SlideShare une entreprise Scribd logo

slide->title; ?>

•Télécharger en tant que PPT, PDF•
•
S
Shyam Singh

slide->imported == 1 || $this->owner_pres == 0) echo "readonly"; ?>>slide->description; ?>

TechnologieBusiness
1  sur  21
Machine Learning for Stock Selection Robert J. Yan Charles X. Ling University of Western Ontario, Canada {jyan, cling}@csd.uwo.ca
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Stock Selection Task ,[object Object],[object Object],[object Object],[object Object],Predictor 1 Predictor 2 Predictor 3 Goal Stock ID Return of week t -1 Return of week t -2 Volume ratio of t -2/ t -1 Return of week t
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Prototype Ranking ,[object Object],[object Object],[object Object],[object Object]
Step 1: Finding Prototypes ,[object Object],[object Object],[object Object],prototypes prototype neighborhood samples
Analysis??? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Step 2: Predicting Test Data ,[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Data ,[object Object],[object Object],[object Object]
Testing PR ,[object Object],[object Object]
Results of Experiment 1 Average Return (1978-2004) Risk (std) (1978-2004)
Experiment 2: Comparison to Cooper’s method ,[object Object],[object Object]
Results of Experiment 2 ,[object Object],[object Object],[object Object]
Results Portfolio Performance 1978-1993 1994-2004 PR CP PR CP 10-stock Ave. Return (%) 1.69 0.89 1.37 0.81 STD (%) 3.30 2.80 6.20 5.10 Sharpe Ratio 0.51 0.32 0.22 0.16 20-stock Ave. Return (%) 1.35 0.80 1.32 0.81 STD (%) 2.60 2.10 5.10 4.30 Sharpe Ratio 0.52 0.38 0.26 0.19 30-stock Ave. Return (%) 1.14 0.67 1.16 0.77 STD (%) 2.20 1.80 4.60 3.50 Sharpe Ratio 0.52 0.37 0.27 0.22
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object]

Recommandé

Lecture 09(introduction to machine learning)
Lecture 09(introduction to machine learning)Lecture 09(introduction to machine learning)
Lecture 09(introduction to machine learning)
Jeet Das
 
Machine Learning - Ensemble Methods
Machine Learning - Ensemble MethodsMachine Learning - Ensemble Methods
Machine Learning - Ensemble Methods
Andrew Ferlitsch
 
Ensemble learning
Ensemble learningEnsemble learning
Ensemble learning
Haris Jamil
 
Ensemble learning
Ensemble learningEnsemble learning
Ensemble learning
Mustafa Sherazi
 
Ensemble methods
Ensemble methods Ensemble methods
Ensemble methods
zekeLabs Technologies
 
Supervised Machine Learning in R
Supervised Machine Learning in RSupervised Machine Learning in R
Supervised Machine Learning in R
Babu Priyavrat
 
(Machine Learning) Ensemble learning
(Machine Learning) Ensemble learning (Machine Learning) Ensemble learning
(Machine Learning) Ensemble learning
Omkar Rane
 
Ensemble hybrid learning technique
Ensemble hybrid learning techniqueEnsemble hybrid learning technique
Ensemble hybrid learning technique
DishaSinha9
 

Recommandé

Lecture 09(introduction to machine learning)
Lecture 09(introduction to machine learning)Lecture 09(introduction to machine learning)
Lecture 09(introduction to machine learning)
Jeet Das
 
Machine Learning - Ensemble Methods
Machine Learning - Ensemble MethodsMachine Learning - Ensemble Methods
Machine Learning - Ensemble Methods
Andrew Ferlitsch
 
Ensemble learning
Ensemble learningEnsemble learning
Ensemble learning
Haris Jamil
 
Ensemble learning
Ensemble learningEnsemble learning
Ensemble learning
Mustafa Sherazi
 
Ensemble methods
Ensemble methods Ensemble methods
Ensemble methods
zekeLabs Technologies
 
Supervised Machine Learning in R
Supervised Machine Learning in RSupervised Machine Learning in R
Supervised Machine Learning in R
Babu Priyavrat
 
(Machine Learning) Ensemble learning
(Machine Learning) Ensemble learning (Machine Learning) Ensemble learning
(Machine Learning) Ensemble learning
Omkar Rane
 
Ensemble hybrid learning technique
Ensemble hybrid learning techniqueEnsemble hybrid learning technique
Ensemble hybrid learning technique
DishaSinha9
 
Ensemble learning Techniques
Ensemble learning TechniquesEnsemble learning Techniques
Ensemble learning Techniques
Babu Priyavrat
 
[Paper Introduction] Training a Natural Language Generator From Unaligned Data
[Paper Introduction] Training a Natural Language Generator From Unaligned Data[Paper Introduction] Training a Natural Language Generator From Unaligned Data
[Paper Introduction] Training a Natural Language Generator From Unaligned Data
NAIST Machine Translation Study Group
 
Understanding Bagging and Boosting
Understanding Bagging and BoostingUnderstanding Bagging and Boosting
Understanding Bagging and Boosting
Mohit Rajput
 
Ensemble modeling and Machine Learning
Ensemble modeling and Machine LearningEnsemble modeling and Machine Learning
Ensemble modeling and Machine Learning
StepUp Analytics
 
Introduction to Machine Learning Classifiers
Introduction to Machine Learning ClassifiersIntroduction to Machine Learning Classifiers
Introduction to Machine Learning Classifiers
Functional Imperative
 
Ensemble methods
Ensemble methodsEnsemble methods
Ensemble methods
Christopher Marker
 
Developing a Computerized Adaptive Test
Developing a Computerized Adaptive TestDeveloping a Computerized Adaptive Test
Developing a Computerized Adaptive Test
Nathan Thompson
 
activelearning.ppt
activelearning.pptactivelearning.ppt
activelearning.ppt
butest
 
Lecture 01: Machine Learning for Language Technology - Introduction
Lecture 01: Machine Learning for Language Technology - Introduction Lecture 01: Machine Learning for Language Technology - Introduction
Lecture 01: Machine Learning for Language Technology - Introduction
Marina Santini
 
Bag the model with bagging
Bag the model with baggingBag the model with bagging
Bag the model with bagging
Chode Amarnath
 
Presentation on supervised learning
Presentation on supervised learningPresentation on supervised learning
Presentation on supervised learning
Tonmoy Bhagawati
 
Machine Learning presentation.
Machine Learning presentation.Machine Learning presentation.
Machine Learning presentation.
butest
 
L4. Ensembles of Decision Trees
L4. Ensembles of Decision TreesL4. Ensembles of Decision Trees
L4. Ensembles of Decision Trees
Machine Learning Valencia
 
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Lecture 3: Basic Concepts of Machine Learning - Induction & EvaluationLecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Marina Santini
 
Generating SPSS training materials in StatJR
Generating SPSS training materials in StatJRGenerating SPSS training materials in StatJR
Generating SPSS training materials in StatJR
University of Southampton
 
Cmpe 255 cross validation
Cmpe 255 cross validationCmpe 255 cross validation
Cmpe 255 cross validation
Abraham Kong
 
Sampling and measurement
Sampling and measurementSampling and measurement
Sampling and measurement
Praveen Minz
 
RapidMiner: Learning Schemes In Rapid Miner
RapidMiner: Learning Schemes In Rapid MinerRapidMiner: Learning Schemes In Rapid Miner
RapidMiner: Learning Schemes In Rapid Miner
DataminingTools Inc
 
Stareast2008
Stareast2008Stareast2008
Stareast2008
JaAe CK
 
Model Selection Techniques
Model Selection TechniquesModel Selection Techniques
Model Selection Techniques
Swati .
 
Skyventure Club Presentation
Skyventure Club PresentationSkyventure Club Presentation
Skyventure Club Presentation
genaoxana
 
tecnologias
tecnologiastecnologias
tecnologias
gabriel17metal
 

Contenu connexe

Tendances

activelearning.ppt
activelearning.pptactivelearning.ppt
activelearning.ppt
butest
 
Lecture 01: Machine Learning for Language Technology - Introduction
Lecture 01: Machine Learning for Language Technology - Introduction Lecture 01: Machine Learning for Language Technology - Introduction
Lecture 01: Machine Learning for Language Technology - Introduction
Marina Santini
 
Machine Learning presentation.
Machine Learning presentation.Machine Learning presentation.
Machine Learning presentation.
butest
 

Tendances (20)

Ensemble learning Techniques
Ensemble learning TechniquesEnsemble learning Techniques
Ensemble learning Techniques
 
[Paper Introduction] Training a Natural Language Generator From Unaligned Data
[Paper Introduction] Training a Natural Language Generator From Unaligned Data[Paper Introduction] Training a Natural Language Generator From Unaligned Data
[Paper Introduction] Training a Natural Language Generator From Unaligned Data
 
Understanding Bagging and Boosting
Understanding Bagging and BoostingUnderstanding Bagging and Boosting
Understanding Bagging and Boosting
 
Ensemble modeling and Machine Learning
Ensemble modeling and Machine LearningEnsemble modeling and Machine Learning
Ensemble modeling and Machine Learning
 
Introduction to Machine Learning Classifiers
Introduction to Machine Learning ClassifiersIntroduction to Machine Learning Classifiers
Introduction to Machine Learning Classifiers
 
Ensemble methods
Ensemble methodsEnsemble methods
Ensemble methods
 
Developing a Computerized Adaptive Test
Developing a Computerized Adaptive TestDeveloping a Computerized Adaptive Test
Developing a Computerized Adaptive Test
 
activelearning.ppt
activelearning.pptactivelearning.ppt
activelearning.ppt
 
Lecture 01: Machine Learning for Language Technology - Introduction
Lecture 01: Machine Learning for Language Technology - Introduction Lecture 01: Machine Learning for Language Technology - Introduction
Lecture 01: Machine Learning for Language Technology - Introduction
 
Bag the model with bagging
Bag the model with baggingBag the model with bagging
Bag the model with bagging
 
Presentation on supervised learning
Presentation on supervised learningPresentation on supervised learning
Presentation on supervised learning
 
Machine Learning presentation.
Machine Learning presentation.Machine Learning presentation.
Machine Learning presentation.
 
L4. Ensembles of Decision Trees
L4. Ensembles of Decision TreesL4. Ensembles of Decision Trees
L4. Ensembles of Decision Trees
 
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Lecture 3: Basic Concepts of Machine Learning - Induction & EvaluationLecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
 
Generating SPSS training materials in StatJR
Generating SPSS training materials in StatJRGenerating SPSS training materials in StatJR
Generating SPSS training materials in StatJR
 
Cmpe 255 cross validation
Cmpe 255 cross validationCmpe 255 cross validation
Cmpe 255 cross validation
 
Sampling and measurement
Sampling and measurementSampling and measurement
Sampling and measurement
 
RapidMiner: Learning Schemes In Rapid Miner
RapidMiner: Learning Schemes In Rapid MinerRapidMiner: Learning Schemes In Rapid Miner
RapidMiner: Learning Schemes In Rapid Miner
 
Stareast2008
Stareast2008Stareast2008
Stareast2008
 
Model Selection Techniques
Model Selection TechniquesModel Selection Techniques
Model Selection Techniques
 

En vedette

Skyventure Club Presentation
Skyventure Club PresentationSkyventure Club Presentation
Skyventure Club Presentation
genaoxana
 
Everyone Needs Friends
Everyone Needs FriendsEveryone Needs Friends
Everyone Needs Friends
Chris Verhesen
 
Carta a Yamazares
Carta a YamazaresCarta a Yamazares
Carta a Yamazares
san gines
 
apresentação 1º período
apresentação 1º períodoapresentação 1º período
apresentação 1º período
grupoA
 
Amparito
AmparitoAmparito
Amparito
Marylda
 
Undefectoenlamujer
UndefectoenlamujerUndefectoenlamujer
Undefectoenlamujer
paquitaguapa
 

En vedette (20)

Skyventure Club Presentation
Skyventure Club PresentationSkyventure Club Presentation
Skyventure Club Presentation
 
tecnologias
tecnologiastecnologias
tecnologias
 
פתיחת חשבון
פתיחת חשבוןפתיחת חשבון
פתיחת חשבון
 
My official photo as a CartesianRecruiter
My official photo as a CartesianRecruiterMy official photo as a CartesianRecruiter
My official photo as a CartesianRecruiter
 
Everyone Needs Friends
Everyone Needs FriendsEveryone Needs Friends
Everyone Needs Friends
 
Desenhos Joana
Desenhos JoanaDesenhos Joana
Desenhos Joana
 
Noenfermarse
NoenfermarseNoenfermarse
Noenfermarse
 
Sav Luxury Watches Michel Grand 2008
Sav Luxury Watches Michel Grand 2008Sav Luxury Watches Michel Grand 2008
Sav Luxury Watches Michel Grand 2008
 
Priceless Moustaches
Priceless MoustachesPriceless Moustaches
Priceless Moustaches
 
Carta a Yamazares
Carta a YamazaresCarta a Yamazares
Carta a Yamazares
 
Snow & Ice Festival
Snow & Ice FestivalSnow & Ice Festival
Snow & Ice Festival
 
apresentação 1º período
apresentação 1º períodoapresentação 1º período
apresentação 1º período
 
Yellow on the Outside, Shame on the Inside: Asian Culture Revealed
Yellow on the Outside, Shame on the Inside: Asian Culture RevealedYellow on the Outside, Shame on the Inside: Asian Culture Revealed
Yellow on the Outside, Shame on the Inside: Asian Culture Revealed
 
Amparito
AmparitoAmparito
Amparito
 
Momentum Technology Centers
Momentum Technology CentersMomentum Technology Centers
Momentum Technology Centers
 
A BibliaköZepe
A BibliaköZepeA BibliaköZepe
A BibliaköZepe
 
龟兔赛跑最新演讲版 12.17
龟兔赛跑最新演讲版 12.17龟兔赛跑最新演讲版 12.17
龟兔赛跑最新演讲版 12.17
 
Undefectoenlamujer
UndefectoenlamujerUndefectoenlamujer
Undefectoenlamujer
 
Ascrevel
AscrevelAscrevel
Ascrevel
 
Pamela Findorff Resume
Pamela Findorff ResumePamela Findorff Resume
Pamela Findorff Resume
 

Similaire à slide->title; ?>

Ensemble Learning Featuring the Netflix Prize Competition and ...
Ensemble Learning Featuring the Netflix Prize Competition and ...Ensemble Learning Featuring the Netflix Prize Competition and ...
Ensemble Learning Featuring the Netflix Prize Competition and ...
butest
 
Learning to Search Henry Kautz
Learning to Search Henry KautzLearning to Search Henry Kautz
Learning to Search Henry Kautz
butest
 
Learning to Search Henry Kautz
Learning to Search Henry KautzLearning to Search Henry Kautz
Learning to Search Henry Kautz
butest
 
32_Nov07_MachineLear..
32_Nov07_MachineLear..32_Nov07_MachineLear..
32_Nov07_MachineLear..
butest
 
Probability density estimation using Product of Conditional Experts
Probability density estimation using Product of Conditional ExpertsProbability density estimation using Product of Conditional Experts
Probability density estimation using Product of Conditional Experts
Chirag Gupta
 
NEURAL Network Design Training
NEURAL Network Design TrainingNEURAL Network Design Training
NEURAL Network Design Training
ESCOM
 
Machine Learning
Machine LearningMachine Learning
Machine Learning
butest
 
Introduction
IntroductionIntroduction
Introduction
butest
 

Similaire à slide->title; ?> (20)

Unit 3.pptx
Unit 3.pptxUnit 3.pptx
Unit 3.pptx
 
Ensemble Learning Featuring the Netflix Prize Competition and ...
Ensemble Learning Featuring the Netflix Prize Competition and ...Ensemble Learning Featuring the Netflix Prize Competition and ...
Ensemble Learning Featuring the Netflix Prize Competition and ...
 
Learning to Search Henry Kautz
Learning to Search Henry KautzLearning to Search Henry Kautz
Learning to Search Henry Kautz
 
Learning to Search Henry Kautz
Learning to Search Henry KautzLearning to Search Henry Kautz
Learning to Search Henry Kautz
 
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHES
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHESIMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHES
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHES
 
Real-time Ranking of Electrical Feeders using Expert Advice
Real-time Ranking of Electrical Feeders using Expert AdviceReal-time Ranking of Electrical Feeders using Expert Advice
Real-time Ranking of Electrical Feeders using Expert Advice
 
32_Nov07_MachineLear..
32_Nov07_MachineLear..32_Nov07_MachineLear..
32_Nov07_MachineLear..
 
Week 1.pdf
Week 1.pdfWeek 1.pdf
Week 1.pdf
 
evaluation and credibility-Part 2
evaluation and credibility-Part 2evaluation and credibility-Part 2
evaluation and credibility-Part 2
 
Metabolomic Data Analysis Workshop and Tutorials (2014)
Metabolomic Data Analysis Workshop and Tutorials (2014)Metabolomic Data Analysis Workshop and Tutorials (2014)
Metabolomic Data Analysis Workshop and Tutorials (2014)
 
Collaborative Filtering Survey
Collaborative Filtering SurveyCollaborative Filtering Survey
Collaborative Filtering Survey
 
Probability density estimation using Product of Conditional Experts
Probability density estimation using Product of Conditional ExpertsProbability density estimation using Product of Conditional Experts
Probability density estimation using Product of Conditional Experts
 
NEURAL Network Design Training
NEURAL Network Design TrainingNEURAL Network Design Training
NEURAL Network Design Training
 
Machine Learning
Machine LearningMachine Learning
Machine Learning
 
Lecture 9: Machine Learning in Practice (2)
Lecture 9: Machine Learning in Practice (2)Lecture 9: Machine Learning in Practice (2)
Lecture 9: Machine Learning in Practice (2)
 
Thesis presentation: Applications of machine learning in predicting supply risks
Thesis presentation: Applications of machine learning in predicting supply risksThesis presentation: Applications of machine learning in predicting supply risks
Thesis presentation: Applications of machine learning in predicting supply risks
 
Exposé Ontology
Exposé OntologyExposé Ontology
Exposé Ontology
 
Machine learning session6(decision trees random forrest)
Machine learning session6(decision trees random forrest)Machine learning session6(decision trees random forrest)
Machine learning session6(decision trees random forrest)
 
A Combination of Simple Models by Forward Predictor Selection for Job Recomme...
A Combination of Simple Models by Forward Predictor Selection for Job Recomme...A Combination of Simple Models by Forward Predictor Selection for Job Recomme...
A Combination of Simple Models by Forward Predictor Selection for Job Recomme...
 
Introduction
IntroductionIntroduction
Introduction
 

Plus de Shyam Singh

Plus de Shyam Singh (9)

demo
demo demo
demo
 
d234
d234d234
d234
 
pdf file test
pdf file testpdf file test
pdf file test
 
KDD
KDDKDD
KDD
 
Demo
DemoDemo
Demo
 
iip
iipiip
iip
 
iipu
iipuiipu
iipu
 
iip
iipiip
iip
 
demo1
demo1demo1
demo1
 

Dernier

+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Principled Technologies
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
Safe Software
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Joaquim Jorge
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
Igalia
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
sammart93
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
vu2urc
 

Dernier (20)

Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfTech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdf
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
Developing An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilDeveloping An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of Brazil
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 

slide->title; ?>

  • 1. Machine Learning for Stock Selection Robert J. Yan Charles X. Ling University of Western Ontario, Canada {jyan, cling}@csd.uwo.ca
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16. Results of Experiment 1 Average Return (1978-2004) Risk (std) (1978-2004)
  • 17.
  • 18.
  • 19. Results Portfolio Performance 1978-1993 1994-2004 PR CP PR CP 10-stock Ave. Return (%) 1.69 0.89 1.37 0.81 STD (%) 3.30 2.80 6.20 5.10 Sharpe Ratio 0.51 0.32 0.22 0.16 20-stock Ave. Return (%) 1.35 0.80 1.32 0.81 STD (%) 2.60 2.10 5.10 4.30 Sharpe Ratio 0.52 0.38 0.26 0.19 30-stock Ave. Return (%) 1.14 0.67 1.16 0.77 STD (%) 2.20 1.80 4.60 3.50 Sharpe Ratio 0.52 0.37 0.27 0.22
  • 20.
  • 21.