The biggest news this week is that Deribit is moving ahead with launching futures on their DVOL Bitcoin volatility index. Like with every new product launch, I am cautiously optimistic, but given that Deribit has ~ 90% market share in cryptocurrency options volume, I think that the product has a

Science has been in a “replication crisis” for more than a decade. Researchers have discovered, over and over, that lots of findings in fields like psychology, sociology, medicine, and economics don’t hold up when other researchers try to replicate them. There are many interesting questions of

Negative exclusionary screening refers to an investment strategy in which socially controversial firms in particular sectors are excluded from the portfolio. The Global Sustainable Investment Review reports that, in 2020, more than $15 trillion (43% of total sustainable investments) were invested

Readers have asked for our take on “the single greatest predictor of future stock market returns”, aka the Aggregate Investor Allocation to Equities. This indicator was first shared by Philosophical Economics back in 2013, and recently resurrected by Portfolio Optimizer (two excellent sites you

Neither equity generalists nor sector specialists have generated alpha on average There is no consistency in alpha generation by either type of fund manager The most consistent alpha generators produced no alpha out-of-sample INTRODUCTION When I joined Citigroup as an analyst in their mergers &

Skewness is one of the less-known but practical measures from statistics that can be used in trading. It is defined as a measure of the asymmetry of the probability distribution of a random variable around its mean. Financial mathematics and most quantitative models assume some kind of symmetric

Another experiment with ChatGPT-4: Active reading a semi-technical book. This book by Michael Isichenko is probably the best I have read so far in this field. Let’s dive into it! You can (and should) buy this book. Chapter 1 Market Data Gautier’s Prompt: The author mentions in his book that

The past decade has witnessed a dramatic increase in spending on intangibles (not just research and development and advertising expenditures, but also expenses related to human capital) relative to tangible capital expenditures on plants and equipment. Given the change, it is not surprising that

We recently hosted our 6th Annual Democratize Quant Conference (sign up here for updates). This post is a recap of what we learned at the conference and some resources we can make available to the public. The agenda for the 2023 conference is outlined below: Date Time Topic Presenter Notes 5/18

Rotational trading is a strategy used by investors that involves purchasing top-performing assets and simultaneously selling the underperforming ones in their portfolio. It’s a great way to periodically manage a portfolio by holding winners and selling losers. Backtesting a rotational trading

This paper helps investors better understand drawdowns and recoveries, in terms of empirical facts, practical implications, and strategies for handling them. It shows the importance of the “interplay” between drawdowns and recoveries (which the authors call “submergence”), which should not

This research was inspired by Alpha Architect’s coverage of a new paper looking at how the distance from a stock’s 1-year high has affected the performance of momentum strategies and the likelihood of “momentum crashes”. We look at the same question applied to a stock index: the S&P 500.

In his 2013 post The Single Greatest Predictor of Future Stock Market Returns, Jesse Livermore1 from the blog Philosophical Economics introduced an indicator to forecast long-term U.S. stock market returns and empirically demonstrated that it outperformed all the commonly used stock market valuation

This a test of Mark Virag’s paper “Momentum Based Balancing for the Diversified Portfolio” (NAAIM Wagner Award winner, 2014). This is a relative momentum strategy that provides an interesting contrast to a popular strategy we track: FinancialMentor.com’s Optimum 3. More on this later.

Long-only trend following in equities was more effective than long-short trend following in the US Same for European and Asian stock markets Perhaps explained by the negative skewness of stock markets INTRODUCTION Imagine a world where economic growth is anemic. Governments and central banks do

In Reimagining Index Funds (Arnott, Brightman, Liu and Nguyen 2023), the authors propose a new methodology for forming an index fund, designed to avoid the “buy high, sell low” behavior that can emerge in traditional index funds while retaining the depth of liquidity and capacity. Specifically,

Abstract: In times of extreme macroeconomic events, including war, hyperinflation, or significant economic recessions, many investors believe gold investing is a safe haven. Is that belief warranted? Investors have concerns about the increased risks of inflation and a recession, Congress’ ability

Trend following can benefit from consideration of macro trends. One reason is that macroeconomic data indicate headwinds (or tailwinds) for the continuation of market price trends. This is particularly obvious in the foreign-exchange space. For example, the positive return trend of a currency is

In this article we will access the Polygon API and download a month of intraday minutely Forex data. We will show you how to access the API, creating a Python function that can be easily adapted to extract FX data for various pairs across different timespans. We will also create and visualise a

In a previous post, we saw how to use genetic algorithms to make implicit optimizations. We used that technique to construct a portfolio, but in a very simple manner: we were just limited to 5 stocks, and we were looking for a constant weight for each of them, assuming daily rebalancing. Today, we

Momentum crashes are a blight on the performance of momentum strategies. Although there has been a fair amount of research on the topic, few practical solutions have emerged to mitigate the impact on portfolios. In this study, the authors document the outperformance of momentum stocks, made somewhat

Emerging markets offer divergent factor exposures across and within regions Smart beta ETFs do not necessarily offer high factor exposures It is all about fund selection INTRODUCTION We highlighted previously that the case for exposure to emerging markets (“EM”) stocks is not as clear as

I recently upgraded my live production system to include all the extra instruments I've added on recently. I also did a little consolidation of trading rules, simplifying things slightly by removing some rules that didn't really have much allocation, and adding a couple from my new book.

Given the strong empirical evidence demonstrating the persistence, pervasiveness, robustness, and implementability of premiums for the factors of size, value, momentum, and profitability in the cross-section of returns, investors may be tempted to gain exposure to those factors across industries and

As the name implies, today we want to consider almost trivially simple models. Although the current trend points towards complex models, even for time-series models, I am still a big believer in simplicity. In particular, when your dataset is small, the subsequent ideas might be useful. To be fair,

In this blog post, I will demonstrate how to work with stock price data using the DuckDB database management system in R. DuckDB is a fast and lightweight analytical database engine that is designed to work with various programming languages, including R. You can use Duck DB from the command line or

The Gerber statistic is a measure of co-movement similar in spirit to the Kendall’s Tau coefficient that has been introduced in Gerber et al.1 to estimate correlation matrices within the Markowitz’s mean-variance framework. In this post, after providing the necessary definitions, I will

Downside betas do not help to identify diversifying strategies These need to be combined with upside betas Betas to the S&P 500 were more useful than betas to the VIX INTRODUCTION In our article Downside Betas vs Downside Correlations (read Downside Betas vs Downside Correlations) we contrasted

Collaboration with ChatGPT. I am still useful to package the experiment, and advertise it, but for how long? 🙂 In this joint work, I felt more like the robot copy-pasting rather than the author of the experiment. Sure, I did the prompting, but that too could be automated, after all building

Here is part two of my annual review. Part one looked at my overall portfolio, including long only, but there was only a cursory look at my futures. Here in this second part I will be looking a my futures trading account in a lot more detail. It's important to say why I'm doing this.

Indicators of growth and inflation cycles are plausible and successful predictors of asset class returns. For proof of concept, we propose a single balanced “cyclical strength score” based on point-in-time quantamental indicators of excess GDP growth, labor market tightening, and excess

A series of events has led to significantly increased interest in stock and options trading by retail investors: The arrival of investing platforms (such as Robinhood) with zero trading commissions and no account minimums. The COVID-19 pandemic, causing many workers to largely remain at home for

So, it’s been a little while. But after a couple of years of some grunt work analytics jobs *and* consulting for a $1B AUM fund, I’ve decided that I had a bit more in the tank to share as far as quant content creation–quantent creation (?)–goes. And a function I’ve searched for in Python

The expense ratio aside, the cost of transacting in an ETF depends on the size of the bid/ask spread at any point in time during the trading day. The ETF investor should make evidence-based trading decisions since the bid/ask spread can range from 1 basis point (bp) to several hundred bps. What are

A good book for an introduction to volatility from a trading perspective. Some excerpts from Volatility Trading by Sinclair: I am a trader. I am not a mathematician, financial engineer, or philosopher. My success is measured in profits. The tools I use and develop need only be useful. They need not

Stocks can be ranked by their upside and downside betas to the S&P 500 Results in strong sector biases and factor exposures Excess returns from upside stocks were negative, zero for downside stocks INTRODUCTION Most capital allocators use correlation to identify strategies that may add

A bit late this year, due to a confluence of holidays, book launches, university exam writing and various other things. Here lies within my performance for the UK tax year 2022-23. Previous years can be found here. TLDR: Not great, absolute or relative. It was indeed a complete anus - horrible!.

Some of the strategies we track use economic data, like the unemployment rate, when making investment decisions. Like 99.99% of strategy backtests you’ll encounter, we’ve always taken the shortcut of basing our historical results on that economic data as it looks today. The problem is that

Over the almost 100 years that we have had data for U.S. stocks, the value premium (the annual average difference in returns, relative to accounting measures, from buying stocks whose market prices are low versus stocks whose market prices are high) has averaged 4.4% per year (when using

Welcome back, fellow traders and machine learning enthusiasts! We hope you’ve been enjoying our journey towards building a successful machine learning trading strategy. If you missed Part 1 of our series, don’t fret – you can always catch up on our exploration of various financial data

We will host our 6th annual “Democratize Quant” conference on May 18th via Zoom. The event is 100% free, but we do screen participants to enforce our “no spammers” policy. Request access Conference website Our speaker line-up is excellent, and we look forward to some exciting discussions.

Stock momentum trading is popular in practice and extensively investigated in academic studies. The paper finds a new option momentum, extending a recent study by Heston et al. (2022), who show that options also display momentum. Our risk-based option momentum is substantially stronger, has a risk

I have seen some chat about the Russell “Death Cross” that occurred on Friday and the potential bearish implications for the market. A “Death Cross” is a catchy (though not terribly accurate) term for when the 50-day moving average of a security cross below its 200-day moving average. It is

Thanks to the courtesy of Packt Publishing, I had the pleasure of receiving, reading, and studying the new release of Python for Finance Cookbook, the book by Eryk Lewinson. This is the second (and probably the last) edition, according to the author himself. Therefore, it must be solid and

Investors typically use correlation to identify diversifying strategies, but the metric can be misleading Upside and downside betas and correlations provide a better perspective Common hedge fund strategies failed to provide attractive diversification benefits INTRODUCTION Are investors as rational

What are the Research Questions? Communication in social networks is not new. In fact, it goes back to coffee houses in the 17th century. According to Standage (2006), “the drama of the South Sea Bubble, a fraudulent investment scheme that collapsed in September 1720, ruining thousands of

In our book “Your Complete Guide to Factor-Based Investing,” Andrew Berkin and I provided evidence that among the hundreds of equity factors identified in the literature, there were only five that met our criteria for investment. The factor must have provided a premium that was: persistent

As a quantitative researcher, your main goal is to find new financial edges. In this article, we will show an overview of the pipeline for designing alpha-generating investment strategies, with associated python code as usual. Here are the main steps that will be presented. Investment rationale Data

Financial data is one of kind: we think is non-stationary, and the samples are non-iid, which means one cannot simply apply common machine learning techniques. In this post, we briefly cover some pitfalls when using machine learning algorithms in a financial context. 1. Harry Potter and the model

Tommorrow marks the official release of my 4th book: Henceforth to be known as AFTS (I've had authors copies since the 24th February, but modern supply chains being what they are it takes considerably longer for the book to arrive in the hands of my readers; although plenty of people on twitter