When someone mentions a financial crisis, most people immediately think of the global financial crisis of 2007-2008. Even though this is the most significant economic crisis in recent years, there have been many more significant crisis periods in the past 100 years. This article examines the biggest crises in three asset classes: stocks, bonds, and commodities, during the past century. Additionally, we analyze the behavior of our trend-following strategy during each of the crisis periods and propose it as a hedge for the stock, bond, and/or commodity markets.

We focused the first part of this article more on tendencies and trends among these US Equity Value strategies. Today we will talk solely about specific top-performing equity value strategies. More specifically, about numbers #7 to #1 from Quantpedia’s ranking. We will also take a look at combining all the value strategies together into a diversified portfolio of value strategies.

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

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.

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.

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.