own-research - QuantPedia

The Worst One-Day Shocks and The Biggest Geopolitical Events of the Past Century

July 11, 2022

We dedicated several articles to how we created 100-year history for bonds, stocks, and commodities . Now we analyze the 50 worst one-day shocks and the following days in each of the abovementioned asset classes. In addition to that, we also look at how the multi-asset trend-following strategy performed during the same periods. Further, the second part of this article focuses on critical geopolitical events (the starts of major wars, international crises, and deterioration of US presidents’ health) and their effect on bonds, stocks, commodities, and the multi-asset trend-following strategy.

Quantpedia Introduces 3rd Party Factors

June 28, 2022

Every year, Quantpedia’s team investigates thousands of academic research papers to bring you the most promising ideas from the academic world. We read papers, identify ideas and backtest them to build our unique database. As a result, we have already identified hundreds of factors and built tools to help you orient better in the broad universe of trading strategies and systematic investment factors.

And now, we are opening the possibility to all external researchers, quants, and portfolio managers to contribute to Quantpedia.

Skewness/Lottery Trading Strategy in Cryptocurrencies

June 21, 2022

A recent spring 2022 crisis in the cryptocurrency market emphasized the importance of market-neutral crypto trading strategies. It’s not enough just to HODL crypto market and hope for the everlasting bull market. Therefore, we continue our series of research articles about the cryptocurrency market and offer an analysis of the skewness anomaly. So after our description of the skewness effect in commodities, an article about the multi-asset skewness strategy, and observation of the skewness/lottery effect in ETFs, we have one more asset class, where we can find lottery/skewness anomaly – in cryptocurrencies.

Trend-Following in the Times of Crisis

June 10, 2022

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.

Best Performing Value Strategies – Part 2

June 8, 2022

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.

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.

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.