Most of you have probably heard the saying that somebody “averaged” into or out of his investment position. But what does it exactly mean, and what different dollar-cost averaging strategies exist? We plan to unveil our new “Dollar-Cost Averaging” report for Quantpedia Pro clients next week, and this article serves as a short introduction to this term.
Do common equity factors suffer during bear markets? Undoubtedly, the market factor is a rather unpleasant investment during bear markets, but what about the long-short factors? Are they able to deliver performance? The research paper by Geertsema and Lu (2021) provides several answers and interesting insights.
Since recessions and bear markets come hand in hand for several asset classes, recession predictions have always been the foremost concern. The yield curve slope, defined as the difference between long and short-term rates, is the leading indicator backed by numerous research papers. Hansen (2021) builds on this theorem, but the author improves the recession prediction by his empirical observation that the VIX index (index of implied equity volatility or fear index) and the slope co-move in counterclockwise cycles, which align with business cycles.
Indisputably, we are witnesses of an ETF mega boom. From passive to active ETFs, their numbers seem to be ever-increasing. Since these exchange-traded funds can be excellent (accessible, transparent, liquid) instruments, it is a great necessity to examine their possible usage in active and systematic trading or investing. Therefore, the short research critically assesses the possibility of using ETFs in the Skewness Trading Strategy.
The critical question of this research is to examine whether the quality factor could be found in the aggregated groups of similar stocks such as industries or sectors. Additionally, instead of constructing a comprehensive quality metric like other papers, we examine the individual ratios aggregated to the whole sector. The aim is to investigate the fundamental ratios on which quality is based rather than the composite quality score of sectors.
The high-frequency data in cryptocurrency markets is available at any time of the day, which facilitates the studies of periodicity measures beyond what’s possible in other markets. The research paper by Hansen, Kim, and Kimbrough (2021) investigates the periodicity in volatility and liquidity in two major cryptocurrencies, Bitcoin and Ether, using data from three exchanges, Binance, Coinbase Pro, and Uniswap V2. In particular, the authors measure relative volatility and relative volume across days, hours, and minutes. Their results have confirmed the presence of recurrent patterns in volatility and volume in studied cryptocurrencies for the periods day-of-the-week, hour-of-the-day, and within the hour.
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