Finance Research is typically very quantitative, analytical, logical, and you should be good at managing numbers and data.It presumes methodical search, collection, and analysis of information in order to form the right decisions.These people are perhaps the most successful data miners in all of finance.Tags: Laufende Dissertationen DatenbankThesis Binding LeatherUs Federal Seal Watermark PaperAnalytical Essay SaisResearch Proposal For PhdQuick Concise Cover LetterVolleyball EssaysThe American Dream For Immigrants Essay
When you take these problems to the authors and publishers, their first response is always “oh no! ”, but at some point I think they all go: “what has reality done for me lately?
It certainly didn’t publish this paper.” Despite steadily improving market data availability and a large open source software community, academic research in algorithmic trading and quantitative investing risks committing classes of errors beyond the well-known “data mining” risk.
Minor oversights in data preparation and small software bugs lead to invalid results that can go unnoticed for years.
Industry best practices and a more rigorous standard of evidence are required to mitigate these risks and ensure the integrity of ongoing research.
From long-term factor model research to high-frequency order book analysis, researchers are writing more lines of code of increasing complexity.
There is an often-cited statistic that the professional software industry produces 15-50 errors per 1,000 lines of code delivered.
The other big problem, with a worrisome trend (especially among lower quality journals, and lower quality academics), is how much of the research and publishing apparatus is completely blasé about the differences in the domain knowledge required to analyze large cross-sections of quarterly stock data versus high-frequency trade and order book data.
Examples: Each of those papers has numerous other problems.
In data-driven trading or quantitative investment research, data mining is a big part of what you have to do.
But successfully employing those techniques requires a lot of attention to the nuances of the domain, critical evaluation of your assumptions, and a commitment to exploring philosophies of statistics and science.