000			03138cam a2200337 i 4500
001			22482877
003			OSt
005			20240311142206.0
008			220328s2022 nju b 001 0 eng
010			_a 2022014482
020			_a9789811238307 _q(hardcover)
040			_aDLC _beng _erda _cIIMV _dDLC
042			_apcc
050	0	0	_aHG173 _b.T56 2022
082	0	0	_a332.0285 _223/eng/20220411
100	1		_aTing, Christopher Hian Ann, _eauthor. _933250
245	1	0	_aAlgorithmic finance : _ba companion to data science / _cChristopher Hian-Ann Ting, Hiroshima University, Japan.
264		1	_aNew Jersey : _bWorld Sceintific, _c[2022]
300			_axvi, 392 pages ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references (pages 377-379) and index.
520			_a"Why is data science a branch of science? Is data science just a catchy rebranding of statistics? Data science provides tools for statistical analysis and machine learning. But, as much as application problems without tools are lame, tools without application problems are vain. Through example after example, this book presents the algorithmic aspects of statistics and show how some of the tools are applied to answer questions of interest to finance. This book champions a fundamental principle of science - objective reproducibility of evidence independently by others. From a companion web site, readers can download many easy-to-understand Python programs and real-world data. Independently, readers can draw for themselves the figures in the book. Even so, readers are encouraged to run the statistical tests described as examples to verify their own results against what the book claims. This book covers some topics that are seldom discussed in other textbooks. They include the methods to adjust for dividend payment and stock splits, how to reproduce a stock market index such as Nikkei 225 index, and so on. By running the Python programs provided, readers can verify their results against the data published by free data resources such as Yahoo! finance. Though practical, this book provides detailed proofs of propositions such as why certain estimators are unbiased, how the ubiquitous normal distribution is derived from the first principles, and so on. This see-for-yourself textbook is essential to anyone who intends to learn the nuts and bots of data science, especially in the application domain of finance. Advanced readers may find the book helpful in its mathematical treatment. Practitioners may find some tips from the book on how an ETF is constructed, as well as some insights on a novel algorithmic framework for pair trading to generate statistical arbitrage"-- _cProvided by publisher.
650		0	_aFinance _xData processing. _933251
650		0	_aFinance _xStatistical methods. _931801
650		0	_aExchange traded funds. _933252
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK
999			_c6179 _d6179