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Quantocracy’s Daily Wrap for 07/31/2020

This is a summary of links featured on Quantocracy on Friday, 07/31/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • I like to MVO it! [OSM]

    In our last post, we ran through a bunch of weighting scenarios using our returns simulation. This resulted in three million portfolios comprised in part, or total, of four assets: stocks, bonds, gold, and real estate. These simulations relaxed the allocation constraints to allow us to exclude assets, yielding a wider range of return and risk results, while lowering the likelihood of achieving our

Quantocracy’s Daily Wrap for 07/30/2020

This is a summary of links featured on Quantocracy on Thursday, 07/30/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Boundary corrected kernel density [Eran Raviv]

    Density estimation is now a trivial one-liner script in all modern software. What is not so easy is to become comfortable with the result, how well is is my density estimated? we rarely know. One reason is the lack of ground-truth. Density estimation falls under unsupervised learning, we dont actually observe the actual underlying truth. Another reason is that the theory around density
  • The Effectiveness of Selected Crisis Hedge Strategies [Quantpedia]

    During past months we made a set of articles analyzing the performance of equity factors and selected systematic strategies during coronavirus crisis. These articles were short-ranged with data only from the start of the year 2020, which is enough for the purpose of the quick blog posts, but very short-sighted to see the nature of these strategies. Therefore, we expanded the time range by 20
  • Why ML in Finance is Hard (3 / 4) [Tr8dr]

    Following on from the prior post, want to discuss the problem of sample independence. Many machine learning models in finance deal with timeseries data, where samples used in training may be close together in time and not be independent of one another. There are very few features in finance that do not make use of lookback periods, for example: almost all technical indicators (SMA being the most
  • Is Systematic Value Dead??? [Alpha Architect]

    There is a large body of academic research demonstrating that the value premium has been persistent over long periods, pervasive across asset classes (stocks, bonds, commodities, and currencies) and also across and within industries, countries, and regions, robust to various fundamental metrics, and is implementable (survives transactions costs). In addition, there are intuitive risk- and

Quantocracy’s Daily Wrap for 07/29/2020

This is a summary of links featured on Quantocracy on Wednesday, 07/29/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Connecting to the Interactive Brokers Native Python API [Quant Start]

    Interactive Brokers has always been a popular brokerage with systematic traders. Initially this could partially be attributed to the fact that IB provided an Application Programming Interface (API) that allowed quants to obtain market data and place trades directly in code. Many competing brokerages took some time to develop their own APIs, allowing IB to gain a reasonable early-mover advantage in
  • Introduction to NLP: Sentiment analysis and Wordclouds [Quant Dare]

    I think one of the most interesting areas in the data analysis field is Natural Language Processing (NLP). These last years this discipline has grown exponentially and now its a huge area with a lot of problems we can attempt to solve, like text classification, translations or text generation In this post, I will show one of the simplest ways to approach to text processing. Im going to focus
  • Detailed Logging with a Low-Level CBT [Quant For Hire]

    Recently a student of my CBT course asked why he wasnt seeing the usual output (including dates) when he selected AmiBrokers Detailed Log option and ran a backtest that utilizes a low-level CBT. The answer is that much of the Detailed Log output comes from AmiBrokers ProcessTradeSignals method, which isnt used in a low-level CBT. However, its fairly straightforward to add your
  • Are Asset Class Correlations At A New Permanently High Plateau? [Capital Spectator]

    The coronavirus crisis reordered many things in economics and finance and you can add asset correlations to the list. After markets crashed in March, followed by a strong (so far) rebound, asset classes have continued to move with an unusually deep and broad degree of unison. High, or at least higher return correlations arent unusual around periods of severe market corrections. The question is

Quantocracy’s Daily Wrap for 07/27/2020

This is a summary of links featured on Quantocracy on Monday, 07/27/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why ML in Finance is Hard (part 1) [Tr8dr]

    I have used machine learning in trading strategies over the past 10 years. However my use of ML has often played a relatively small role in the overall design and success of the strategies. I use ML in specific signals or strategy sub-problems where the data / problem setup tends to have a robust statistical solution. This is as opposed to the Nirvana scenario where fundamental features and
  • Seasonality Factor [Dual Momentum]

    Our first look at calendar influences was in analyzing the best time during the month to execute dual momentum trades. Studies here, here, and here show that stocks perform best early in the month. This is when institutional investors make changes to their portfolios. Prices then are most representative of their true value. Here are the Sharpe and Sortino ratios for our Global Equities Momentum
  • Relative Skewness: A New Risk Factor? [Alpha Architect]

    In the search for more and better factors, this article examines the cross-sectional relationship between historical skewness (see Jacks post here) and the returns on a robust set of assets and documents the premium for taking on skewness risk. The authors construct long/short portfolios across four global asset classes including equity indices, government bonds, commodities, and currencies
  • Global Macro: Masters of the Universe? [Factor Research]

    The alpha of global macro funds has been shrinking consistently over time However, correlations to equities & bonds were low on average, offering diversification benefits Capital allocators have been cautious on the strategy in recent years INTRODUCTION He-Man and the Masters of the Universe was a popular TV cartoon show in the 1980s, where a handsome Prince Adam was battling the evil Skeletor
  • Nowcasting for financial markets [SR SV]

    Nowcasting is a modern approach to monitoring economic conditions in real-time. It makes financial market trading more efficient because economic dynamics drive corporate profits, financial flows and policy decisions, and account for a large part of asset price fluctuations. The main technology behind nowcasting is the dynamic factor model, which condenses the information of numerous correlated

Quantocracy’s Daily Wrap for 07/24/2020

This is a summary of links featured on Quantocracy on Friday, 07/24/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Petra on Programming: The Compare Price Momentum Oscillator [Financial Hacker]

    Vitali Apirine, inventor of the OBVM indicator, presented another new tool for the believing technical analyst in the Stocks & Commodities August 2020 issue. His new Compare Price Momentum Oscillator (CPMO) is based on the Price Momentum Oscillator (PMO) by a Carl Swenlin. So we got another indicator with an impressive name but has it any use? Lets check.
  • Weighting on a friend [OSM]

    Our last few posts on portfolio construction have simulated various weighting schemes to create a range of possible portfolios. Weve then chosen portfolios whose average weights yield the type of risk and return wed like to achieve. However, weve noted there is more to portfolio construction than simulating portfolio weights. We also need to simulate return outcomes given that our use of
  • Introduction to Artificial Neural Networks and the Perceptron [Quant Start]

    In this article we begin our discussion of artificial neural networks (ANN). We first motivate the need for a deep learning based approach within quantitative finance. Then we outline one of the most elementary neural networks known as the perceptron. We discuss the architecture of the perceptron and its ability to function as a supervised linear classifier, using step function based activation

Quantocracy’s Daily Wrap for 07/23/2020

This is a summary of links featured on Quantocracy on Thursday, 07/23/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • My NAAIM Webinar [Quantifiable Edges]

    Last week I had the honor of being a guest speaker for the National Association of Active Investment Managers (NAAIM)) webinar series. The topic I discussed was Quantifiable Edges for Active Investing. That recording is now available to view on the NAAIM website (email registration required). And if you are an investment manager, you may also want to learn more about NAAIM.
  • Fundamental Momentum, the Carry Trade, and Currency Returns [Alpha Architect]

    Momentum in prices is the tendency of assets that have performed well recently (such as over the prior year) to outperform assets in the same asset class that have performed poorly over the prior year. For a more thorough review of momentum check out this post by Wes Gray. This phenomenon has been found to exist not only in stocks all around the globe, but in bonds, commodities, and currencies as

Quantocracy’s Daily Wrap for 07/22/2020

This is a summary of links featured on Quantocracy on Wednesday, 07/22/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The importance of testing different exits [Alvarez Quant Trading]

    When developing a strategy, exits are often not given a second thought. If you are creating a mean reversion, you may default to using Close greater than the 2-period RSI. If you are trading a trend strategy, you may default to trailing exit using 14-day ATR. You try a bunch of entry filters but rarely try a different exit. Or maybe a slight change in the exit. If you are having success, with your
  • The secret sauce that makes Deep Learning frameworks so powerful [Quant Dare]

    Inside most of the Deep Learning frameworks that are available lies a powerful technique called Automatic Differentiation. If you ever encountered these words but dont know what they mean or how this procedure works, this post is for you. In a previous post, we saw how to built a deep learning framework using NumPy. In that post, I mentioned that we could implement the computations at operation

Quantocracy’s Daily Wrap for 07/21/2020

This is a summary of links featured on Quantocracy on Tuesday, 07/21/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Simple Neural Network for Indicator Prognosis [Philipp Kahler]

    Technical indicators are the base of algorithmic trading. So wouldnt it be nice to know tomorrows indicator value in advance? This article is about how to use a simple neural network to do so. Python and Tradesignal will be used to do the programming. A single linear neuron A single neuron / perceptron is the most simple form of a neural network. It consists of several inputs which are weighted
  • EM Equities vs Debt: Same, Same, but Different? [Factor Research]

    Some EM asset classes are highly correlated, to the point they can almost be considered interchangeable EM equities and hard-currency government debt are highly correlated to US equities and bonds In crisis times, all EM exposure is sold off and fails to provide meaningful diversification benefits INTRODUCTION Dreaming of vacationing in Europe conjures images of towns and villages rich in
  • What is Impact Investing? [Alpha Architect]

    Can we do impact investing that is both good for us and tastes better? In the past, if an investment had positive non-financial outcomes (positive impact), a return trade-off was expected. Today, some investors find that incorporating aspects such as diversity, stakeholders, and environmental sustainability leads to stronger companies and investments. Clearly not all healthy meals are tasty.

Quantocracy’s Daily Wrap for 07/18/2020

This is a summary of links featured on Quantocracy on Saturday, 07/18/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Machine Learning Model Validation [Only VIX]

    I just came across an excellent and highly relevant piece of research "A comparison of machine learning model validation schemes for non-stationary time series data" by Matthias Schnaubelt. Features like non-stationarity, concept drift, and structural breaks present serious modelling challenges, and properly validating ML time series models requires knowing proper validation strategies.
  • Research Review | 17 July 2020 | Smart Beta Revisited [Capital Spectator]

    The Smart Beta Mirage Shiyang Huang (University of Hong Kong), et al. June 2020 We document sharp performance deterioration of smart beta indexes after the corresponding smart beta ETFs are listed for investments. Adjusted by aggregate market return, the average return of smart beta indexes drops from 2.77% per year on paper before ETF listing to 0.44% per year after ETF listing. This

Quantocracy’s Daily Wrap for 07/16/2020

This is a summary of links featured on Quantocracy on Thursday, 07/16/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • ESG Scores and Price Momentum Are More Than Compatible [Quantpedia]

    Momentum in stocks is not only a key strategy in the many portfolios of practitioners, but it is also an attractive research topic for academics. The original idea behind momentum, is that past winner tend to perform well in the near future, and vice versa, past loser tend to underperform (Jegadeesh and Titman, 2001). Later, the momentum anomaly was found practically everywhere, Moskowitz, Ooi,
  • Backtesting Basics: Four biases to know by heart [Auquan]

    In God we trust. All others must bring data. Backtesting is probably the single best method we have to quickly evaluate new trading strategies. However, if used incorrectly it can be our greatest weakness guiding us on a false path to ruin. For the uninitiated, backtesting is the process where you simulate a trading strategy with the help of historical data. Effectively youre seeing what
  • Installing TensorFlow 2.2 on Ubuntu 18.04 with an Nvidia GPU [Quant Start]

    Earlier in the year we carried out our 2020 QuantStart Content Survey and Advanced Machine Learning & Deep Learning was voted the most popular topic. This article constitutes the first in a series on the topic of modern machine learning via deep learning as applied to systematic trading research. In this article we will demonstrate how to install a modern deep learning research environment on