I'm curious as to how many academic studies and industry white papers are actually using daily data to report intramonth drawdowns; specifically, when the papers are often reporting monthly signals, statistics, and performance. I would think it would be obvious that honest reporting would report risk statistics using daily data granularity, but considering they do not often explicitly state such, it is hard to say with certainty.
Many of the daily data series themselves are hard to gather and guarantee same results (dividends, etc) over the long term. But there are numerous papers on topics such as multi-asset momentum strategies going back to the seventies. There would be a large difference in some risk metrics like drawdown if only monthly closing data points were sampled.
Any first-hand experience or references on the matter are appreciated.
edit: Thanks for replies so far. Just for clarification; I'm not really asking about the merits or pitfalls of sampling at different intervals-- I'm well aware of that. I'm asking more about experiences with various papers(academic) and white-papers(industry) that show monthly statistics back to the seventies (or more) and whether or not you've found that they divulge risk metrics (esp. drawdowns) based on daily or only monthly granularity. It's important for comparison purposes to understand if they are underestimating risk measures in such old data. If some paper, displaying only monthly results, charts, and tables, tells me that the worst drawdown over 40 + years was -25% (some use data going back to twenties), I want to know if that included daily granularity or not. Unfortunately, I don't often see that clarification and so I'm wondering if it is the norm to only use monthly sampling for long term systematic studies with potentially sparse daily data available on total return series. There are some high low data available from CRSP and IDSI going back to the 60s, so I agree with Freddy that it can be done, just more interested in what has actually been applied in papers with older data, so they can be compared reliably.