COVID-19 Beta

[ quant-finance ]

In this short post, we compute and visualise “COVID-19 betas” for stocks in the S&P500 index, to quantitatively and visually understand which companies were most affected (positively and negatively) by COVID-19.

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Option-implied probability distributions, part 2

[ finance ]

In Part 1 of this series, we demonstrated that the prices of option butterfly spreads imply a probability distribution of prices for the underlying asset. In this post, we will first examine the limiting case of butterfly spreads. Then, we will tackle the industry-standard approach for constructing PDFs from option prices: interpolating in volatility space to generate a volatility surface, converting this into a continuous set of option prices, then applying the Breeden-Litzenberger formula to find the PDF.

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Option-implied probability distributions, part 1

[ finance ]

The goal of this two-part series is to understand what option prices can tell us about the implied probability distribution of future asset prices. Part 1 lays the groundwork and examines an intuitive approach using butterfly spreads.

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A critical look at Greenblatt's Magic Formula

[ quant-finance ]

As the saying goes, when something sounds too good to be true, it probably is – all the more so when it comes to investing. In this short post, we look at the Magic Formula of Joel Greenblatt, as described in The Little Book That Still Beats the Market, critically examining the strategy and attempting to quantify its alpha.

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The Big Shorts: what is the smart money betting against?

[ finance ]

Hedge funds in the UK are legally obligated to disclose to the Financial Conduct Authority (FCA) whenever their net short position in a particular listed company reaches 0.5% of the issued equity capital. In this post, I investigate a publicly available dataset containing information about these large institutional short positions in UK equities and attempt to understand the value that this data contains.

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Statistical arbitrage in closed-end funds

[ quant-finance ]

Sometimes, it is cheaper to buy a basket of assets than it is to buy the assets in the basket. In this post, we discuss closed-end funds and why they often trade at a discount to their net asset value. Furthermore, we explore whether this could be the basis for an algorithmic trading strategy.

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A Tanker Trade

[ finance ]

April 2020 has been a volatile month for oil. Last week, the May WTI contract traded at a low of minus \$40 a barrel. In a desperate search for storage space, people have been chartering oil tankers to use as floating storage units, leading to a price surge in shares of tanker companies like Nordic American Tanker (46%), Teekay (30%), and Scorpio Tankers (59%). In this post, we aim to build a framework for forecasting the revenue of DHT Holdings (NYSE:DHT), a tanker company.

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Understanding the market's expectations of COVID-19

[ finance ]

One of the reasons why I find markets fascinating is that they are capable of integrating huge amounts of information, misinformation, hope, fear and uncertainty into a single number – the price of an asset. In this post, we work backwards, quantitatively examining what the current price of an asset can tell us about its future prospects.

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Rebuilding PyPortfolioOpt: an open source adventure

[ programming ]

A few weeks ago, a user raised an issue on the GitHub repository for PyPortfolioOpt, my open-source portfolio optimisation software library. In this nontechnical post, I discuss why a seemingly innocuous error resulted in a ground-up rebuild of a large chunk of PyPortfolioOpt, and share some reflections on open-source in general.

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Black-Litterman allocation in algorithmic trading

[ quant-finance ]

In December 2019, I released a major update to PyPortfolioOpt, my python portfolio optimisation package. The most significant addition was an implementation of the Black-Litterman (BL) method. Although BL optimisation is commonly used as part of a pipeline to optimise a multiasset/equity portfolio, in this post I argue that BL is particularly well suited to the problem of optimally weighting signals in an algorithmic trading context.

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