A Quantitative Primer on Investments with R - Rosenthal, Dale W. R.
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A Quantitative Exploration of Investments - So You Can Be a Better Analyst! Quantitative analysts and financial engineers often skip taking an investments course. Many would-be analysts take a less quantitative investments course. This omission robs them of the fundamental knowledge needed to create better, more profitable models. A Quantitative Primer on Investments with R fills that gap by taking a quantitative approach to investments and analyzing real data using R, the open source statistical computing language. This illuminates the commonalities among investment theories and builds…mehr

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Produktbeschreibung
A Quantitative Exploration of Investments - So You Can Be a Better Analyst! Quantitative analysts and financial engineers often skip taking an investments course. Many would-be analysts take a less quantitative investments course. This omission robs them of the fundamental knowledge needed to create better, more profitable models. A Quantitative Primer on Investments with R fills that gap by taking a quantitative approach to investments and analyzing real data using R, the open source statistical computing language. This illuminates the commonalities among investment theories and builds intuition. This text collects the author's two decades of experience in finance - from positions at Goldman Sachs, Morgan Stanley's Equity Trading Lab, and hedge fund Long-Term Capital Management to the quantitative background of a PhD in statistics, teaching at some of the world's top universities, and presenting research at central banks, regulatory agencies, and trading firms. The explanations, questions, and exercises have been tested over a decade and enabled many students to enter the world of quantitative finance and succeed. Supplemental materials: For instructors and self-studying readers, slides and an exercise solution manual are available.
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Autorenporträt
Dale Rosenthal is the head partner of Q36, a quantitative algorithmic finance firm. He was previously an equity derivatives strategist at Long-Term Capital Management, a proprietary algorithmic trader and quantitative researcher at Morgan Stanley's Equity Trading Lab, a programmer/analyst at Goldman Sachs, and an assistant professor of finance at the University of Illinois at Chicago. He has also taught at the University of Chicago, the University of Notre Dame, and Renmin University of China; and, he was a visiting scholar at Peking University and a fellow at the Institute on Computational Economics. He has presented his work to central banks, regulators, and trading firms; had work published in top finance and econometric journals; been featured in national print, radio, and TV; and, testified before political bodies on financial issues. He holds a PhD in statistics from the University of Chicago and a Bachelors in electrical engineering from Cornell University. Since 2009 he has co-organized the R/Finance conference.