Investment Week (December Issue)
“Quantitative forecasts are based on probability models that cannot help but assume the future will be correlated to the past, and qualitative scenarios are based on, well, a combination of experience and common sense. Either way, most methodologies it would seem leave little room for discussion of true outliers and surprises.”
Tags: Forecasts, Future, Outliers, Probability models, Qualitative scenarios, Quantitative, Surprises
Institutional Investor (April 6)
“PNC Capital Advisors is putting its large-cap mutual funds under a factor-based computer model.” Now that quant funds tend to outperform human stock pickers, at least in the large-cap space, the move seeks to reduce costs, improve performance and attract more investors. “Quantitative factor-based models” appear to “reduce human being’s tendency to make behavioral mistakes. Portfolio managers are prone to confirmation bias as human beings are rarely swayed by new information that goes against long-held beliefs.”
Tags: Confirmation bias, Costs, Investors, Large caps, Mutual funds, Performance, PNC, Portfolio managers, Quantitative, Stock pickers
Institutional Investor (December 14)
It’s difficult for analysts to rise above groupthink since they “tend to use the same quantitative information…and similar methodologies.” Remarkably, some do. Institutional Investor recognizes them annually. Still, it seems that one day algorithms will “displace analysts entirely, meaning that these algorithms will themselves become eligible, in principle, for election to the II All-America Research Team.”
Tags: Algorithms, All-America Research Team, Analysts, Groupthink, Information, Methodologies, Quantitative
Institutional Investor (May 30)
Firms “are doubling down on machine learning and other quantitative investing efforts.” More advanced than rule-based algorithms, “with machine learning, a computer sifts through billions of data points, picking up patterns. Armed with this knowledge, it learns trading behaviors such as buying dips or selling high over time, based on what it has gleaned about the market from past and present data.” Despite the inroads, however, human ingenuity remains essential.
Tags: Algorithms, Computer, Data points, Dip, Firms, Ingenuity, Investing, Machine learning, Market, Patterns, Quantitative, Trading
