For the mean reversion strategies that I have created in the past and are trading now, they typically enter at the next day’s open or wait for a further pullback intraday before entering. My current mean reversion strategy, which enters on a limit down, was doing great until a few months ago when

Remember HML? It was the original formulation for estimating the “value” premium published by Fama & French in 1992. In that seminal article, FF argued based on the results they obtained, that the risk of owning equity is multidimensional. One of those dimensions of risk they used was

Financial research has consistently shown that correlations between assets tend to increase during crises and tend to decrease during recoveries1. The recent COVID-19 market crash was no exception, as illustrated on Alvarez Quant Trading blog post Correlations go to One for both the individual

The year 2021 has been an incredible year for passive equity investors so far. However, in the first part of our article, we talked about quantitative strategies which achieved even better results in 2021 than passive US equity investors. Indeed, there do exist such strategies, at least definitely

Long volatility strategies can be built without using options Securities can be selected on different risk metrics like the VIX or high yield spread Although portfolios differ, the strategies exhibited similar trends INTRODUCTION We started our exploration of long volatility strategies by analyzing

Unlike market price trends, macroeconomic trends are hard to track in real-time. Conventional econometric models are immutable and not backtestable for algorithmic trading. That is because they are built with hindsight and do not aim to replicate perceived economic trends of the past (even if their

I have a collection of crypto stat/arb strategies I plan to trade as a portfolio of strategies. Each strategy trades a small mean-reversion portfolio of loosely cointegrated coins, based on a bayesian state-based model. The returns in cryptos for this sort of strategy are phenomenal, however,

rsims is a new package for fast, quasi event-driven backtesting in R. You can find the source on GitHub, docs here, and an introductory blog post here. Our use case for rsims was accurate but fast simulation of trading strategies. I’ve had a few questions about how I made the backtester as fast as

A firm’s stock price should reflect the value of both its tangible and intangible capital. While tangible capital has been widely studied, intangible capital has been receiving more attention due to its increasing importance in economic values. According to a December 29, 2020, Forbes article,

In 1982, a group of inexperienced traders were recruited to be a part of an experiment that would make many of them multi-millionaires. Richard Dennis bet his partner William Eckhardt that anyone could be a successful trader given they had training and a system to follow. It was a re-hash of the

Unfamiliar terms have a way of impressing us. I remember the first time I heard about the ‘Monte Carlo’ method. The name conjured up an image of a sophisticated technique, born out of deep discussions by brilliant mathematicians in a Spanish cafe. Turns out, it’s just a by-word for running

This paper contributes to the literature on understanding the limits of arbitrage and the resulting dynamics of price discovery. Specifically, it studies the context of "merger arbitrage," which is a well-known investment strategy and unless there are limits to arbitrage, this market

Machine learning is a need in almost every sector today. Sectors like medicine, transportation, healthcare, advertising and financial technology are tremendously reliant on machine learning. Speaking about the financial technology domain, algorithmic trading practice is extremely efficient with the

If indicators didn’t help your trading so far, just pimp them by preprocessing their input data. John Ehlers proposed in his TASC September article the windowing technique: multiply the input data with an array of factors. Let’s see how triangle, Hamming, and Hann factor arrays can improve the

Foreign stock ownership is low in China and the market is dominated by retail investors This provides an opportunity for investors to deploy quant strategies Factor investing has been far more attractive in Chinese than U.S. equities in recent years INTRODUCTION The latest chapter in the complicated

You can find the reproducible experiment in this Colab Notebook. In econometrics and financial research, categorical variables, and especially sectors and industries, are usually encoded as dummy variables (also called one-hot encoding in the machine learning community). You can find plenty of such

rsims is a new package for fast, realistic (quasi event-driven) backtesting of trading strategies in R. Really?? Does the world really need another backtesting platform…?? It’s hard to argue with that sentiment. Zipline, QuantConnect, Quantstrat, Backtrader, Zorro… there are certainly plenty

This blog post is the continuation of series about Quantconnect´s Alpha market strategies. This part is related to the factor strategies notoriously known from the majority of asset classes. Although the results are insightful, they are not straightforward, and further analysis could be made.

Decomposing Momentum: Eliminating its Crash Component Pascal Büsing (University of Muenster), et al. July 15, 2021 We propose a purely cross-sectional momentum strategy that avoids crash risk and does not depend on the state of the market. To do so, we simply split up the standard momentum return

This paper studies the relationship between aggregate investor attention and subsequent market returns over the following week. The authors create two different investor attention indicators—one for aggregate retail attention (ARA) and one for aggregate institutional attention (AIA). ARA is found

We track a lot of tactical strategies, and it can be difficult to understand how they all fit together in the big picture. The usual correlation matrix (example) is helpful when drilling down on a single strategy, but it’s near impossible to see the forest for the trees among the 1000’s of data

I recently outlined two models for estimating the US stock market’s return for the decade ahead. Let’s add a third model to the mix with the plan to take the average as a relatively robust forecast. The previous two models (see here and here) used valuation to estimate ex ante performance for

Recent research, including the 2020 studies “Explaining the Recent Failure of Value Investing” and “Intangible Capital and the Value Factor: Has Your Value Definition Just Expired?,” have investigated the impact on U.S. value strategies of the increase in the relative importance of

Of the dozen indicators and metrics we have researched, the fortunes of the US Trade-Weighted U.S Dollar Index (TWDI) has the biggest impact on Bitcoin USD prices. When the TWDI depreciates, this boosts Bitcoin prices strongly. When the TWDI becomes stronger, Bitcoin prices face significant

In our previous article, we’ve discussed a couple of trading strategies exploiting arbitrage between similar stocks using stochastic optimal control methods. A major shortcoming of those approaches is that we restricted ourselves to constructing delta-neutral portfolios. Along with this, the ratio

An inflation portfolio can be created by systematically selecting stocks correlated to inflation This would have resulted in a portfolio with strong sector and factor biases However, the correlation to inflation would not have been significantly higher than for stocks overall INTRODUCTION Measuring

The Kelly Criterion gives an optimal result for betting based on the probability of winning a bet and how much you receive for winning. If you check out Wikipedia or Investopedia, you’ll see formulas like this: f∗=p−1−pb−1f^{*} = p - \frac{1-p}{b-1} f∗=p−b−11−p​ which gives you

Dimension reduction methods of machine learning are suited for detecting latent factors of a broad set of asset prices. These factors can then be used to improve estimates of the covariance structure of price changes and – by extension – to improve the construction of a well-diversified minimum

Of the major machine learning algorithms, the convolutional neural network (CNN) is my favourite. CNNs form some of our company’s most cherished elements that give strength to our investment algorithms. My curiosity was therefore piqued when I came across Sezer and Ozbayoglu’s paper titled

While there may be debates about passive and active investing, and even blogs about the numbers of active funds that were outperformed by the market, history taught us that the outperformance of active or passive investing is cyclical. As a proxy for active investing, the paper examines factor

The internet is flooded with anecdotal evidence about how to swing trade and how to make money. Unfortunately, almost all articles consist of unproven and untested swing trades. To make money swing trading is difficult, but we believe you face much better odds the more you backtest and generate

We have discussed Basic Distance Approach in the previous blog post. In this post, we’ll look into one of the advanced methods in the Distance Approach and its differences to the Basic Distance Approach. If you haven’t read the previous blog post, we recommend reading it before you read this

First off, for the hiring managers out there, after about a one-year contracting role at Bank of America doing some analytical reporting coding for them in Python, I am on the job market. Feel free to find my LinkedIn here. This post will cover how to make tactical asset allocation strategies a bit

Long volatility strategies can be built without using options Portfolios would have primarily consisted of certain currency pairs and treasury bonds They lack explosive returns when volatility spikes, but they also lack the bleed INTRODUCTION Almost all asset classes are implicitly short volatility

nternational markets have been a fertile testbed for factor research because they offer an opportunity to test old ideas on new data. Much of the previous work studying factor structure and risk premia in international markets uses highly aggregated test assets, such as country portfolios, industry

Fluctuations of economic growth are observed throughout multiple measures of business activity and among countries. Due to their synchronized manner, they are often referred to as business cycles.[1] The problem with this designation is a lack of strict periodicity – as we'll see below, the

My experience in financial markets to date has mostly been related to trading and investment banking. From executing index arbitrage and various other strategies, to product managing a team building execution algorithms for automating strategies which minimize market impact and make relative and

Around a month ago, we launched a series of short videos called “Quantpedia Explains“, in which we plan to show and explain some of the themes out of quantitative finance that we think are worth mentioning. We have started with a quick intro to individual Quantpedia Pro reports, and now, we have

We have been working on some option strategies and wanted to get a sense of how well BTC and ETH options are priced on Deribit, i.e. is there a substantial IV premium over realized volatility or are options fairly priced. At first glance, based on the documentation, it seemed that Deribit options

While environmental, social, and governance (ESG) investing continues to gain in popularity, economic theory suggests the share prices of “sin” businesses (typically those involved in the gambling, tobacco, alcohol, guns, and defense industries) will become depressed if a large enough proportion

March for the Fallen (#MFTF) will happen on September 25, 2021. COVID can't kill the event this year! Action Item: Please let us know your trip details so we can support you as much as possible. Here are the links to prior updates if you'd like to review: Footwear and foot care

The first (annual) Algo Trading Summit was a huge success! Over 2,500 registered for the event, with an average of 500 people watching the live stream at any given moment — and, so far, the video recordings have over 5,000 views. Not too shabby. In this post, I want to share and summarize some of

Ordinary least squares (OLS) regression is probably the most commonly used statistical method in quantitative finance (and likely in other quantitative fields). It is very fast to compute, and the results are often quite interpretable. Due to its simplicity, it serves as the cornerstone for many

The most common approach to measuring portfolio (risk) factor exposures is linear regression analysis, which describes the relationship between a dependent variable - portfolio returns - and explanatory variables - factors - as linear. One of the outputs of this analysis are the partial regression

Algorithmic trader Cesar Alvarez from Alvarez Quant Trading joins us to discuss low effort trading strategies, including: An explanation of rotational trading and the benefits/challenges of using rotational strategies, Why rotational trading is a fantastic way to diversify time (and also get to

Divergences occur when price and your indicator move in opposite directions. For example, you’re trading with the RSI and it last had a peak at 80, now it peaks at 70. The underlying security you’re trading was at $14 when RSI hit 80, and now hits a new peak at $18. This is a divergence. Traders

Digital asset ETFs have outperformed tech stocks in recent years However, they provide no exposure to cryptocurrencies Their returns are explained by market beta and equity factors INTRODUCTION Cathie Wood, the founder and CEO of Ark Invest, an ETF manager, is the latest entrant to launching a

My August 17, 2020, article for Advisor Perspectives, “Factor-Based Investing Beats Active Management for Bonds,” provided the evidence from a series of academic papers on the ability of common factors to explain the variation of returns of bond funds, results which have important implications

Corporate balance sheet data are important building blocks of quantitative-fundamental (“quantamental”) investment factors. However, accounting terms are easily misunderstood and confused with economic concepts. Accounting is as much driven by assessment of risk as it is by economic value. For

I’m always on the lookout for interesting ways to time the market. Using a market timing model can help to avoid painful bear markets and indicate when is a good time to buy stocks. Recently, I was looking at some of the strategies on Allocate Smartly and came across one called Growth Trend