Blog - QuantPedia

Quantpedia in May 2022

June 6, 2022

Hello all,

What have we accomplished in the last month?

– A new Quantpedia Pro report – Monte Carlo Analysis
– 9 new Quantpedia Premium strategies have been added to our database
– 15 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 10 new backtests written in QuantConnect code
– And finally, 5+3 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

Quantpedia Premium Update – 4th June

June 6, 2022

Five new strategies have been added.
Five new related research papers have been included into existing strategy reviews and four short free blog posts have been published during last few weeks.
Plus, five trading strategies have been backtested in QuantConnect in the previous two weeks.

Introduction and Examples of Monte Carlo Strategy Simulation

May 30, 2022

The Monte Carlo method (Monte Carlo simulations) is a class of algorithms that rely on a repeated random sampling to obtain various scenario results. Monte Carlo simulations are used to predict the probability of different outcomes when it would be difficult to use other approaches such as optimization. The main aim is to create alternative scenarios, which account for possible risk and help with decision making. The simulations are used in various fields, from finance and quantitative analysis to engineering or science. We plan to unveil our new “Monte Carlo” report for Quantpedia Pro clients in a next few days, and this article is our introduction to different methodologies that can be used for Monte Carlo calculation.

100-Years of Multi-Asset Trend-Following

May 27, 2022

Trend-following strategies have gained extreme popularity in the recent decade. Almost every asset manager utilizes trend following, or momentum, in some form – whether consciously or subconsciously. We at Quantpedia are convinced that each and every strategy has to be scrutinized thoroughly before it’s put into use. This is one of our motivations why we will introduce to you our framework for building a 100-year daily history of a multi-asset trend-following strategy today.

Extending Historical Daily Commodities Data to 100 Years

May 25, 2022

Finding a high-quality data source is crucial for quantitative trading strategies. Also, having a long history is beneficial. Fama & French, for example, offer free historical data for stocks and a variety of factors. However, it is very hard to get good-quality and free data for other asset classes. For this reason, we have already examined how to extend historical daily bond data to 100 years.

For any event-driven analysis or to perform stress tests of various historical situations, long-enough data can only help. Whether one wants to analyze past market patterns, or simply examine the risk of their portfolio under different historical scenarios, the use case for long data is pretty straightforward.

Following the theme of our previous article, we decided to extend historical data of another asset class, commodities. This article explains our commodity data methodology and introduces data sources, which helped us extend historical daily commodities data to 100 years.

Best Performing Value Strategies – Part 1

May 23, 2022

Equity Value strategies have suffered hardly during years 2018, 2019 and also 2020. Due to the poor performance of Value during this period, many investors have abandoned the strategy, often expressing view that “Value strategy is not working anymore”. Nevertheless, equity Value strategies have managed a strong comeback recently, turning attention of investors and traders back to them. In our blog today, we will take a close look at many different equity Value strategies, their performance and how they behave.

Quantpedia Premium Update – 18th May

May 20, 2022

Four new strategies have been added.
Ten new related research papers have been included into existing strategy reviews and four short free blog posts have been published during last few weeks.
Plus, five trading strategies have been backtested in QuantConnect in the previous two weeks.

Extending Historical Daily Bond Data to 100 Years

May 18, 2022

Finding a good data source with quality data and long history is one of the greatest challenges in quantitative trading. There definitely are some data sources with very long histories. However, they tend to be on the more expensive side. On the other hand, cheap or free data usually lacks quality and/or has shorter time frames.

This article explains how to combine multiple data sources to create a 100-year daily data history for US 10-year bonds. Having a 100-year history of daily data can be very beneficial to understanding the market patterns and analyzing history and extending backtests to arrive at a new source of out-of-sample data.

Furthermore, suppose you want to examine how your portfolio would have performed during various historical events or to backtest a strategy during multiple market phases. In that case, the long history provides more opportunities. Besides, investors are always on the run to better understand the market. So, having substantial knowledge of history is crucial.

Grading and Merging ESG Scores from Multiple Providers

May 13, 2022

Socially responsible investing, also known as ESG investing, is a recent trend in the world of portfolio management. More and more investors have started to look into the Environmental, Social, and Governance scores of the companies they invest in. However, one major problem with ESG scoring is that there is not one universal scoring system. Many companies sell ESG data, but the scores are not comparable, and additionally, the ESG data providers are not very transparent about how they create the ratings. These problems with ESG data mean we need to have a method to grade and merge the information from multiple providers.

How Often Should We Rebalance Equity Factor Portfolios?

May 10, 2022

Quantpedia has already covered a countless number of factor investing strategies and articles, from strategies in our Screener to multiple blog posts. Therefore, we can confidently say that we do like factor investing. However, there is always new research with a unique point of view. For example, we recently found a paper focused on the decay of the factor exposures of equity factor strategies. The study examines five factors: Value, Momentum, Quality, Investment, and Low Volatility, across 12 developed and emerging markets over a 20-year period. This research aims to find out how long it takes for a factor to decay after the portfolio is assembled. In other words, how often should the portfolio be rebalanced?