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Quantocracy’s Daily Wrap for 01/29/2018

This is a summary of links featured on Quantocracy on Monday, 01/29/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Timing Bonds with Value, Momentum, and Carry [Flirting with Models]

    Bond timing has been difficult for the past 35 years as interest rates have declined, especially since bonds started the period with high coupons. With low current rates and higher durations, the stage may be set for systematic, factor-based bond investing. Strategies such as value, momentum, and carry have done well historically, especially on a risk-adjusted basis. Diversifying across these
  • Factor Allocation Models [Factor Research]

    Factor timing and factor risk management are related concepts, but have different objectives Factors have unique characteristics that require a tailored risk management approach A multi-dimensional factor risk management model shows consistent increases in risk-return ratios and decreases in maximum drawdowns across markets INTRODUCTION Smart beta funds surpassed $1 trillion assets under
  • Bitcoin exponential growth [Eran Raviv]

    Is bitcoin a bubble? I dont know. What defines a bubble? The price should drastically overestimate the underlying fundamentals. I simply dont know much about blockchain to have an opinion there. A related characteristic is a run-away price. Going up fast just because it is going up fast. How fast the price of bitcoin moves up? In order to quantify this we can design a short exercise testing

Quantocracy’s Daily Wrap for 01/27/2018

This is a summary of links featured on Quantocracy on Saturday, 01/27/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Dynamic WARIMAX-gjrGARCH Market Strategy [Alpha Macro]

    In this article, I am going to explore an alternative forecasting technique that currently has merits in the field of dam displacement, a structural engineering problem. I will then apply this technique and measure its forecasting capability on the S&P/TSX Composite Index (GSPTSE). The model is the Wavelet Auto-Regressive Integrated Moving Average model with eXogenous variables and Generalized

Quantocracy’s Daily Wrap for 01/26/2018

This is a summary of links featured on Quantocracy on Friday, 01/26/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bitcoin Return Based on Supply and Demand Model [CXO Advisory]

    Does the increase in number of Bitcoin wallets at a rate that far exceeds growth in number of Bitcoins explain the dramatic rise in Bitcoin price? In the December revision of his paper entitled Metcalfes Law as a Model for Bitcoins Value, Timothy Peterson models Bitcoin price according to Metcalfe Law, which posits that the value of a network (Bitcoin) is a function of the number of
  • SPX at Highs with XIV at Lows [Quantifiable Edges]

    XIV is an inverse-VIX ETN. In other words, it was designed to generally trade inversely to VIX futures on a daily basis. Since VIX and SPX typically trade opposite each other, you would think that XIV and SPX would often close in the same direction. And you would be right. Of course, XIV depends on more than just the movement in the VIX to determine its price. Among other things, it is influenced

Quantocracy’s Daily Wrap for 01/24/2018

This is a summary of links featured on Quantocracy on Wednesday, 01/24/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Which Implied Volatility Ratio Is Best? [QuantStrat TradeR]

    This post will be about comparing a volatility signal using three different variations of implied volatility indices to predict when to enter a short volatility position. In volatility trading, there are three separate implied volatility indices that have a somewhat long history for tradingthe VIX (everyone knows this one), the VXV (more recently changed to be called the VIX3M), which is like
  • Are There Any Simple Calendar Effects in Bitcoin Market? [Quantpedia]

    There is a large literature that reports time-specific anomalies in equity markets such as the Monday effect, the January effect and the Halloween effect. This study is the first to report intra-day time-of-day, day-of-week, and month-of-year effects for Bitcoin returns and trading volume. Using more than 15 million price and trading volume observations from seven global Bitcoin exchanges reveal
  • Equity Curve Monte Carlo Analysis [Alvarez Quant Trading]

    Imagine the following. You spent time developing a strategy with a compounded annual return of 24% and max drawdown of 18%. Profitable 10 of the last 11 years. An average 21 day rolling correlation with the SPY of .20. Passes your out-of-sample testing. Passes your parameter sensitivity testing. Raise your hand if you would trade this? I would be the guy jumping up and down saying yes!. Now
  • Machine Learning K-Nearest Neighbors (KNN) Algorithm In Python [Quant Insti]

    Machine Learning is one of the most popular approaches in Artificial Intelligence. Over the past decade, Machine Learning has become one of the integral parts of our life. It is implemented in a task as simple as recognizing human handwriting or as complex as self-driving cars. It is also expected that in a couple of decades, the more mechanical repetitive task will be over. With the increasing
  • When distance is the issue [Quant Dare]

    Rankings are everywhere. They are sometimes useful and, at other times, contradicting. In such a case, we need to come up with a consensus ranking but how do we evaluate ranking consensus? The other day I was reading about something called rank aggregation, which is just a fancy name for combining preferences expressed through rankings. I bet you know that rankings are everywhere. The page

Quantocracy’s Daily Wrap for 01/23/2018

This is a summary of links featured on Quantocracy on Tuesday, 01/23/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Deep Learning for Trading Part 3: Feed Forward Networks [Robot Wealth]

    This is the third in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful signals from historical market data. If you
  • Can mutual fund investors beat the market? [Mathematical Investor]

    Many individual investors employ mutual funds as an alternative to direct ownership of stocks or bonds. Indeed, mutual funds have some advantages: Diversity: Even a single fund can encapsulate a large sector of the market. Peace of mind: One is less likely to stress out about sudden bad news regarding a particular firm if one owns shares in it only indirectly as part of a large mutual funds
  • When New Years Begin With A Steady Stream Of Up Days [Quantifiable Edges]

    The start to 2018 has been fairly remarkable. The SPX has only closed down 3 days so far, while closing up 11 days. That is a substantial hot streak, and one might think that such a strong run to start the year would almost certainly have to pullback soon. So I checked. 2018-01-23 The imminent pullback theory certainly does not seem to work here. All 6 previous instances were higher 2, 7, 8, and 9

Quantocracy’s Daily Wrap for 01/22/2018

This is a summary of links featured on Quantocracy on Monday, 01/22/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantifying Timing Luck [Flirting with Models]

    When two managers implement identical strategies, but merely choose to rebalance on different days, we call variance between their returns timing luck. Timing luck can easily be overcome by using a method of overlapping portfolios, but few firms do this in practice. We believe the magnitude of timing luck impact is much larger than most believe, particularly in tactical strategies. We derive
  • Gold Price Prediction Using Machine Learning In Python [Quant Insti]

    Is it possible to predict where the Gold price is headed? Yes, lets use machine learning regression techniques to predict the price of one of the most important precious metal, the Gold. We will create a machine learning linear regression model that takes information from the past Gold ETF (GLD) prices and returns a prediction of the Gold ETF price the next day. GLD is the largest ETF to invest
  • Can You Short The S&P Successfully? (with @DBurgh) [System Trader Success]

    A short signal for the S&P500? Believe it or not, some do exist. Although these can often be hard to find they can also contribute greatly to your success so tons of traders search endlessly for a complimentary short system or two for their portfolios. I want to talk about a simple short edge that I have recently been tweeting about for the past year or so as it has been an interesting
  • A Historical Look At Market Reaction To New Fed Chairmen [Quantifiable Edges]

    Jerome Powell is expected to take over for Janet Yellen as the new Fed chairman on Feb 3rd. A few days ago in the letter I looked at SPX performance after a new chairman takes over. I used the SPX and looked back to 1970. Tonight I decided to take the analysis back to 1923 using my Dow data. Like with the SPX, I found the first few weeks to be the most consistent and interesting data. Once we look
  • The Government Shutdown [Highly Evolved Vol]

    Over the last ten years, a number of congress members have been elected on a fairly nihilistic platform, voting against practically any spending bill (unless it buys tanks). This is a good way to get elected but it makes it hard to govern. The government has to spend money. While the Republicans have majorities in both houses, there is a huge difference in political philosophies between members of

Quantocracy’s Daily Wrap for 01/19/2018

This is a summary of links featured on Quantocracy on Friday, 01/19/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mixture Model Trading (Part 4 – Strategy Implementation) [Black Arbs]

    This notebook will walkthrough the algorithm implementation process on the quantconnect platform. Please be advised that this notebook will not actually run the algorithm as I have not installed the quantconnect backtesting engine locally. This is a demonstration of the process. The script is available to copy and paste into the quantconnect environment within the ./scripts/ directory of the
  • Value and Momentum Factor Portfolio Construction: Combine, Intersect or Sequence? [Alpha Architect]

    Wes asked that I contribute to the ongoing debates regarding the construction of value and momentum portfolios. There are three key research pieces on the topic, all with different viewpoints: Alpha Architects take AQRs take Newfound Researchs Take I encourage everyone to dig into the three articles above and then tackle my article below. And if you are interesting in learning more about
  • Most popular posts 2017 [Eran Raviv]

    Writing this, I cant believe how quickly the year 2017 has gone by. Also weird, we are already three weeks into 2018, unreal. Time flies when youre having fun I guess. The analytics report shows that the three most popular posts for 2017 are: Understanding False Discovery Rate (4 minutes average time on page) R vs MATLAB round 4 Understanding K-Means Clustering Own personal
  • Research Review | 19 January 2018 | The Business Cycle [Capital Spectator]

    Fama-French Factors and Business Cycles Arnav Sheth and Tee Lim (Saint Marys College of California) December 4, 2017 We examine the behavior of Fama-French factors across business cycles measured in various ways. We first split up the business cycles into four stages and examine the cumulative returns of factors in each of those stages. We then look at the behavior of the factors after a yield

Quantocracy’s Daily Wrap for 01/18/2018

This is a summary of links featured on Quantocracy on Thursday, 01/18/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mixture Model Trading (Part 3 – Strategy Research) [Black Arbs]

    This is the beginning of a three part series that I completed towards the end of 2017 as a learning module for Quantinsti.com. The purpose of the series is to demonstrate a research workflow focused around the theory and application of mixture models as the core framework behind a algorithmic trading strategy. Below is a quote taken from the README of the github repo: The primary goal of this
  • The Mother of All Momentum Research Reports. A Must Read! [Alpha Architect]

    J.P. Morgan researchers, Marko Kolanovic and Zhen Wei, produced an incredibly detailed report on all aspects of momentum (one of our favorite topics!) Here is a link to the report 188 pages of pure effort and information. Here is a summary of what is examined in the research: As the virtually unlimited number of possible implementations may confound an investor, we first provide a framework for
  • Crash Sensitivity Explains the Momentum Effect in Stocks [Quantpedia]

    This paper proposes a risk-based explanation of the momentum anomaly on equity markets. Regressing the momentum strategy return on the return of a self-financing portfolio going long (short) in stocks with high (low) crash sensitivity in the USA from 1963 to 2012 reduces the momentum effect from a highly statistically significant 11.94% to an insignificant 1.84%. We find additional supportive
  • Highly Unusual Behavior Between SPX and VIX [Quantifiable Edges]

    Wednesday saw both SPX and VIX close at 40-day highs (about 2 months). Since they commonly trade opposite each other, to have them both be extended up like this is very rare. In fact, it has only happened 4 other times. Below is a list of those instances along with their 4-day results. 2018-01-18 The takeaway here is not that they all lost money over the next few days. Though that is notable, it

Quantocracy’s Daily Wrap for 01/17/2018

This is a summary of links featured on Quantocracy on Wednesday, 01/17/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mixture Model Trading (Part 2 – Gaussian Mixtures) [Black Arbs]

    This is the beginning of a three part series that I completed towards the end of 2017 as a learning module for Quantinsti.com. The purpose of the series is to demonstrate a research workflow focused around the theory and application of mixture models as the core framework behind a algorithmic trading strategy. Below is a quote taken from the README of the github repo: The primary goal of this
  • Covered Call Options Strategy using Machine Learning [Quant Insti]

    A covered call is used by an investor to make some small profit while holding the stock. Mostly the reason why a trader would want to create a covered call is because the trader is bullish on the underlying stock and wants to hold for long-term, but the stock doesnt pay any dividend.The stock is expected to go up over a period of next 6 months, and in the meantime, you would want to use this
  • Cointegration in Economy: a long-term relationship [Quant Dare]

    The relationship between series can be measured by different methods. The most common is to check if both series move in the same way. Wed like to go further, and see if the difference between them is always the same. We call it cointegration. In many cases, we are interested in expressing one series according to another, or looking for common characteristics from which we can draw conclusions

Quantocracy’s Daily Wrap for 01/14/2018

This is a summary of links featured on Quantocracy on Sunday, 01/14/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Investing: Gross to Net Returns [Factor Research]

    Long-short multi-factor portfolios generate attractive returns before fees Returns are much less attractive post fees charged historically However, some fees in the long-short space are likely justified given higher complexity INTRODUCTION Reality is the murder of a beautiful theory by a gang of ugly facts (Robert Glass, 2002). Factor investing can be considered one of the beautiful theories of