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Quantocracy’s Daily Wrap for 06/21/2019

This is a summary of links featured on Quantocracy on Friday, 06/21/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bond. Treasury Bond [Robot Wealth]

    The Federal Reserve publishes the yield-to-maturity of US Treasury bonds. However, the actual returns earned by investors are not publicly available. Nor are they readily and intuitively discerned from historical yields, since a bonds return equals its yield only if its yield stays constant and if all coupons (cash payments) are reinvested at that same yield (Tuckman and Angel, 2013,
  • Trading China A-Share Stocks Based On Social Media Data Analysis In Python [Quant Insti]

    In this article, we will understand how natural language processing, sentiment analysis and social media play a role in the share markets with the help of Python. This would be explained with respect to the trading in China markets A-share stocks. This article is the final project submitted by the authors as a part of their coursework in the Executive Programme in Algorithmic Trading (EPAT) at

Quantocracy’s Daily Wrap for 06/20/2019

This is a summary of links featured on Quantocracy on Thursday, 06/20/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Matter of Scale [Quant Dare]

    When dealing with mathematical modeling, choosing the right scale to frame the equations can make the difference between a successful and lasting model, or poor description of reality. In todays post, we explore two important scaling procedures that arise in finance: the annualisation of returns and volatility. These are common terms in the industry and building bricks to many other metrics, so

Quantocracy’s Daily Wrap for 06/19/2019

This is a summary of links featured on Quantocracy on Wednesday, 06/19/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • DeepTrading with TensorFlow III [Todo Trader]

    We are now closer to applying our knowledge of neural networks (NN) to our trading systems. But, we still have to tune our rudiments a bit on TensorFlow. If you are not yet familiar with our supervised machine learning flowchart, take a look at the first two posts in this series. DeepTrading with Tensorflow DeepTrading with TensorFlow II As usual, the calculations contained in this post are part
  • A Python Implementation of Triangles for Visualising Long-Term Investment Metrics [Scalable Capital]

    We introduce triangle plots for visualising long-term investment metrics. Return triangles are well suited to showcase the performance of a strategy or asset for a huge number of possible subperiods. Sensitivity analysis with respect to the length of the holding period as well as the start and end dates can thus be easily performed and visualised. The pairwise comparison of two assets or

Quantocracy’s Daily Wrap for 06/18/2019

This is a summary of links featured on Quantocracy on Tuesday, 06/18/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Shannon Entropy: A Genius Gambler’s Guide to Market Randomness [Robot Wealth]

    Before you commit your precious time to read this blog post, I need to warn you that this is one of those posts that market nerds like myself will get a kick out of, but which probably wont add much of practical value to your trading. The purpose of this post is to scratch the surface of the markets from an information theoretic perspective, using tools developed by none other than the father
  • Factor Investing Research On Steriods [Alpha Architect]

    What are the research questions? Do the most prominent long/short factors value, momentum, carry, and defensive survive out of sample? Can long/short factors be timed? What are the Academic Insights? YES. All of the factors exist out of sample, albeit their magnitudes are generally muted.(1) NOT REALLY. After a mind-numbing battery of tests, the evidence is mixed and inconsistent across

Quantocracy’s Daily Wrap for 06/17/2019

This is a summary of links featured on Quantocracy on Monday, 06/17/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Time-Series Signals and Multi-Sector Bonds [Flirting with Models]

    We expand last weeks commentary to explore momentum, carry, value, and long-term reversal signals in a time-series context. Using these signals, we generate long/short portfolios for each asset class. We use a sub-sampling methodology to bootstrap and annualized return distribution. We find that the signals are only selectively significant, and rarely consistent. We believe this initial study
  • A Horse Race of Liquid Alternatives [Factor Research]

    Investors can access alternative strategies via mutual funds and ETFs Most of these show moderate to high correlations to equities, which is concerning Bonds would have been a better diversifier in recent years INTRODUCTION Investing is challenging as it is complex and complicated, which requires continuous learning and updating of mental frameworks. Conflicts and contradictions are found

Quantocracy’s Daily Wrap for 06/15/2019

This is a summary of links featured on Quantocracy on Saturday, 06/15/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • DeepTrading with TensorFlow II [Todo Trader]

    OK, you know what tensors are or perhaps you dont, but you are sure you want to use TensorFlow to trade with it. This post introduces you to how to create elemental NN tensors in TensorFlow. This is the second post of the serie, so you need to be familiarized with the concepts exposed in the first post DeepTrading with Tensorflow. Tensors Tensors (of order higher than two) are data structures
  • DeepTrading with TensorFlow [Todo Trader]

    Do you want to maximize your trading knowledge using TensorFlow? Here are several tips that will surely help you. Introduction Within TodoTraders commitment related to the generation and dissemination of knowledge, I want to offer a series of tutorials on the use of TensorFlow for algorithmic trading. The objective of these tutorials, which will be published periodically, is to offer in a
  • Natural language processing for financial markets [SR SV]

    News and comments are major drivers for asset prices, maybe more so than conventional price and economic data. Yet it is impossible for any financial professional to read and analyse the vast and growing flow of written information. This is becoming the domain of natural language processing; a technology that supports the quantitative evaluation of humans natural language. It delivers textual

Quantocracy’s Daily Wrap for 06/12/2019

This is a summary of links featured on Quantocracy on Wednesday, 06/12/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Market Timing with a Canary, Gold, Copper, LQD, IEF and much more [Alvarez Quant Trading]

    One commonality in my strategies is the inclusion of a market timing component. This could be a signal to go into cash or reduce position size or enter a safe ETF. This applies to my swing trading strategies, my monthly rotation strategies and my Tactical Assert Allocation strategies. As a researcher, I am always on a looking to improve this part of my strategies. There have been a handful
  • The Threat of Rising Rates and the Impact on TAA vs B&H Investing [Allocate Smartly]

    Weve written a lot here and on our sister site BetterBuyAndHold.com about the threat of rising interest rates. You can read some of our past work projecting returns for Treasury ETFs and other interest rate sensitive assets here and here. In a nutshell, assuming that were near the tail end of this 37+ year march lower in interest rates, and in the coming decade(s) rates either rise or remain

Quantocracy’s Daily Wrap for 06/11/2019

This is a summary of links featured on Quantocracy on Tuesday, 06/11/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Value Factor Valuations Over Time: US and Developed [Alpha Architect]

    We built a simple tool recently to review so-called value spreads over time. (1) This tool maps out the median valuations for the top decile and bottom decile cheap stock portfolios (as measured by EBIT/TEV). Why might this be useful? This tool allows one to identify the valuation spread between the cheapest stocks and the most expensive stocks in the universe. Some research suggests
  • The Cross-Section of Emerging Market Stock Returns [Alpha Architect]

    As a non-academic finance person, I was never really exposed to academic research until I started working on articles for Alpha Architect. Fortunately (or unfortunately, depending on your perspective), I am now very familiar with the so-called cross-section of expected returns debates. (explained here). This research seeks to identify the core factors that explain why stock move the way they

Quantocracy’s Daily Wrap for 06/10/2019

This is a summary of links featured on Quantocracy on Monday, 06/10/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio construction tilting towards higher moments [Eran Raviv]

    When you build your portfolio you must decide what is your risk profile. A pension funds risk profile is different than that of a hedge fund, which is different than that of a family office. Everyones goal is to maximize returns given the risk. Sinfully but commonly risk is defined as the variability in the portfolio, and so we feed our expected returns and expected risk to some optimization
  • Quantitative Styles and Multi-Sector Bonds [Flirting with Models]

    In this commentary we explore the application of several quantitative signals to a broad set of fixed income exposures. Specifically, we explore value, momentum, carry, long-term reversals, and volatility signals. We find that value, 3-month momentum, carry, and 3-year reversals all create attractive quantile profiles, potentially providing clues for how investors might consider pursuing higher
  • Strategy Risk vs Asset Risk [Two Centuries Investments]

    Alternative Title: How to Avoid Bad Manager Timing Lets look at the two types of risks in most investments: Strategy Risk: If you own a black-box go-anywhere hedge fund that invests long and short and uses futures and derivatives at any frequencies, you are mostly exposed to the strategy risk. It doesnt really matter what is happening to the underlying assets – the strategy either
  • The Case Against Small Caps [Factor Research]

    The performance of the Size factor in the US was positive since 1926, but not particularly attractive Returns in Europe were more favorable, but not in Japan Alternative metrics to market capitalization would not have resulted in better performance SMALL VERSUS LARGE STOCKS In the David vs. Goliath scenario, a smaller, weaker character faces down and defeats a larger and stronger opponent. Such
  • State of Trend Following in May [Au Tra Sy]

    Strong result in May for the Trend Following index, taking the Year-to-Date performance in positive territory. Please check below for more details. Detailed Results The figures for the month are: May return: 5.01% YTD return: 2.94% Below is the chart displaying individual system results throughout May: StateTF May And in tabular format: System May Return YTD Return BBO-20 16% 6.37% Donchian-20

Quantocracy’s Daily Wrap for 06/09/2019

This is a summary of links featured on Quantocracy on Sunday, 06/09/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Future-Proofing Quant Conference from QuantMinds, September 9 – 11 in Boston

    Join experts from banks, buy-side, Silicon Valley and academia to meet, network and share ideas at America's leading quant finance event. 3 key themes shaping the agenda: 1. Innovations in machine learning, HFT, AI and data 2. Quant techniques in investment and trading 3. Advances in option pricing, trading and modelling Learn from the brightest quant minds 50+ quant strategists, analysts,
  • Selection of Sparse Mean-reverting Portfolios – Part 1 [Alex Botsula]

    Mean-reverting portfolio construction is an exciting area that involves a wide range of forecasting and optimisation techniques. In Part 1 of the setries, I demonstrate the approach to the construction of optimal mean reverting portfolios satisfying sparsity and volatility constraints.
  • A theory of hedge fund runs [SR SV]

    Hedge funds capital structure is vulnerable to market shocks because most of them offer high liquidity to loss-sensitive investors. Moreover, hedge fund managers form expectations about each other based on market prices and investor flows. When industry-wide position liquidations become a distinct risk they will want to exit early, in order to mitigate losses. Under these conditions, market