Although the factors in asset pricing models offer a premium in the long run, they are undergoing bull and bear market cycles in the short term. One would expect that it is due to their connection to the business cycles as the factor premium represents a reward for bearing the macroeconomic risks. A novel study by Blitz (2021) finds that traditional business cycle indicators can’t explain much of the time variation of factor returns as the factors are a behavioral phenomenon driven by investor sentiment. To capture the large factor cyclical variation, the author proposes a quant cycle that is defined by the peaks and troughs in the factor returns corresponding to the bull and bear markets.

https://quantpedia.com/the-quant-cycle-the-time-variation-in-factor-returns/

This blog post is the continuation (and finale) of series about Quantconnect’s AlphaMarket 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 last part. So, without further ado, we continue where we have left last time.

https://quantpedia.com/community-alpha-of-quantconnect-part-4-composite-social-trading-multi-factor-strategy/

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…

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…

By observing an increased amount of Bitcoin located in exchange reserves, it can be assumed that investors transferred their Bitcoin back to the exchange. In most cases, this means that they plan to sell. Price pressure is created, followed by a negative effect on the Bitcoin price. On the other hand, withdrawing Bitcoins into the private wallets most likely means positive long-term future returns, as the investors are preparing to hold for a prolonged amount of time. This means that long-term returns are associated with (only) decreased exchange reserves, while short-term returns are being influenced by both increasing and decreasing bitcoin exchange reserves.

https://quantpedia.com/bitcoin-returns-and-volatility-predicted-by-bitcoin-exchange-reserves/

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…

Understanding the risks of any quantitative trading strategy is one of the pillars of successful portfolio management. Of course, we can hope for good future performance, but to survive market whipsaws, we must have tools for sound risk management. The “Value at Risk” measure is such a standard tool used to assess the riskiness of trading and investment strategies over time. We plan to unveil our new “Value at Risk” report for Quantpedia Pro clients next week, and this article is our introduction to different methodologies that can be used for VaR calculation.

https://quantpedia.com/an-introduction-to-value-at-risk-methodologies/

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