonable fund selection criteria are applied Performance does not seem effective for fund selection for a full market cycle CHASING PERFORMANCE Chasing mutual fund performance suffers from a bad reputation these days. Of course, perspectives change all the time in finance. What was once
  • PPI and the Stock Market [CXO Advisory]

    Inflation at the producer level (derived from the Producer Price Index PPI) is arguably an advance indicator for inflation downstream at the consumer level (derived from the Consumer Price Index CPI). Do investors therefore reliably react to changes in PPI as an indicator of the future wealth discount rate? In other words, is a high (low) producer-level inflation rate bad (good) for the

Quantocracy’s Daily Wrap for 09/02/2018

This is a summary of links featured on Quantocracy on Sunday, 09/02/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in August [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent tactical asset allocation 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 you

Quantocracy’s Daily Wrap for 09/01/2018

This is a summary of links featured on Quantocracy on Saturday, 09/01/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Beta herding [SR SV]

    Beta herding means convergence of market betas of individual stocks that arises from investors biased perceptions. Adverse beta herding denotes the dispersion of such betas that arises from a reversal of the bias. A new paper suggests that overconfidence in predictions of overall market direction and positive sentiment are key drivers of beta convergence, while uncertainty and negative
  • R Code Best practices [R Trader]

    Nothing is more frustrating than a long piece of code with no standard way of naming elements, presenting code or organizing files. Its not only unreadable but more importantly not reusable. Unfortunately, unlike other programming languages, R has no widely accepted coding best practices. Instead there has been various attempts to put together a few sets of rules. This post is trying to fill

Quantocracy’s Daily Wrap for 08/31/2018

This is a summary of links featured on Quantocracy on Friday, 08/31/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How Members Are Using Our Site and What That Says About TAA Investors [Allocate Smartly]

    Were in unique position to analyze the behavior of Tactical Asset Allocation investors. Our platform helps members analyze 40+ published TAA strategies from many angles, including: historical performance, tax efficiency, exposure to rising interest rates, etc. Members can combine those strategies together into what we call custom model portfolios, which they can then follow in near
  • Enhanced Factor Portfolios [Quantpedia]

    We dissect the performance of factor-based equity portfolios using a characteristics-based multi-factor expected return model. We show that generic single-factor portfolios, which invest in stocks with high scores on one particular factor, are sub-optimal, because they ignore the possibility that these stocks may be unattractive from the perspective of other factors. We also show that differences
  • Video Digest: Trade Optimization [Flirting with Models]

Quantocracy’s Daily Wrap for 08/29/2018

This is a summary of links featured on Quantocracy on Wednesday, 08/29/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Timing the Market with Google Trends Search Volume Data [iMarketSignals]

    Past research suggests that the relative change in the volume of Google searches for financial terms such as debt or stocks can be used to anticipate stock market trends. In this analysis the search term debt was used to obtain monthly search volume data from Google Trends. The analysis shows, that a decrease in search volume typically preceded price increases of the S&P 500
  • A Short Introduction On Using R For Tail-Risk Analytics [Capital Spectator]

    Interactive Brokers (IB) just published the second installment in a series Im writing for the brokerage firm about using R for portfolio analysis: Modeling Tail Risk In R With Value at Risk. Todays update (part deux) is more or less adapted from my recent book: Quantitative Investment Portfolio Analytics In R: An Introduction To R For Modeling Portfolio Risk and Return. Theres so much
  • Two New Strategies Added: Defensive Asset Allocation and Accelerating Dual Momentum [Allocate Smartly]

    Weve begun tracking two new tactical asset allocation strategies: Defensive Asset Allocation (DAA) and Accelerating Dual Momentum (ADM). Well be introducing both in more detail on our blog in the coming weeks. Members can review their historical performance and begin tracking them in near real-time in our members area now: DAA | ADM. Defensive Asset Allocation DAA is the latest strategy from

Quantocracy’s Daily Wrap for 08/27/2018

This is a summary of links featured on Quantocracy on Monday, 08/27/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trade Optimization [Flirting with Models]

    Trade optimization is more technical topic than we usually cover in our published research. Therefore, this note will relies heavily on mathematical notation and assumes readers have a basic understanding of optimization. Accompanying the commentary is code written in Python, meant to provide concrete examples of how these ideas can be implemented. The Python code leverages the PuLP optimization
  • Factor Momentum [Factor Research]

    The Momentum strategy can be applied to stocks, sectors, countries and factors Factor momentum shows positive excess returns across regions However, single-stock Momentum performance is comparable and less complex to implement INTRODUCTION We recently investigated applying the long-short Momentum strategy to sectors and countries in Europe, which revealed positive excess returns (Sector versus
  • Crypto-asset Risks and Returns [CXO Advisory]

    How do the major crypto-assets (Bitcoin, Ripple, and Ethereum) stack up against conventional asset classes? In their August 2018 paper entitled Risks and Returns of Cryptocurrency, Yukun Liu and Aleh Tsyvinski apply standard tools of asset pricing to measure crypto-asset exposures to: 160 equity factors. Macroeconomic factors (non-durable consumption growth, durable consumption growth,
  • Fintwit Might Matter for Momentum and Mean Reversion in Stock Prices [Alpha Architect]

    Do users of social media provide valuable information about liquidity that can be used to predict future liquidity? Does social media provide useful information, over and above that provided by traditional, fundamental news sources? Do positive and negative sentiment have the same effects on markets? Does information gleaned from social-media improve trading strategies? What are the Academic
  • New Highs On Low Volume During August [Quantifiable Edges]

    SPX closed at a new all-time high on Friday. But NYSE volume came in at the lowest level since mid-July. Low volume at new highs can sometimes be a negative. Of course August frequently has low volume as many market participants are on vacation and not trading as actively. So I decided to look back at other times the SPX made a long-term high on light volume during the month of August. Results

Quantocracy’s Daily Wrap for 08/24/2018

This is a summary of links featured on Quantocracy on Friday, 08/24/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Regime-Switching & Market State Modeling [Jonathan Kinlay]

    The Excel workbook referred to in this post can be downloaded here. Market state models are amongst the most useful analytical techniques that can be helpful in developing alpha-signal generators. That term covers a great deal of ground, with ideas drawn from statistics, econometrics, physics and bioinformatics. The purpose of this short note is to provide an introduction to some of the key ideas
  • Academic Factor Portfolios are Extremely Painful. Unless you are an Alien [Alpha Architect]

    Imagine you are an alien. You land on planet earth in 1927 and are given a mission. You are told that you need to solve a problem: compound $1,000,000. The goal: compound your extraterrestrial face off. The options: FF_VAL: Top decile B/M, annually rebalanced, market-cap weighted. FF_MOM: Top decile 2-12 Momentum, monthly rebalanced, market-cap weighted. SP500: Own the biggest 500 stocks,
  • What Works (and Doesn’t Work) in Cryptocurrencies [Quantpedia]

    If behavioral biases explain asset pricing anomalies, they should also materialize in cryptocurrency markets. I test more than 20 stock return anomalies based on daily cryptocurrency data, and document strong evidence of price momentum. Unlike stock markets, price reversal and risk-based anomalies are weak, controlling for market and size. Cryptocurrency anomalies can be explained by behavioral

Quantocracy’s Daily Wrap for 08/22/2018

This is a summary of links featured on Quantocracy on Wednesday, 08/22/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pre-inclusion Bias: How to create a false strategy [Alvarez Quant Trading]

    In the previous post I described a simple rule to double the returns of a mean reversion strategy. In this post, I show how pre-inclusion bias can take a losing strategy and make it a winning one. Recently I had reader send me the rules for a stock trend following strategy. He knew these are the strategies I have been researching lately. The rules were few and I had time, so I coded it up. Here is
  • Resources for Quantitative Analysts [Jonathan Kinlay]

    Two of the smartest econometricians I know are Prof. Stephen Taylor of Lancaster University, and Prof. James Davidson of Exeter University. I recall spending many profitable hours in the 1980s with Stephens book Modelling Financial Time Series, which I am pleased to see has now been reprinted in a second edition. For a long time this was the best available book on the topic and it remains a

Quantocracy’s Daily Wrap for 08/21/2018

This is a summary of links featured on Quantocracy on Tuesday, 08/21/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 2D Asset Allocation using PCA (Part 2) [CSS Analytics]

    In the last post we showed how to use PCA to create Offense and Defense portfolios by focusing on the first principal component or PC1. After rotation has been completed it is possible to derive weights or portfolios for each principal component. Another good primer on using PCA for asset allocation is written by a reader of the blog- Dr. Rufus Rankin. The link for this book is here. We can
  • Our Own Worst Enemy [Alpha Scientist]

    "We have met the enemy, and he is us" – Walt Kelly It has long been noted that investors – individual and institutional – tend to be their own worst enemies. They have an uncanny ability to buy stocks near market tops and sell near market bottoms. As a consequence, investor returns, in aggregate, have a tendency to significantly lag investment returns in aggregate since investors are
  • Can Machine Learning Be Used To Predict Market Direction? The 1,000,000 Model Test [Jonathan Kinlay]

    During the 1990s the advent of Neural Networks unleashed a torrent of research on their applications in financial markets, accompanied by some rather extravagant claims about their predicative abilities. Sadly, much of the research proved to be sub-standard and the results illusionary, following which the topic was largely relegated to the bleachers, at least in the field of financial market
  • Video Digest: A Factor-Based Approach to Disruptor-Based Sectors [Flirting with Models]

Quantocracy’s Daily Wrap for 08/20/2018

This is a summary of links featured on Quantocracy on Monday, 08/20/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading Metrics that Actually Matter [Quant Fiction]

    Traders love their performance metrics. Anyone whos used their platforms backtesting features has probably come across a few dozen of them, and everyones got their favorite. Anybody whos anybody in the finance world has one named after them: Sharpe, Sortino, Calmar, Treynor, Gartman, etc. (OK, maybe not the last one). But which ones are the most important? There should be some kind of
  • The State of Risk Management [Flirting with Models]

    We compare and contrast different approaches to risk managing equity exposure; including fixed income, risk parity, managed futures, tactical equity, and options-based strategies; over the last 20 years. We find that all eight strategies studied successfully reduce risk, while six of the eight strategies improve risk-adjusted returns. The lone exceptions are two options-based strategies that
  • Looking at Alternatives? Avoid Complexity and Magical Backtests [Alpha Architect]

    The paper investigates the following research question: Does persistence (out of sample performance) exist for alternative beta strategies sponsored by investment banks? Does adding complexity to a strategy increase the risk of backtesting overfitting? Do the strategies capture the factor exposure they seek to exploit? And, does the exposure remain consistent between backtesting and live periods?
  • On Testing Direction Prediction Accuracy [Jonathan Kinlay]

    As regards the question of forecasting accuracy discussed in the paper on Forecasting Volatility in the S&P 500 Index, there are two possible misunderstandings here that need to be cleared up. These arise from remarks by one commentator as follows: An above 50% vol direction forecast looks good,.. but direction is biased when working with highly skewed distributions! ..so it would be
  • Low Volatility, Low Beta & Low Correlation [Factor Research]

    The Low Volatility, Low Beta and Low Correlation factors are interrelated Low-risk factors generate attractive risk-adjusted returns, but require beta-neutrality Currently they feature moderate to high interest-rate sensitivity INTRODUCTION Coca-Cola versus Bitcoin Investment Trust, Mattel versus Groupon, Ventas versus Facebook. Which stocks would you prefer? These stock pairs represent low versus
  • Range-Based EGARCH Option Pricing Models (REGARCH) [Jonathan Kinlay]

    The research in this post and the related paper on Range Based EGARCH Option pricing Models is focused on the innovative range-based volatility models introduced in Alizadeh, Brandt, and Diebold (2002) (hereafter ABD). We develop new option pricing models using multi-factor diffusion approximations couched within this theoretical framework and examine their properties in comparison with the