This blog post is the continuation (and finale) of series about Quantconnect’s Alpha market strategies. This part is related to the multi-factor strategies notoriously known from the majority of asset classes. We continue in the examination of factor strategies built on top of social trading strategies, but the investment universe is reduced based on the insights of the previous part. So, without further ado, we continue where we have left last time.

Today we will again talk more about the portfolio management theory, and we will focus on techniques for combining quantitative strategies into one multi-strategy portfolio. So, let’s imagine we already have a set of profitable investment strategies, and we need to combine them. The goal of such “strategy allocation” usually is to achieve the best risk-adjusted return possible. There is no single correct solution to this task, but there are a few methods that we can try.

The “appropriate combination” highly depends on the type of strategies we are about to combine. Are we combining equity and bond strategies together? Are we combining equity strategies, with each one having an entirely different logic? Or do we rather need to assign weights to strategies that are similar in nature yet still different? We will focus this article on the last option – combining similar yet different strategies.

Nobody would argue that nowadays, we live in an information-rich society – the amount of available information (data) is constantly rising, and news is becoming more accessible and frequent. It is indisputable that this evolvement has also affected financial markets. Machine learning algorithms can chew up big chunks of data. We can analyze the sentiment (which is frequently related to the news). Big data does not seem to be a problem anymore, and high-frequent trading algorithms can react almost instantly. But how important is the news? Kerssenfischer and Schmeling (2021) provide several answers by studying the impact of scheduled and unscheduled news (frequently omitted in other news-related studies) in connection with high-frequency changes in bond yields and stock prices in the EU and US as well. The research points out that the effect is tremendous and significant.

In the modern world full of technologies, cryptocurrencies are gaining popularity every day. The most famous cryptocurrency, bitcoin, was introduced in 2009. Ever since its launch and its subsequent success, when within a few years, its price skyrocketed, and it has been the subject of many price predicting studies. These, however, primarily focus on the market and macro factors, entirely omitting the nature of bitcoin – which is blockchain technology. In this study, authors Hoang and Baur try to capture and research this interconnection between behaviour of investors, bitcoin exchanges, and blockchain.

Extremely high bitcoin returns and drawdowns come hand in hand with significant volatility. As Bitcoin is becoming an unignorable part of finance with substantial institutional participation, it is necessary to understand the key drivers of returns and volatility, which is comparably persistent as in other, more established asset classes. In addition, other cryptocurrencies are extremely correlated with Bitcoin, so understanding of key drivers of Bitcoin volatility might also carry to other cryptos. The research of Lyócsa et al. (2020) examines several possible drivers of the volatility. The authors study the realized volatility and its jump component and identify whether the volatility is influenced by various factors such as news about the regulation of bitcoin, hacking attacks on bitcoin exchanges, investor sentiment, and various types of macroeconomic news. The study identifies the significant impact of two intuitive factors: news about the regulation or cryptocurrency exchange hacks. Lagged volatility is also an essential factor, as shown by regression analysis. Regarding macroeconomic data, economic fundamentals do not seem to influence the volatility, except for forward-looking indicators (e.g., the consumer confidence index). Lastly, the authors study the investor sentiment extracted from Google searches, but only the positive sentiment has some impact. Overall, the research is a vital addition to the literature that helps us understand Bitcoin’s volatility.

Hello all,

What have we accomplished in the last month?

– A new Value-at-Risk Quantpedia Pro report
– 10 new Quantpedia Premium strategies have been added to our database
– 10 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 11 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