Distress risk and corporate failure modelling: the state of the art
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Abingdon, Oxon ; New York, NY
Routledge
2023
|
| Schriftenreihe: | Routledge advances in management and business studies
|
| Schlagwörter: | |
| Abstract: | The Relevance and Utility of Distress Risk and Corporate Failure Forecasts -- Searching for the Holy Grail: Alternative Statistical Modelling Approaches -- The Rise of the Machines -- An Empirical Application of Modern Machine Learning Methods -- Corporate Failure Models for Private Companies, Not-for Profits and Public Sector Entities -- Whither Corporate Failure Research? "This book is an introduction text to distress risk and corporate failure modelling techniques. It illustrates how to apply a wide range of corporate bankruptcy prediction models and in turn, highlights their strengths and limitations under different circumstances. It also conceptualises the role and function of different classifiers in terms of a trade-off between model flexibility and interpretability. Jones's illustrations and applications which are based on actual company failure data and samples. Its practical and lucid presentation of basic concepts covers various statistical learning approaches, including machine learning which has come into prominence in recent years. The material covered will help readers better understand a broad range of statistical learning models, ranging from relatively linear techniques such as linear discriminant analysis to state-of-the-art machine learning methods such as gradient boosting machines, adaptive boosting, random forests, and deep learning. The book's comprehensive review and use of real-life data will make this a valuable, easy-to-read text for researchers, academics, institutions and professionals who make use of distress risk and corporate failure forecasts"-- |
| Beschreibung: | Includes bibliographical references and index 2209 |
| Umfang: | xi, 230 Seiten Diagramme |
| ISBN: | 9781138652491 9781138652507 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV049326215 | ||
| 003 | DE-604 | ||
| 005 | 20240104 | ||
| 007 | t| | ||
| 008 | 230914s2023 xxk|||| |||| 00||| eng d | ||
| 020 | |a 9781138652491 |c hardback |9 978-1-138-65249-1 | ||
| 020 | |a 9781138652507 |c paperback |9 978-1-138-65250-7 | ||
| 035 | |a (OCoLC)1369614499 | ||
| 035 | |a (DE-599)KXP1809215846 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a xxk |c XA-GB | ||
| 049 | |a DE-N2 | ||
| 082 | 0 | |a 332.7/5 | |
| 084 | |a QP 760 |0 (DE-625)141936: |2 rvk | ||
| 100 | 1 | |a Jones, Stewart |d 1964- |e Verfasser |0 (DE-588)139244581 |4 aut | |
| 245 | 1 | 0 | |a Distress risk and corporate failure modelling |b the state of the art |c Stewart Jones |
| 264 | 1 | |a Abingdon, Oxon ; New York, NY |b Routledge |c 2023 | |
| 300 | |a xi, 230 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Routledge advances in management and business studies | |
| 500 | |a Includes bibliographical references and index | ||
| 500 | |a 2209 | ||
| 520 | 3 | |a The Relevance and Utility of Distress Risk and Corporate Failure Forecasts -- Searching for the Holy Grail: Alternative Statistical Modelling Approaches -- The Rise of the Machines -- An Empirical Application of Modern Machine Learning Methods -- Corporate Failure Models for Private Companies, Not-for Profits and Public Sector Entities -- Whither Corporate Failure Research? | |
| 520 | 3 | |a "This book is an introduction text to distress risk and corporate failure modelling techniques. It illustrates how to apply a wide range of corporate bankruptcy prediction models and in turn, highlights their strengths and limitations under different circumstances. It also conceptualises the role and function of different classifiers in terms of a trade-off between model flexibility and interpretability. Jones's illustrations and applications which are based on actual company failure data and samples. Its practical and lucid presentation of basic concepts covers various statistical learning approaches, including machine learning which has come into prominence in recent years. The material covered will help readers better understand a broad range of statistical learning models, ranging from relatively linear techniques such as linear discriminant analysis to state-of-the-art machine learning methods such as gradient boosting machines, adaptive boosting, random forests, and deep learning. The book's comprehensive review and use of real-life data will make this a valuable, easy-to-read text for researchers, academics, institutions and professionals who make use of distress risk and corporate failure forecasts"-- | |
| 653 | 0 | |a Insolvenz / (DE-627)091401992 / (DE-2867)12302-3 | |
| 653 | 0 | |a Prognoseverfahren / (DE-627)091384680 / (DE-2867)15072-0 | |
| 653 | 0 | |a Mathematik / (DE-627)091376807 / (DE-2867)15045-3 | |
| 653 | 0 | |a Statistische Methode / (DE-627)091392101 / (DE-2867)15064-6 | |
| 653 | 0 | |a Künstliche Intelligenz / (DE-627)091373379 / (DE-2867)15611-3 | |
| 653 | 0 | |a Bankruptcy / Forecasting / Mathematical models | |
| 653 | 0 | |a Corporations / Finance / Mathematical models | |
| 653 | 0 | |a Risk / Mathematical models | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-315-62322-1 |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-034587070 | |
Datensatz im Suchindex
| _version_ | 1818991210467950592 |
|---|---|
| any_adam_object | |
| author | Jones, Stewart 1964- |
| author_GND | (DE-588)139244581 |
| author_facet | Jones, Stewart 1964- |
| author_role | aut |
| author_sort | Jones, Stewart 1964- |
| author_variant | s j sj |
| building | Verbundindex |
| bvnumber | BV049326215 |
| classification_rvk | QP 760 |
| ctrlnum | (OCoLC)1369614499 (DE-599)KXP1809215846 |
| dewey-full | 332.7/5 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.7/5 |
| dewey-search | 332.7/5 |
| dewey-sort | 3332.7 15 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03336nam a2200469 c 4500</leader><controlfield tag="001">BV049326215</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20240104 </controlfield><controlfield tag="007">t|</controlfield><controlfield tag="008">230914s2023 xxk|||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781138652491</subfield><subfield code="c">hardback</subfield><subfield code="9">978-1-138-65249-1</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781138652507</subfield><subfield code="c">paperback</subfield><subfield code="9">978-1-138-65250-7</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1369614499</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1809215846</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">xxk</subfield><subfield code="c">XA-GB</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-N2</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">332.7/5</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QP 760</subfield><subfield code="0">(DE-625)141936:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jones, Stewart</subfield><subfield code="d">1964-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)139244581</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Distress risk and corporate failure modelling</subfield><subfield code="b">the state of the art</subfield><subfield code="c">Stewart Jones</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Abingdon, Oxon ; New York, NY</subfield><subfield code="b">Routledge</subfield><subfield code="c">2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xi, 230 Seiten</subfield><subfield code="b">Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Routledge advances in management and business studies</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">2209</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">The Relevance and Utility of Distress Risk and Corporate Failure Forecasts -- Searching for the Holy Grail: Alternative Statistical Modelling Approaches -- The Rise of the Machines -- An Empirical Application of Modern Machine Learning Methods -- Corporate Failure Models for Private Companies, Not-for Profits and Public Sector Entities -- Whither Corporate Failure Research?</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">"This book is an introduction text to distress risk and corporate failure modelling techniques. It illustrates how to apply a wide range of corporate bankruptcy prediction models and in turn, highlights their strengths and limitations under different circumstances. It also conceptualises the role and function of different classifiers in terms of a trade-off between model flexibility and interpretability. Jones's illustrations and applications which are based on actual company failure data and samples. Its practical and lucid presentation of basic concepts covers various statistical learning approaches, including machine learning which has come into prominence in recent years. The material covered will help readers better understand a broad range of statistical learning models, ranging from relatively linear techniques such as linear discriminant analysis to state-of-the-art machine learning methods such as gradient boosting machines, adaptive boosting, random forests, and deep learning. The book's comprehensive review and use of real-life data will make this a valuable, easy-to-read text for researchers, academics, institutions and professionals who make use of distress risk and corporate failure forecasts"--</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Insolvenz / (DE-627)091401992 / (DE-2867)12302-3</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Prognoseverfahren / (DE-627)091384680 / (DE-2867)15072-0</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Mathematik / (DE-627)091376807 / (DE-2867)15045-3</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Statistische Methode / (DE-627)091392101 / (DE-2867)15064-6</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Künstliche Intelligenz / (DE-627)091373379 / (DE-2867)15611-3</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Bankruptcy / Forecasting / Mathematical models</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Corporations / Finance / Mathematical models</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Risk / Mathematical models</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-1-315-62322-1</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034587070</subfield></datafield></record></collection> |
| id | DE-604.BV049326215 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T20:06:39Z |
| institution | BVB |
| isbn | 9781138652491 9781138652507 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034587070 |
| oclc_num | 1369614499 |
| open_access_boolean | |
| owner | DE-N2 |
| owner_facet | DE-N2 |
| physical | xi, 230 Seiten Diagramme |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Routledge |
| record_format | marc |
| series2 | Routledge advances in management and business studies |
| spelling | Jones, Stewart 1964- Verfasser (DE-588)139244581 aut Distress risk and corporate failure modelling the state of the art Stewart Jones Abingdon, Oxon ; New York, NY Routledge 2023 xi, 230 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Routledge advances in management and business studies Includes bibliographical references and index 2209 The Relevance and Utility of Distress Risk and Corporate Failure Forecasts -- Searching for the Holy Grail: Alternative Statistical Modelling Approaches -- The Rise of the Machines -- An Empirical Application of Modern Machine Learning Methods -- Corporate Failure Models for Private Companies, Not-for Profits and Public Sector Entities -- Whither Corporate Failure Research? "This book is an introduction text to distress risk and corporate failure modelling techniques. It illustrates how to apply a wide range of corporate bankruptcy prediction models and in turn, highlights their strengths and limitations under different circumstances. It also conceptualises the role and function of different classifiers in terms of a trade-off between model flexibility and interpretability. Jones's illustrations and applications which are based on actual company failure data and samples. Its practical and lucid presentation of basic concepts covers various statistical learning approaches, including machine learning which has come into prominence in recent years. The material covered will help readers better understand a broad range of statistical learning models, ranging from relatively linear techniques such as linear discriminant analysis to state-of-the-art machine learning methods such as gradient boosting machines, adaptive boosting, random forests, and deep learning. The book's comprehensive review and use of real-life data will make this a valuable, easy-to-read text for researchers, academics, institutions and professionals who make use of distress risk and corporate failure forecasts"-- Insolvenz / (DE-627)091401992 / (DE-2867)12302-3 Prognoseverfahren / (DE-627)091384680 / (DE-2867)15072-0 Mathematik / (DE-627)091376807 / (DE-2867)15045-3 Statistische Methode / (DE-627)091392101 / (DE-2867)15064-6 Künstliche Intelligenz / (DE-627)091373379 / (DE-2867)15611-3 Bankruptcy / Forecasting / Mathematical models Corporations / Finance / Mathematical models Risk / Mathematical models Erscheint auch als Online-Ausgabe 978-1-315-62322-1 |
| spellingShingle | Jones, Stewart 1964- Distress risk and corporate failure modelling the state of the art |
| title | Distress risk and corporate failure modelling the state of the art |
| title_auth | Distress risk and corporate failure modelling the state of the art |
| title_exact_search | Distress risk and corporate failure modelling the state of the art |
| title_full | Distress risk and corporate failure modelling the state of the art Stewart Jones |
| title_fullStr | Distress risk and corporate failure modelling the state of the art Stewart Jones |
| title_full_unstemmed | Distress risk and corporate failure modelling the state of the art Stewart Jones |
| title_short | Distress risk and corporate failure modelling |
| title_sort | distress risk and corporate failure modelling the state of the art |
| title_sub | the state of the art |
| work_keys_str_mv | AT jonesstewart distressriskandcorporatefailuremodellingthestateoftheart |