I will be publishing some of my research notebooks, starting with downloader for VIX data, fitting Nelson-Siegel model for term structure ( static ) , and dynamic ( Kalman Filter ), and possibly some recent work I did on regime clusters in VIX and ML for VIX trading. Here is the link to the first

In the last article, we discussed how Decision Trees and Random Forests can be used for forecasting. While mean and point forecasts are the most obvious applications, they might not always be the most useful ones. Consider the classic example of financial returns, where the conditional mean is hard,

Previously on this blog, we wrote about a machine-learning-based parameter optimization technique we invented, called Conditional Parameter Optimization (CPO). It appeared to work well on optimizing the operating parameters of trading strategies, but increasingly, we found that its greatest power

In order for investors to determine which of the hundreds of factors in what John Cochrane famously called the “zoo of factors” were worthy of investment, Andrew Berkin and I set out seven criteria in our book “Your Complete Guide to Factor-based Investing.” For a factor to be considered, it

The Factor Multiverse: The Role of Interest Rates in Factor Discovery Jules H. van Binsbergen (University of Pennsylvania), et al. September 2022 We study the importance of the decline in interest rates in the discovery of asset pricing anomalies. We investigate 153 discovered anomalies as well as

If you follow Quantpedia’s blogs, you probably know that Quantpedia PRO already contains multiple risk management and portfolio construction tools for your quantitative investment strategies. For example, Crisis Hedge can find you suitable investment hedges for negative months and for bear

Value is leading the performance scoreboard in YTD 2022 Low volatility is the worst-performing factor Oddly, the value and low volatility factors are strongly positively correlated INTRODUCTION We present the performance of five well-known factors on an annual basis for the last 10 years.

Transaction costs are a key consideration for the development of trading strategies; and not just in final profitability checks. Indeed, disregard for trading costs at the design stage leads to excessive reliance on fleeting small-scale characteristics for return predictors. It also skews the

In the first post of this series about the Sharpe ratio considered as a statistical estimator, I introduced a probabilistic framework to answer the question What is the probability that an estimated Sharpe ratio is statistically significantly greater than a reference Sharpe ratio? In this second

Bonds are typically considered safe investments However, there were decades of negative real returns Drawdowns reached 50% for U.S. Treasuries and Bonds INTRODUCTION Inflation greater than 10% was unknown for the majority of people in developed markets before this year, but it is nothing

So much useful data has a date or time component. Often, data has a timestamp to represent when the data was acquired, or when an event will take place, or as an identifying attribute like an expiration date. For this reason, understanding how to work with dates and times effectively can be a very

Inflation-linked bonds are considered inflation-hedges However, these have lost almost as much as plain-vanilla bonds in 2022 The sensitivity to interest rates matters more than that to inflation INTRODUCTION Inflation is the biggest issue facing the U.S. and is more important to citizens than

Manager-selected benchmarks are suboptimal as they are not free of conflict of interests Investors can use sectors to identify more appropriate benchmarks However, this ignores factors, which are better at explaining investment returns INTRODUCTION In our last research article (Mirror, Mirror on the

Consumer demand drives the cash flows of consumer-oriented companies. Thus, they should serve as a reliable source of information to predict future fundamentals above and beyond the information contained in financial statements and readily available market data. For example, Jiekun Huang, author of

In 2019, the SEC proposed that all brokers and advisors be required to determine whether or not their clients understood the risks of investing in levered and inverse exchange traded products before selling such products to them. The SEC moved on this requirement in response to a series of fund

We’re happy to share that today we published the code for 15 brokerage integrations to our open-source platform, LEAN. One step toward the future we’re building. LEAN handles all of the data and brokerage infrastructure for you so you can focus on what matters most: creating brilliant

The past few years in the stock market have been pretty crazy. And the pinnacle of “crazy” was during March 2020 — peak chaos in the stock market. Below is a chart of US large-cap stocks and small-cap stocks in 2020. Note the monster crash in March — watch out below! Source: koyfin.com As an

Today, Deep Learning dominates many areas of modern machine learning. On the other hand, Decision Tree based models still shine particularly for tabular data. If you look up the winning solutions of respective Kaggle challenges, chances are high that a tree model is among them. A key advantage of

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