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Quantocracy’s Daily Wrap for 06/04/2020

This is a summary of links featured on Quantocracy on Thursday, 06/04/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why Aren’t Call Options More Expensive Than Put Options? [Robot Wealth]

    Why arent calls more expensive than puts for an asset which is more likely to go up than down? We have an asset trading at $100 for which the distribution of future returns is a known fact. It has annual returns described by a normal distribution with mean 5% and standard deviation 10%. This is, therefore, an asset with positive drift. It is more likely to go up than down. Because we are
  • Do Interest Rates Explain Value s Underperformance? [Alpha Architect]

    From January 2017 through March 2020, the value premium, defined by HML (the return of high book-to-market stocks minus the return of low book-to-market stocks experienced a drawdown of 42 percent. 1 If we extend the period back to January 2007, the drawdown of about 51 percent is the largest ever. There have been many attempts to explain the reason for the dramatic underperformance; weve even

Quantocracy’s Daily Wrap for 06/03/2020

This is a summary of links featured on Quantocracy on Wednesday, 06/03/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why We Use Apache Beam For Our Systematic Trading Data Pipeline [Robot Wealth]

    In the world of Big Data, there are lots of tools and technologies to choose from. Choosing the right one depends on the things that you are building and the problems you are trying to solve. Trading firms have skilled teams that monitor and deploy data pipelines for their organisation and the technical overhead that comes with that. Firms invest in data infrastructure and research because
  • Variational autoencoder as a method of data augmentation [Quant Dare]

    In this blog weve talked about autoencoders several times, both as outliers detection and as dimensionality reduction. Now, we present another variation of them, variational autoencoder, which makes possible data augmentation. If you have ever faced Machine Learning problems, you will have dealt with the lack of data to train models. Well, this method will give you an interesting way of getting

Quantocracy’s Daily Wrap for 06/02/2020

This is a summary of links featured on Quantocracy on Tuesday, 06/02/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Petra on Programming: The Correlation Cycle Indicator [Financial Hacker]

    The previous article dealt with indicators based on correlation with a trend line. This time well look into another correlation-based indicator by John Ehlers. The new Correlation Cycle indicator (CCY) measures the price curve correlation with a sine wave. This works surprisingly well not for generating trade signals, but for a different purpose. Ehlers published the indicator together with
  • How I Explain Crappy Returns [Alpha Architect]

    Diversification. It looks great on paper, but for the past ten years, being globally and Factor diversified has been anything but great. Understandably, many diversified investors are looking at their returns and wondering why they should follow such a strategy when the S&P 500 which costs almost nothing has performed so well. To answer this question, lets come back to my
  • Machine learning is simply statistics – part 2 [Eran Raviv]

    Another opinion piece. If you cant explain it simply you dont understand it well enough. (Albert Einstein) Rant in progress A bit on Deep Learning What is so deep about deep learning? Nothing. There is nothing deep about it. If you read through the excellent Deep Learning book you can see (p. 167 in my copy) that a deep learning model with say three layers, omitting dependency on parameters,
  • Working with Tidy Financial Data in tidyr [Robot Wealth]

    Holding data in a tidy format works wonders for ones productivity. Here we will explore the tidyr package, which is all about creating tidy data. In particular, lets develop an understanding of the tidyr::pivot_longer and tidyr::pivot_wider functions for switching between different formats of tidy data. In this video, youll learn: What tidy data looks like Why its a sensible approach
  • How to Get (Almost) Free Tick Data [Black Arbs]

    Access to high quality, cost effective market data is a continuing problem for retail traders. I was recently told about the ongoing efforts of the startup brokerage Alpaca. The gentleman I spoke with said the API gave access to the tick data of thousands of stocks everyday and without cost. I thought it was too good to be true but recently I took a little bit of time to investigate. In this
  • Is value dead? Has the story changed? No. [Alpha Architect]

    Although there is widespread agreement that systematic value strategies have turned in at least a decade of underperformance, there is little agreement as to the underlying cause or cause(s). However, a number of rationalizations and critiques have emerged that question the long term viability of value strategies. The authors address in detail the relevant research or empirically test each of the
  • Mean Reversion Strategies in Python (Course Review) [Black Arbs]

    In this post I will be reviewing the course Mean Reversion Strategies by Dr. E.P. Chan ( His research and publications have garnered widespread appreciation, over the years. Unfortunately for Python programmers most of his past research was done in Matlab. Matlab was a very popular tool for researchers at one point but has been overtaken by the ubiquity of the Python programming language. So

Quantocracy’s Daily Wrap for 06/01/2020

This is a summary of links featured on Quantocracy on Monday, 06/01/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Exploiting The Non-Farm Payrolls Drift [Robot Wealth]

    Anyone thats been around the markets knows that the monthly release of the United States Department of Labors Non-Farm Payrolls (NFP) data can have a tremendous impact, especially in the short term. NFP is a snapshot of the state of the employment situation in the US, representing the total number of paid workers, excluding farm employees and public servants. We know your barn is hiding a
  • Bonds & The Invisible Thief [Factor Research]

    US bonds generated positive total returns in most inflation regimes Returns were mixed when inflation was above 4% Real returns were strongly negative when inflation was high INTRODUCTION Inflation is like cancer. It largely happens out of plain sight and requires focus to be noticed. The data is not easily understood and the perspective changes frequently. The outlook is difficult to predict. And

Quantocracy’s Daily Wrap for 05/31/2020

This is a summary of links featured on Quantocracy on Sunday, 05/31/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Online Portfolio Selection: Pattern Matching [Hudson and Thames]

    Pattern matching locates similarly acting historical market windows and makes future predictions based on the similarity. They combine the strengths of both momentum and mean reversion by exploiting the statistical correlations of the current market window to the past. In the following blog post, we will examine three variations of Correlation Driven Nonparametric Strategies strategies:
  • The Livermore System: Part 1 | Trading Strategy (Filters) [Oxford Capital]

    I. Trading Strategy Source: Kaufman, P. J. (2020). Trading Systems and Methods. New Jersey: John Wiley & Sons, Inc. Concept: Momentum trading strategy based on Jesse Livermores approach to swing trading. Research Goal: Performance verification of Swing Filter and Penetration Filter. Specification: Table 1. Results: Figure 1-2. Trade Entry/Exit: Table1. Portfolio: 42 futures markets from

Quantocracy’s Daily Wrap for 05/29/2020

This is a summary of links featured on Quantocracy on Friday, 05/29/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mad methods [OSM]

    Over the past few weeks, weve examined the three major methods used to set return expectations as part of the portfolio allocation process. Those methods were historical averages, discounted cash flow models, and risk premia models. Today, well bring all these models together to compare and contrast their accuracy. Before we make these comparisons, we want to remind readers that were now
  • Two Different Methods to Apply Some Corey Hoffstein Analysis to your TAA [QuantStrat TradeR]

    So, first off: I just finished a Thinkful data science in python bootcamp program that was supposed to take six months, in about four months. All of my capstone projects I applied to volatility trading; long story short, none of the ML techniques worked, and the more complex the technique I tried, the worse it performed. Is there a place for data science in Python in the world? Of course. Some
  • Smart(er) Investing – The Easy Way [Alpha Architect]

    Most of the time we make you earn your education by reading our posts to build up your knowledge of the latest and greatest academic research concepts. That said, we understand there are different ways to educate, which is why were experimenting with video and audio. In the videos below, 3 of our teammates at Alpha Architect Perth Tolle, Tommi Johnsen, PhD, and Elisabetta Basilico,

Quantocracy’s Daily Wrap for 05/28/2020

This is a summary of links featured on Quantocracy on Thursday, 05/28/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Performant R Programming: Chunking a Problem into Smaller Pieces [Robot Wealth]

    When data is too big to fit into memory, one approach is to break it into smaller pieces, operate on each piece, and then join the results back together. Heres how to do that to calculate rolling mean pairwise correlations of a large stock universe. Background Weve been using the problem of calculating mean rolling correlations of ETF constituents as a test case for solving in-memory
  • S&P 500 Dividend Aristocrats [Alvarez Quant Trading]

    Back in 2018, I wrote a post, Backtesting a Dividend Strategy, which was conceptually based on the S&P 500 Dividend Aristocrats. Just recently, Norgate Data started offering historical constituent data for the S&P 500 Dividend Aristocrats index. This would be a much cleaner version compared to what I was trying to do in my original post. Would using this index produces better
  • SPX Historically Bullish On Thursday After Memorial Day [Quantifiable Edges]

    Thursday after Memorial Day has been a day that has exhibited a bullish bias for many years. I last showed this on the blog a couple of years ago. The chart below shows updated results. SPX Perfromance on Thursday After Memorial Day Single-day seasonality can certainly be overrun by other forces, but the Thursday after Memorial Day has been a good one for many years. That may be something that

Quantocracy’s Daily Wrap for 05/27/2020

This is a summary of links featured on Quantocracy on Wednesday, 05/27/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Fill Gaps in Large Stock Data Universes Using tidyr and dplyr [Robot Wealth]

    When youre working with large universes of stock data youll come across a lot of challenges: Stocks pay dividends and other distributions that have to be accounted for. Stocks are subject to splits and other corporate actions which also have to be accounted for. New stocks are listed all the time you wont have as much history for these stocks as for other stocks. Stocks are delisted,

Quantocracy’s Daily Wrap for 05/26/2020

This is a summary of links featured on Quantocracy on Tuesday, 05/26/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Two Centuries of Value and Momentum [Two Centuries Investments]

    As a quant, I have been obsessed with systematic Value and Momentum since the first day I ran a backtest. Part of me knows that the future for this combo is unlikely to be as good as the past. In my R&D, I moved on to other factors more than a decade ago. But another part of me is still in love with the magical duo and wishes for them to survive. Value and Momentum have been the most beautiful
  • Tactically Adjusting Everything in a Financial Crisis? Bad Idea. [Alpha Architect]

    With the current market conditions and the wild ride weve all been on, weve pivoted our attention to focus on supplying academic research on responding to a crisis. This article investigates what the appropriate tactical adjustments investors should consider when making changes to their portfolio holdings following large losses in wealth during a crisis. What are the Academic Insights? In a
  • How to develop, test and optimize a trading strategy – complete guide [Milton FMR]

    Developing a trading strategy from start to finish is a complex process. The process follows the following steps: Formulation of the strategy Write Pseudo Code Transform into working code Start first backtests Optimize Evaluate test results Go live Monitor performance Evaluate and adjust Optimization process We will discuss each of this points separately. Here is a visualization of the design
  • Tactical ETFs: Tactfully No, Thank You? [Factor Research]

    Tactical investing aims to deliver better risk-adjusted returns and/or reduced drawdowns Tactical ETFs have not achieved either objective in recent years It is challenging to explain the consistent underperformance across different types of tactical ETFs INTRODUCTION Every investor is a tactician, whether they actively try or not. Warren Buffett and his lieutenants at Berkshire Hathaway pursue a
  • Find Cheap Options for Effective Crash Protection Using Crash Regressions [Robot Wealth]

    One way we can quantify a stocks movement relative to the market index is by calculating its beta to the market. To calculate the beta of MSFT to SPY (for example) we: calculate daily MSFT returns and daily SPY returns align the returns with one another regress MSFT returns against SPY returns. This shows the procedure, graphically:

Quantocracy’s Daily Wrap for 05/25/2020

This is a summary of links featured on Quantocracy on Monday, 05/25/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Defensive Equity with Machine Learning [Flirting with Models]

    Defensive equity strategies are comprised of stocks that lose less than the market during bear markets while keeping up with the market during a bull market. Coarse sorts on metrics such as volatility, beta, value, and momentum lead to diversified portfolios but have mixed results in terms of their defensive characteristics, especially through different crisis periods that may favor one metric
  • Long-Short vs Long-Only Implementation of Equity Factors [Quantpedia]

    How should be equity factor strategies implemented? In a long-only (smart beta) way? As a long-short strategy, as most of the hedge funds usually do? Or in a partially-hedged fashion by going long equity factor and shorting market to offset some of the market risks? There is no one universal answer as it depends on the investment mandate and constraints of each fund manager contemplating to
  • Rolling and Expanding Windows For Dummies [Robot Wealth]

    In todays article, we are going to take a look at rolling and expanding windows. By the end of the post, you will be able to answer these questions: What is a rolling window? What is an expanding window? Why are they useful? What is a Rolling or Expanding window? Here is a normal window. We use normal windows because we want to have a glimpse of the outside, the bigger the window the more of
  • Joint predictability of FX and bond returns [SR SV]

    When macroeconomic conditions change rational inattention and cognitive frictions plausibly prevent markets from adjusting expectations for futures interest rates immediately and fully. This is an instance of information inefficiency. The resulting forecast errors give rise to joint predictability of currency and bond market returns. In particular, an upside shock to the rates outlook in a country