Machine Learning and ESG Production Networks in Multi ... · Machine Learning and ESG Production...

Machine Learning and ESG Production Networks in Multiobjective Behavioral Portfolio Selection

Gordon H Dash*Nina Kajiji**

*College of Business and Interdisciplinary Neuroscience Program**Computer Science and Statistics

University of Rhode Island

Email: [email protected]: [email protected]

For presentation at:Northfield’s 31st Annual Research Conference

(Long version: ~50 min)

October 24-27, 2019Washington, DC

Research was funded by grants from:•The NKD Group, Inc., USA

Corporate website: www.nkd-group.global

http://www.nkd-group.global/

Motivation

To extend dynamic hierarchical portfolio optimization: ESG production and expected shortfall (ES) bias (Jain, 2015 and Giglio, 2019)To reconcile and add new evidence on:

Disentanglement of Pervasive ESG Factors (Pukthuanthong, Roll, and Subrahmanyam (PRS), 2018). Pervasive factors are approximate economic factors that contribute to the security return generating process.E,S, & G pervasive factor design (Dash and Kajiji, 2018)

To extend Gu, et.al., 2018 by uncovering network produced security returns using ML/AI.To extract real-time option-priced ES (Barone-Adesi, 2017) Northfield 2019

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Outline

1. ESG: Definitions and Literature2. Pervasive (non-priced) Factor Extraction from TR/S-

Networks ESG Large Cap Portfolios3. The Four-Step PRS (2018) Algorithm for Investor

Portfolio Disentanglement4. Firm ESG Strategy and the Impact on Firm Production

of Market Returns5. Option Implied Expected Shortfall (ES)6. Investor Hierarchical Bias (objectives)7. Modeling Results8. Summary & Conclusions

Diversification of Behavioral Bias, ESG, and ES

Definition: ESG: Environmental, Social and Governance factors.ESG investing adds value in at least 3 ways:

Allows investors to make a social impact beyond just obtaining financial returns.It augments the usefulness of risk-adjusted returns.Integrates non-financial indexes into the portfolio building process.

Question: How to account for firm-level ESG performance and ES risk in efficiently diversified behaviorally biased portfolios?

Northfield 2019Slide: 4 / 36

Literature Contributions: ESG

Hoepner, 2010 Hoepner, Rezec et al., 2013

ESG criteria contribute to overall portfolio diversification

Kremer, et al., 2019

Bloom, et al., 2013

Bernard, et al., 2019

Shefrin and Statman, 2000 Statman 1999, 2004

Behaviorally biased portfolios formed as layered pyramids where each layer is aligned to a specific investment objective

Companies with high ESG scores tend to have less company-specific risk

Bouslah, Kryzanowski et al. 2011, Oikonomou, Brooks et al. 2012

Reported how both small- and large-investors incorporate ESG screening information in: a) valuation models, and b) inputs into portfolio optimization models

Amel-Zadeh and Serafeim, 2017

Examined the efficient characteristics of three ESG-tilted portfolio strategies to demonstrate superiority to non-ESG biased portfolios

Nagy, Z., et al., 2013

Giglio, Maggiori, Stroebel, & Utkus, 2019

The layered pyramid of objectives approach finds new support.

“Pervasive” Factor Extraction From Extant ESG Portfolios


Step 1

• For the k ⸦750 securities across the E,S, and G portfolios compute daily after market residuals using Vasicek (1973) “Smart” betas (see

Sarker, 2013 for implications and Fiorentini, et al., 2019 for score orthogonality – predictability).

Step 2• Test residual matrix for structure detection using the Kaiser-

Meyer-Olkin (KMO). If KMO > 0.8 subject each matrix to EFA

Step 3• Implemented the Kaiser-Guttman selection criterion (λ ≥ 1.0) for

factor retention. Rotate factors using Varimax orthogonal rotation.

Step 4

• Compute factor scores using Thomson (1951) Maximum Validity(or regression) method: the set of composite variables are the time-series of latent, but pervasive, ESG factors.

Thomson Reuters/S-Network ESG Indices

The Thomson Reuters/S-Network ESG Best Practices Ratings and Indices:

a suite of benchmarks designed to measure the performance of companies with superior ratings for ESG practices (http://www.trcri.com/ ).

7 tickers (ANDV, APTV, BHGE, HPE, PYPL, TPR, and YUMC) with incomplete data were removed.


Dimension Filename Original (K0) Research (K)

Environment TRENVUS 250 245

Social TRSCUS 248 245

Governance TRCGVUS 248 243

Varimax Score Weighted Pervasive ESG Factors

Obtain the daily pervasive factor score indexes (FSIs) by computing a simple average across the C factor scores (fti) derived from the unique portfolios (E, S, G)

𝐹𝐹𝐹𝐹𝐹𝐹𝑡𝑡𝐸𝐸 = ∑ 𝑓𝑓𝑡𝑡𝑡𝑡𝐶𝐶𝐸𝐸

𝑖𝑖 = 1, … ,𝐶𝐶𝐸𝐸

𝐹𝐹𝐹𝐹𝐹𝐹𝑡𝑡𝑆𝑆 = ∑ 𝑓𝑓𝑡𝑡𝑡𝑡𝐶𝐶𝑆𝑆

𝑖𝑖 = 1, … ,𝐶𝐶𝑆𝑆

𝐹𝐹𝐹𝐹𝐹𝐹𝑡𝑡𝐺𝐺 = ∑ 𝑓𝑓𝑡𝑡𝑡𝑡𝐶𝐶𝐺𝐺

𝑖𝑖 = 1, … ,𝐶𝐶𝐺𝐺

Prior to the derivation of efficient portfolios, each index is rescaled to a monthly time seriesFSI indexes express pervasive factors; not “price” factors


Disentangle ESG Tilt in Investor Portfolio Using Pervasive ESG Factors

Implement the time-series based PRS Algorithm (2018).Investor’s portfolio: 65 instruments (equities + ETFs). A naively diversified fund with a “sustainability” tilt.Historical monthly price data for all instruments were obtained over the period from Jan 2015 through Apr 2018, inclusive.Estimate the log-differenced monthly returns for all securities and the market proxy.

𝑟𝑟𝑡𝑡 = ln 𝑃𝑃𝑡𝑡 − ln(𝑃𝑃𝑡𝑡−1)


Investor Portfolio Disentanglement: The Four-step PRS Algorithm


Step 1• Assess the diversification and heterogeneity structure of the

investor portfolio.

Step 2• Extract L Principal Components from the time series structure of

security returns using the investor portfolio.

Step 3• Create set of latent after-market ESG Factors (i.e. the newly created

pervasive indices for E, S, and G) and L Principal Components.

Step 4• Canonical Correlation analysis between the pervasive indices and

eigenvectors of the investor portfolio

PRS Step 1: Sector Diversification step i of ii

Industry classification of the Investor Portfolio Source:Yahoo!


PRS Step 1: Sufficient Heterogeneity? step ii of ii

Fruchterman-Reingold (FR) network analysis of Investor portfolio return correlations.


PRS Step 2:Compute Investor Portfolio Eigenvectors

The time series of security returns were subjected to PCAThe cutoff point for the cumulative variance was set to 90%16 components were retained and 16 eigenvectors were computed


View of the first 3 PCs

Excess Returns across Time. Legend: PC-1=Green; PC-2=Blue; PC-3=Lime

PRS Steps 3 & 4: Pervasive Factors andCanonical Correlation

Step 3: Collect factors - 𝐹𝐹𝐹𝐹𝐹𝐹𝑡𝑡𝐸𝐸;𝐹𝐹𝐹𝐹𝐹𝐹𝑡𝑡𝑆𝑆;𝑎𝑎𝑎𝑎𝑎𝑎,𝐹𝐹𝐹𝐹𝐹𝐹𝑡𝑡𝐺𝐺

Step 4: Subject pervasive factors and investor portfolio assets to a canonical correlation:

Ho: The E, S, G Indexes influence the movement of asset prices systematically.The factor candidates (E, S, & G) are conditionally related tothe covariance matrix of real returns (λ = 0.6411, F48, 63.253 = 2.0, p < 0.05) Discussion on dimension 1 for the covariate set

E = -1.1622; infers that E leads to a -1.1622 unit decrease in the first dimension of the covariate set with other predictors held constant. S = -0.5230G = 1.0375


Does an ESG Strategy Alter Firm Production?

Atkin, et al., 2017Supports Duffio, et al. and Bloom, et al. Argues ESG metrics must have a clear link to the optimization of shareholder value and long-term business strategy..

Kremer, et al., 2019“Present Bias” explains why high returns in the absence of high growth often dampens management’s adoption of an efficient production function.

Bloom, et al., 2013

Supports Duffio et al.

Salazar & Guzmán, 2017Firm management should

respond to investor sustainability bias by adopting

new technological (sustainable) investments

Bernard, et al., 2019A firm’s intensified and

competitive adoption of ESG factors can lead to a shifting

(growing) market share which can account for changes in firm size and financial performance

Duffio, et al., 2008“Limited Attention Bias” impacts optimal production choices across sectors when irrational managers do not take advantage of useful and readily available information..

Firm Production Networks, ESG, & Market Returns

Ozsoylev & Walden, 2011

An asset pricing model that includes (ESG) pricing factors within an interconnected information network

Kremer, et al., 2019

Bloom, et al., 2013

Hong, et al., 2004

How fund managers exhibit collective (or, networked) behaviors

Report findings of collective behavior among individual investors

Ivkovic & Weisbenner, 2007

Provided evidence that peer effect networks interact with production functions to transform inputs into outputs

Horrace, et al., 2016

Extend the classic factor-based asset pricing model to include network linkages of exogenous lagged and contemporaneous links across assets

Billio, et al., 2016

Roukny, Battiston, & Stiglitz, 2018Evolutionary financial network theory and the interconnectedness to asset returns as a source of uncertainty in systematic risk

Herskovic, 2018Extends asset pricing theory by uncovering a link between equilibrium asset prices and the two production-theoretic network attributes that drive systematic risk --network concentration and network sparsity

Machine Learning and Production Function Estimation

Found “shallow” learning outperforms “deep” learning,…, is undoubtedly due to the comparative dearth of data and low signal to-noise ratio in asset pricing problems.

Machine learning is well suited for this type of challenging prediction problems: reduces degrees of freedom and condenses redundant variation among predictors.

Relative to traditional empirical methods in asset pricing, machine learning accommodates a far more expansive list of potential predictor variables and richer specifications of functional form.

ML Preferred

Power of ML

Shallow v/s Deep Networks

Gu et al., 2018

Arreola and Johnson, 2015 Commonly used Cobb Douglas production function results in very competitive approximations

ESG and the Production of Market Returns

For all j firms in n, output is produced using a Cobb-Douglas production technology, 𝑦𝑦𝑗𝑗 = 𝐴𝐴𝑗𝑗𝑘𝑘𝑗𝑗𝛼𝛼𝑙𝑙𝑗𝑗1−𝛼𝛼

Without considering a firm’s age or it’s learning rate we assume 𝐴𝐴𝑗𝑗 = 𝑒𝑒𝛽𝛽𝑗𝑗∆𝑎𝑎, where ∆𝑎𝑎is a common interconnected ESG shock. It affects returns productivity of all firms and 𝛽𝛽𝑗𝑗is the firm-specific exposure to the common shock ∆𝑎𝑎.Prior to implementing a firm specific production decision, firm j observes a unique noisy ESG signal: 𝑠𝑠𝑖𝑖𝑠𝑠𝑗𝑗 = 𝛽𝛽𝑗𝑗 + 𝜖𝜖𝑗𝑗where 𝜖𝜖𝑗𝑗~𝑖𝑖. 𝑖𝑖.𝑎𝑎. ,𝑁𝑁(0, 1

Δ𝑎𝑎𝜏𝜏2). 𝜏𝜏2captures noise.

Perfect information: τ = 0. As τ →∞ the signal to firm j is not informative or firm management is numb to ESG factors.


Network Estimation of Market Returns with ESG Production

We define the model of firm returns as: 𝐸𝐸 𝑟𝑟𝑗𝑗 =𝑓𝑓 𝜲𝜲 + 𝜀𝜀𝑗𝑗Network estimation of the ESG production impact on a firm’s market returns augments the Fama and French 3-factor model (1992) with FSI indexes:


𝐸𝐸 𝑟𝑟𝑗𝑗 = 𝛼𝛼𝑗𝑗 + 𝛽𝛽1𝑙𝑙𝑎𝑎(1 + (𝑟𝑟𝑀𝑀− 𝑟𝑟𝑓𝑓)) + 𝛽𝛽2𝑙𝑙𝑎𝑎 1 + 𝐹𝐹𝐹𝐹𝐹𝐹𝐸𝐸 + 𝛽𝛽3𝑙𝑙𝑎𝑎 1 + 𝐹𝐹𝐹𝐹𝐹𝐹𝑆𝑆 +𝛽𝛽4𝑙𝑙𝑎𝑎 1 + 𝐹𝐹𝐹𝐹𝐹𝐹𝐺𝐺 + 𝛽𝛽5𝑙𝑙𝑎𝑎 1 + 𝐹𝐹𝑆𝑆𝑆𝑆 + 𝛽𝛽6𝑙𝑙𝑎𝑎 1 + 𝐻𝐻𝑆𝑆𝐻𝐻 + 𝜖𝜖𝑗𝑗

ML: RBFN ESG Weight Approxiation

The traditional Radial Basis Function ANN (RBFN) is stated as, y = f(x) where y, the output vector, is a function x, which is a p by n input matrix where n is the number of observations. The function can be restated as:

The Kajiji (2001) extension to the traditional RANN specification introduced multiple objectives within a Bayesian RANN framework.

Efficient Mapping: is achieved by adding a weight penalty, Tikhonov’s (1977) regularization parameter, to the SSE optimization objective. The modified SSE is restated as the following cost function:

Optimal Weight Decay. Additionally, Kajiji proposed a closed-form solution to the estimation of the weight parameter based on Hemmerle’s extensions (1975) to the traditional ridge-regression parameters and Crouse’s incorporation of priors (1995). Under the Kajiji specification the function to be minimized is stated as:

( )2 2

1 1

ˆ ( )p m

i i j ji j

C y f x k w= =

= − +∑ ∑


2 2

1 1

argmin ( ( | ))p m

i i j ji j

y f x k k wk

ς−

= =

− +

∑ ∑

RBFN Weight Extraction – Exxon Returns


The K4-RANN (i.e.,shallow) model is solved for the 65 securities of the investor portfolioModel assumptions

Transformation: STDError minimization rule: GCVTransfer function: Inverse Multiquadric

𝐸𝐸 𝑟𝑟𝑗𝑗 = 𝛼𝛼𝑗𝑗 + 𝛽𝛽1(1 + (𝑟𝑟𝑀𝑀− 𝑟𝑟𝑓𝑓)) + 𝛽𝛽2𝑙𝑙𝑎𝑎 1 + 𝐹𝐹𝐹𝐹𝐹𝐹𝐸𝐸 + 𝛽𝛽3𝐻𝐻𝑎𝑎 1 + 𝐹𝐹𝐹𝐹𝐹𝐹𝑆𝑆 +𝛽𝛽4𝐻𝐻𝑎𝑎 1 + 𝐹𝐹𝐹𝐹𝐹𝐹𝐺𝐺 + 𝛽𝛽5𝐻𝐻𝑎𝑎 1 + 𝐹𝐹𝑆𝑆𝑆𝑆 + 𝛽𝛽6𝐻𝐻𝑎𝑎 1 + 𝐻𝐻𝑆𝑆𝐻𝐻 + 𝜖𝜖𝑗𝑗

Repeated for presentation clarity

Used to compute RtS

Portfolio: FSI Production Elasticity Weights


*Scale = E_FSI + S_FSI + G_FSI

Selected Results : All tickers in the Investor Portfolio from the Financial and Energy Sectors

Ticker E_FSI S_FSI G_FSI Scale*

Banks – Global (Financial Services)

JPM 0.0101 0.3325 0.3058 0.6484WFC -0.1957 0.2673 0.2562 0.3278BAC 0.0808 0.2226 0.1862 0.4896C -0.2620 -0.0870 -0.1650 -0.5140

Oil & Gas (Energy)XOM -0.0943 -0.0388 0.7870 0.6538CVX -0.2114 0.5529 0.7629 1.1045COP 0.4275 0.3749 0.7556 1.5580

iShares Oil Equip & Services ETF

IEZ 0.1224 0.0645 0.7072 0.8942

Investor Portfolio: Gravitation of ESG Elasticities


Heavy lines indicate a +ve marginal weightDotted lines indicate -ve marginal weightThe thicker the line, the further away from zero.

Investor Portfolio: ESG Interconnectedness


How are production elasticities distributed for each security in the portfolio across the E, S, & G domains?

ESG Returns and ES Tilt in Portfolio Allocations

Beliefs are reflected in portfolio allocations

Beliefs are mostly characterized by large and persistent individual heterogeneity; demographic characteristics explain only a small part of why some individuals are optimistic and some are pessimistic.

Expected returns and the subjective probability of rare disasters are negatively related, both within and across investors

E(r) and Rare Events

Individual Heterogeneity

Portfolio Allocations

Giglio et al., 2019

Jain et al., 2015Individual investor makes his/her investment decision under the influence of some combination of behavioral biases, which mainly include disposition effect, mental accounting, investors’ overconfidence, representativeness, narrow framing, aversion to ambiguity, anchoring, availability bias, and regret aversion

Extreme Risk Measurement: Option-Implied ES

The sub-additive CVaR is the expected dollar loss beyond VaR for the underlying (S). As such it is affected by fatness in the tail of the distribution of S. It is stated as:

𝐶𝐶𝐶𝐶𝑎𝑎𝐶𝐶 =1𝛼𝛼�−∝

𝑋𝑋𝐻𝐻 𝐹𝐹 𝑓𝑓(𝐹𝐹)𝑎𝑎𝐹𝐹

Following Barone Adesi (2017) for a given 𝛼𝛼, estimate CVaR using real-time S and X(p), the risk-free rate (r), and time to expiration (T). Then if p = BSOPM(p)

𝐶𝐶𝐶𝐶𝑎𝑎𝐶𝐶 = 𝑒𝑒𝑟𝑟𝑟𝑟𝑝𝑝𝛼𝛼

+ 𝐶𝐶𝑎𝑎𝐶𝐶


Investor Hierarchical Bias


MINLGP: Separable Goal Programming


Bi-Objective MV-NLGP Model

Sharpe Single Index Model (1964)Efficient portfolio diversification

Mean-Variance v/s Sharpe Single Index Model (SIM)Frankfruter (1977)

Identical for MRP portfolioSlightly inefficient estimation of global minimum variance portfolio

NLGP ApproachBi-Goal Optimization of SIMP1: fix the 𝐸𝐸(𝑟𝑟)𝑃𝑃P2: Min 𝜎𝜎𝑃𝑃 efficient diversification of 𝐸𝐸(𝑟𝑟)𝑃𝑃


Hierarchical Pyramid Portfolio Model

Model 1: 𝑆𝑆𝑖𝑖𝑎𝑎 𝑍𝑍 = 𝑃𝑃1 ℎ4− ,𝑃𝑃2 ℎ1+ ,𝑃𝑃3 ℎ8+

Model 2: 𝑆𝑆𝑖𝑖𝑎𝑎 𝑍𝑍 = 𝑃𝑃1 ℎ4− ,𝑃𝑃2 ℎ8+ ,𝑃𝑃3[ℎ1+]Model 3: 𝑆𝑆𝑖𝑖𝑎𝑎 𝑍𝑍 = 𝑃𝑃1 ℎ4− + ℎ5− ,𝑃𝑃2 ℎ1+ + ℎ6− ,𝑃𝑃3[ℎ8+ + ℎ7−]Model 4: 𝑆𝑆𝑖𝑖𝑎𝑎 𝑍𝑍 = 𝑃𝑃1 ℎ4− + ℎ5− + ℎ6− + ℎ7− ,𝑃𝑃2 ℎ1+ ,𝑃𝑃3[ℎ8+]

Structural:1. ∑𝑘𝑘=1𝑘𝑘+1 ε𝑘𝑘2𝑥𝑥𝑗𝑗 − ℎ1+ = 0.02. ∑𝑘𝑘𝐾𝐾 𝛽𝛽𝑘𝑘𝑣𝑣 𝑥𝑥𝑘𝑘 = 𝛽𝛽𝑀𝑀3. ∑𝑘𝑘𝐾𝐾 𝑥𝑥𝑘𝑘 = 1.0

Policy Preferences4. ∑𝑘𝑘𝐾𝐾 𝐸𝐸 𝑟𝑟𝑘𝑘 𝑥𝑥𝑘𝑘 + ℎ4− − ℎ4+ = 𝐶𝐶𝑝𝑝5. ∑𝑘𝑘𝐾𝐾 𝐶𝐶𝑅𝑅𝐹𝐹𝑘𝑘𝐸𝐸 + ℎ5− − ℎ5+ = 𝐶𝐶𝐹𝐹𝑃𝑃𝐸𝐸

6. ∑𝑘𝑘𝐾𝐾 𝐶𝐶𝑅𝑅𝐹𝐹𝑘𝑘𝑆𝑆 + ℎ6− − ℎ6+ = 𝐶𝐶𝐹𝐹𝑃𝑃𝑆𝑆

7. ∑𝑘𝑘𝐾𝐾 𝐶𝐶𝑅𝑅𝐹𝐹𝑘𝑘𝐺𝐺 + ℎ7− − ℎ7+ = 𝐶𝐶𝐹𝐹𝑃𝑃𝐺𝐺

8. ∑𝑘𝑘𝐾𝐾 𝐶𝐶𝐶𝐶𝑎𝑎𝐶𝐶𝑘𝑘𝐵𝐵𝑆𝑆(𝑝𝑝) + ℎ8− − ℎ8+ = 0.0

Goal Constraints


Where: 𝐶𝐶𝐹𝐹𝑃𝑃𝐸𝐸 ,𝐶𝐶𝐹𝐹𝑃𝑃𝑆𝑆,𝐶𝐶𝐹𝐹𝑃𝑃𝐺𝐺 = 1, respectively; 𝑘𝑘 = 1. .𝐾𝐾 securities

Comparative NLGP/Behavioral Portfolios


Both axis are truncated

Quick Reference: Model 1: R, V, CModel 2: R, C, VModel 3: RE, VS, CGModel 4: RESG, V, C

Behavioral Portfolio Characteristics


Quick Reference: Model 1: R, V, CModel 2: R, C, VModel 3: RE, VS, CGModel 4: RESG, V, C

M1: smallest CV set but no ESG

ESG focus needs to be spread out

Summary & Conclusions

Illustrated lexicographical ESG portfolio management with:

Pervasive E, S, and G factorsML estimation of market returnsInclusion of option-priced shortfall goal

FF with ESG returns production produced meaningful scale elasticities for ESG component.Employed the Barone Adesi(2017) methodology for estimating option priced CVaR from real-time put trades.

MINLGP Sharpe-Diagonal model (SDM) enumeration of Behaviorally efficient portfolios:

Canonical MV/SIM dominate behaviorally efficient portfoliosFirm adoption of ESG results in increased portfolio riskESG portfolio diversification ratios (CV) demonstrates how the firm’s commitment to ESG can result in portfolio efficiency – but only at moderate expected return levels.Bias: ESG is preferred to CVaR

Future research questions:Stability of FSI pervasive factorsAugmentation to reflect trade and portfolio adjustments base on real-time news (Zaldokas, 2019)


Selected References: MINLGP

Dash, G.H., and Kajiji, N. (2014): Multiobjective portfolio optimization with combinatorial methods to dynamically hedge interim period performance.Kwon, R., Stoyan, Steven J. (2011): incorporated a wide set of real-world trading constraints to the mean-variance portfolio framework. Solved both MV and MAD. Focus: MIP and trading system constraintsAnagnostopoulos, K.P and Mamanis, G. (2010): formulated the MV problem as a bi-objective linear mixed integer optimization problem.Xidonas, P; Mavrotas, G; and, Psarras, J. (2010): Multiple Criteria Decision-Making Approach. MO/MCFabozzi, Frank; et.al. (2010): Survey of recent contributions in robust portfolio strategies from OR and Finance


Additional References

Bouslah, K., Kryzanowski, L., and M’Zali, B.: Relationship Between Firm Risk and Individual Dimensions of Social Performance. In: Proceedings of the Annual Conference of the Administrative Science Association of Canada, Montreal, (Canada). 32(1), 105-122 (2011)Daryl, C.R.M. and Shawn, L.K.J.: The Attenuation of Idiosyncratic Risk under Alternative Portfolio Weighting Strategies: Recent Evidence from the UK Equity Market. International Journal of Economics and Finance. 4(11), 1-14 (2012)Dash, G.H. and Kajiji, N.: A Nonlinear Goal Programming Model for Efficient Asset -Liability Management of Property-Liability Insurers. Information Systems and Operational Research. 43(2), 135-156 (2005)Dash, G.H. and Kajiji, N.: Efficient Multiple Objective Neural Network Mapping of State-Wide High School Achievement. Journal of Applied Operational Research. 4(3) (2012)Dash Jr., G.H. and Kajiji, N.: On Mulitobjective Combinatorial Optimization and Dynamic Interim Hedging of Efficient Portfolios.International Transactions in Operational Research. DOI: 10.1111/itor.12067, (2014)Hemmerle, W. J. "An Explicit Solution for Generalized Ridge Regression." Technometrics 17(3): 309-314 (1975).Hoepner, A.G.F.: Portfolio Diversification and Environmental, Social or Governance Criteria: Must Responsible Investments Really Be Poorly Diversified? Social Science Research Network Working Paper Series. Available at http://ssrn.com/abstract=1599334(2010)Hoepner, A.G.F., Rezec, M., and Siegl, K.S.: Does Pension Funds' Fiduciary Duty Prohibit the Integration of Environmental Responsibility Criteria in Investment Processes?: A Realistic Prudent Investment Test. Social Science Research Network Working Paper Series. Available at http://ssrn.com/abstract=1930189 (2013)Hoerl, A. E. and R. W. Kennard. "Ridge Regression: Biased Estimation for Nonorthogonal Problems." Technometrics 12(3): 55-67. (1970)Oikonomou, I., Brooks, C., and Pavelin, S.: The Impact of Corporate Social Performance on Financial Risk and Utility: A Longitudinal Analysis. Financial Management. 41(2), 483-515 (2012)Pennanen, T., Introduction to Convex Optimization in Financial Markets. Mathematical Programming. 134(1), 91-110 (2012)Pukthuanthong, Roll, & Subrahmanyam, A Protocol for Factor Identification, Review of Financial Studies, 2018Thomson, G. H. The Factorial Analysis of Human Ability. London, University of London Press. (1951)Tikhonov, A. and V. Arsenin. Solutions of Ill-Posed Problems. New York, Wiley (1977)Urwin, R., Allocations to Sustainable Investing. Towers Watson Technical Paper No. 1656955. Available at http://ssrn.com/abstract=1656955 (2010)


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