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Quantocracy’s Daily Wrap for 08/09/2020

This is a summary of links featured on Quantocracy on Sunday, 08/09/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio Optimisation with MlFinLab: Estimation of Risk [Hudson and Thames]

    Risk has always played a very large role in the world of finance with the performance of a large number of investment and trading strategies being dependent on the efficient estimation of underlying market risk. With regards to this, one of the most popular and commonly used representation of risk in finance is through a covariance matrix higher covariance values mean more volatility in the

Quantocracy’s Daily Wrap for 08/08/2020

This is a summary of links featured on Quantocracy on Saturday, 08/08/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 10 Learnings from Open Source [Hudson and Thames]

    As many of you will know by now, Hudson & Thames is pivoting towards an open-core business model and away from our dreams of pure open source and the unlocking the commons. What follows is a very brief history of our learnings with open-source. Starting Out MlFinLab started as an ambitious project for Ashutosh and my (Jacques) Masters in Financial Engineering at WorldQuant University. We
  • Measures of market risk and uncertainty [SR SV]

    In financial markets, risk refers to the probability distribution of future returns. Uncertainty is a broader concept that encompasses ambiguity about the parameters of this probability distribution. There are various types of measures seeking to estimate risk and uncertainty: [1] realized and derivatives-implied distributions of returns across assets, [2] news-based measures of policy and

Quantocracy’s Daily Wrap for 08/07/2020

This is a summary of links featured on Quantocracy on Friday, 08/07/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What is the probability of profit of your next trade? (Introducing PredictNow.Ai) [EP Chan]

    What is the probability of profit of your next trade? You would think every trader can answer this simple question. Say you look at your historical trades (live or backtest) and count the winners and losers, and come up with a percentage of winning trades, say 60%. Is the probability of profit of your next trade 0.6? This might be a good initial estimate, but it is also a completely useless
  • Factor Investing in Singapore [Factor Research]

    Singapores stock market has unique features given its strong sector biases However, despite these, there were no structural factor exposures over time Like in other markets, investors can pursue factor investing to generate outperformance INTRODUCTION One of the stories of how Singapore received its name is about a Sumatran prince who came across a mythical beast called Janggi while hunting.
  • Research Review | 7 August 2020 | Gold [Capital Spectator]

    Is Gold a Hedge or Safe Haven Asset during COVID19 Crisis? Md Akhtaruzzaman (Australian Catholic University), et al. May 15, 2020 The COVID19 pandemic has shaken the global financial markets. Our study examines the role of gold as a safe haven asset during the different phases of this COVID19 crisis by utilizing an intraday dataset. The empirical findings show that dynamic conditional

Quantocracy’s Daily Wrap for 08/06/2020

This is a summary of links featured on Quantocracy on Thursday, 08/06/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantamental: How to Create a Google Style News Recommender for Your Stocks [Auquan]

    This article is accompanied by a Google Colab notebook, which contains all the code and additional mathematical details. You can find the notebook here: https://links.quant-quest.com/KGNotebook What Will You Learn in This Article? In this article we will explore how you can automatically identify relevant news about a company, using a technology called knowledge graphs (KGs). You will be able to
  • Cross-Asset Signals and Time-Series Momentum [Alpha Architect]

    In their paper Time Series Momentum, published in the May 2012 issue of the Journal of Financial Economics, Tobias Moskowitz, Yao Hua Ooi and Lasse Pedersen documented significant time-series momentum (trend) in equity index, currency, commodity and bond futuresdelivering substantial abnormal returns with little exposure to standard asset pricing factors and performing best during extreme

Quantocracy’s Daily Wrap for 08/04/2020

This is a summary of links featured on Quantocracy on Tuesday, 08/04/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Buy / Sell Imbalance [Tr8dr]

    It is fairly easy to recognize price momentum with price-based indicators ex-post or with lag. Price based momentum signals tend to have lag issues in recognizing the start and end of a price move as there is a tradeoff between noise and lag [1] that cant be defeated without future information (due to principles from signal processing). [1] For those interested see impulse-response and the
  • Creating Anti-Fragile Portfolios [Factor Research]

    Most asset classes are bets on economic growth Diversified endowment-style portfolios are essentially short volatility Long volatility strategies can be used to create anti-fragile portfolios LONG OR SHORT VOLATILITY? In what by now seems like a galaxy far far away, I once worked as an equity derivatives intern at Credit Suisse First Boston in London. As at other investment banks, the team had

Quantocracy’s Daily Wrap for 08/02/2020

This is a summary of links featured on Quantocracy on Sunday, 08/02/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio Optimisation with MlFinLab: Hierarchical Equal Risk Contribution [Hudson and Thames]

    Harry Markowitzs Modern Portfolio Theory (MPT) was seen as an amazing accomplishment in portfolio optimization, earning him a Nobel Prize for his work. it is based on the hypothesis that investors can optimize their portfolios based on a given level of risk. While this theory works very well mathematically, it fails to translate to real-world investing. This can be mainly attributed to two

Quantocracy’s Daily Wrap for 08/01/2020

This is a summary of links featured on Quantocracy on Saturday, 08/01/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to learn Python for finance [Cuemacro]

    The question I get asked most is, what is your favourite burger joint? The answer.. well, youll have to ask me! The second question I get asked a lot, particularly in recent months, is how can I learn Python if Im working in finance? I will endeavour to answer that question, updating and adding to articles Ive written before. If you work in finance there are lots of good reasons to learn

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