Quantitative portfolio management : the art and science of statistical arbitrage / Michael Isichenko.
By: Isichenko, Michael [author.].
Publisher: Hoboken, New Jersey : John Wiley & Sons, Inc., ©2021Description: 261 p.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781119821328.Subject(s): Portfolio management -- Mathematical models | ArbitrageGenre/Form: Print books.Summary: "Quantitative trading of financial securities is a multi-billion dollar business employing thousands of portfolio managers and quantitative analysts ("quants") trained in mathematics, physics, or other "hard" sciences. The quants trade stocks and other instruments creating liquidity for investors and competing, as best they can, at finding and exploiting any mispricings. The result is highly efficient financial markets not immune to occasional events of crowding, bubbling, and liquidation panic. This book covers all the major parts of the quantitative trading process starting with sourcing financial data, learning future asset returns from historical data, generating and combining multiple forecasts, dealing with risk, building optimal portfolio of stocks subject to risk preferences and trading costs, and executing trades. The exposition seeks a balance between financial insight, mathematical ideas of statistical and machine learning, practical computational aspects, actual events and thoughts "from the trenches", as observed by a quantitative portfolio manager, and even actual questions asked at countless quant interviews. The intended audience includes practicing quants who will encounter things both familiar and novel (such lesser known ML algorithms or multi-period portfolio optimization), students and scientists thinking of joining the quant workforce (and wondering if it's worth it), and the general public interested in quantitative and algorithmic trading from a broad scientific, and occasionally ironic, standpoint"--Current location | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|
On Shelf | HG4529.5 .I83 2021 (Browse shelf) | Available | AU00000000017834 |
Browsing Alfaisal University Shelves , Shelving location: On Shelf Close shelf browser
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
||
HG4529.5 .E45 2013 Winning the loser's game : timeless strategies for successful investing / | HG4529.5 .E47 2014 Modern portfolio theory and investment analysis / | HG4529.5 .H378 2021 Strategic risk management designing portfolios and managing risk / | HG4529.5 .I83 2021 Quantitative portfolio management : the art and science of statistical arbitrage / | HG4529.5 .K563 2021 Asset allocation : from theory to practice and beyond / | HG4529.5 .L366 2016 Rational investing : the subtleties of asset management / | HG4529.5 .N366 2021 Artificial intelligence for asset management and investment : a strategic perspective / |
Includes bibliographical references and index.
"Quantitative trading of financial securities is a multi-billion dollar business employing thousands of portfolio managers and quantitative analysts ("quants") trained in mathematics, physics, or other "hard" sciences. The quants trade stocks and other instruments creating liquidity for investors and competing, as best they can, at finding and exploiting any mispricings. The result is highly efficient financial markets not immune to occasional events of crowding, bubbling, and liquidation panic. This book covers all the major parts of the quantitative trading process starting with sourcing financial data, learning future asset returns from historical data, generating and combining multiple forecasts, dealing with risk, building optimal portfolio of stocks subject to risk preferences and trading costs, and executing trades. The exposition seeks a balance between financial insight, mathematical ideas of statistical and machine learning, practical computational aspects, actual events and thoughts "from the trenches", as observed by a quantitative portfolio manager, and even actual questions asked at countless quant interviews. The intended audience includes practicing quants who will encounter things both familiar and novel (such lesser known ML algorithms or multi-period portfolio optimization), students and scientists thinking of joining the quant workforce (and wondering if it's worth it), and the general public interested in quantitative and algorithmic trading from a broad scientific, and occasionally ironic, standpoint"--