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datacamp_old.txt
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project title: Systematic Investment Strategies
The exclamation !!! marks signify very good papers
get Andrew Ang book Asset Management: A Systematic Approach to Factor Investing
!!! Bloch ebook Quantitative Portfolio Management.pdf
!!! Tourin dynport.zip
!!! Cochrane Advanced Investments
C:\Research\Academic\Cochrane Advanced Investments
!!! Pfaff Package Development in R.pdf
##### General projects:
- [x] create IB account
- [ ] create simple quantstrat scripts
http://timelyportfolio.blogspot.com/2011/06/reit-momentum-in-quantstrat.html
- [ ] install gcc
- [ ] reproduce factors results
http://timelyportfolio.blogspot.com/2014/04/all-factors-more-looks.html
http://timelyportfolio.blogspot.com/2014/04/exploring-factors-with-rcharts-and.html
- [ ] create R scripts for loading Reuters data
- [ ]
*** Loading and scrubbing time series data: packages xts and quantmod,
# download OHLC data from Quandl
# Maintaining a database of price files in R
http://www.thertrader.com/2015/12/13/maintaining-a-database-of-price-files-in-r/
# convert all synthetic t-series to log-normal process
# Yollin tutorial rDataAccess
recreate CRSPpanel.txt fundamental financial data for 265 S&P 500 stocks
trellis plots
nice barchart, dotplot, bwplot, and data munging
*** Estimating risk and performance measures: volatility, skew, CVaR, Cornish-Fisher VaR=Modified VaR, risk-return ratios (Sharpe, Sortino, Calmar), package PerformanceAnalytics,
Normal (Gaussian) distribution is a bad model for price returns,
because extreme returns are frequent and determine the mean
For example, half of the return of the stock market over the past 50 years
was associated with just 10 days with the greatest daily change (Taleb) ?
# use MAD instead of STDEV
http://edge.org/response-detail/25401
The Pareto distribution has infinite variance but has finite MAD.
Show that when volatility is stochastic then STDEV is much higher than MAD.
create plot displaying ratio of STDEV over MAD as function of volatility of volatility.
Show that ratio of STDEV over MAD is related to kurtosis
# add: slide for measures of dispersion:
Maximum absolute deviation
trailing range stat
Hurst
# demonstrate the persistence autoregression of volatility in real return data
fit GARCH model into real return data
# fit returns into Pareto distribution
# create study of bias-variance tradeoff using volatility estimation example:
1. create xts of random prices with changing time-dependent deterministic vol parameter,
2. estimate volatility use look-back window parameter,
3. too short look-back window increases variance,
4. too long look-back window increases bias,
5. tune filter parameters in-sample: study bias-variance tradeoff,
6. create rCharts and shiny visualizations
# estimate volatility using OHLC data
demonstrate that estimator standard error is lower using OHLC data
use bootstrap to determine estimator standard error confidence intervals
use simulated data with constant volatility
# range OHLC volatility estimation
http://eranraviv.com/intraday-volatility-measures/
http://eranraviv.com/multivariate-volatility-forecasting-2/
http://eranraviv.com/multivariate-volatility-forecasting-3-exponentially-weighted-model/
http://eranraviv.com/multivariate-volatility-forecasting-5-orthogonal-garch/
Brandt High Frequency Realized Volatility Estimation.pdf
Chou Range OHLC GARCH Volatility Estimators.pdf
Bencik Range OHLC HAR GARCH Volatility Estimators.pdf
Yang OHLC Range Volatility Estimators.pdf
# GARCH volatility models
http://jonathankinlay.com/index.php/2011/03/long-memory-and-regime-shifts-in-asset-volatility/
http://www.jonathankinlay.com/articles/Modeling%20Asset%20Volatility.pdf
http://jonathankinlay.com/index.php/2011/03/regarch-option-pricing-models/
# Simulate GARCH
http://stackoverflow.com/questions/9969962/simulation-of-garch-in-r?rq=1
# value-at-risk and conditional value-at-risk as function of skewness and kurtosis parameters.
subadditive risk measures, ETL (ES/ETL/CVaR), Omega, Hurst,
FamaBeta
VaR generalized Pareto distribution
Shalizi: pareto.R
Maximize portfolio mean return per unit ES/ETL/CVaR=STARR Ratio
ES=Expected Shortfall=Conditional VaR=CVaR=STARR (Stable Tail Adjusted Return Ratio)
method="historical", method="gaussian" or method="modified"
# conditional value at risk (CVaR)
!!! Maillard Cvar Cornish Fisher Portfolio.pdf
# demonstrate that a small change in the alpha parameter (less than its mean error)
changes the CVaR by large amount (plot the relationship)
https://edge.org/conversation/nassim_nicholas_taleb-the-fourth-quadrant-a-map-of-the-limits-of-statistics
# add:
if cumulative returns are mostly generated by a few large returns,
then focusing on predicting small returns is a waste of time
bin returns according to their magnitude
what are the cumulative returns for each return magnitude bucket?
# Tail-Risk Analysis
http://www.capitalspectator.com/tail-risk-analysis-in-r-part-i/
https://gist.github.com/jpicerno1/c3af6285713c76a5d124
### topics to add
create Normal mixture model and show that it has fat tails,
show that Normal mixture model is similar to t-distr
create distribution with large skew - Poisson
Matthieu Lestel PerformanceAnalytics
PerformanceAnalytics PA-Bacon.pdf
*** CAPM model: market portfolio, regressions of asset returns, alpha, beta, CML, SML, package PerformanceAnalytics,
in this course we will study both investment and speculation
The main difference between investment and speculation lies in the time horizon.
Investment is concerned with capturing returns on the long-run with lower risk, while speculation is concerned with achieving returns over a short period of time.
Speculation attempts to switch between investments to achieve the best return versus risk.
Investment attempts to choose the best investments and hold onto them to achieve the best return versus risk over the long term.
http://blogs.wsj.com/moneybeat/2015/12/24/this-simple-way-is-the-best-way-to-predict-the-market/
# add:
!!! Cazalet CAPM Factor Models.pdf
Fernandez CAPM Stock Model Review.pdf
Black CAPM Empirical Tests.pdf
Steiner Alpha Misleading Performance Measure.pdf
# beta robust regression shrinkage-estimator-for-beta
http://eranraviv.com/a-shrinkage-estimator-for-beta/
# beta confidence intervals using bootstrap
http://eranraviv.com/bootstrap-example/
http://statistics.ats.ucla.edu/stat/r/library/bootstrap.htm
Fox Regression Bootstrap.pdf
# lm() Model Variable selection
# shrinkage
AIC, AIC, BIC
update()
# add:
Ormos Entropy Asset Pricing Model.pdf
# add: Equity premium puzzle
returns on stocks are much higher than predicted by CAPM model using volatility of equity returns and returns on government bonds
The fact that stocks are riskier than bonds doesn't explain the magnitude of the difference,
https://en.wikipedia.org/wiki/Equity_premium_puzzle
# add: derive CAPM from utility
CAPM = market return is frontier return
The efficient frontier consists of convex combinations of any two efficient frontier portfolios.
Any convex combination of efficient frontier portfolios is also an efficient frontier portfolio.
the Market Portfolio is the optimal portfolio with the highest utility
the Market Portfolio isn't necessarily equal to the portfolio of all assets
how can Market Portfolio be obtained ?
Market Portfolio can be obtained by optimizing Sharpe ratio
Market Price of Risk == highest Sharpe ratio
logarithmic utility implies max Sharpe ?
derive Security Market Line (SML) from Capital Market Line (CML) ?
if the residuals of returns in SML were correlated with each other,
then a different Market Portfolio would exist
portfolios on the CML satisfy the SML equation
provide reasons why CAPM may not hold?
Market Portfolio isn't same as highest Sharpe portfolio
# Efficient Frontier Portfolios
https://gist.github.com/jpicerno1/565be39ca4226ecd004c
http://www.capitalspectator.com/efficient-frontier-portfolios-impractical-but-still-useful/
*** Factor models: CAPM, Fama-French, Barra, statistical,
Distinguish between cross-sectional regressions (in-sample),
and predictive regressions (out-of-sample),
Statistical factor models examine returns over many time periods, and from them identify relationships between and among the different assets, unlike fundamental factor models, which from the outset group assets that are likely to experience similar returns.
# Fama and French three-factor model tutorial
http://www.bogleheads.org/wiki/Fama_and_French_three-factor_model
https://www.bogleheads.org/wiki/Fama-French_three-factor_model_analysis
https://www.bogleheads.org/wiki/CAPM_-_Capital_Asset_Pricing_Model
https://www.bogleheads.org/wiki/Category:Financial_theory
http://www.capitalspectator.com/portfolio-analysis-in-r-part-v-risk-analysis-via-factors/
http://jonathankinlay.com/index.php/2015/03/combining-momentum-mean-reversion-strategies/
C:\Research\Academic\Cochrane Advanced Investments
Cochrane_asset_pricing_CH12_229-250.pdf
C:\Research\R\Tutorials\Zivot\research
factorModels.r
Zivot Factor Models.pdf
# use data:
data(package="factorAnalytics")
data(package="GARPFRM")
data(package="mpo")
# AQR data library
https://www.aqr.com/library
https://www.aqr.com/library/data-sets/quality-minus-junk-factors-monthly
http://stackoverflow.com/questions/28031008/reading-an-online-xlsx-file-into-r
# Fama-MacBeth two-pass regressions to explain cross-sectional returns/values by factors
Campbell Market Factors Stock Forecasting.pdf
# Equity Factors with Principal Component Analysis
http://www.calculatinginvestor.com/2013/03/01/principal-component-analysis/
http://www.calculatinginvestor.com/2013/03/18/pca-factors-vs-fama-french-factors/
http://www.calculatinginvestor.com/octave-code/calculating-fama-french-loading/
# Fama-French three factor model viewer
http://systematicinvestor.wordpress.com/2012/06/20/factor-attribution/
https://systematicinvestor.wordpress.com/category/factor-model/
# Factor Attribution
https://systematicinvestor.wordpress.com/2012/06/
# Barra and Northfield factor models
https://systematicinvestor.wordpress.com/2012/02/21/multiple-factor-model-building-risk-model/
https://systematicinvestor.wordpress.com/2012/01/29/multiple-factor-model-fundamental-data/
# timely factors
http://timelyportfolio.blogspot.com/2014/04/exploring-factors-with-rcharts-and.html
http://timelyportfolio.github.io/rCharts_factor_analytics/factors_with_new_R.html
# qmj factor package David Kane Hutchin Hill
portfolio.pdf
portfolio Vignette.pdf
https://github.com/anttsou/qmj
https://github.com/anttsou/qmjdata
# PCA
CFA Stock Premium Factors.pdf
CFM Principal Component Market Factors.pdf
scatterplot of two stocks
rotate axes to get PComps
PCA: don't scale factors
PCA: stock regressed against PCA factors
https://tgmstat.wordpress.com/2013/11/28/computing-and-visualizing-pca-in-r/
apply ADF test to higher order PC's to demonstrate that higher order PC's are more stationary
http://fabian-kostadinov.github.io/2015/01/27/comparing-adf-test-functions-in-r/
http://www.statmethods.net/advstats/factor.html
http://davetang.org/wiki/tiki-index.php?page=Principal+component+analysis
CFM Principal Component Market Factors.pdf
Alexander Principal Component Multivariate GARCH Model.pdf
# PCA as eigenvectors by hand
http://eranraviv.com/multivariate-volatility-forecasting-4-factor-models/
https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix
# PCA Principal Components and Clustering
http://systematicinvestor.wordpress.com/2012/12/22/visualizing-principal-components
http://systematicinvestor.wordpress.com/2012/12/29/clustering-with-selected-principal-components
http://systematicinvestor.wordpress.com/2013/01/17/optimal-number-of-clusters
http://gestaltu.com/2015/11/tactical-alpha-in-theory-and-practice-part-ii-principal-component-analysis.html
Guttman (1954) and Kaiser (1960, 1970), asserted that in order to be significant, a factor must account for at least as much variance as an individual variable (Nunnally and Bernstein, 1994).
Horn proposed that factors should only be considered significant if they explain a greater proportion of variance than what might be expected from random chance.
Kakushadze Factor Model Stock Alpha Forecasting.pdf
Kakushadze Factor Models Alpha Streams.pdf
# factorAnalytics use fundamental and return data for 447 NYSE stocks
stock (Stock.df)
# add fundamental factor model from factorAnalytics:
fitFfm
?Stock.df
?fitFfm
https://r-forge.r-project.org/scm/viewvc.php/pkg/FactorAnalytics/?root=returnanalytics
# fitFundamentalFactorModel is is now called fitFfm
factorAnalytics YiAnChen.pdf
http://r.789695.n4.nabble.com/factoranalytics-vs-factoranalyticsuw-td4709951.html
# factorAnalytics vignettes
https://r-forge.r-project.org/scm/viewvc.php/pkg/FactorAnalytics/vignettes/?root=returnanalytics
https://r-forge.r-project.org/projects/factoranalytics/
http://rpackages.ianhowson.com/rforge/factorAnalytics/
http://timelyportfolio.blogspot.com/2014/08/famafrench-factors-in-1-line-of-code.html
https://github.com/R-Finance/FactorAnalytics/blob/master/vignettes/fundamentalFM.Rnw
ls("package:factorAnalytics")
[1] "dCornishFisher" "fitFfm" "fitSfm" "fitTsfm" "fitTsfmLagBeta"
[6] "fitTsfmMT" "fitTsfmUpDn" "fmCov" "fmEsDecomp" "fmmc"
[11] "fmmc.estimate.se" "fmmcSemiParam" "fmSdDecomp" "fmVaRDecomp" "paFm"
[16] "pCornishFisher" "qCornishFisher" "rCornishFisher"
data(package="factorAnalytics")
Data sets in package factorAnalytics:
factors.M (CommonFactors) Factor set of several commonly used factors
factors.Q (CommonFactors) Factor set of several commonly used factors
managers Hypothetical Alternative Asset Manager and Benchmark Data
r.M (StockReturns) Stock Return Data
r.W (StockReturns) Stock Return Data
stock (Stock.df) Fundamental and return data for 447 NYSE stocks
tr.yields (TreasuryYields) Treasury yields at different maturities
# Exploring Factors with rCharts and factorAnalytics
http://timelyportfolio.blogspot.com/2014/04/exploring-factors-with-rcharts-and.html
# Value strategies can be implemented in many different ways, leading to widely different performance
http://investorfieldguide.com/three-value-investors-meet-in-a-bar/
Stock value can be measured in several different ways including book value, earnings, and sales.
The Russell 1000 Value has underperformed the Russell 1000 by -22% and the Russell 1000 Growth by -43% over the past decade (10 years ending 11/30/15).
idea: apply value investing to different value indices: buy more of the cheap ones
# factor data mining: 24 return factors forecast monthly stock returns
Green Factor Models Stock Forecasting.pdf
quote:
Fama and French (1992, FF92) measured the dimensionality of the cross-section of
expected monthly U.S. stock returns by regressing the potential factors beta,
firm size, book-to-market, earnings-to-price and leverage.
They found that beta was not explanatory of expected returns, but size and
book-to-market were, and that they absorbed the explanatory power of
earnings-to-price and leverage.
FF92 concluded that the cross-section of expected monthly U.S. stock returns was
two-factor, although neither factor was consistent with the CAPM.
A third factor in the form of 12-month return momentum (Jegadeesh, 1990;
Jegadeesh and Titman, 1993) was incorporated by Fama and French (1996) and
Carhart (1997) to create the three-factor model of risks that explain equity returns.
# most predictive factors of monthly stock returns from Green Factor Models
sue standardized unexpected earnings
bm book-to-market
mom12m 12-month momentum
mom36m 36-month momentum
chfeps Change in forecasted annual EPS
ear three-day return centered on the most recent earnings announcement
sfe the ratio of forecasted annual earnings-to-price
rsup quarterly sales growth
indmom 12-month industry return momentum
turn Share turnover
dolvol trading volume in month t-2
rsup Revenue surprise
mve log of market cap at month-end immediately prior to signal date
# additional sources for above Green Factor Models
Lewellen Factor Models Stock Forecasting.pdf
Harvey Factor Model Data Mining Bonferroni Adjustment.pdf
# demonstrate that the time variation of factors may lead to spurious evidence
of additional risk factors
# variance inflation factor for multicollinearity in explanatory variables regression analysis
https://en.wikipedia.org/wiki/Variance_inflation_factor
# LASSO reduces artefacts from multicollinearity of explanatory variables
# BARRA factor model with factorAnalytics
https://github.com/BradGalton/R-Factor-Models/blob/master/Barra%20Industry
# factorAnalytics fundamentalFM.Rnw - BARRA fundamental factor model - can't find pdf
https://github.com/R-Finance/FactorAnalytics/blob/master/vignettes/fundamentalFM.Rnw
# data
C:\Research\R\Packages\factorAnalytics\extdata
Pukthuanthong Ranking Factor Models Stock Forecasting.pdf
*** Asset pricing anomalies: size, value, momentum, volatility,
Fama French Dissecting Anomalies.pdf
C:\Research\Academic\Cochrane Advanced Investments\new_anomalies.pdf
Vogel Factor Model Momentum Anomaly.pdf
CFM Momentum Trend Following Strategy Anomaly.pdf
Han Trend Factor Cross-Section Momentum Stock Returns.pdf
Asness Fama French Small-Cap Anomalies.pdf
# Asness data files in:
Asness*.xlsx
# Anomalies aren't persistent
Edwards Market Anomaly Smart Beta Persistent Spurious.pdf
# low volatility anomaly
Gray Volatility Anomaly.pdf
Baker Volatility Anomaly.pdf
Li Low Volatility Anomaly FAJ.pdf
Han Volatility Decile Cross-Sectional Momentum Anomaly.pdf
# low beta anomaly caused by demand for positive skewness (lottery) which reduces future returns
Bali Betting Against Beta Lottery Demand.pdf
# skewness enhanced momentum is about twice as large as traditional momentum
skewness is among the most important cross-sectional determinants of momentum
Jacobs Skewness Cross-Sectional Momentum Anomaly.pdf
# credit risk produces time-varying skewness in Merton model
# CAPM betas overestimate true market risk
Schneider Volatility Anomaly Skew Risk Premium.pdf
Israel Size Value Momentum Anomalies.pdf
DeBondt Stock Premium January Anomaly.pdf
# R code to replicate main results in Ang, Hodrick, Xing, and Zhang (2006)
Ang Idiosyncratic Volatility Anomaly.pdf
https://gist.github.com/alexchinco/d58ebd7750904db1b94c
https://gist.github.com/alexchinco
https://github.com/alexchinco
# Treasury Curve Anomaly
Gayed Treasury Curve Anomaly Asset Allocation.pdf
### seasonal anomalies
# demonstrate sell in May anomaly by subsetting S&P returns
Bouman Sell in May Halloween Seasonal Anomaly.pdf
Afik Sell in May Halloween Seasonal Anomaly
Matilde Sell in May Halloween Seasonal Anomaly.pdf
Dzhabarov Seasonal Anomalies.pdf
Cieslak Market Timing FOMC Calendar Seasonal Anomaly.pdf
# daily overnight seasonal anomaly
buy ES in last 30min, and sell ES next morning in first 30min - trade only on reversals
Gray Lou Overnight Momentum Seasonal Anomaly.pdf
Lou Overnight Momentum Seasonal Anomaly CAPM Factor Models.pdf
Cliff Overnight Momentum Seasonal Anomaly.pdf
Donninger Overnight Momentum Seasonal Anomaly.pdf
http://jonathankinlay.com/index.php/2015/11/overnight-trading-in-the-e-mini-sp-500-futures/
http://www.priceactionlab.com/Blog/2015/11/overnight-trading-anomaly-backtesting-r/
http://blog.fosstrading.com/2015/11/overnight-spy-anomaly.html
http://systemtradersuccess.com/overnight-edge/
http://systemtradersuccess.com/market-seasonality-study/
http://systemtradersuccess.com/seasonality-sp-market-session/
# daily overnight overreaction gap reversal anomaly
create morning strategy based on open-close (daytime) and close-open (overnight) returns
is there price gap in morning ? what is best rule based on combination of all three returns?
Donninger Intraday Reversal Stock Forecasting.pdf
Kudryavtsev Intraday Reversal Stock Forecasting.pdf
Kudryavtsev abstract Intraday Reversal Stock Forecasting.pdf
*** Investor risk preferences and utility functions: investor prudence and temperance,
*** Kelly and CAPM,
Ilmanen Buying Selling Insurance Lottery Tickets.pdf
Nekrasov Kelly Criterion Multivariate Portfolios.pdf
*** Performing rolling calculations using vectorized functions: package caTools,
# rolling regression illustrate variance-bias tradeoff
*** Performing factor model regularization shrinkage
Regularization (shrinkage) is an example of Occams Razor, which was postulated
by the fourteenth-century philosopher Sir William of Ockham.
Occams Razor states that the most likely solution to be correct is the
simplest solution, and that any solution should not be more complicated
than necessary ("the law of parsimony").
http://blogs.wsj.com/moneybeat/2015/12/24/this-simple-way-is-the-best-way-to-predict-the-market/
Bogle Investing Factor Models.pdf
Bogle's message is: it's better to invest in indices,
unless you're a genius stock picker or a genius speculator.
Bogle postulates that long-term returns on investments consist of an "investment
return" (initial yield plus earnings growth) plus the "speculative return"
(discount factor determined by investor psychology and risk appetite).
The cumulative investment return is positive, while the cumulative speculative
return is close to zero.
*** Estimating model parameters,
# two resampling methods: cross-validation and bootstrap
C:\Research\R\Tutorials\Stanford Statistical Learning\cv_boot.pdf
C:\Research\R\Tutorials\Zivot\Econ 424\bootStrapPowerPoint.pdf
C:\Research\R\Tutorials\Shalizi Advanced Data Analysis\which-bootstrap-when.pdf
# Kalman filter
http://bilgin.esme.org/BitsBytes/KalmanFilterforDummies.aspx
http://intelligenttradingtech.blogspot.com/2010/05/kalman-filter-for-financial-time-series.html
http://stats.stackexchange.com/questions/8055/how-to-use-dlm-with-kalman-filtering-for-forecasting
http://www.magesblog.com/2015/01/extended-kalman-filter-example-in-r.html
Arnold Kalman Filter Expectation Maximization.pdf
Sorensen Kalman Filter.pdf
*** Portfolio optimization: Akaike and Bayesian information criteria, coefficient shrinkage,
# PortfolioAnalytics Portfolio Optimization
PortfolioAnalytics Bennett Random Portfolios Swarm Optimization.pdf
C:\Research\R\Packages\PortfolioAnalytics Bennett
https://github.com/rossb34/PortfolioAnalyticsPresentation2015
# package covmat for asset return correlation matrix estimation
https://github.com/rstats-gsoc/gsoc2015/wiki/Covariance-Matrix-Estimators
https://github.com/arorar/covmat
data(package="covmat")
Cholesky
mis-specified correlation matrix: cause and how to fix
correlation estimation error bands
Kwan Correlation Estimation Error.pdf
# demonstrate that the term structure of correlation decreases with tenor
# show that correlation depends on time scale, and decreases with shorter time scale
on short time scales correlation is very small
on intermediate time scales correlation is greater
on long time scales correlation is lower
# study Lo and MacKinlay variance ratio test
Kinlaw Variance Ratio Correlation Term Structure.pdf
# correlation covariance estimation and shrinkage
!!! https://bwlewis.github.io/covariance-shrinkage/
http://bwlewis.github.io/covar/missing.html
# correlation parameter uncertainty
indeterminate correlation matrix
Cholesky fails
positive definite correlation matrix
demonstrate how correlation parameter uncertainty increases with smaller number of observations or larger number of assets
# SVD and covariance matrix inverse:
inverse of covariance matrix using factors
Karhunen-Loeve Decomposition
# package irlba
Lewis RFinance 2012 Cointegration SVD.pdf
C:\Research\R\R-Finance 2015\BryanLewis.html
# Factor Augmented Regression for shrinking correlation matrix
Fit asset returns into multifactor model (start with CAPM),
Fitted asset returns should equal weighted sum of factors plus random uncorrelated residual,
Calculate correlation matrix of the fitted asset returns,
The correlation matrix should depend only on the factor correlations and asset betas,
Chiara Factor Model Forecasting.pdf
# Multivariate volatility and correlation forecasting DCC GARCH model
http://eranraviv.com/multivariate-volatility-forecasting-1/
# shrinkage Constructing Efficient portfolios
http://www.finance-r.com/s/efficient_frontier_fPortfolio/complete/
http://www.finance-r.com/s/simple_portfolio_optimization_tseries/complete/
http://www.portfolioprobe.com/2011/04/28/a-test-of-ledoit-wolf-versus-a-factor-model
http://quant.stackexchange.com/questions/10101/portfolio-optimization-shrinkage-of-covariance-matrix-when-data-is-available
http://systematicinvestor.wordpress.com/2011/11/11/resampling-and-shrinkage-solutions-to-instability-of-mean-variance-efficient-portfolios/
http://quant.stackexchange.com/questions/1504/robust-portfolio-optimization-re-balancing-with-transaction-costs
Kakushadze Correlation Shrinkage Factor Models.pdf
# package corpcor
http://strimmerlab.org/software/corpcor/
https://en.wikipedia.org/wiki/Estimation_of_covariance_matrices
http://quant.stackexchange.com/questions/44/what-methods-do-you-use-to-improve-expected-return-estimates-when-constructing-a
http://quant.stackexchange.com/questions/10101/portfolio-optimization-shrinkage-of-covariance-matrix-when-data-is-available
# create risk/return scatterplot for portfolios with two assets: stocks plus bonds
create vector of weights and plot line from stocks to bonds
simulate stock and bond returns using different correlations, and study effect on the line
solve for the most efficient portfolios (highest Sharpe) and create plot of bond percentage as function of correlation
create xts plot with slider for bond weight, display how SR changes
# add: "Zivot portfolio.r" from econ424
# http://faculty.washington.edu/ezivot/econ424/portfolio.r
Zivot Efficient Portfolios in R
C:\Research\R\Tutorials\Zivot\Econ 424\bootstrapPortfoliosPowerpoint.pdf
C:\Research\R\Tutorials\Zivot\Econ 424\bootstrapPortfolio.R
visualize:
chart.VaRSensitivity
chart.RiskReward(risk.col="StdDev") or (risk.col="ES")
# combine portfolios into list and chart
chart.EfficientFrontierOverlay
ECON 424/CFRM 462: Computational Finance and Financial Econometrics
C:\Research\R\Tutorials\Zivot
# add: portfolio optimization using optim
can mean variance portfolio optimization be converted to min variance optimization ?
library(quadprog)
solve.QP
different obj function,
out-of-sample performance,
# optimize portfolio assuming zero or constant correlations
demonstrate that this portfolio outperforms out-of-sample
# package NMOF PMwR
Schumann Take the Best Portfolio Selection Heuristic.pdf
portfolio optimization adds no incremental value because
correlation forecast error is so large that best to rely on marginal risk for portfolio choice.
pick assets that are good on their own, not for diversification,
simple sorting rules or cutoff rules are likely "more optimal" than is sometimes thought.
# Guy Yollins effFrontier and maxSharpe functions3, which use the core function of portfolio.optim in the tseries R package
http://blog.streeteye.com/blog/2012/01/portfolio-optimization-and-efficient-frontiers-in-r/
C:\Research\R\Tutorials\Guy Yollin Presentations
C:\Research\R\Packages\NMOF\doc
C:\Research\R\Packages\NMOF\book
DEopt
PSopt
NMOF Portfolio Optimization Threshold Accepting.pdf
quadprog package
TAopt
solve.QP
# DEoptim
Ardia DEoptim Portfolio Optimization.pdf
# package fPortfolio
Shaw Portfolio Optimization CVaR Omega Utility.pdf
rolling portf optim
stability of weights over time
Levy Alpha Sharpe Portfolio Optimization.pdf
Boudt DEoptim Portfolio Optimization.pdf
Boudt Asset Allocation Conditional Value-at-Risk Budgets.pdf
https://stat.ethz.ch/pipermail/r-sig-finance/2014q3/012564.html
efficient Frontiers
http://zoonek.free.fr/blosxom/R/2012-06-01_Optimization.html
good bullet points:
http://www.londonfs.com/programmes/Modern-Asset-Allocation-Portfolio-Construction/Outline/
Michaud Resampled Efficiency Portfolio Optimization (patented)
https://newfrontieradvisors.com/Research/Articles/MichaudResampledEfficiency.html
https://systematicinvestor.wordpress.com/2011/11/11/resampling-and-shrinkage-solutions-to-instability-of-mean-variance-efficient-portfolios/
# constrained portfolio optimization
# shrinkage
https://systematicinvestor.wordpress.com/2011/11/11/resampling-and-shrinkage-solutions-to-instability-of-mean-variance-efficient-portfolios/
https://systematicinvestor.wordpress.com/2013/10/29/updates-for-proportional-minimum-variance-and-adaptive-shrinkage-methods/
# Random Subspace Optimization
https://systematicedge.wordpress.com/2013/10/14/random-subspace-optimization-max-sharpe/
# tawny package for regularizinging correlation matrices using random matrix theory and shrinkage estimation
Rowe Random Matrix Shrinkage Covariance Estimation.pdf
Gatheral Random Matrix Shrinkage Covariance Estimation.pdf
# Steven Pav - packages SharpeR and MarkowitzR
# Sharpe ratio as Hotelling's T-squared distribution
https://github.com/shabbychef
Pav Sharpe Ratio Notes Hotelling Statistic
Pav Strategy Overfit
Pav code for Cochrane Asset Pricing
https://github.com/shabbychef/coursera_ap2013
SharpeR Vignette.pdf
MarkowitzR Vignette.pdf
MarkowitzR AsymptoticMarkowitz.pdf
finding optimal portfolio in-sample is the same as finding optimal strategy in-sample
both are over-fit and require shrinkage
use MC to create scatterplot of optimal portfolios or weights, due to parameter uncertainty
Plerou Random Matrix Correlation Estimation.pdf
Golts Constrained Shrinkage Portfolio Optimization.pdf
Demiguel Shrinkage Estimators Portfolio Optimization.pdf
Ledoit Wolf Covariance Shrinkage Estimators Portfolio Optimization.pdf
### PortfolioAnalytics
http://blog.fosstrading.com/2014/03/intro-to-portfolioanalytics.html
# demo_efficient_frontier.R
portfolio object specifies the constraints and objectives for the optimization
# create portfolios satisfying combinations of constraints and objectives:
objectives: maxSR, maxSRES, minVAR, minVARES,
constraints: long-only, long-short, neutral, box, leverage (=sum of absolute values of weights),
mean-ES (Expected Shortfall) portfolio
min-ES (Expected Shortfall) portfolio
mean-variance portfolio
mean-variance long-only portfolio
min-variance long-only portfolio
# optimize.portfolio: study and explain effect of choosing different
optimize_method="DEoptim", "random", "ROI",
ROI package=R Optimization Infrastructure
# optimize simultaneously several portfolios with different constraints and objectives
optimize.portfolio.rebalancing
Ardia CAPM Portfolio Optimization Stock Forecasting.pdf
*** Intertemporal portfolio choice, out-of-sample performance of optimized portfolios,
# Merton Dynamic Consumption and Portfolio Choice
# topic: simulate Merton consumption wealth model
Guasoni Merton Optimal Consumption Utility Shortfall Aversion.pdf
An Merton Utility Asset Allocation.pdf
https://en.wikipedia.org/wiki/Intertemporal_portfolio_choice
https://en.wikipedia.org/wiki/Merton%27s_portfolio_problem
Sivaramakrishnan Intertemporal Portfolio Choice.pdf
Garleanu Intertemporal Portfolio Choice.pdf
*** Active portfolio management strategies: tactical asset allocation, risk parity, minimum correlation, minimum variance, maximum Sharpe, maximum CVaR, universal portfolios,
# expand on:
cap-weighted indices have large concentrations and undesirable factor exposures
# factor investing
Blitz Investing Asset Allocation Factor Models.pdf
Bender Smart Beta Asset Allocation Investing Factor Models.pdf
# simulate 500 correlated stocks time series random lognormal with positive drift,
use them for random portfolios
create a value-weighted index
show that the index investors are inherently trend-following
because index keeps buying more of the outperforming stocks
compare to equally weighted index
which investors perform better?
# show that:
terminal price distribution is very skewed
most paths are below expected value
if we start with a portfolio of 500 stocks, most will underperform
therefore most randomly selected portfolios will underperform the index
therefore most PMs who randomly select portfolios will underperform the index
Antti Ilmanen:
The equity risk premium (ERP) refers to the expected return of a broad equity
index in excess of some fixed-income alternative.
Arnott (Research Affiliates):
The ERP Puzzle: Stocks beat bonds by more than they should.
Historical excess returns exhibit large negative correlation.
The correlation between consecutive 10-year stock market excess returns
over 10-year government bonds has been a whopping 38 percent.
When stocks beat bonds by a wide margin in one decade, they reversed with
reasonable reliability over the next decade.
This correlation is both statistically significant and economically meaningful.
# Risk Parity Portfolios
!!! Roncalli Risk Parity Factor Models.pdf
Steiner Risk Parity Portfolios.pdf
Heaton Stock Index Selection Active Portfolio Management.pdf
Griveau-Billion Risk Parity Portfolio Cyclical Coordinate Descent Algorithm.pdf
# package IKTrading - Keller (2014) Elastic Asset Allocation
https://quantstrattrader.wordpress.com/2015/01/30/comparing-flexible-and-elastic-asset-allocation/
# Flexible Asset Allocation returns algorithm
Keller Elastic Asset Allocation.pdf
# Grinold fundamental law of active management
Grinold Synopsis Active Portfolio Management.pdf
# all weather portfolios
Faber Arnott Portfolio Asset Allocation.pdf
# Ian Kaplan (UofWash) minimum variance and tangency portfolios, CVaR portfolio optimization, ETF portfolios, Wharton Research Data Service (WRDS) data set and Factor Model Factors
http://www.bearcave.com/finance/
# nice formulas Global Minimum Variance Weights
http://www.bearcave.com/finance/portfolio_equations/
# Andrew Ang at Columbia book Asset Management: A Systematic Approach to Factor Investing
!!! Ang Factor Models Investing.pdf
http://factorinvestingbook.com/
http://factorinvestingbook.com/book.html
!!! Richard Smart Beta Minimum Variance Factor Models.pdf
Maillard Risk Parity Minimum Variance Portfolios.pdf
Goldberg Value Minimum Variance Portfolio Factor Models
Hsu Minimum Variance Portfolio Factor Models.pdf
Clarke Risk Parity Minimum Variance Portfolios.pdf
Chow Minimum Variance Stock Strategy.pdf
# Tactical Asset Allocation simple script
http://blog.fosstrading.com/2009/11/tactical-asset-allocation-using-blotter.html
http://petewerner.blogspot.com/2012/04/mebane-faber-tactical-asset-allocation.html
https://github.com/petewerner/misc/blob/master/gtaa-script.R
# Tactical Asset Allocation Faber
http://unstarched.net/2013/06/18/the-fallacy-of-1n-and-static-weight-allocation/
# Smart Beta doesn't outperform
Jacobs:
Glushkov found the Sharpe ratios of smart beta funds and their benchmarks to be nearly identical, at 0.46 versus 0.48, respectively, while the average information ratio was 0.08, inconsistent with the idea that smart beta ETFs offer a distinct advantage over traditional cap-weighted indexes. Furthermore, according to an analysis performed for Reuters by ETF.com, and reported in Barlyn [2015], recent smart beta performance results have been disappointing.
Gestalt Adaptive Asset Allocation.pdf
Gestalt Smart Beta Factor Models Active Portfolio Management
Malkiel Smart Beta Dumb Factor Models.pdf
Glushkov Smart Beta Factor Models.pdf
Richard Smart Beta Minimum Variance Factor Models.pdf
Amenc Smart Beta Investing Factor Models.pdf
Amenc Smart Beta Factor Models.pdf
Amenc Smart Beta Factor Models JPM.pdf
# Russell factor model
Barber Russell Smart Beta Factor Models.pdf
Blitz Factor Models Investing.pdf
# Beta Rotation
Bilello Utilities Sector Indicator Beta Rotation Strategy.pdf
Bilello Lumber Gold Indicator Beta Rotation Strategy.pdf
# create strategy for betting against beta
Asness Betting Against Beta.pdf
Frazzini Betting Against Beta.pdf
https://gist.github.com/timelyportfolio/11148198
https://gist.github.com/timelyportfolio/11232439
http://blog.alphaarchitect.com/2014/06/09/betting-beta-demand-lottery/
# Minimum Variance Strategy
even better: Minimum Variance minus High Variance Strategy ?
what about time-dependent beta?
# Markowitzs Critical Line Algorithm (CLA) - function CCLA()
!!! Bailey Prado Critical Line Algorithm Portfolio Selection
# momentum e-book
http://www.investfy.co/little-book-of-momentum/
# the 12M-1M momentum is the 11-month return up to one month ago
12-month-1-month momentum strategy
Practically, it can be viewed as an 11-month momentum strategy executed with a one-month delay.
A third factor in the form of 12-month return momentum (Jegadeesh, 1990;
Jegadeesh and Titman, 1993) was incorporated by Fama and French (1996) and
# Ross Bennett: Momentum with R
http://rbresearch.wordpress.com/2012/08/23/momentum-with-r-part-1
http://rbresearch.wordpress.com/2012/10/20/momentum-in-r-part-2
http://rbresearch.wordpress.com/2012/11/18/momentum-in-r-part-3
http://rbresearch.wordpress.com/2013/02/19/momentum-in-r-part-4-with-quantstrat/
AQR "Dispelling Myths of Momentum": replicate paper with R and rCharts:
http://timelyportfolio.github.io/rCharts_factor_analytics/aqr_fact_fiction_momentum.html
https://github.com/timelyportfolio/rCharts_factor_analytics/
http://timelyportfolio.blogspot.com/2014/06/dispelling-myths-of-momentum-aqr.html
# is momentum waning?
http://www.philosophicaleconomics.com/2015/12/momentum/
# Momentum crashes
Barroso Momentum Volatility Crash Forecasting.pdf
https://quantstrattrader.wordpress.com/2015/09/16/hypothesis-driven-development-part-iv-testing-the-barrososanta-clara-rule/
# invest in portfolio with highest momentum or one year trailing Sharpe Ratio
Gogerty Portfolio Optimization Momentum Asset Allocation.pdf
!!! Keller Momentum Markowitz Asset Allocation.pdf
https://quantstrattrader.wordpress.com/2015/06/05/momentum-markowitz-and-solving-rank-deficient-covariance-matrices-the-constrained-critical-line-algorithm/
https://github.com/drquant/R_Finance/blob/master/Momentum_and_Markowitz/kellerCLAfun.R
http://systematicinvestor.github.io/Review-Momentum-Markowitz/
# similar to above, but applies shrinkage
!!! Keller Momentum Markowitz Shrinkage Asset Allocation.pdf
# use ETFs from:
Antonacci Optimal Momentum.pdf
# PerformanceAnalytics portfolio rebalancing
PerformanceAnalytics Return.portfolio.pdf
http://tradeblotter.wordpress.com/2014/09/25/aggregate-portfolio-contributions-through-time/
Omega ratio
Adjusted Sharpe ratio
add various table.*
table.SpecificRisk()
table.Distributions()
table.DrawdownsRatio()
table.DownsideRiskRatio()
# examples:
http://seekingalpha.com/article/3222126-the-world-country-top-4-etf-strategy-a-way-to-fight-rising-rates-and-a-stalling-u-s-stock-market
http://seekingalpha.com/article/3536476-lower-risk-versions-of-a-dual-momentum-fixed-income-strategy
http://seekingalpha.com/article/3578136-a-paradigm-shift-for-tactical-strategies-trading-mutual-funds-on-a-monthly-basis
*** Benchmarking portfolio management skill:
https://www.dimensional.com/famafrench/essays/luck-versus-skill-in-mutual-fund-performance.aspx
# use: data(edhec) from library(PerformanceAnalytics)
chart.CumReturns(edhec)
# random portfolios can outperform market if they're equally weighted - because they're overweight value and small-cap stocks
Research Affiliates indexes (known as RAFIs) rank stocks based on book value as well as trailing five-year average cash flow, sales and dividends.
# random portfolios indicate additional factors not included in FF4
Arnott Random Portfolios Factor Models.pdf
Amenc Random Portfolios Factor Models.pdf
# not a single of the 1000 random portfolios of size 50 delivers a annualized return below the S&P 500 index
https://predictivealpha.wordpress.com/2015/12/24/towards-a-better-equity-benchmark-random-portfolios/
http://robotwealth.com/benchmarking-backtest-results-against-random-strategies/
http://gestaltu.com/2015/10/apples-and-oranges-a-random-portfolio-case-study.html
Novomestky package: rportfolios
Stein Random Portfolios Fund Analysis.pdf
http://www.capitalspectator.com/using-random-portfolios-to-test-asset-allocation-strategies/
https://gist.github.com/jpicerno1/fbc2e589023be56dde42
http://www.capitalspectator.com/skewed-by-randomness-testing-arbitrary-rebalancing-dates/
https://gist.github.com/jpicerno1/af88861bcbbb80687cfb
# Resampling Methods Bootstrap Cross Validation Random Portfolios
http://www.burns-stat.com/documents/tutorials/the-statistical-bootstrap-and-other-resampling-methods-2/
# benchmarking investor timing skill Merton
Roy D. Henriksson and Robert C. Merton.
# benchmark investor skill portfolio convexity skewness
# Performance Attribution from Bacon
PerformanceAnalytics PA-Bacon.pdf
performance attribution:
asset picking
timing
asset allocation
Stubbs Portfolio Performance Attribution Factor Models.pdf
!!! pa package Kane Performance Attribution.pdf
pa package Lu Performance Attribution.pdf
Guasoni Alpha Actively Managed Funds.pdf
Ferson Portfolio Performance Attribution Bootstrap Factor Models.pdf
# create scatterplot of returns of managed strategy versus benchmark strategy
should illustrate convexity profile
*** Forecasting returns and volatility,
# simulate a time series using the Vasicek and Heston models and use it for forecasting
# simulate Heston model and calibrate it to S&P returns
# use package NMOF
http://stackoverflow.com/questions/15579655/heston-simulation-monte-carlo-slow-r-code
http://stackoverflow.com/questions/27429725/monte-carlo-simulation-in-r
http://stackoverflow.com/questions/15534270/stock-price-simulation-r-code-slow-monte-carlo
# Using the LASSO to Forecast Returns
http://www.alexchinco.com/using-the-lasso-to-forecast-returns/
# Bias in Time-Series Regressions
http://www.alexchinco.com/bias-in-time-series-regressions/
# simulate GARCH model
# stochastic volatility and rebalancing - solve Hamilton-Jacobi-Bellman equation
Goyal GARCH Volatility Forecasting.pdf
Goyal Cross Sectional Factors Stock Forecasting.pdf
Rapach Equity Stock Forecasting.pdf
DeMiguel VAR Model Stock Selection Forecasting.pdf
correlation forecasting - is it possible?
is Hurst exponent forecastable as well?
# momentum
Vogel Absolute Momentum Stock Forecasting.pdf
Gulen Absolute Momentum Stock Forecasting.pdf
# Volatility-adjusted momentum
We calculate volatility-adjusted momentum rankings by dividing the prior twelve
month total return by the realised volatility over the same period and then
ranking in the standard fashion.
Clare Volatility Momentum Trend Following Asset Allocation.pdf
Baltas Volatility Momentum Trend Following Asset Allocation.pdf
Zakamulin Momentum Indicators Stock Forecasting.pdf
# steady momentum frog-in-the-pan indicator: number of winning periods minus number of losing periods
steady momentum indicator should be related to skew: large gains in a short period should produce positive skew
# sort stocks by volatility and test which deciles have highest momentum
Vogel Volatility Momentum Stock Forecasting.pdf
# negative correlation between the monthly return of S&P index versus monthly volatility of returns on the index
unexpected volatility is the difference between the realized volatility minus the GARCH forecast
unexpected volatility predicts future excess return and volatility
two strategies that dynamically reallocate between stocks and the risk-free asset,
depending on the value of unexpected volatility.
Zakamulin Volatility Forecasting Asset Allocation.pdf
Vogel Absolute Momentum Stock Forecasting.pdf
# forecasting returns, variance, skew, kurtosis,
Stroud High Frequency Forecasting Volatility VIX VXX Strategy.pdf
http://www.jonathanrstroud.com/code.html
# forecasting intraday returns after price jumps
Zawadowski Intraday Reversal Stock Forecasting.pdf
Grant Intraday Reversal Stock Forecasting.pdf
Duyvesteyn Intraday Reversal Bond Forecasting.pdf
Schneider Skew Fear Volatility Risk Premium Forecasting.pdf