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reference-keys.txt
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tab:packages
fig:nbassets
fig:datarectangle
eq:ML
eq:MLfin
fig:figscheme2
eq:apt
fig:factportsort
tab:factorImport
fig:riskpremiaFF
eq:factsimple
eq:Bpv
tab:FMreg
tab:betaformat
tab:FamaMacBeth2b
fig:premiaplot
eq:faccompet
tab:faccompet2
fig:facautocorr
eq:optchar
eq:genML
eq:SDFGMM
eq:AEearly
fig:boxcorr
eq:regfun
fig:regfun
fig:histcorr
tab:impex
eq:catlabel
tab:onehot
fig:triplebarrier
eq:macrocond
eq:condvix
tab:fakedata2
tab:datatoyreg
tab:datatoyreg2
eq:regbeta
eq:lasso1
eq:lasso2
eq:ridge
eq:ridge6
fig:lassoridge
eq:elasticnet
eq:elastic
fig:lassoresults
fig:sparseridge
eq:MSR
eq:sparse1
eq:sparseeq
eq:sparse2
eq:sparse3
eq:sparsehedgeeq2
fig:treescheme
eq:node
fig:rpart1
fig:rpart3mkt
fig:RF
eq:adaboostam
eq:adaboostw
eq:adaboosty
tab:adaboost
fig:treeq
eq:xgbweight
fig:monotonic
fig:perceptron
fig:NNnaive
fig:MLperceptron
fig:activationf
eq:univapprox
eq:graddesc
fig:newton
eq:backprop1
fig:backp
eq:gradmom
eq:crossentropy
eq:gradbatch
fig:NN3
fig:NN3C
fig:recnet
fig:RNN2
fig:sparsemax
eq:GAN
eq:GAN2
eq:AEgu
fig:cnnscheme
fig:cnnpooling
fig:AEgu
fig:exoUA
fig:svmscheme
eq:svm0
eq:svm1
eq:svm1b
eq:svm2
fig:svmscheme2
eq:svm3
fig:svmscheme4
eq:svm4
fig:svmscheme3
eq:bayes
eq:bayes2
eq:likelihood
eq:linlike
eq:linprior
eq:cascade
eq:linsig
fig:lmBayesplot
eq:naivebayes
eq:naivebayes2
eq:naivebayes3
eq:transf
fig:NB
eq:BART
eq:bartpost
eq:bart1
eq:bartnode
fig:bartfig
fig:bartsigplot
eq:MAE
eq:MSE
eq:R2
eq:MAPE
eq:MSPE
eq:RMSLE
fig:valconfusion
fig:ROCcurve
fig:roc
eq:biasvariance
fig:archery
fig:varbiastrade
eq:vartrade
fig:ridgetrade
fig:treesimple
fig:overfit
eq:HPO
eq:acquisition
eq:EI
fig:gridvisu
fig:bayesoptfig
fig:stackscheme
fig:stackNN2
fig:ensfred2
fig:backtestoos
fig:backtestoos2
eq:coq
eq:lagrangew
eq:coqw
eq:SRTC
eq:tSR
eq:nolunch
eq:deltak
fig:backtest6
fig:backov3
fig:imlsurr
fig:VItrees
fig:VIglobal2
eq:pdp
eq:pdpMC
fig:pdp
fig:lime
eq:shapley
fig:shapley
eq:breakdown
fig:breakdown
eq:CAM0
eq:CAM
fig:pcalg
fig:structbayplot
fig:conceptchange
fig:statplot
fig:conceptdriftemp
eq:regret
eq:online1
eq:online2
tab:regbroom
fig:instcorrplot2
eq:svd
eq:diagonaliz
eq:pca
eq:covy
fig:pca2
eq:pcascheme
eq:pcaschem2
eq:aescheme2
fig:aekeras3
eq:kmeans
eq:D
fig:mdpscheme
eq:transprob
eq:transprob2
eq:gain6
eq:RLvalue
eq:RLQ
eq:bellman
eq:bellmanq
eq:QLupdate
eq:RLeq
eq:egreedy
eq:SARSAupdate
eq:exSARSAupdate
eq:simplex
eq:policyex
eq:PGT
eq:ascent
eq:ascentAC
eq:parpol
tab:appendix1
fig:ex41b
fig:ex41
fig:ex43
fig:ex5b
fig:ex51c
fig:ex52b
fig:ex53a
fig:ex61
fig:ex71
fig:ex73a
fig:ex73b
fig:ex82b
fig:ex12c
fig:ex130a
fig:ex131d
notdata
notations
dataset
intro
context
portfolio-construction-the-workflow
machine-learning-is-no-magic-wand
factor
introduction
detecting-anomalies
challenges
simple-portfolio-sorts
factors
predictive-regressions-sorts-and-p-value-issues
fama-macbeth-regressions
factor-competition
advanced-techniques
factors-or-characteristics
hot-topics-momentum-timing-and-esg
factor-momentum
factor-timing
the-green-factors
the-links-with-machine-learning
a-short-list-of-recent-references
explicit-connections-with-asset-pricing-models
coding-exercises
Data
know-your-data
missing-data
outlier-detection
feateng
feature-selection
scaling
labelling
simple-labels
categorical-labels
the-triple-barrier-method
filtering-the-sample
horizons
pers
extensions
transforming-features
macrovar
active-learning
additional-code-and-results
impact-of-rescaling-graphical-representation
impact-of-rescaling-toy-example
coding-exercises-1
lasso
penalized-regressions
penreg
forms-of-penalizations
illustrations
sparse-hedging-for-minimum-variance-portfolios
presentation-and-derivations
sparseex
predictive-regressions
literature-review-and-principle
code-and-results
coding-exercise
trees
simple-trees
principle
treeclass
pruning-criteria
code-and-interpretation
random-forests
principle-1
code-and-results-1
adaboost
methodology
illustration
boosted-trees-extreme-gradient-boosting
managing-loss
penalization
aggregation
tree-structure
boostext
boostcode
instweight
discussion
coding-exercises-2
NN
the-original-perceptron
multilayer-perceptron
introduction-and-notations
universal-approximation
backprop
NNclass
howdeep
architectural-choices
frequency-of-weight-updates-and-learning-duration
penalizations-and-dropout
code-samples-and-comments-for-vanilla-mlp
regression-example
classification-example
custloss
RNN
presentation
code-and-results-2
tabular-networks-tabnets
the-zoo-of-layers
sparsemax-activation
feature-selection-1
the-full-architecture
code-and-results-3
other-common-architectures
generative-aversarial-networks
autoencoders
CNN
coding-exercises-3
svm
svm-for-classification
svm-for-regression
practice
coding-exercises-4
bayes
the-bayesian-framework
bayesian-sampling
gibbs-sampling
metropolis-hastings-sampling
bayesian-linear-regression
naive-bayes-classifier
BART
general-formulation
priors
sampling-and-predictions
code
valtune
mlmetrics
regression-analysis
classification-analysis
validation
the-variance-bias-tradeoff-theory
the-variance-bias-tradeoff-illustration
the-risk-of-overfitting-principle
the-risk-of-overfitting-some-solutions
the-search-for-good-hyperparameters
methods
example-grid-search
example-bayesian-optimization
short-discussion-on-validation-in-backtests
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example
stacked-ensembles
two-stage-training
code-and-results-4
extensions-1
exogenous-variables
shrinking-inter-model-correlations
exercise
backtest
protocol
turning-signals-into-portfolio-weights
perfmet
discussion-1
pure-performance-and-risk-indicators
factor-based-evaluation
risk-adjusted-measures
transaction-costs-and-turnover
common-errors-and-issues
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backov
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general-comments
the-no-free-lunch-theorem
first-example-a-complete-backtest
second-example-backtest-overfitting
coding-exercises-5
interp
global-interpretations
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lime
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causality
causality-1
granger
causal-additive-models
structural-time-series-models
nonstat
non-stationarity-yet-another-illustration
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homogeneous-transfer-learning
unsup
corpred
principal-component-analysis-and-autoencoders
a-bit-of-algebra
pca
ae
application
clustering-via-k-means
nearest-neighbors
coding-exercise-1
RL
theoretical-layout
general-framework
q-learning
sarsa
the-curse-of-dimensionality
policy-gradient
principle-2
extensions-2
simple-examples
q-learning-with-simulations
RLemp2
concluding-remarks
exercises
data-description
python
solutions-to-exercises
chapter-3
chapter-4
chapter-5
chapter-6
chapter-7-the-autoencoder-model-universal-approximation
chapter-8
chapter-11-ensemble-neural-network
chapter-12
ew-portfolios-with-the-tidyverse
advanced-weighting-function
functional-programming-in-the-backtest
chapter-15
chapter-16