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These people are perhaps the most successful data miners in all of finance.An ever-increasing risk for catastrophic errors is when academics write code.[Editor’s note: This guest post is by Zachary David, an analyst who advises institutional clients on trading practices (website:
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.
ABI/INFORM Complete/ ABI/INFORM Global/ ABI/INFORM Professional Advanced/ ABI/INFORM Professional Standard/ Banking Information Source (Pro Quest), Business Source Alumni Edition/ Business Source Complete/ Business Source Complete Government Edition/ Business Source Corporate Plus/ Business Source Elite/ Business Source Premier (EBSCO), Cabell's Directory of Publishing Opportunities in Economics and Finance, and Management, Econ Lit, the ERA Journal List (Australian Research Council), Professional Pro Quest Central/ Pro Quest Central (Pro Quest), The Publication Forum (Finland), Scopus, Summon (Pro Quest), Thomson Reuter’s Book Citation Index, TOC Premier (EBSCO). This title is available as part of the Emerald Business, Management and Economics e Book Series Collection.
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Contributions include finance theory and financial practice, plus accounting issues such as reporting derivatives positions, reflecting intangible holdings, or predicting financial distress.
Themed issues may also be guest edited to provide quality insight into important areas of current financial research.When pairs trading was invented at Morgan Stanley in the early 1980’s, that came from a computer science graduate from Columbia who was calculating relative weights and correlations for groups of stocks sorted by industry sectors. Robert Frey, a former managing director, has said that their approach was “decidedly non-theoretical.Discipline is the key, not theory.” The founder, Jim Simons, tells an anecdote about sending people down to the NYFed building to copy historical interest rate information by hand that was kept in a written ledger.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.CMCRC researchers show that China is still on track to reshape the global oil market Talk of a petroyuan replacing the petrodollar has been called “premature”, but analysis from CMCRC shows that China’s new crude oil futures contract is exceeding expectation when compared to the established US and European exchanges in key areas.Information: Author guidelines | Publication ethics Other: Recommend this book series Also available in our: Emerald Business, Management and Economics e Book Series Collection Online access: Online table of contents | Latest Volume RSS seeks to provide a collection of quality research articles that reflect the current and primary issues in financial markets.Yet to the best of my knowledge, there are no finance journals that have professional software developers review submitted code.Most finance journals still either don’t have or don’t enforce code submission and open publishing standards — an asinine move that both increases the uncertainty of the results as well as slows the progress of future research.