This is a summary of links featured on Quantocracy on Thursday, 06/23/2022. To see our most recent links, visit the Quant Mashup. Read on readers!
Using Historical Volatility for Parameter Adjustment [Alvarez Quant Trading]The AllocateSmartly website often has interesting posts. Recently I was reading the article Trending Fast and Slow and thought about other ideas to test. The article is based on research on trading the SPX and depending on the current historical volatility one would either use a 12-month or a 1-month lookback to decide whether to enter or exit the trade. I had tried similar ideas before but not
Can Machine Learning Identify Future Outperforming Active Equity Funds? [Alpha Architect]Ron Kaniel, Zihan Lin, Markus Pelger, and Stijn Van Nieuwerburgh contribute to the asset pricing literature with their January 2022 study Machine-Learning the Skill of Mutual Fund Managers in which they used machine learning in the form of an artificial neural network to examine the universe of actively traded U.S. equity mutual funds between 1980 and 2019 and the stocks they hold in order
Using Institutional Investor’s Trading Data in Factors [Alpha Architect]Can the returns from running factor strategies be enhanced if institutional investors selectively and actively participate? Most of the evidence presented in this paper would suggest the answer is an unqualified YES. The authors argue this would require institutional investors to possess and then capitalize on private information. Consequently, the movement into and out of specific stock positions