PCA is a useful tool for quant trading (stat arb) but in its naive implementation suffers from several forms of instabilities which yield to unnecessary turnover (trading cost…) and spurious trades. In order to regularize the model, several techniques are available: Sparse PCA Robust PCA Kernel

There are already uncounted variants of moving averages. Vitali Apirine invented another one in his article in the Stocks&Commodities September issue. The LREMA is an EMA with a variable period derived from the distance of the current price and a linear regression line. This ensures an optimal

This short blog to illustrate an interesting fact that I found in An Analysis of Eigenvectors of a Stock Market Cross-Correlation Matrix by Nguyen and co-authors: The first eigenvector is not THE market portfolio (market-cap or uniformly weighted) as people usually believe, but a

Near the end of 2021, I wrote an article noting that value portfolios looked historically cheap based on valuation spreads. I found that in the next five years (after the peak), Value investing performed quite well.(1) Following this post, I have received numerous questions related to the following

This is a test of concepts from the paper Cross-Asset Signals and Time Series Momentum. Standard “time series” momentum is a well-documented feature of financial markets. Assets going up tend to continue going up. In this paper, the authors show that stocks and treasuries can be used to time

The Sharpe ratio1 is one of the most commonly used measure of financial portfolio performance, but because it is deeply rooted in mean-variance theory, its usage with return distributions deviating from normality (e.g. hedge funds, cryptocurrencies) is frequently questioned2. One solution to this

I am drawn to ETF rotation strategies. What likely draws me to them is that in general, these are simple strategies that do not trade that often. My goal with these strategies is to match buy and hold with less drawdown. What follows is a strategy I have known about for a while and tested but never

Imagine you’re a quantitative long-short equities trader. If you can predict which stocks will have above-average returns next period and which will have below-average returns, then you can profit by buying the winners and selling short the losers. Return predictability and trading profits are two

The objective of research into asset pricing is to determine which characteristics are most important for predicting returns and then build simplified models using as few factors as possible—to tame the so-called “zoo of factors”—while still providing a high level of explanatory power. In

In an earlier article, we discussed how to replace the conditional Gaussian assumption in a traditional GARCH model. While such gimmicks are a good start, they are far from being useful for actual applications. One primary limitation is the obvious restriction to a single dimensional time-series. In

Cryptocurrencies have been acknowledged as an emerging asset class with a relatively low correlation to traditional asset classes. One of the most important questions for allocators is how much to allocate to Bitcoin and to a portfolios cryptocurrency assets within a broad portfolio which includes

Everyone is aware of the importance of the U.S. elections and we take it for granted that, like many other things, financial markets will end up being affected in some way. But have you ever wondered if there is any seasonality throughout those elections that we can take advantage of when making

Breaking new ground, the authors present a novel view on the nature and source of “momentum” that differs from our current understanding of momentum, whether it be industry momentum, residual, or any other version of momentum. Explanations of the source of profitability for momentum strategies

On September 23rd Columbia U will hold its annual ML in Finance workshop. Event link. Registration link. Topics include robust portfolio construction, NLP-clustering, novel computational technique for online PCA, deep learning for statistical arbitrage (equities) and futures (momentum, mr ), both

Today we’re launching the next step of our journey. We’re proud to be opening ownership of QuantConnect to our community. We want to align our investors and users to continue pioneering as the world’s leading cloud quantitative analytics platform. With more than 210,000 engineers, quants,

Despite the fact that a company’s internally generated intangible investments create future value, (Summary) under current U.S. generally accepted accounting principles, internally developed intangibles are not included in reported assets. While research and development (R&D) is an important

In statistics, a bootstrap method, also called bootstrapping, is a compute-intensive procedure that allows to estimate the distribution of a statistic through repeated resampling from a single observed sample of data1. Bootstrapping has several applications in quantitative finance, for example to

In a previous post, we explained how self-organizing maps work, with a very simple example. In this post, we will explain how to implement self-organizing maps for an investment strategy. Last time, we gave a simple example with a map of colors to explain in detail how self-organizing maps (SOM)

Evaluating manager performance is difficult as it requires an appropriate benchmark The manager’s benchmark selection is often not objective given conflicts of interests Factor exposure analysis can be used to systematically identify the best benchmark INTRODUCTION Although information asymmetries

Have you ever wondered if your trading asset trends or mean-reverts? Everyone involved in trading or investments daily solves the task of – What trading strategy should I apply to my assets to generate profits? As always, we at Quantpedia will try to help you a bit with this never-ending task with

In “Your Complete Guide to Factor-Based Investing,” Andrew Berkin and I established criteria that must be met before considering investing in a factor-based strategy. We established the criteria to minimize the risks that any findings were the result of data-mining exercises. Data mining occurs

The Avoidable Costs of Index Rebalancing Robert D. Arnott (Research Affiliates), et al. May 2022 Traditional capitalization-weighted indices generally add stocks with high valuation multiples after persistent outperformance and sell stocks at low valuation multiples after persistent

This is a test of Dr. Wouter Keller’s tactical strategy “Bold Asset Allocation” (BAA) from his paper Relative and Absolute Momentum in Times of Rising/Low Yields. Backtested results from 1970 follow. Results are net of transaction costs – see backtest assumptions. Learn about what we do and

Inverse ETFs come with significant risk disclosure Analyzing the performance of these products justifies the warnings There is a significant difference between performance of inverse ETFs & the inverse underlying indices INTRODUCTION In May 2022, Allianz, the large German insurance company,

Most pandas users encounter a situation where choosing to append rows to a pandas DataFrame seems like a good idea. A quick search of the API (or your favorite search engine) reveals that pandas has an append method in DataFrame. You may be tempted to use it. In this article I’ll show you why you

At the moment, there is a lot of attention surrounding overnight anomalies in various types of financial markets. While such effects have been well documented in research, especially in US equities and derivatives, there are other asset classes that are not as well addressed. We previously compiled

In this short post, I will provide an overview of the TIC algorithm1 introduced by Marcos Lopez de Prado in his paper Estimation of Theory-Implied Correlation Matrices2, which aims to compute a forward-looking asset correlation matrix blending both empirical and theoretical inputs. I will also

In this post, we will develop a time-varying, probabilistic extension of the Sharpe ratio as a widely used performance metric for financial assets. In particular, we devise a Bayesian regime-switching model to capture different market conditions and infer the full distribution the Sharpe ratio as it

The stem paper for this post is: Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE 12(7): e0180944. https://doi.org/10.1371/journal.pone.0180944 The chief claim by the researchers is that 90% to 95% 1-day

Long volatility strategies are attractive diversifiers, but complex and not easily accessible Tactical asset allocation for equities may be considered as an alternative There is no clear winner between these two options INTRODUCTION Risk management in portfolio construction is primarily achieved via

Relative sentiment is an indicator that measures the positions, flows, and attitudes of institutional investors compared to those of individual investors–where institutions typically consist of large asset managers, insurance companies, pension funds, and endowments. In some instances,

In recent weeks, I have probably OD’ed my readers with philosophical and highbrow topics. Whether it was my Hitchhiker’s Guide to Investment Research or yesterday’s post on the Fiscal Theory of the Price Level. Since this is my last post before my summer break, I wanted to bring it down to

In “Your Complete Guide to Factor-Based Investing” Andrew Berkin and I provided six criteria that had to be met in order to determine which exhibits in the “factor zoo” are worthy of investment. For a factor to be considered, it must meet all of the following tests. To start, it must provide

Generating synthetic data is an extremely useful technique in quantitative finance. It provides the ability to assess behaviour on models using data with known behaviours. This has a myriad of applications, such as testing backtesting simulators for correct functional behaiour as well as allowing

Imagine you are an European insurer. One of your funds is an equity portfolio of EMU stocks. Under Solvency II framework, you might want to reduce your Solvency Capital Requirement (SCR) thanks to the use of derivatives to hedge some of your equity risk. However, due to your size, the only

Finding high-quality data with a long history can be challenging. We have already examined How To Extend Historical Daily Bond Data To 100 years, How To Extend Daily Commodities Data To 100 years, and How To Build a Multi-Asset Trend-Following Strategy With a 100-year Daily History. Following the

Outperformance and alpha are not the same One is the difference from a benchmark, the other is the unexplained return A contribution analysis helps understanding the return drivers INTRODUCTION Almost 90% of US drivers rate themselves safer and more skillful than average. Obviously, such perceptions

While linear correlation (aka Pearson correlation) is by far the most common type of dependence measure there are few arguably better ways to characterize\estimate the degree of dependence between variables. This is a fascinating topic I keep coming back to. There is so much for a typical geek to

In the 1970's, Richard Donchian began the trend following trend by introducing a simple breakout trading system that would make him millions over the following decades. This system was predicated on an indicator that came to bear his name the Donchian Channel. We're going to show you how

In this article, the authors explore an alternative measure of consumer demand from a unique dataset of individual credit and debit card daily transactions ( available one week after the transaction was made on average) from January 2013 to December 2019. They ask the following: Can more timely

I am introducing my most recent research on log-normal stochastic volatility model with applications to assets with positive implied volatility skews, such as VIX index, short index ETFs, cryptocurrencies, and some commodities. Together with Parviz Rakhmonov, we have extended my early work on the

We were recently asked what we thought about bonds as an investment. A lot of this was inspired by my comments on bonds via a discussion on how I personally invest. I’ll repost what I said on bonds below: What are your thoughts on bonds and commodities? In general, I’m not a fan of corporate

What are the research questions? Theories abound in the financial literature explaining the drivers of momentum and reversal in one way or another. Not surprisingly, most portray the role of public and private information as key to the underlying relationships and weigh the type of information

There has been a boom in launching tactical asset allocation ETFs However, the recent track record of these has been poor Declining stock and bond markets have created a challenging environment for these INTRODUCTION Most investment strategies become popular through short rather than long periods,

Combining Factors Christoph Reschenhofer (Vienna University of Economics and Business) July 2022 While the academic literature primarily investigates factor exposures based on covariances (i.e. beta exposure), most practitioners apply characteristics-based scorings to obtain factor portfolios. It

A new theoretical paper illustrates the logic behind runs and crashes in modern safe asset markets. Safe assets are characterized by stable value and high liquidity. In times of distress “flight for safety” increases demand for these assets, while “dash for cash” increases supply. However,

In our book “Your Complete Guide to Factor-Based Investing,” Andrew Berkin and I presented the evidence demonstrating that momentum, both cross-sectional (CSMOM) and time-series (TSMOM), has provided a premium that has been found to be persistent across time and economic regimes, pervasive

In my last blog post, Using Historical Volatility for Parameter Adjustment, I tested using historical volatility to determine trade rules. While reading the July 2022 Technical Analysis of Stocks & Commodities, I came across an article, “Is It Too Volatile To Trade?” by Perry Kaufman. I

Just recently we posted the trend-following weights for our Robust Asset Allocation model. Something interesting happened — the model suggested zero exposure to every asset, except commodities(1) source: https://alphaarchitect.com/indexes/trend/#trendasset My knee-jerk reaction was, “Wow, never

This is the forth and final post of a short series of posts on extreme events in financial time series. In the first post, we have introduced power-law theory to describe and extrapolate the chance of extreme price movements of the S&P500 index. In the second post, we took a closer look at how