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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

Quantocracy’s Daily Wrap for 07/15/2020

This is a summary of links featured on Quantocracy on Wednesday, 07/15/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Forex Intraday Seasonality [Dekalog Blog]

    Over the last week or so I have been reading about/investigating this post's title matter. Some quotes from various papers' abstracts on the matter are: "We provide empirical evidence that the unique signature of the FX market seasonality is indeed due to the different time zones market participants operate from. However, once normalised using our custom-designed procedure, we
  • Finance Factors Coordination? Cascade Selection [Quant Dare]

    Currently, strategies based on premium factors are everywhere: from funds or ETFs built on ratios or statistics perfectly specified, trying to exploit specific factor premia, to boutique instruments more or less opaque that following one or more risk premia. In any case, one of the questions we may pose is what the best way to combine several risk premia is and what interaction can expect. In this
  • Left Tail Risk and Left Tail Momentum [Alpha Architect]

    The positive trade-off between risk and expected return is the most fundamental concept in financial economics. Most investors are risk-averse. In order to hold higher-risk securities, they demand higher compensation in the form of higher expected returns. And risk-averse investors are more sensitive to downside risk, the left tail in the distribution of potential outcomes. I covered tail risk

Quantocracy’s Daily Wrap for 07/14/2020

This is a summary of links featured on Quantocracy on Tuesday, 07/14/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How To Be a Quant Trader – Experiments with @QuantConnect [Robot Wealth]

    This post presents an analysis of the SPY returns process using the QuantConnect research platform. QuantConnect is a strategy development platform that lets you research ideas, import data, create algorithms, and trade in the cloud, all in one place. For this research, Ive used their online research notebook, and it came preinstalled with all the libraries and data (intraday) I needed to

Quantocracy’s Daily Wrap for 07/13/2020

This is a summary of links featured on Quantocracy on Monday, 07/13/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sixty-Forty Over the Long-Run [Two Centuries Investments]

    Based on many years of reviewing investor portfolios, I concluded that most end up closely resembling a 60% Stocks / 40% Bonds Allocation. Yes, many portfolios also have alternatives, nuanced sub-asset classes, individual security selection, and perhaps some tactical components. But when you look at their returns, a simple 60/40 can usually explain 99% of these more diversified allocations. (This
  • Reducing Estimation Error in Mean-Variance Optimization [Alpha Architect]

    As a general rule, we recommend you kick your spidey senses into high gear anytime there is a geek bearing formulas (especially if they are trying to sell you something). Simple is always a nice cheap default because complexity often leads to confusion, which leans to a need to have an expert, which leads to advice fees, and so the game goes. With that disclosure out of the way, complexity is not
  • Cap-Weighted Benchmarks: Good Momentum Bets? [Factor Research]

    After strong momentum rallies, investors frequently ask if cap-weighted benchmarks are good Momentum bets Factor exposure analysis shows this is not the case Investors should seek smart beta and long-short products if they want Momentum exposure INTRODUCTION Old myths are hard to kill. Good old myths are nearly impossible to kill. Good old myths with elements of pseudo credibility are like
  • Portfolio Optimisation with MlFinLab: Theory-Implied Correlation Matrix [Hudson and Thames]

    Traditionally, correlation matrices have always played a large role in finance. They have been used in tasks ranging from portfolio management to risk management and are calculated based on historical empirical observations. Although they are used so frequently, these correlation matrices often have poor predictive power and prove to be unreliable estimators. Additionally, there are also