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

Most investors analyze investment products based on their holdings However, holdings often misportray of what is determining the risk profile A factor exposure analysis can identify the performance & risk contributors INTRODUCTION It is difficult to watch an hour of European television and not

What are the research questions? The authors argue that factor exposures are random variables governed by a specific distribution that drives the behavior factor exposures over time. Five factors are examined including value, momentum, quality, investment and low volatility, over 12 developed and

Trading in financial markets can be a challenging and complex endeavour, with ever-changing conditions and numerous factors to consider. With markets becoming increasingly competitive all the time, it is a never ending struggle to stay ahead of the curve. Machine learning (ML) has made several

Modified Value-at-Risk (mVaR) is a parametric approach to computing Value-at-Risk introduced by Zangari1 that adjusts Gaussian Value-at-Risk for asymmetry and fat tails present in financial asset returns2 through a mathematical technique called Cornish–Fisher expansion. Since its publication, mVaR

Several years ago, I started using the phrase, “It’s long/short portfolios all the way down.” I think it’s clever. Spoiler: it has not caught on. The point I was trying to make is that the distance between any two portfolios can be measured as a long/short strategy. This simple point, in my

This article analyzes six trend-following indicators from a digital signal processing (DSP) frequency domain perspective in which the indicators are considered as digital filters and their frequency response characteristics are determined. In addition, potential trading signals generated by each

At the end of 2018, researchers at Google AI Language made a significant breakthrough in the Deep Learning community. The new technique for Natural Language Processing (NLP) called BERT (Bidirectional Encoder Representations from Transformers) was open-sourced. An incredible performance of the BERT

All other things equal, an improvement in a country’s terms of trade, the ratio of export to import prices, translates into increased demand for its currency and a boost for its growth outlook. However, terms of trade are a rather subtle and sporadic influence. Therefore, many market participants

The authors ask the following questions: Does the rise of index investing change information production in the economy? Does it affect the informational efficiency of stock prices? What are the Academic Insights? The authors augment (and improve) the Grossman and Stiglitz (1980) model of endogenous

US tech funds underperformed their benchmark indices by 4% between 2018 and 2022 The factor contributions were marginal This resulted in a median alpha of -4% over the 5-year period INTRODUCTION Every day, fortunes are won and lost on the stock market. However, investing in stocks is not a zero-sum

Interest rates are not necessarily a pure random walk. This assumption falls out from noticing that yields of different bond maturities must be in some way related. Have a look at the yields of the 30 year and 3 year U.S. Treasuries in the plot below. Notice that the 3 year yield bounces up and down

All the popular ‘smoothing’ indicators, like SMA or lowpass filters, exchange more lag for more smoothing. In TASC 4/2023, John Ehlers suggested the undersampling of price curves for achieving a better compromise between smoothness and lag. We will check that by applying a Hann filter to the

The empirical evidence from studies such as the 2017 paper “A Century of Evidence on Trend-Following Investing” and the 2020 paper “Time Series Momentum in the US Stock Market: Empirical Evidence and Theoretical Analysis” has found that time-series momentum (TSMOM) has demonstrated

Here is a link to our recent chat with Isaiah Douglass and Josh Bennett. An overview of the conversation is below: On this week’s episode, Isaiah is joined by expert Dr. Wesley Gray, CEO of Alpha Architect, to discuss the concepts of value

This article deals with the degree of market vulnerability to spillovers from disruptions in the cryptocurrency markets. This study investigates the impact of the FTX collapse and bankruptcy across global financial markets. What do responses of financial markets to the collapse of FTX say about

The EURUSD currency pair has been one of the most closely watched and traded pairs in the forex market for years. Its movements can have a significant impact on the global economy and particular investments. In 2022, we witnessed a significant moment in the history of the global currency market. In

After a great 2022 for factor investing, this year has started negatively for all traditional factors Perhaps this can be attributed to a revival of the growth theme as growth ETFs have outperformed again Size performed best, low volatility worst INTRODUCTION We present the performance of five

A costless collar, sometimes referred to as a hedged equity or defined outcome strategy, is a risk management strategy that combines holding a long position in a stock or index with buying a put spread defined by a specific set of strikes (e.g. 5% OTM long put, 20% OTM short put). This provides

Investment strategy should be based on three fundamental principles. First, markets are highly, though not perfectly, efficient. That leads to the conclusion that active management is the loser’s game. Second, if markets are efficient, it must follow that you should believe that all unique sources

It’s always fun to push the boundaries of technology and see what it can do. The AI chatbots are the hot topic of actual discussion in the quant blogosphere. So we have decided to test OpenAI’s ChatGPT abilities. Will we persuade it to become a data analyst for us? While we may not be there yet,

Simple equity ETFs often have exposures to other asset classes Gold stocks are bond proxies & growth stocks are short commodities Investors may have unintended bets in their portfolios INTRODUCTION ETFs used to be like surgical instruments. StateStreet’s SPY tracks the stocks of the S&P

Towards the end of last year, we ran a couple of free Zoom webinars on: The Basics of Edge Extraction – the “trader smarts” of getting an edge Data Analysis for Traders – an interactive research session. Here are the recordings: Basics of Edge Extraction Data analysis for Traders The colab

From the Volume and Mean Reversion post, a reader sent a suggestion to instead use the ratio of 10 day moving average of the Close times Volume divided by the 63-day moving average of the Close times Volume (CV10/63). I had not tried this before and wanted to see how well it would work. First Steps