Point&Figure charts have been around for more than a 100 years and they are still quite popular, especially with commodities and forex traders. This article will do some statistical analysis of the most basic Point&Figure signal. Point&Figure Charts – price movements only Unless bar

Back in the 1/20/2009 blog I looked at inauguration day returns. I wondered at the time whether a new president brought about new hope and optimism for the market. I have decided to update that study today. I limited the instances to only those inaugurations where a new president was entering

Risk factor models are at the core of quantitative investing. We’ve been exploring their application within our portfolio series to see if we could create such a model to quantify risk better than using a simplistic volatility measure. That is, given our four portfolios (Satisfactory, Naive, Max

A couple of weeks ago I was casually chatting with a friend, masks on, social distance, the usual stuff. He was telling me how he was trying to, and I quote, detox from the broker app he was using. I asked him about the meaning of the word detox in this particular context, worrying that he might go

As a quant trader, you are always on the lookout to create and optimise your trading strategies. Backtesting forms a very important part of this process. And for backtesting, access to historical data is a necessity. But it’s a very daunting task to find decent historical price data for

Long-Term Stock Forecasting Magnus Pedersen (Hvass Laboratories) December 17, 2020 When plotting the relation between valuation ratios and long-term returns on individual stocks or entire stock-indices, we often see a particular pattern in the plot, where higher valuation ratios are strongly

The Black-Litterman (BL) model is one of the many successfully used portfolio allocation models out there. Developed by Fischer Black and Robert Litterman at Goldman Sachs, it combines Capital Asset Pricing Theory (CAPM) with Bayesian statistics and Markowitz’s modern portfolio theory

Interest in factor investing was hot several years back but seems to have died on the back of poor relative performance and a move to hotter products in thematics and ESG. But, for better or worse, we haven’t moved on. We are boring and we trust the process. We still believe that markets do a

Although the ETF market has grown exponentially over the recent 20 years, ETFs that are less popular are not always liquid. A majority of the dollars flowing into ETFs are concentrated in 3 products, accounting for 46.7% of total ETF trading volume (see Figure 3 below). If the next 8 ETFs are

There are few alternatives to regression analysis when explaining investment performance Too few as well as too many independent variables can be problematic The results are often not intuitive, but also encourage asking further questions that may prove insightful INTRODUCTION The older I become,

In this post we discuss the algorithms we use to accurately recover implied dividend and interest rates from option markets. Implied dividends and interest rates show up in a wide variety of applications: to link future-, call-, and put-prices together in a consistent market view de-noise market

Typically, we cannot predict a meaningful portion of daily or higher-frequency market returns. A more realistic approach is classifying the state of the market for a particular day or hour. A powerful tool for this purpose is artificial neural networks. This is a popular machine learning method that

The predictive abilities of value and momentum strategies are among the strongest and most pervasive empirical findings in the asset pricing literature. (here is a deep dive) For example, the study “Value and Momentum Everywhere” by Clifford Asness, Tobias Moskowitz and Lasse Pedersen, published

Earlier versions of Zorro used to ship with a script for converting market data in Zorro binary format to CSV. That script seems to have disappeared with the recent versions of Zorro, so I thought I’d post it here. When you run this script by selecting it and pressing [Test] on the Zorro

Risk. Love it or hate it, well as a trader you have to deal with it even though none of us really like it. No, we'd all prefer to be one of those mythical traders you hear about on youtube or instagram who consistently make $1000 a day, and never lose any money. Sadly I am not in that unicorn

An age-old question in the quant community asks whether systematic traders should stick with simple quant strategies or expend the effort to implement more advanced approaches. It is often the perception that retail algo traders solely utilise simpler strategies while quantitative hedge funds carry

The Hurst exponent is a measure for the behaviour of the market. It shows if the market behaves in a random, trending or mean-reversion manner. This can be used to select the right trading strategy for your market. Hurst Exponent hurst spx hurst exponent spx The hurst exponent describes the self

Momentum has been the clear winner across markets in 2020 Value has been the laggard like in recent years Low Volatility ended a 10-year fantastic run INTRODUCTION We present the performance of five well-known factors on an annual basis for the last 10 years. We only present factors where academic

I have been thinking a lot about different models for backtesting and strategy development. While I would like to think it's possible to develop one universal backtester, I believe that different time horizons require materially different programming interfaces. In particular, tick-by-tick

Building on the work of Robert Shiller, in recent posts I investigated the use of the CAPE ratio to predict future stock market performance and examine for the structural change in market valuation over time. This work revealed that stock market returns depend significantly on valuation and are

Another day, another record. Today, at 17:35 GST+1, Bitcoin crossed U$33,000 in trading at Coinbase Pro exchange and did not fall sharply down. It took about 4.5 hours to accelerate from a psychological level of U$30k with more greed among investors rather than fear of bursting Bitcoin (second)

In our last post, we looked at using a risk factor model to identify potential sources of variance for our 30,000 portfolio simulations. We introduced the process with a view ultimately to construct a model that could help to quantify, and thus mitigate, sources of risk beyond a simplistic

The American Finance Association Annual Meetings are here. 1 The conference is virtual this year but that doesn’t mean the organization hasn’t done a good job collecting the brightest minds in academia to discuss hundreds of new finance research papers — a gold mine for new and exciting ideas!

Littered with Corona, this year was not easy. But looking around me, I feel grateful. The following quote by Socrates comes to mind: “If all our misfortunes were laid in one common heap whence everyone must take an equal portion, most people would be content to take their own and depart.” On

Many traders use strategies based on trends that occur in stock, bond, currency, commodity, and other financial asset price time series in order to “buy low” and “sell high”. A trend is considered to be the overall direction of prices over a period of time. If prices have generally increased

This research is an update to “Documentation of the File Drawer Problem in Academic Finance Journals” published by the same authors in the Journal of Investment Management in 2018. A summary of that article can be found here. The “file drawer problem” refers to the idea that journal editors

In 2020 we published more than 50 research notes on mostly factor investing and smart beta ETFs, but also on topics like ESG, tail risk hedge funds, long volatility strategies, and private equity. The Research Compendium 2020 contains all of our research published this year. We would like to thank

In Part 1 we looked at using TimescaleDB and SQLAlchemy to build a relational database model of the Sharadar equity dataset with a Python object model sitting on top. The initial cut of this project ran on my desktop and broke up each of the dataset loads into a simple script that I could run in

The purpose of seasonal adjustment is to remove seasonal and calendar effects from economic time series. It is a common procedure but also a complex one, with side effects. Seasonal adjustment has two essential stages. The first accounts for deterministic effects by means of regression and selects a

In the prior post I showed results for a new equities strategy which uses a combination of signals to create and risk manage a high-momentum portfolio. Further investigation revealed that I had neglected on a couple of fronts: failed to account for dividends (which are substantial) some data issues

The premise of using either time-series momentum or “trend-following” using moving averages is the same only the math differs very slightly (see Which Trend Is Your Friend? by AQR): using some fixed lookback you can time market cycles and capture more upside than downside and therefore improve

Japanese traders invented candle patterns in the 17th century. Some traders believe that those patterns are still valid. But alas, no one yet got rich with them. Still, trading book authors are all the time inventing new patterns, in hope to find one that is really superior to randomly entering

I’ve posted and updated the “Twas 3 Nights Before Christmas” study on the blog here several times since 2008. The study will kick in at the close today (12/22). This year I will again show the Nasdaq version of the study. While all the major indices have performed well during this period, the

This example investigates the performance of R in comparison to Julia language. Additionally shows how to easily call Julia inside R code. With that being said, we will load JuliaCall library that enables us to do so. Alternatively, there is also XRJulia library available. library(JuliaCall) It is

NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Using daily adjusted close data from 20201118 to 20201218 for Dow 30 stocks, we compute correlation coefficients, apply a threshold of 0.8 to find similar stocks, and

Is there a better way than the Fed model to measure relative attractiveness of equities and bonds. In his October 2020 paper entitled “Towards a Better Fed Model”, Raymond Micaletti examines seven Fed Model alternatives, each comparing a 10-year forward annualized estimate of equity returns to

Suppose, during a nightly TV weather broadcast, that a reporter presented forecasts by persons, with no credentials in mathematical meteorology, who based their analysis on eyeballing a few charts and graphs. If anyone took such amateur forecasts seriously, when a severe storm was approaching,

I have been thinking about the recent institutional buying that has propelled the price of bitcoin to stratospheric levels; in particular considering how one might detect some of this interest early. Bitcoin and crypto in general is quite interesting in that at some level, due to the decentralized

In his famous 1981 paper, “The Relationship Between Return and Market Value of Common Stocks,” Rolf Banz found that small firms have higher risk-adjusted returns than large firms. This was one of the first major challenges to the capital asset pricing model (CAPM) and market efficiency in

A subscriber asked about a strategy that switches between an equal-weighted portfolio of Invesco QQQ Trust (QQQ) and iShares Russell 2000 ETF (IWM) when the S&P 500 Index is above its 200-day simple moving average (SMA200) and an equal-weighted portfolio of SPDR Gold Shares (GLD) and iShares 20+

One of the most appealing areas of Artificial Intelligence is Reinforcement Learning, for its applicability to a variety of areas. It can be applied to different kinds of problems, in the present article we will analyze an interesting one: Reinforcement Learning for trading strategies. Reinforcement

We’re returning to our portfolio discussion after detours into topics on the put-write index and non-linear correlations. We’ll be investigating alternative methods to analyze, quantify, and mitigate risk, including risk-constrained optimization, a topic that figures large in factor research.

The rise of intangibles has been increasingly used as an argument for the poor performance of the value factor However, this idea is not supported by data The type of market environment is marginally more useful for explaining the value factor performance INTRODUCTION Great storytelling may be the

I have written many times over the years about the bullish tendency of the market during opex week in December. I used to refer to it as “The Most Wonderful Week of the Year”. And it was…up until 2018. So below is an updated look at the stats and profit curve for owning SPX from the close of

Theory and plausibility suggest that precious metal prices benefit from inflation and negative real interest rates. This makes gold, silver, platinum, and palladium natural candidates for hedges against inflationary monetary policy. Long-term empirical evidence supports the inflation-precious metal

We would like to present our newest own-research about factor allocation and passive versus active strategies clash. However, respecting our blog format, this version is largely shortened and we invite you to read the full version. Introduction In the equity market, there are two types of investing:

The most common question I get is how do you determine that a strategy is no longer working. It is also the question that I don’t have a good answer for. I have written several posts about this: Trading the Equity Curve, How to turn off a strategy using historical volatility, Broken Strategy or

Investing is never easy, but some times are easier than others. Buying US government bonds at 10%+ yields when inflation was steadily decreasing in the 1980s was probably less worrying than buying them today at negative real yields. In today’s world, bonds may have largely lost their

Several years ago, I wrote about some experimentation I’d done with data mining for predictive features from financial data. The article has had several tens of thousands of views and nearly 100 comments. I think the popularity of the article lay in its demonstration of various tools and modeling

Survey data and asset prices can be combined to estimate high-frequency growth expectations. This is a specific form of nowcasting that implicitly captures all types of news on the economy, not just official data releases. Methods for estimation include the Kalman filter, MIDAS regression, and