Machine Learning for Financial Engineering.:
This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfol...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Singapore :
World Scientific,
2012.
|
| Series: | Advances in computer science and engineering. Texts.
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment. The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and eng. |
| Item Description: | 5.4. Universally Consistent Predictions: Unbounded Y. |
| Physical Description: | 1 online resource (261 pages) |
| ISBN: | 9781848168145 1848168144 |
Staff View
MARC
| LEADER | 00000cam a2200000Mu 4500 | ||
|---|---|---|---|
| 001 | ZDB-4-EBU-ocn794328402 | ||
| 003 | OCoLC | ||
| 005 | 20241004212047.0 | ||
| 006 | m o d | ||
| 007 | cr |n|---||||| | ||
| 008 | 120528s2012 si o 000 0 eng d | ||
| 040 | |a EBLCP |b eng |e pn |c EBLCP |d OCLCQ |d LGG |d CDX |d YDXCP |d N$T |d OCLCA |d OCLCQ |d DEBSZ |d OCLCO |d OCLCQ |d OCLCO |d OCLCQ |d OCLCO |d OCLCQ |d OCLCO |d AGLDB |d ZCU |d MERUC |d OCLCQ |d STF |d OCLCF |d JBG |d OCLCQ |d VTS |d ICG |d OCLCQ |d TKN |d DKC |d OCLCQ |d AJS |d OCLCO |d OCLCQ |d OCLCO |d OCLCL | ||
| 019 | |a 794262982 | ||
| 020 | |a 9781848168145 |q (electronic bk.) | ||
| 020 | |a 1848168144 |q (electronic bk.) | ||
| 020 | |z 9781848168138 | ||
| 020 | |z 1848168136 | ||
| 035 | |a (OCoLC)794328402 |z (OCoLC)794262982 | ||
| 050 | 4 | |a Q325.5 .M321 2012 | |
| 072 | 7 | |a COM |x 005030 |2 bisacsh | |
| 072 | 7 | |a COM |x 004000 |2 bisacsh | |
| 082 | 7 | |a 006.31 | |
| 049 | |a MAIN | ||
| 100 | 1 | |a Gyorfi, Laszlo. | |
| 245 | 1 | 0 | |a Machine Learning for Financial Engineering. |
| 260 | |a Singapore : |b World Scientific, |c 2012. | ||
| 300 | |a 1 online resource (261 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Advances in Computer Science and Engineering: Texts | |
| 505 | 0 | |a Preface; Contents; 1. On the History of the Growth-Optimal Portfolio M.M. Christensen; 1.1. Introduction and Historical Overview; 1.2. Theoretical Studies of the GOP; 1.2.1. Discrete Time; 1.2.2. Continuous-Time; 1.3. The GOP as an Investment Strategy; 1.3.1. Is the GOP Better? -- The Samuelson Controversy; 1.3.2. Capital Growth and the Mean-Variance Approach; 1.3.2.1. Discrete time; 1.3.2.2. Continuous time; 1.3.3. How Long Does it Take for the GOP to Outperform other Portfolios?; 1.4. The GOP and the Pricing of Financial Assets and Derivatives; 1.4.1. Incomplete Markets. | |
| 505 | 8 | |a 1.4.1.1. Utility-Based Pricing1.4.1.2. The Minimal Martingale Measure; 1.4.1.3. Good-Deal Bounds; 1.4.2. A World Without a Risk-Neutral Measure; 1.5. Empirical Studies of the GOP; 1.5.1. Composition of the GOP; 1.5.1.1. Discrete-Time Models; 1.5.1.2. Continuous Time Models; 1.6. Conclusion; References; 2. Empirical Log-Optimal Portfolio Selections: A Survey L. Gyorfi, Gy. Ottucsak and A. Urban; 2.1. Introduction; 2.2. Constantly-Rebalanced Portfolio Selection; 2.2.1. Log-Optimal Portfolio for Memoryless Market Process; 2.2.2. Examples for the Constantly-Rebalanced Portfolio. | |
| 505 | 8 | |a 2.2.3. Semi-Log-Optimal Portfolio2.3. Time-Varying Portfolio Selection; 2.3.1. Log-Optimal Portfolio for Stationary Market Process; 2.3.2. Empirical Portfolio Selection; 2.3.3. Regression Function Estimation; 2.3.4. Histogram-Based Strategy; 2.3.5. Kernel-Based Strategy; 2.3.6. Nearest-Neighbor-Based Strategy; 2.3.7. Numerical Results on Empirical Portfolio Selection; References; 3. Log-Optimal Portfolio-Selection Strategies with Proportional Transaction Costs L. Gyorfi and H. Walk; 3.1. Introduction; 3.2. Mathematical Setup: Investment with Proportional Transaction Cost. | |
| 505 | 8 | |a 3.3. Experiments on Heuristic Algorithms3.4. Growth-Optimal Portfolio Selection Algorithms; 3.5. Portfolio Selection with Consumption; 3.6. Proofs; References; 4. Growth-Optimal Portfolio Selection with Short Selling and Leverage M. Horvath and A. Urban; 4.1. Introduction; 4.2. Non-Leveraged, Long-Only Investment; 4.3. Short Selling; 4.3.1. No-Ruin Constraints; 4.3.2. Optimality Condition for Short Selling with Cash Account; 4.4. Long-Only Leveraged Investment; 4.4.1. No-Ruin Condition; 4.4.2. Kuhn-Tucker Characterization; 4.5. Short Selling and Leverage; 4.6. Experiments; References. | |
| 505 | 8 | |a 5. Nonparametric Sequential Prediction of Stationary Time Series L. Gyorfi and Gy. Ottucsak5.1. Introduction; 5.2. Nonparametric Regression Estimation; 5.2.1. The Regression Problem; 5.2.2. Regression Function Estimation and L2 Error; 5.2.3. Partitioning Estimate; 5.2.4. Kernel Estimate; 5.2.5. Nearest-Neighbor Estimate; 5.2.6. Empirical Error Minimization; 5.3. Universally Consistent Predictions: Bounded Y; 5.3.1. Partition-Based Prediction Strategies; 5.3.2. Kernel-Based Prediction Strategies; 5.3.3. Nearest-Neighbor-Based Prediction Strategy; 5.3.4. Generalized Linear Estimates. | |
| 500 | |a 5.4. Universally Consistent Predictions: Unbounded Y. | ||
| 520 | |a This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment. The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and eng. | ||
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Financial engineering |x Data processing. | |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Investments |x Data processing. | |
| 650 | 6 | |a Ingénierie financière |x Informatique. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Investissements |x Informatique. | |
| 650 | 7 | |a COMPUTERS |x Enterprise Applications |x Business Intelligence Tools. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Investments |x Data processing |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 700 | 1 | |a Ottucsak, Gyorgy. | |
| 700 | 1 | |a Walk, Harro. | |
| 758 | |i has work: |a Machine learning for financial engineering (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGV37pwgqjQcgR7FmM4XMK |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Gyorfi, Laszlo. |t Machine Learning for Financial Engineering. |d Singapore : World Scientific, ©2012 |z 9781848168138 |
| 830 | 0 | |a Advances in computer science and engineering. |p Texts. |0 http://id.loc.gov/authorities/names/no2007041420 | |
| 966 | 4 | 0 | |l DE-862 |p ZDB-4-EBU |q FWS_PDA_EBU |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=457208 |3 Volltext |
| 966 | 4 | 0 | |l DE-863 |p ZDB-4-EBU |q FWS_PDA_EBU |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=457208 |3 Volltext |
| 938 | |a Coutts Information Services |b COUT |n 22579779 | ||
| 938 | |a EBL - Ebook Library |b EBLB |n EBL919109 | ||
| 938 | |a EBSCOhost |b EBSC |n 457208 | ||
| 938 | |a YBP Library Services |b YANK |n 7466860 | ||
| 994 | |a 92 |b GEBAY | ||
| 912 | |a ZDB-4-EBU | ||
| 049 | |a DE-862 | ||
| 049 | |a DE-863 | ||
Record in the Search Index
| DE-BY-FWS_katkey | ZDB-4-EBU-ocn794328402 |
|---|---|
| _version_ | 1826942415911518210 |
| adam_text | |
| any_adam_object | |
| author | Gyorfi, Laszlo |
| author2 | Ottucsak, Gyorgy Walk, Harro |
| author2_role | |
| author2_variant | g o go h w hw |
| author_facet | Gyorfi, Laszlo Ottucsak, Gyorgy Walk, Harro |
| author_role | |
| author_sort | Gyorfi, Laszlo |
| author_variant | l g lg |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | Q325 |
| callnumber-raw | Q325.5 .M321 2012 |
| callnumber-search | Q325.5 .M321 2012 |
| callnumber-sort | Q 3325.5 M321 42012 |
| callnumber-subject | Q - General Science |
| collection | ZDB-4-EBU |
| contents | Preface; Contents; 1. On the History of the Growth-Optimal Portfolio M.M. Christensen; 1.1. Introduction and Historical Overview; 1.2. Theoretical Studies of the GOP; 1.2.1. Discrete Time; 1.2.2. Continuous-Time; 1.3. The GOP as an Investment Strategy; 1.3.1. Is the GOP Better? -- The Samuelson Controversy; 1.3.2. Capital Growth and the Mean-Variance Approach; 1.3.2.1. Discrete time; 1.3.2.2. Continuous time; 1.3.3. How Long Does it Take for the GOP to Outperform other Portfolios?; 1.4. The GOP and the Pricing of Financial Assets and Derivatives; 1.4.1. Incomplete Markets. 1.4.1.1. Utility-Based Pricing1.4.1.2. The Minimal Martingale Measure; 1.4.1.3. Good-Deal Bounds; 1.4.2. A World Without a Risk-Neutral Measure; 1.5. Empirical Studies of the GOP; 1.5.1. Composition of the GOP; 1.5.1.1. Discrete-Time Models; 1.5.1.2. Continuous Time Models; 1.6. Conclusion; References; 2. Empirical Log-Optimal Portfolio Selections: A Survey L. Gyorfi, Gy. Ottucsak and A. Urban; 2.1. Introduction; 2.2. Constantly-Rebalanced Portfolio Selection; 2.2.1. Log-Optimal Portfolio for Memoryless Market Process; 2.2.2. Examples for the Constantly-Rebalanced Portfolio. 2.2.3. Semi-Log-Optimal Portfolio2.3. Time-Varying Portfolio Selection; 2.3.1. Log-Optimal Portfolio for Stationary Market Process; 2.3.2. Empirical Portfolio Selection; 2.3.3. Regression Function Estimation; 2.3.4. Histogram-Based Strategy; 2.3.5. Kernel-Based Strategy; 2.3.6. Nearest-Neighbor-Based Strategy; 2.3.7. Numerical Results on Empirical Portfolio Selection; References; 3. Log-Optimal Portfolio-Selection Strategies with Proportional Transaction Costs L. Gyorfi and H. Walk; 3.1. Introduction; 3.2. Mathematical Setup: Investment with Proportional Transaction Cost. 3.3. Experiments on Heuristic Algorithms3.4. Growth-Optimal Portfolio Selection Algorithms; 3.5. Portfolio Selection with Consumption; 3.6. Proofs; References; 4. Growth-Optimal Portfolio Selection with Short Selling and Leverage M. Horvath and A. Urban; 4.1. Introduction; 4.2. Non-Leveraged, Long-Only Investment; 4.3. Short Selling; 4.3.1. No-Ruin Constraints; 4.3.2. Optimality Condition for Short Selling with Cash Account; 4.4. Long-Only Leveraged Investment; 4.4.1. No-Ruin Condition; 4.4.2. Kuhn-Tucker Characterization; 4.5. Short Selling and Leverage; 4.6. Experiments; References. 5. Nonparametric Sequential Prediction of Stationary Time Series L. Gyorfi and Gy. Ottucsak5.1. Introduction; 5.2. Nonparametric Regression Estimation; 5.2.1. The Regression Problem; 5.2.2. Regression Function Estimation and L2 Error; 5.2.3. Partitioning Estimate; 5.2.4. Kernel Estimate; 5.2.5. Nearest-Neighbor Estimate; 5.2.6. Empirical Error Minimization; 5.3. Universally Consistent Predictions: Bounded Y; 5.3.1. Partition-Based Prediction Strategies; 5.3.2. Kernel-Based Prediction Strategies; 5.3.3. Nearest-Neighbor-Based Prediction Strategy; 5.3.4. Generalized Linear Estimates. |
| ctrlnum | (OCoLC)794328402 |
| dewey-full | 006.31 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.31 |
| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>06381cam a2200685Mu 4500</leader><controlfield tag="001">ZDB-4-EBU-ocn794328402</controlfield><controlfield tag="003">OCoLC</controlfield><controlfield tag="005">20241004212047.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr |n|---|||||</controlfield><controlfield tag="008">120528s2012 si o 000 0 eng d</controlfield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">EBLCP</subfield><subfield code="b">eng</subfield><subfield code="e">pn</subfield><subfield code="c">EBLCP</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">LGG</subfield><subfield code="d">CDX</subfield><subfield code="d">YDXCP</subfield><subfield code="d">N$T</subfield><subfield code="d">OCLCA</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">DEBSZ</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">OCLCO</subfield><subfield code="d">AGLDB</subfield><subfield code="d">ZCU</subfield><subfield code="d">MERUC</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">STF</subfield><subfield code="d">OCLCF</subfield><subfield code="d">JBG</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">VTS</subfield><subfield code="d">ICG</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">TKN</subfield><subfield code="d">DKC</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">AJS</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCL</subfield></datafield><datafield tag="019" ind1=" " ind2=" "><subfield code="a">794262982</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781848168145</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1848168144</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">9781848168138</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">1848168136</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)794328402</subfield><subfield code="z">(OCoLC)794262982</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">Q325.5 .M321 2012</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="x">005030</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="x">004000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="082" ind1="7" ind2=" "><subfield code="a">006.31</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">MAIN</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gyorfi, Laszlo.</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine Learning for Financial Engineering.</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Singapore :</subfield><subfield code="b">World Scientific,</subfield><subfield code="c">2012.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (261 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Advances in Computer Science and Engineering: Texts</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Preface; Contents; 1. On the History of the Growth-Optimal Portfolio M.M. Christensen; 1.1. Introduction and Historical Overview; 1.2. Theoretical Studies of the GOP; 1.2.1. Discrete Time; 1.2.2. Continuous-Time; 1.3. The GOP as an Investment Strategy; 1.3.1. Is the GOP Better? -- The Samuelson Controversy; 1.3.2. Capital Growth and the Mean-Variance Approach; 1.3.2.1. Discrete time; 1.3.2.2. Continuous time; 1.3.3. How Long Does it Take for the GOP to Outperform other Portfolios?; 1.4. The GOP and the Pricing of Financial Assets and Derivatives; 1.4.1. Incomplete Markets.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1.4.1.1. Utility-Based Pricing1.4.1.2. The Minimal Martingale Measure; 1.4.1.3. Good-Deal Bounds; 1.4.2. A World Without a Risk-Neutral Measure; 1.5. Empirical Studies of the GOP; 1.5.1. Composition of the GOP; 1.5.1.1. Discrete-Time Models; 1.5.1.2. Continuous Time Models; 1.6. Conclusion; References; 2. Empirical Log-Optimal Portfolio Selections: A Survey L. Gyorfi, Gy. Ottucsak and A. Urban; 2.1. Introduction; 2.2. Constantly-Rebalanced Portfolio Selection; 2.2.1. Log-Optimal Portfolio for Memoryless Market Process; 2.2.2. Examples for the Constantly-Rebalanced Portfolio.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.2.3. Semi-Log-Optimal Portfolio2.3. Time-Varying Portfolio Selection; 2.3.1. Log-Optimal Portfolio for Stationary Market Process; 2.3.2. Empirical Portfolio Selection; 2.3.3. Regression Function Estimation; 2.3.4. Histogram-Based Strategy; 2.3.5. Kernel-Based Strategy; 2.3.6. Nearest-Neighbor-Based Strategy; 2.3.7. Numerical Results on Empirical Portfolio Selection; References; 3. Log-Optimal Portfolio-Selection Strategies with Proportional Transaction Costs L. Gyorfi and H. Walk; 3.1. Introduction; 3.2. Mathematical Setup: Investment with Proportional Transaction Cost.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.3. Experiments on Heuristic Algorithms3.4. Growth-Optimal Portfolio Selection Algorithms; 3.5. Portfolio Selection with Consumption; 3.6. Proofs; References; 4. Growth-Optimal Portfolio Selection with Short Selling and Leverage M. Horvath and A. Urban; 4.1. Introduction; 4.2. Non-Leveraged, Long-Only Investment; 4.3. Short Selling; 4.3.1. No-Ruin Constraints; 4.3.2. Optimality Condition for Short Selling with Cash Account; 4.4. Long-Only Leveraged Investment; 4.4.1. No-Ruin Condition; 4.4.2. Kuhn-Tucker Characterization; 4.5. Short Selling and Leverage; 4.6. Experiments; References.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">5. Nonparametric Sequential Prediction of Stationary Time Series L. Gyorfi and Gy. Ottucsak5.1. Introduction; 5.2. Nonparametric Regression Estimation; 5.2.1. The Regression Problem; 5.2.2. Regression Function Estimation and L2 Error; 5.2.3. Partitioning Estimate; 5.2.4. Kernel Estimate; 5.2.5. Nearest-Neighbor Estimate; 5.2.6. Empirical Error Minimization; 5.3. Universally Consistent Predictions: Bounded Y; 5.3.1. Partition-Based Prediction Strategies; 5.3.2. Kernel-Based Prediction Strategies; 5.3.3. Nearest-Neighbor-Based Prediction Strategy; 5.3.4. Generalized Linear Estimates.</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">5.4. Universally Consistent Predictions: Unbounded Y.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment. The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and eng.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Financial engineering</subfield><subfield code="x">Data processing.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85079324</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Investments</subfield><subfield code="x">Data processing.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Ingénierie financière</subfield><subfield code="x">Informatique.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Apprentissage automatique.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Investissements</subfield><subfield code="x">Informatique.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS</subfield><subfield code="x">Enterprise Applications</subfield><subfield code="x">Business Intelligence Tools.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS</subfield><subfield code="x">Intelligence (AI) & Semantics.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Investments</subfield><subfield code="x">Data processing</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machine learning</subfield><subfield code="2">fast</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ottucsak, Gyorgy.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Walk, Harro.</subfield></datafield><datafield tag="758" ind1=" " ind2=" "><subfield code="i">has work:</subfield><subfield code="a">Machine learning for financial engineering (Text)</subfield><subfield code="1">https://id.oclc.org/worldcat/entity/E39PCGV37pwgqjQcgR7FmM4XMK</subfield><subfield code="4">https://id.oclc.org/worldcat/ontology/hasWork</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Gyorfi, Laszlo.</subfield><subfield code="t">Machine Learning for Financial Engineering.</subfield><subfield code="d">Singapore : World Scientific, ©2012</subfield><subfield code="z">9781848168138</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">Advances in computer science and engineering.</subfield><subfield code="p">Texts.</subfield><subfield code="0">http://id.loc.gov/authorities/names/no2007041420</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-862</subfield><subfield code="p">ZDB-4-EBU</subfield><subfield code="q">FWS_PDA_EBU</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=457208</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-863</subfield><subfield code="p">ZDB-4-EBU</subfield><subfield code="q">FWS_PDA_EBU</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=457208</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">Coutts Information Services</subfield><subfield code="b">COUT</subfield><subfield code="n">22579779</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBL - Ebook Library</subfield><subfield code="b">EBLB</subfield><subfield code="n">EBL919109</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBSCOhost</subfield><subfield code="b">EBSC</subfield><subfield code="n">457208</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">7466860</subfield></datafield><datafield tag="994" ind1=" " ind2=" "><subfield code="a">92</subfield><subfield code="b">GEBAY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-EBU</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-862</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-863</subfield></datafield></record></collection> |
| id | ZDB-4-EBU-ocn794328402 |
| illustrated | Not Illustrated |
| indexdate | 2025-03-18T14:27:39Z |
| institution | BVB |
| isbn | 9781848168145 1848168144 |
| language | English |
| oclc_num | 794328402 |
| open_access_boolean | |
| owner | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| physical | 1 online resource (261 pages) |
| psigel | ZDB-4-EBU FWS_PDA_EBU ZDB-4-EBU |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | World Scientific, |
| record_format | marc |
| series | Advances in computer science and engineering. Texts. |
| series2 | Advances in Computer Science and Engineering: Texts |
| spelling | Gyorfi, Laszlo. Machine Learning for Financial Engineering. Singapore : World Scientific, 2012. 1 online resource (261 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Advances in Computer Science and Engineering: Texts Preface; Contents; 1. On the History of the Growth-Optimal Portfolio M.M. Christensen; 1.1. Introduction and Historical Overview; 1.2. Theoretical Studies of the GOP; 1.2.1. Discrete Time; 1.2.2. Continuous-Time; 1.3. The GOP as an Investment Strategy; 1.3.1. Is the GOP Better? -- The Samuelson Controversy; 1.3.2. Capital Growth and the Mean-Variance Approach; 1.3.2.1. Discrete time; 1.3.2.2. Continuous time; 1.3.3. How Long Does it Take for the GOP to Outperform other Portfolios?; 1.4. The GOP and the Pricing of Financial Assets and Derivatives; 1.4.1. Incomplete Markets. 1.4.1.1. Utility-Based Pricing1.4.1.2. The Minimal Martingale Measure; 1.4.1.3. Good-Deal Bounds; 1.4.2. A World Without a Risk-Neutral Measure; 1.5. Empirical Studies of the GOP; 1.5.1. Composition of the GOP; 1.5.1.1. Discrete-Time Models; 1.5.1.2. Continuous Time Models; 1.6. Conclusion; References; 2. Empirical Log-Optimal Portfolio Selections: A Survey L. Gyorfi, Gy. Ottucsak and A. Urban; 2.1. Introduction; 2.2. Constantly-Rebalanced Portfolio Selection; 2.2.1. Log-Optimal Portfolio for Memoryless Market Process; 2.2.2. Examples for the Constantly-Rebalanced Portfolio. 2.2.3. Semi-Log-Optimal Portfolio2.3. Time-Varying Portfolio Selection; 2.3.1. Log-Optimal Portfolio for Stationary Market Process; 2.3.2. Empirical Portfolio Selection; 2.3.3. Regression Function Estimation; 2.3.4. Histogram-Based Strategy; 2.3.5. Kernel-Based Strategy; 2.3.6. Nearest-Neighbor-Based Strategy; 2.3.7. Numerical Results on Empirical Portfolio Selection; References; 3. Log-Optimal Portfolio-Selection Strategies with Proportional Transaction Costs L. Gyorfi and H. Walk; 3.1. Introduction; 3.2. Mathematical Setup: Investment with Proportional Transaction Cost. 3.3. Experiments on Heuristic Algorithms3.4. Growth-Optimal Portfolio Selection Algorithms; 3.5. Portfolio Selection with Consumption; 3.6. Proofs; References; 4. Growth-Optimal Portfolio Selection with Short Selling and Leverage M. Horvath and A. Urban; 4.1. Introduction; 4.2. Non-Leveraged, Long-Only Investment; 4.3. Short Selling; 4.3.1. No-Ruin Constraints; 4.3.2. Optimality Condition for Short Selling with Cash Account; 4.4. Long-Only Leveraged Investment; 4.4.1. No-Ruin Condition; 4.4.2. Kuhn-Tucker Characterization; 4.5. Short Selling and Leverage; 4.6. Experiments; References. 5. Nonparametric Sequential Prediction of Stationary Time Series L. Gyorfi and Gy. Ottucsak5.1. Introduction; 5.2. Nonparametric Regression Estimation; 5.2.1. The Regression Problem; 5.2.2. Regression Function Estimation and L2 Error; 5.2.3. Partitioning Estimate; 5.2.4. Kernel Estimate; 5.2.5. Nearest-Neighbor Estimate; 5.2.6. Empirical Error Minimization; 5.3. Universally Consistent Predictions: Bounded Y; 5.3.1. Partition-Based Prediction Strategies; 5.3.2. Kernel-Based Prediction Strategies; 5.3.3. Nearest-Neighbor-Based Prediction Strategy; 5.3.4. Generalized Linear Estimates. 5.4. Universally Consistent Predictions: Unbounded Y. This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment. The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and eng. Print version record. Financial engineering Data processing. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Investments Data processing. Ingénierie financière Informatique. Apprentissage automatique. Investissements Informatique. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Investments Data processing fast Machine learning fast Ottucsak, Gyorgy. Walk, Harro. has work: Machine learning for financial engineering (Text) https://id.oclc.org/worldcat/entity/E39PCGV37pwgqjQcgR7FmM4XMK https://id.oclc.org/worldcat/ontology/hasWork Print version: Gyorfi, Laszlo. Machine Learning for Financial Engineering. Singapore : World Scientific, ©2012 9781848168138 Advances in computer science and engineering. Texts. http://id.loc.gov/authorities/names/no2007041420 |
| spellingShingle | Gyorfi, Laszlo Machine Learning for Financial Engineering. Advances in computer science and engineering. Texts. Preface; Contents; 1. On the History of the Growth-Optimal Portfolio M.M. Christensen; 1.1. Introduction and Historical Overview; 1.2. Theoretical Studies of the GOP; 1.2.1. Discrete Time; 1.2.2. Continuous-Time; 1.3. The GOP as an Investment Strategy; 1.3.1. Is the GOP Better? -- The Samuelson Controversy; 1.3.2. Capital Growth and the Mean-Variance Approach; 1.3.2.1. Discrete time; 1.3.2.2. Continuous time; 1.3.3. How Long Does it Take for the GOP to Outperform other Portfolios?; 1.4. The GOP and the Pricing of Financial Assets and Derivatives; 1.4.1. Incomplete Markets. 1.4.1.1. Utility-Based Pricing1.4.1.2. The Minimal Martingale Measure; 1.4.1.3. Good-Deal Bounds; 1.4.2. A World Without a Risk-Neutral Measure; 1.5. Empirical Studies of the GOP; 1.5.1. Composition of the GOP; 1.5.1.1. Discrete-Time Models; 1.5.1.2. Continuous Time Models; 1.6. Conclusion; References; 2. Empirical Log-Optimal Portfolio Selections: A Survey L. Gyorfi, Gy. Ottucsak and A. Urban; 2.1. Introduction; 2.2. Constantly-Rebalanced Portfolio Selection; 2.2.1. Log-Optimal Portfolio for Memoryless Market Process; 2.2.2. Examples for the Constantly-Rebalanced Portfolio. 2.2.3. Semi-Log-Optimal Portfolio2.3. Time-Varying Portfolio Selection; 2.3.1. Log-Optimal Portfolio for Stationary Market Process; 2.3.2. Empirical Portfolio Selection; 2.3.3. Regression Function Estimation; 2.3.4. Histogram-Based Strategy; 2.3.5. Kernel-Based Strategy; 2.3.6. Nearest-Neighbor-Based Strategy; 2.3.7. Numerical Results on Empirical Portfolio Selection; References; 3. Log-Optimal Portfolio-Selection Strategies with Proportional Transaction Costs L. Gyorfi and H. Walk; 3.1. Introduction; 3.2. Mathematical Setup: Investment with Proportional Transaction Cost. 3.3. Experiments on Heuristic Algorithms3.4. Growth-Optimal Portfolio Selection Algorithms; 3.5. Portfolio Selection with Consumption; 3.6. Proofs; References; 4. Growth-Optimal Portfolio Selection with Short Selling and Leverage M. Horvath and A. Urban; 4.1. Introduction; 4.2. Non-Leveraged, Long-Only Investment; 4.3. Short Selling; 4.3.1. No-Ruin Constraints; 4.3.2. Optimality Condition for Short Selling with Cash Account; 4.4. Long-Only Leveraged Investment; 4.4.1. No-Ruin Condition; 4.4.2. Kuhn-Tucker Characterization; 4.5. Short Selling and Leverage; 4.6. Experiments; References. 5. Nonparametric Sequential Prediction of Stationary Time Series L. Gyorfi and Gy. Ottucsak5.1. Introduction; 5.2. Nonparametric Regression Estimation; 5.2.1. The Regression Problem; 5.2.2. Regression Function Estimation and L2 Error; 5.2.3. Partitioning Estimate; 5.2.4. Kernel Estimate; 5.2.5. Nearest-Neighbor Estimate; 5.2.6. Empirical Error Minimization; 5.3. Universally Consistent Predictions: Bounded Y; 5.3.1. Partition-Based Prediction Strategies; 5.3.2. Kernel-Based Prediction Strategies; 5.3.3. Nearest-Neighbor-Based Prediction Strategy; 5.3.4. Generalized Linear Estimates. Financial engineering Data processing. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Investments Data processing. Ingénierie financière Informatique. Apprentissage automatique. Investissements Informatique. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Investments Data processing fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 |
| title | Machine Learning for Financial Engineering. |
| title_auth | Machine Learning for Financial Engineering. |
| title_exact_search | Machine Learning for Financial Engineering. |
| title_full | Machine Learning for Financial Engineering. |
| title_fullStr | Machine Learning for Financial Engineering. |
| title_full_unstemmed | Machine Learning for Financial Engineering. |
| title_short | Machine Learning for Financial Engineering. |
| title_sort | machine learning for financial engineering |
| topic | Financial engineering Data processing. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Investments Data processing. Ingénierie financière Informatique. Apprentissage automatique. Investissements Informatique. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Investments Data processing fast Machine learning fast |
| topic_facet | Financial engineering Data processing. Machine learning. Investments Data processing. Ingénierie financière Informatique. Apprentissage automatique. Investissements Informatique. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Investments Data processing Machine learning |
| work_keys_str_mv | AT gyorfilaszlo machinelearningforfinancialengineering AT ottucsakgyorgy machinelearningforfinancialengineering AT walkharro machinelearningforfinancialengineering |