Most of the time we make you “earn your education” by reading our posts to build up your knowledge of the latest and greatest academic research concepts. That said, we understand there are different ways to educate, which is why we’re experimenting with video and audio. In the videos below, 3

When data is too big to fit into memory, one approach is to break it into smaller pieces, operate on each piece, and then join the results back together. Here’s how to do that to calculate rolling mean pairwise correlations of a large stock universe. Background We’ve been using the problem of

Back in 2018, I wrote a post, Backtesting a Dividend Strategy, which was conceptually based on the S&P 500 Dividend Aristocrats. Just recently, Norgate Data started offering historical constituent data for the S&P 500 Dividend Aristocrats index. This would be a much ‘cleaner’ version

Thursday after Memorial Day has been a day that has exhibited a bullish bias for many years. I last showed this on the blog a couple of years ago. The chart below shows updated results. SPX Perfromance on Thursday After Memorial Day Single-day seasonality can certainly be overrun by other forces,

When you’re working with large universes of stock data you’ll come across a lot of challenges: Stocks pay dividends and other distributions that have to be accounted for. Stocks are subject to splits and other corporate actions which also have to be accounted for. New stocks are listed all the

As a quant, I have been obsessed with systematic Value and Momentum since the first day I ran a backtest. Part of me knows that the future for this combo is unlikely to be as good as the past. In my R&D, I moved on to other factors more than a decade ago. But another part of me is still in love

With the current market conditions and the wild ride we’ve all been on, we’ve pivoted our attention to focus on supplying academic research on responding to a crisis. This article investigates what the appropriate tactical adjustments investors should consider when making changes to their

Developing a trading strategy from start to finish is a complex process. The process follows the following steps: Formulation of the strategy Write Pseudo Code Transform into working code Start first backtests Optimize Evaluate test results Go live Monitor performance Evaluate and adjust

Tactical investing aims to deliver better risk-adjusted returns and/or reduced drawdowns Tactical ETFs have not achieved either objective in recent years It is challenging to explain the consistent underperformance across different types of tactical ETFs INTRODUCTION Every investor is a tactician,

One way we can quantify a stock’s movement relative to the market index is by calculating its “beta” to the market. To calculate the beta of MSFT to SPY (for example) we: calculate daily MSFT returns and daily SPY returns align the returns with one another regress MSFT returns against SPY

Defensive equity strategies are comprised of stocks that lose less than the market during bear markets while keeping up with the market during a bull market. Coarse sorts on metrics such as volatility, beta, value, and momentum lead to diversified portfolios but have mixed results in terms of their

How should be equity factor strategies implemented? In a long-only (smart beta) way? As a long-short strategy, as most of the hedge funds usually do? Or in a partially-hedged fashion by going long equity factor and shorting market to offset some of the market risks? There is no one universal answer

In today’s article, we are going to take a look at rolling and expanding windows. By the end of the post, you will be able to answer these questions: What is a rolling window? What is an expanding window? Why are they useful? What is a Rolling or Expanding window? Here is a normal window. We use

When macroeconomic conditions change rational inattention and cognitive frictions plausibly prevent markets from adjusting expectations for futures interest rates immediately and fully. This is an instance of information inefficiency. The resulting forecast errors give rise to joint predictability

The Law of Regression to the Tail: How to Mitigate COVID-19, Climate Change, and Other Catastrophic Risks Bent Flyvbjerg (University of Oxford) 13 May 2020 Regression to the mean is nice and reliable, regression to the tail is reliably scary. We live in the age of regression to the tail. It is only

Speaking to Legends is a quest for ideas, insights, and stories from the lives of the most successful hedge fund managers. We learn about their spectacular careers, we share life lessons and dissect their investment techniques. Join us for this journey.

Recently, we wrote about calculating mean rolling pairwise correlations between the constituent stocks of an ETF. The tidyverse tools dplyr and slider solve this somewhat painful data wrangling operation about as elegantly and intuitively as possible. Why did you want to do that? We’re building a

In Finance and Trading, a large amount of data is generated every day. This data comes in the form of News, Scheduled Economic releases, employment figures, etc. It is clear that the news has a great impact on the prices of stocks. Every trader takes great efforts in keeping track of the latest news

Without much ado, here is the code ## Copyright (C) 2020 dekalog ## ## This program is free software: you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 3 of the License, or ## (at your option)

Working with modern APIs you will often have to wrangle with data in JSON format. This article presents some tools and recipes for working with JSON data with R in the tidyverse. We’ll use purrr::map functions to extract and transform our JSON data. And we’ll provide intuitive examples of the

After closing down more than 1% yesterday, SPY is set to open up enough to erase all of yesterday’s losses. I decided to look back at other times this has happened. SPY Big Gap Up Erases Yesterdays Loss - Open to Close Results Not exactly a consistent edge, but I thought the general upside

Can a Sharpe ratio of 1.55 be better than a Sharpe ratio of 1.63 in a 1 year track-record? Not necessarily. Sharpe ratios are not comparable, unless we control the skewness and kurtosis of the returns. In this post we are going to analyze the advantages of the Probabilistic Sharpe Ratio exposed by

In our last post, we applied machine learning to the Capital Aset Pricing Model (CAPM) to try to predict future returns for the S&P 500. This analysis was part of our overall project to analyze the various methods to set return expectations when seeking to build a satisfactory portfolio. Others

The field of behavioral finance provides us with fascinating insights into individual investor behavior, including how individuals view risk, as well as the impact of those views on asset prices. Prospect theory plays a major role in explaining investor behavior. The theory, formulated in 1979 by

In this post, we look at tools and functions from the field of digital signal processing. Can these tools be useful to us as quantitative traders? What’s a Digital Signal? A digital signal is a representation of physical phenomena created by sampling that phenomena at discrete time intervals. If

Now that market volatility is back, tail risk management strategies are gaining some attention. A lot of investors are dipping their toe into the water and exploring trend-following strategies on the S&P 500 — arguably the most popular U.S. stock market index.This paper explores multiple trend

For those systematic traders who are considering a long-term investment horizon one of the most common forms of generating a portfolio involves static proportional capital allocation amongst a collection of (hopefully) diversifying asset classes, which is periodically rebalanced to maintain the

Longer is better for analyzing the stock market, which is why Professor Robert Shiller’s data set (with an 1871 starting date) is one of the great free resources on the internet for studying the history of US equities. With that in mind, let’s review how the current drawdown for the S&P 500

Just a quick post to illustrate the latest of my ongoing chart iterations which combines a levels chart, as I have recently been posting about, but with the addition of a refined methodology of creating the horizontal histograms to more clearly represent the volumes over distinct periods. The main

We track 50+ public Tactical Asset Allocation (TAA) strategies in near real-time, allowing us to draw broad conclusions about TAA as a trading style. By design, most of those strategies trade just once per month, and most assume that next month’s asset allocation is both calculated and executed on

How might we calculate rolling correlations between constituents of an ETF, given a dataframe of prices? For problems like this, the tidyverse really shines. There are a number of ways to solve this problem … read on for our solution, and let us know if you’d approach it differently! First, we

This research note was originally published at Alpha Architect. Here is the link. SUMMARY Factors can be valued like stocks Factor valuations have not changed structurally over the last 30 years Cheap factors outperformed expensive ones on average INTRODUCTION Tesla (TSLA) breached the $100 billion

As a risk factor, Value is very much alive. Confusing the risk side and return side of factors creates the misconceived question of whether the value factor is dead. Something that is dead, does not move. A dead factor is a flat horizontal line with random noise. By contrast, value has been moving

Earlier in May I posted about Market Profile with some charts and video. Further work on this has made me realise that my earlier post should more accurately be described as Volume Profile, so apologies to readers for that. Another, similar type of chart I have seen described as a TPO chart (TPO

Modern data science is fundamentally multi-lingual. At a minimum, most data scientists are comfortable working in R, Python and SQL; many add Java and/or Scala to their toolkit, and it’s not uncommon to also know one’s way around JavaScript. Personally, I prefer to use R for data analysis. But,

It has been 47 trading days since SPX posted its last 3-day pullback. That is a long time. And it is especially long considering SPX is still below its 200ma. Should SPX fail to rally out of this early hole this morning, we will finally see the 1st 3-day pullback since March 9th. Bulls could look at

Nearly two months ago, in a time of the highest turmoil during the current pandemic crisis, we performed a quick assessment of the status of performance of equity factor strategies. The world has still not been able to ward-off health-care crisis completely, but a lot of countries have made

We recently covered a paper by David Blitz that highlighted the potential problems with passively investing in “active” strategies. The research piece is great and surfaces a lot of great concepts. Like a lot of research we publish/summarize this article appears to “shoot Alpha Architect in

The Bitcoin’s 3rd halving was the most anticipated event this year. This a moment when a reward for all Bitcoin block miners is cut by half. It happens every 4 years or every 210,000 blocks on the Bitcoin blockchain. The previous two halving events took place in 2012 and 2016, respectively. Before

In this post, we’re going to show how a quant trader can manipulate stock price data using the dplyr R package. Getting set up and loading data Load the dplyr package via the tidyverse package. if (!require('tidyverse')) install.packages('tidyverse') library(tidyverse) First,

Attention all finance geeks. The latest and greatest from academic researchers is available for all to review. The WFA recently released their sessions. WFA is one of the more prestigious academic conferences and papers presented at the conference often find their way into top-tier academic

Quantitative factor portfolios generally use historical company fundamental data in portfolio construction. The key assumption behind this approach is that past fundamentals proxy for elements of risk and/or systematic mispricing. However, what if we could forecast fundamentals, with a small margin

Every aspiring millionaire who comes to the markets armed with some programming ability has implemented a systematic Get Rich Quick (GRQ) trading strategy. Of course, they don’t work. Deep down even the greenest of newbies knows this. Yet, still, we are compelled to give them a try, just once,

The price of energy changes hourly, which opens up the possibility of temporal arbitrage: buying energy at a low price, storing it, and selling it later at a higher price. To successfully execute any temporal arbitrage strategy, some amount of confidence in future prices is required, to be able to

In this post, we are going to construct snapshots of historic S&P 500 index constituents, from freely available data on the internet. Why? Well, one of the biggest challenges in looking for opportunities amongst a broad universe of stocks is choosing what stock “universe” to look at. One

When hunting for diversity, the typical investor considers only average correlations. However, when measuring an asset’s diversification benefits utilizing average correlations tend to mislead investors. For example, when both U.S. and non-U.S. equities produce returns greater than one standard

The convex payoff profile of trend following strategies naturally lends itself to comparative analysis with option strategies. Unlike options, however, the payout of trend following is not guaranteed. To compare and contrast the two approaches, we replicate simple trend following strategies with

If you want to make money trading, you’re going to need a way to identify when an asset is likely to be cheap and when it is likely to be expensive. You want to be a net buyer of the cheap stuff and a net seller of the expensive stuff. Thanks, Capitain Obvious. You’re welcome. How does this

In last week’s post we extended the systematic value factor (or at least a pretty good proxy of it) back to 1871. The response from readers was encouraging, perhaps because of the pain that value investing has been causing lately. Long-run history gives some relief. This week we dig deeper,

Factor investing is likely the best option for investors seeking to outperform the market However, the cyclicality of factors makes factor investing challenging when it underperforms Investors that do not understand this cyclicality are likely better served by plain, rather than smart beta FREE