Machine learning in empirical asset pricing View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03

AUTHORS

Alois Weigand

ABSTRACT

The tremendous speedup in computing in recent years, the low data storage costs of today, the availability of “big data” as well as the broad range of free open-source software, have created a renaissance in the application of machine learning techniques in science. However, this new wave of research is not limited to computer science or software engineering anymore. Among others, machine learning tools are now used in financial problem settings as well. Therefore, this paper mentions a specific definition of machine learning in an asset pricing context and elaborates on the usefulness of machine learning in this context. Most importantly, the literature review gives the reader a theoretical overview of the most recent academic studies in empirical asset pricing that employ machine learning techniques. Overall, the paper concludes that machine learning can offer benefits for future research. However, researchers should be critical about these methodologies as machine learning has its pitfalls and is relatively new to asset pricing. More... »

PAGES

93-104

References to SciGraph publications

  • 2013-09. The supraview of return predictive signals in REVIEW OF ACCOUNTING STUDIES
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11408-019-00326-3

    DOI

    http://dx.doi.org/10.1007/s11408-019-00326-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112389482


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of St. Gallen", 
              "id": "https://www.grid.ac/institutes/grid.15775.31", 
              "name": [
                "Swiss Institute of Banking and Finance (s/bf), University of St. Gallen, Unterer Graben 21, 9000, St. Gallen, Switzerland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Weigand", 
            "givenName": "Alois", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1111/j.1540-6261.2008.01371.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001159041"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.trpro.2014.10.067", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003208765"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jbankfin.2016.07.015", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003460370"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/for.867", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008639588"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/rfs/hhv059", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015226242"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0305-0483(99)00066-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020361323"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/rfs/hhm014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025150849"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/mind/lix.236.433", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027055246"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1540-6261.1995.tb04055.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029480363"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0304-405x(86)90070-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036640597"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1146/annurev-financial-102710-144905", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037572691"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11142-013-9231-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046018897", 
              "https://doi.org/10.1007/s11142-013-9231-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1540-6261.1994.tb00081.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050008564"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jbankfin.2010.06.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050010735"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jeconom.2015.02.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052992844"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1147/rd.33.0210", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063181662"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1561/104.00000024", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1068001034"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1257/jep.31.2.87", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085319572"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1142/s1793351x17500015", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085483922"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2139/ssrn.3032013", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091940047"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2139/ssrn.2740751", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1102455488"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2139/ssrn.2865922", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1102467737"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2139/ssrn.3147971", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103357710"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2139/ssrn.2799443", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103575591"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2139/ssrn.3233119", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1106426151"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2139/ssrn.3232721", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107813799"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2139/ssrn.3275654", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1109951382"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-03", 
        "datePublishedReg": "2019-03-01", 
        "description": "The tremendous speedup in computing in recent years, the low data storage costs of today, the availability of \u201cbig data\u201d as well as the broad range of free open-source software, have created a renaissance in the application of machine learning techniques in science. However, this new wave of research is not limited to computer science or software engineering anymore. Among others, machine learning tools are now used in financial problem settings as well. Therefore, this paper mentions a specific definition of machine learning in an asset pricing context and elaborates on the usefulness of machine learning in this context. Most importantly, the literature review gives the reader a theoretical overview of the most recent academic studies in empirical asset pricing that employ machine learning techniques. Overall, the paper concludes that machine learning can offer benefits for future research. However, researchers should be critical about these methodologies as machine learning has its pitfalls and is relatively new to asset pricing.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s11408-019-00326-3", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1346484", 
            "issn": [
              "1934-4554", 
              "2373-8529"
            ], 
            "name": "Financial Markets and Portfolio Management", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "33"
          }
        ], 
        "name": "Machine learning in empirical asset pricing", 
        "pagination": "93-104", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "2a6efb7deed75255cd98316ad824b8155b06f719da0bd1e67b4e71eead4a7ea4"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11408-019-00326-3"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1112389482"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11408-019-00326-3", 
          "https://app.dimensions.ai/details/publication/pub.1112389482"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T11:58", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000359_0000000359/records_29222_00000003.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs11408-019-00326-3"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11408-019-00326-3'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11408-019-00326-3'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11408-019-00326-3'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11408-019-00326-3'


     

    This table displays all metadata directly associated to this object as RDF triples.

    142 TRIPLES      21 PREDICATES      54 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11408-019-00326-3 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Ne7f872a34a454fafb0e9d581f11de8a1
    4 schema:citation sg:pub.10.1007/s11142-013-9231-1
    5 https://doi.org/10.1002/for.867
    6 https://doi.org/10.1016/0304-405x(86)90070-x
    7 https://doi.org/10.1016/j.jbankfin.2010.06.001
    8 https://doi.org/10.1016/j.jbankfin.2016.07.015
    9 https://doi.org/10.1016/j.jeconom.2015.02.011
    10 https://doi.org/10.1016/j.trpro.2014.10.067
    11 https://doi.org/10.1016/s0305-0483(99)00066-3
    12 https://doi.org/10.1093/mind/lix.236.433
    13 https://doi.org/10.1093/rfs/hhm014
    14 https://doi.org/10.1093/rfs/hhv059
    15 https://doi.org/10.1111/j.1540-6261.1994.tb00081.x
    16 https://doi.org/10.1111/j.1540-6261.1995.tb04055.x
    17 https://doi.org/10.1111/j.1540-6261.2008.01371.x
    18 https://doi.org/10.1142/s1793351x17500015
    19 https://doi.org/10.1146/annurev-financial-102710-144905
    20 https://doi.org/10.1147/rd.33.0210
    21 https://doi.org/10.1257/jep.31.2.87
    22 https://doi.org/10.1561/104.00000024
    23 https://doi.org/10.2139/ssrn.2740751
    24 https://doi.org/10.2139/ssrn.2799443
    25 https://doi.org/10.2139/ssrn.2865922
    26 https://doi.org/10.2139/ssrn.3032013
    27 https://doi.org/10.2139/ssrn.3147971
    28 https://doi.org/10.2139/ssrn.3232721
    29 https://doi.org/10.2139/ssrn.3233119
    30 https://doi.org/10.2139/ssrn.3275654
    31 schema:datePublished 2019-03
    32 schema:datePublishedReg 2019-03-01
    33 schema:description The tremendous speedup in computing in recent years, the low data storage costs of today, the availability of “big data” as well as the broad range of free open-source software, have created a renaissance in the application of machine learning techniques in science. However, this new wave of research is not limited to computer science or software engineering anymore. Among others, machine learning tools are now used in financial problem settings as well. Therefore, this paper mentions a specific definition of machine learning in an asset pricing context and elaborates on the usefulness of machine learning in this context. Most importantly, the literature review gives the reader a theoretical overview of the most recent academic studies in empirical asset pricing that employ machine learning techniques. Overall, the paper concludes that machine learning can offer benefits for future research. However, researchers should be critical about these methodologies as machine learning has its pitfalls and is relatively new to asset pricing.
    34 schema:genre research_article
    35 schema:inLanguage en
    36 schema:isAccessibleForFree false
    37 schema:isPartOf N208124dfcc054c6baf7568d1ad051c78
    38 Nedf1c9e78f294271865f1cd46303ac03
    39 sg:journal.1346484
    40 schema:name Machine learning in empirical asset pricing
    41 schema:pagination 93-104
    42 schema:productId N119ea3384ec24c3fa6a5b43708cc9800
    43 N1e7dd7564ba04c68b199ffacdf73aacb
    44 Nddc02f1f5a6949198dbc28e6252f97fd
    45 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112389482
    46 https://doi.org/10.1007/s11408-019-00326-3
    47 schema:sdDatePublished 2019-04-11T11:58
    48 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    49 schema:sdPublisher N49f30c5a662546b796d77affd827420c
    50 schema:url https://link.springer.com/10.1007%2Fs11408-019-00326-3
    51 sgo:license sg:explorer/license/
    52 sgo:sdDataset articles
    53 rdf:type schema:ScholarlyArticle
    54 N119ea3384ec24c3fa6a5b43708cc9800 schema:name doi
    55 schema:value 10.1007/s11408-019-00326-3
    56 rdf:type schema:PropertyValue
    57 N1e7dd7564ba04c68b199ffacdf73aacb schema:name dimensions_id
    58 schema:value pub.1112389482
    59 rdf:type schema:PropertyValue
    60 N208124dfcc054c6baf7568d1ad051c78 schema:volumeNumber 33
    61 rdf:type schema:PublicationVolume
    62 N26ae33bbc0fb4d0488e43f2c8a7fb203 schema:affiliation https://www.grid.ac/institutes/grid.15775.31
    63 schema:familyName Weigand
    64 schema:givenName Alois
    65 rdf:type schema:Person
    66 N49f30c5a662546b796d77affd827420c schema:name Springer Nature - SN SciGraph project
    67 rdf:type schema:Organization
    68 Nddc02f1f5a6949198dbc28e6252f97fd schema:name readcube_id
    69 schema:value 2a6efb7deed75255cd98316ad824b8155b06f719da0bd1e67b4e71eead4a7ea4
    70 rdf:type schema:PropertyValue
    71 Ne7f872a34a454fafb0e9d581f11de8a1 rdf:first N26ae33bbc0fb4d0488e43f2c8a7fb203
    72 rdf:rest rdf:nil
    73 Nedf1c9e78f294271865f1cd46303ac03 schema:issueNumber 1
    74 rdf:type schema:PublicationIssue
    75 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    76 schema:name Information and Computing Sciences
    77 rdf:type schema:DefinedTerm
    78 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    79 schema:name Artificial Intelligence and Image Processing
    80 rdf:type schema:DefinedTerm
    81 sg:journal.1346484 schema:issn 1934-4554
    82 2373-8529
    83 schema:name Financial Markets and Portfolio Management
    84 rdf:type schema:Periodical
    85 sg:pub.10.1007/s11142-013-9231-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046018897
    86 https://doi.org/10.1007/s11142-013-9231-1
    87 rdf:type schema:CreativeWork
    88 https://doi.org/10.1002/for.867 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008639588
    89 rdf:type schema:CreativeWork
    90 https://doi.org/10.1016/0304-405x(86)90070-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1036640597
    91 rdf:type schema:CreativeWork
    92 https://doi.org/10.1016/j.jbankfin.2010.06.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050010735
    93 rdf:type schema:CreativeWork
    94 https://doi.org/10.1016/j.jbankfin.2016.07.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003460370
    95 rdf:type schema:CreativeWork
    96 https://doi.org/10.1016/j.jeconom.2015.02.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052992844
    97 rdf:type schema:CreativeWork
    98 https://doi.org/10.1016/j.trpro.2014.10.067 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003208765
    99 rdf:type schema:CreativeWork
    100 https://doi.org/10.1016/s0305-0483(99)00066-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020361323
    101 rdf:type schema:CreativeWork
    102 https://doi.org/10.1093/mind/lix.236.433 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027055246
    103 rdf:type schema:CreativeWork
    104 https://doi.org/10.1093/rfs/hhm014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025150849
    105 rdf:type schema:CreativeWork
    106 https://doi.org/10.1093/rfs/hhv059 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015226242
    107 rdf:type schema:CreativeWork
    108 https://doi.org/10.1111/j.1540-6261.1994.tb00081.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1050008564
    109 rdf:type schema:CreativeWork
    110 https://doi.org/10.1111/j.1540-6261.1995.tb04055.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1029480363
    111 rdf:type schema:CreativeWork
    112 https://doi.org/10.1111/j.1540-6261.2008.01371.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1001159041
    113 rdf:type schema:CreativeWork
    114 https://doi.org/10.1142/s1793351x17500015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085483922
    115 rdf:type schema:CreativeWork
    116 https://doi.org/10.1146/annurev-financial-102710-144905 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037572691
    117 rdf:type schema:CreativeWork
    118 https://doi.org/10.1147/rd.33.0210 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063181662
    119 rdf:type schema:CreativeWork
    120 https://doi.org/10.1257/jep.31.2.87 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085319572
    121 rdf:type schema:CreativeWork
    122 https://doi.org/10.1561/104.00000024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068001034
    123 rdf:type schema:CreativeWork
    124 https://doi.org/10.2139/ssrn.2740751 schema:sameAs https://app.dimensions.ai/details/publication/pub.1102455488
    125 rdf:type schema:CreativeWork
    126 https://doi.org/10.2139/ssrn.2799443 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103575591
    127 rdf:type schema:CreativeWork
    128 https://doi.org/10.2139/ssrn.2865922 schema:sameAs https://app.dimensions.ai/details/publication/pub.1102467737
    129 rdf:type schema:CreativeWork
    130 https://doi.org/10.2139/ssrn.3032013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091940047
    131 rdf:type schema:CreativeWork
    132 https://doi.org/10.2139/ssrn.3147971 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103357710
    133 rdf:type schema:CreativeWork
    134 https://doi.org/10.2139/ssrn.3232721 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107813799
    135 rdf:type schema:CreativeWork
    136 https://doi.org/10.2139/ssrn.3233119 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106426151
    137 rdf:type schema:CreativeWork
    138 https://doi.org/10.2139/ssrn.3275654 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109951382
    139 rdf:type schema:CreativeWork
    140 https://www.grid.ac/institutes/grid.15775.31 schema:alternateName University of St. Gallen
    141 schema:name Swiss Institute of Banking and Finance (s/bf), University of St. Gallen, Unterer Graben 21, 9000, St. Gallen, Switzerland
    142 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...