ABEAI
Proceedings
of the Sixteenth Annual Conference
of the
Applied Business and Entrepreneurship Association International
Conference Chair
Lisa Andrus
Program Co-Chairs
Bahram Adrangi
Arjun Chatrath
Pamplin School of Business Administration
The University of Portland
November 2019
Kauai, Hawaii, U.S.A.
Articles published in this Conference Proceedings are accepted based on the double-blind peer-review process.
Table of Contents
Passively Active Investing – A Five Year Test………………..3
Screening Leaders for Success in Turbulent Environments………….11
Use of Alternative Data in Consumer Lending Models: The Case of “Upstart”…………………………………………………. …………….31
Passively Active Investing – A Five Year Test
Jeffry Haber
LaPenta School of Business
Iona College
jhaber@iona.edu
Abstract
This paper took five years of the Council on Foundations-Commonfund study of foundations and using the average asset allocation of large, private foundations replaced the active managers with ETFs with a similar strategy to form a replacement portfolio. Over the five years the active portfolio outperformed the replacement portfolio in four of the five years, and also produced superior returns in Fixed Income, Non-US Equities and Alternative Strategies. The replacement portfolio produced similar returns to the active portfolio in US Equities and Short-term Securities/Cash.
The five year return of the actively managed portfolio was 8.1% versus 4.8% for the replacement portfolio. The replacement portfolio was rebalanced annually to the new average asset allocation, and all ETFs chosen were based on Morningstar ratings and/or designation as an “all-star.” When removing an ETF (due to rebalancing) the last one in the list was removed, not the worst performing. The ETFs chosen also had to have expense ratios of less than 20 basis points, except for alternative strategies, which could have an expense ratio of up to 80 basis points.
Introduction
In investment circles one of the longest running debates is whether active management can outperform (net of fees) passive investing. The literature contains ample evidence of the persistence of this argument. Both the academic and professional literature has countless examples of papers written on the subject. The papers usually take a focus that follow one of a few paths:
A. Whether active management outperforms passive on an annual basis[1]
B. Whether active management outperforms passive on a cumulative return basis[2]
C. Whether a passive index is still attractive when subjected to the diligence that an active manager would be[3]
This paper looks at the debate in a different way. The Council on Foundations and the Commonfund produce an annual report that compiles the investment allocations and returns from a number of foundations. This paper uses the last five years (2013 – 2017) that the report was produced. The 2013 report included just private foundations, whereas starting in 2014 the report included private and community foundations. This paper takes the average asset allocation reported for the large ($500 million and over in endowment) private foundation and uses exchange-traded-funds (ETFs) to populate the asset allocations.
ETFs were chosen over indices because not all indices are investable, and most reported indices are gross of fees. To use the investable indices (even if imputing a reasonable management fee) would require also filling some asset allocations with a different type of security. Using all ETFs provides a homogenous security type for replacing the asset allocations in the average portfolio.
Procedure
A comparative portfolio will be developed comprised of ETFs that are intended to mirror the asset allocation of the average, large ($500 million and above) private foundation (the “replacement portfolio”). Each year the replacement portfolio will be rebalanced to the new asset allocation. The initial $500 million replacement portfolio will use the asset allocation reported in the 2013 Study. The returns will be applied and the ending balance of the asset allocations in the replacement portfolio at the end of 2013 will be used in the asset allocation for 2014. This will continue for each of the years.
The E-Trade website was used to select the ETFs that will replace managers in the portfolio. In order to try and select passive ETFs, rather than active investment management provided in an ETF framework, a maximum expense ratio of 20 basis points was initially chosen. Additionally, since the initial portfolio is set beginning on January 1, 2013, the ETF has to have a history going back at least that far.
For some broad groupings the Study had unspecified allocations, using the term “other.” This happened with alternatives and cash. For cash, the allocation to “other” was simply added to the allocation of cash. For alternatives, the allocation to “other” was reallocated to the specified alternative allocations within the alternative grouping on a pro-rata basis.
As a general rule, no individual allocation could be greater than 5% of the portfolio balance. It became apparent that it would be difficult to find ETFs to replicate alternative managers for 20 basis points. So the cap was raised to 80 basis points for alternative managers (with a priority on using lower cost ETFs where they existed). The ETFs utilized were:
| | | | |Expense |
|Asset Class |ETF |Name |Ratio |
| | | | | |
|US equities | | | |
| |Active |MGK |Vanguard Mega Cap Growth Index |0.07% |
| |Active |VYM |Vanugard High Dividend Yield |0.08% |
| |Active |VOE |Vanguard Mid-Cap Value |0.07% |
| |Active |VOT |Vanugard Mid-Cap Growth |0.07% |
| |Passive |VOO |Vanugard S&P 500 |0.04% |
| | | | | |
|Fixed income | | | |
| |US investment grade (active) |VCIT |Vanguard Intermediate Term Corporate Bond |0.07% |
| |US investment grade (passive) |BIV |Vanugard Intermediate Term Bond |0.07% |
| |US non-investment grade |VMBS |Vanguard Mortgage-Backed Securities |0.07% |
| |Non-US investment grade |BWX |SPDR Int'l Treasury Bond |0.35% |
| |Emerging markets |EMAG |VanEck Emerging Markets Aggregate Bond |0.35% |
| | | | | |
|Non-US equities | | | |
| |Active MSCI, EAFE |IEFA |iShares Core MSCI EAFE |0.08% |
| |Active MSCI, EAFE |IXUS |iShares Core MSCI Total International Stock |0.10% |
| |Active MSCI, EAFE |VEU |Vanguard Int'l Equity Ind FD FTSE All World ex US |0.11% |
| |Passive index MSCI, EAFE |VXUS |Vanguard Total Int'l Stock Index |0.11% |
| |Emerging markets |VWO |Vanguard Emerging Markets FTSE |0.14% |
| |Emerging markets |IEMG |iShares Core MSCI Emerging Markets |0.14% |
| | | | | |
|Alternative investments | | | |
| |Private equity |BDCS |UBS Securities Linked Wells Fargo Business |0.85% |
| |Private equity |BDCL |UBS 2x Leveraged Long Linked Wells |0.85% |
| |Private equity |QAI |IndexIQ Hedge Multi-Strategy Tracker |0.79% |
| |Marketable alternatives |MNA |IndexIQ Merger Arbitrage |0.78% |
| |Marketable alternatives |HDG |ProShares Hedge Replication |0.95% |
| |Marketable alternatives |PBP |Invesco S&P 500 Buy Write |0.49% |
| |Marketable alternatives |MRGR |ProShares Merger |0.75% |
| |Venture capital |IWC |iShares Micro Cap |0.60% |
| |Venture capital |IPO |Renaissance IPO |0.60% |
| |Private real estate |USRT |iShares Core US REIT |0.08% |
| |Energy, natural resources |VAW |Vanguard Materials |0.10% |
| |Commodities and managed futures |GSP |Barclays Traded Notes Linked to GSCI |0.75% |
| |Distressed debt |ANGL |VanEck Fallen Angel High Yield |0.35% |
| | | | | |
|Cash, short-term |VGSH |Vanguard Short Term Treasury |0.07% |
Where multiple ETFs were available to fill allocations the selection criteria was to use the Morningstar rating and/or whether the ETF was classified as an “all-star.” No information about returns was used (prior or future). And once an ETF was selected it remained in the portfolio unless an allocation change required that it be dropped. Where an allocation decreased and there were multiple ETFs covering that mandate, the last ETF in the list was dropped, regardless of the prior reported returns.
Private foundations have a distribution requirement, but since the ETFs are all liquid, any distribution could be done pro-rata across all allocations, thereby not affecting the reported returns. The study uses a minimum assets under management of $500 million to define the “large” foundation class, so it is probable that the average large foundation in the study had an endowment that was greater than $500 million. Given how the comparative portfolio is developed it would not matter how large the assumed beginning portfolio balance was.
Average Asset Allocation
The average asset allocation shown in the 2013-2017 studies were:
| | | |Asset Allocation Percentages |
| | | |2013 |2014 |2015 |
| |Active |19 |18 |18 |17 |18 |
| |Indexed |3 |5 |5 |5 |4 |
| | | | | | |
| |Domestic investment grade - active |3 |5 |5 |4 |5 |
| |Domestic investment grade - passive |1 |2 |3 |1 |2 |
| |Domestic non-investment grade |1 |0 |0 |1 |0 |
| |International bonds |1 |1 |0 |0 |0 |
| |Emerging markets |1 |0 |0 |0 |0 |
| | | | | | |
| |Active MSCI EAFE |11 |8 |10 |9 |14 |
| |Passive/index MSCI/EAFE |2 |4 |2 |5 |2 |
| |Emerging markets |7 |6 |5 |6 |5 |
| | | | | | |
| |Private equity |10 |10 |9 |9 |9 |
| |Marketable alternative strategies |17 |18 |20 |19 |18 |
| |Venture capital |5 |6 |7 |9 |8 |
| |Private equity real estate |5 |5 |5 |4 |4 |
| |Energy and natural resources |5 |5 |3 |4 |4 |
| |Commodities and managed futures |0 |0 |1 |0 |1 |
| |Distressed debt |4 |3 |3 |4 |4 |
| | | | | | |
| | | | | | | |
| | | | | | | |
Using the 2013 asset allocation and a $500 million starting balance the 2013 portfolio was:
| | | | |Beginning | |Ending |
| | | |ETF |Balance |Return |Balance |
|Domestic equities | | | | |
| |Active |MGK |23,750,000 |27.99% |30,397,316 |
| |Active |VYM |23,750,000 |23.50% |29,332,144 |
| |Active |VOE |23,750,000 |32.59% |31,490,361 |
| |Active |VOT |23,750,000 |28.62% |30,546,352 |
| |Indexed |VOO |15,000,000 |27.24% |19,085,678 |
|Subtotal | |110,000,000 |28.05% |140,851,850 |
| | | | | | | |
|Fixed income | | | | |
| |Domestic investment grade - active |VCIT |15,000,000 |-6.05% |14,091,787 |
| |Domestic investment grade - passive |BIV |5,000,000 |-7.31% |4,634,672 |
| |Domestic non-investment grade |VMBS |5,000,000 |-2.43% |4,878,585 |
| |International bonds |BWX |5,000,000 |-5.38% |4,731,236 |
| |Emerging markets |EMAG |5,000,000 |-11.24% |4,437,970 |
|Subtotal | |35,000,000 |-19.04% |28,336,280 |
| | | | | | | |
|International equities | | | | |
| |Active MSCI EAFE |IEFA |18,333,334 |17.63% |21,566,332 |
| |Active MSCI EAFE |IXUS |18,333,333 |10.59% |20,274,634 |
| |Active MSCI EAFE |VEU |18,333,333 |9.38% |20,053,000 |
| |Passive/index MSCI/EAFE |VXUS |10,000,000 |9.31% |10,931,000 |
| |Emerging markets |VWO |17,500,000 |-8.84% |15,953,000 |
| |Emerging markets |IEMG |17,500,000 |-5.86% |16,474,500 |
|Subtotal | |100,000,000 |5.25% |105,252,465 |
| | | | | | | |
|Alternative strategies | | | | |
| |Private equity |BDCS |25,000,000 |6.15% |26,536,643 |
| |Private equity |BDCL |25,000,000 |10.95% |27,736,411 |
| |Marketable alternative strategies |MNA |21,250,000 |6.08% |22,540,967 |
| |Marketable alternative strategies |HDG |21,250,000 |2.63% |21,809,348 |
| |Marketable alternative strategies |PBP |21,250,000 |4.69% |22,247,099 |
| |Marketable alternative strategies |MRGR |21,250,000 |-6.69% |19,828,067 |
| |Venture capital |IWC |25,000,000 |38.06% |34,515,714 |
| |Private real estate |USRT |25,000,000 |-5.99% |23,503,586 |
| |Energy and natural resources |VAW |25,000,000 |19.78% |29,944,896 |
| |Distressed debt |ANGL |20,000,000 |0.15% |20,029,630 |
|Subtotal | |230,000,000 |8.13% |248,692,361 |
| | | | | | | |
|Short-term securities, cash, other | | | | |
| |Short-term securities, cash |VGSH |25,000,000 |0.05% |25,012,325 |
| | | | | | | |
|Total | | |500,000,000 |9.63% |548,145,281 |
Taking the ending balance from 2013 and using it as the beginning balance in 2014, and then applying the average asset allocation to the beginning balance and applying the returns produced a portfolio of:
| | | | |Beginning | |Ending |
| | | |ETF |Balance |Return |Balance |
|Domestic equities | | | | |
| |Active |MGK |24,666,538 |12.50% |27,749,855 |
| |Active |VYM |24,666,538 |10.66% |27,294,777 |
| |Active |VOE |24,666,538 |12.41% |27,726,602 |
| |Active |VOT |24,666,538 |12.91% |27,851,616 |
| |Indexed |VOO |27,407,264 |11.79% |30,638,631 |
|Subtotal | |126,073,415 |12.05% |141,261,481 |
| | | | | | | |
|Fixed income | | | | |
| |Domestic investment grade - active |VCIT |27,407,264 |4.04% |28,513,357 |
| |Domestic investment grade - passive |BIV |10,962,906 |3.57% |11,354,438 |
| |International bonds |BWX |5,481,453 |-3.66% |5,281,052 |
|Subtotal | |43,851,623 |2.96% |45,148,847 |
| | | | | | | |
|International equities | | | | |
| |Active MSCI EAFE |IEFA |21,925,811 |-7.89% |20,195,403 |
| |Active MSCI EAFE |IXUS |21,925,811 |-6.63% |20,473,058 |
| |Passive/index MSCI/EAFE |VXUS |21,925,811 |-6.73% |20,449,425 |
| |Emerging markets |VWO |16,444,358 |-1.21% |16,245,451 |
| |Emerging markets |IEMG |16,444,358 |-3.90% |15,803,028 |
|Subtotal | |98,666,151 |-5.57% |93,166,366 |
| | | | | | | |
|Alternative strategies | | | | |
| |Private equity |BDCS |27,407,264 |-15.20% |23,240,791 |
| |Private equity |BDCL |27,407,264 |-28.21% |19,676,304 |
| |Marketable alternative strategies |MNA |24,666,538 |5.72% |26,077,389 |
| |Marketable alternative strategies |HDG |24,666,538 |1.83% |25,117,936 |
| |Marketable alternative strategies |PBP |24,666,538 |0.14% |24,702,286 |
| |Marketable alternative strategies |MRGR |24,666,538 |-2.08% |24,154,315 |
| |Venture capital |IWC |16,444,358 |2.52% |16,858,260 |
| |Venture capital |IPO |16,444,358 |4.20% |17,135,021 |
| |Private real estate |USRT |27,407,264 |25.12% |34,291,725 |
| |Energy and natural resources |VAW |27,407,264 |4.15% |28,544,914 |
| |Distressed debt |ANGL |16,444,358 |-3.59% |15,853,798 |
|Subtotal | |257,628,282 |-0.77% |255,652,739 |
| | | | | | | |
|Short-term securities, cash, other | | | | |
| |Short-term securities, cash |VGSH |21,925,811 |0.02% |21,929,413 |
| | | | | | | |
|Total | | |548,145,281 |1.64% |557,158,845 |
| | | | | | |
| | | | | | |
Continuing this procedure for 2015:
| | | | |Beginning | |Ending |
| | | |EFT |Balance |Return |Balance |
|Domestic equities | | | | |
| |Active |MGK |25,072,148 |1.68% |25,492,729 |
| |Active |VYM |25,072,148 |-3.36% |24,229,997 |
| |Active |VOE |25,072,148 |-4.49% |23,946,562 |
| |Active |VOT |25,072,148 |-2.31% |24,492,445 |
| |Indexed |VOO |27,857,942 |-1.25% |27,510,619 |
|Subtotal | |128,146,534 |-1.93% |125,672,352 |
| | | | | | | |
|Fixed income | | | | |
| |Domestic investment grade - active |VCIT |27,857,942 |-2.47% |27,169,733 |
| |Domestic investment grade - passive |BIV |16,714,765 |-1.91% |16,394,998 |
|Subtotal | |44,572,708 |-2.26% |43,564,731 |
| | | | | | | |
|International equities | | | | |
| |Active MSCI EAFE |IEFA |27,857,942 |-2.16% |27,256,475 |
| |Active MSCI EAFE |IXUS |27,857,942 |-7.38% |25,803,276 |
| |Passive/index MSCI/EAFE |VXUS |11,143,177 |-6.68% |10,398,608 |
| |Emerging markets |VWO |27,857,942 |-18.12% |22,810,083 |
|Subtotal | |94,717,004 |-8.92% |86,268,442 |
| | | | | | | |
|Alternative strategies | | | | |
| |Private equity |BDCS |25,072,148 |-11.44% |22,203,632 |
| |Private equity |BDCL |25,072,148 |-26.49% |18,429,297 |
| |Marketable alternative strategies |MNA |27,857,942 |-0.04% |27,848,021 |
| |Marketable alternative strategies |HDG |27,857,942 |-0.05% |27,844,711 |
| |Marketable alternative strategies |PBP |27,857,942 |-0.82% |27,628,936 |
| |Marketable alternative strategies |MRGR |27,857,942 |-0.55% |27,705,713 |
| |Venture capital |IWC |19,500,560 |-6.64% |18,205,236 |
| |Venture capital |IPO |19,500,560 |-0.21% |19,459,445 |
| |Private real estate |USRT |27,857,942 |-12.54% |24,363,413 |
| |Energy and natural resources |VAW |16,714,765 |-37.33% |10,474,908 |
| |Commodities and managed futures |GSP |5,571,588 |-37.33% |3,491,714 |
| |Distressed debt |ANGL |16,714,765 |-6.31% |15,660,064 |
|Subtotal | |267,436,246 |-9.02% |243,315,091 |
| | | | | | | |
|Short-term securities, cash, other | | | | |
| |Short-term securities, cash |VGSH |22,286,354 |-0.23% |22,235,121 |
| | | | | | | |
|Total | | |557,158,845 |-6.48% |521,055,737 |
And for 2016:
| | | | |Beginning | |Ending |
| | | |EFT |Balance |Return |Balance |
|Domestic equities | | | | |
| |Active |MGK |22,144,869 |6.80% |23,651,784 |
| |Active |VYM |22,144,869 |15.20% |25,511,885 |
| |Active |VOE |22,144,869 |14.68% |25,395,396 |
| |Active |VOT |22,144,869 |7.64% |23,836,694 |
| |Indexed |VOO |26,052,787 |11.72% |29,106,479 |
|Subtotal | |114,632,262 |11.23% |127,502,239 |
| | | | | | | |
|Fixed income | | | | |
| |Domestic investment grade - active |VCIT |20,842,229 |1.91% |21,241,278 |
| |Domestic investment grade - passive |BIV |5,210,557 |-0.18% |5,201,166 |
| |Domestic non-investment grade |VMBS |5,210,557 |-0.98% |5,159,494 |
|Subtotal | |31,263,344 |1.08% |31,601,937 |
| | | | | | | |
|International equities | | | | |
| |Active MSCI EAFE |IEFA |23,447,508 |0.30% |23,517,671 |
| |Active MSCI EAFE |IXUS |23,447,508 |4.21% |24,434,772 |
| |Passive/index MSCI/EAFE |VXUS |26,052,787 |3.15% |26,872,794 |
| |Emerging markets |VWO |15,631,672 |12.37% |17,565,993 |
| |Emerging markets |IEMG |15,631,672 |10.49% |17,271,434 |
|Subtotal | |104,211,147 |5.23% |109,662,664 |
| | | | | | | |
|Alternative strategies | | | | |
| |Private equity |BDCS |23,447,508 |13.85% |26,694,629 |
| |Private equity |BDCL |23,447,508 |25.48% |29,421,396 |
| |Marketable alternative strategies |MNA |24,750,147 |4.98% |25,982,778 |
| |Marketable alternative strategies |HDG |24,750,147 |2.70% |25,419,230 |
| |Marketable alternative strategies |PBP |24,750,147 |4.62% |25,894,522 |
| |Marketable alternative strategies |MRGR |24,750,147 |-2.38% |24,160,536 |
| |Venture capital |IWC |23,447,508 |20.73% |28,308,496 |
| |Venture capital |IPO |23,447,508 |0.34% |23,527,184 |
| |Private real estate |USRT |20,842,229 |4.63% |21,808,100 |
| |Energy and natural resources |VAW |20,842,229 |21.20% |25,261,402 |
| |Distressed debt |ANGL |20,842,229 |18.44% |24,684,747 |
|Subtotal | |255,317,311 |10.12% |281,163,021 |
| | | | | | | |
|Short-term securities, cash, other | | | | |
| |Short-term securities, cash |VGSH |15,631,672 |0.21% |15,665,101 |
| | | | | | | |
|Total | | |521,055,737 |8.55% |565,594,962 |
And finally for 2017:
| | | | |Beginning | |Ending |
| | | |EFT |Balance |Return |Balance |
|Domestic equities | | | | |
| |Active |MGK |25,451,773 |27.15% |32,361,191 |
| |Active |VYM |25,451,773 |12.33% |28,590,258 |
| |Active |VOE |25,451,773 |13.73% |28,946,527 |
| |Active |VOT |25,451,773 |19.92% |30,523,009 |
| |Indexed |VOO |22,623,798 |18.68% |26,850,162 |
|Subtotal | |124,430,892 |18.36% |147,271,148 |
| | | | | | | |
|Fixed income | | | | |
| |Domestic investment grade - active |VCIT |28,279,748 |2.27% |28,921,793 |
| |Domestic investment grade - passive |BIV |11,311,899 |1.23% |11,451,232 |
|Subtotal | |39,591,647 |1.97% |40,373,025 |
| | | | | | | |
|International equities | | | | |
| |Active MSCI EAFE |IEFA |26,394,432 |22.71% |32,387,820 |
| |Active MSCI EAFE |IXUS |26,394,432 |24.34% |32,820,042 |
| |Active MSCI EAFE |VEU |26,394,432 |23.05% |32,478,149 |
| |Passive/index MSCI/EAFE |VXUS |11,311,899 |23.10% |13,924,789 |
| |Emerging markets |VWO |28,279,748 |26.89% |35,885,109 |
|Subtotal | |118,774,942 |24.18% |147,495,909 |
| | | | | | | |
|Alternative strategies | | | | |
| |Private equity |BDCS |25,451,773 |-8.79% |23,215,238 |
| |Private equity |BDCL |25,451,773 |-16.50% |21,252,877 |
| |Marketable alternative strategies |MNA |25,451,773 |5.65% |26,889,335 |
| |Marketable alternative strategies |HDG |25,451,773 |5.15% |26,762,442 |
| |Marketable alternative strategies |PBP |25,451,773 |0.56% |25,594,962 |
| |Marketable alternative strategies |MRGR |25,451,773 |1.71% |25,887,273 |
| |Venture capital |IWC |22,623,798 |10.19% |24,928,894 |
| |Venture capital |IPO |22,623,798 |35.49% |30,654,052 |
| |Private real estate |USRT |22,623,798 |1.99% |23,074,972 |
| |Energy and natural resources |VAW |22,623,798 |20.90% |27,351,846 |
| |Commodities and managed futures |GSP |5,655,950 |5.79% |5,983,217 |
| |Distressed debt |ANGL |22,623,798 |3.85% |23,494,548 |
|Subtotal | |271,485,582 |5.01% |285,089,656 |
| | | | | | | |
|Short-term securities, cash, other | | | | |
| |Short-term securities, cash |VGSH |11,311,899 |-0.63% |11,241,037 |
| | | | | | | |
|Total | | |565,594,962 |11.65% |631,470,774 |
Analysis
The returns from the Studies versus the returns from the replacement portfolio were:
| | |Replacement |
|Year |Study |Portfolio |
| | | |
|2013 |11.9% |9.6% |
|2014 |7.1% |1.6% |
|2015 |1.1% |-6.5% |
|2016 |6.7% |8.6% |
|2017 |14.3% |11.7% |
| | | |
|5-year return |8.1% |4.8% |
The actively managed portfolios from the various Studies outperformed the replacement portfolio is four of the five years, and the cumulative recalculated 5-year return was also superior.
The returns can also be compared based on the asset class allocation:
|US Equities |
| | |Replacement |
|Year |Study |Portfolio |
| | | |
|2013 |31.8% |28.1% |
|2014 |9.9% |12.2% |
|2015 |-1.3% |-1.9% |
|2016 |10.2% |11.2% |
|2017 |21.5% |18.4% |
|5- year |13.9% |13.1% |
|Fixed Income |
| | |Replacement |
|Year |Study |Portfolio |
| | | |
|2013 |-0.7% |-19.0% |
|2014 |4.2% |3.0% |
|2015 |0.1% |-2.3% |
|2016 |2.9% |1.1% |
|2017 |3.8% |2.0% |
|5- year |2.0% |-3.4% |
|Non-US Equities |
| | |Replacement |
|Year |Study |Portfolio |
| | | |
|2013 |15.9% |5.3% |
|2014 |0.2% |-5.6% |
|2015 |-5.0% |-8.9% |
|2016 |4.6% |5.2% |
|2017 |28.2% |24.2% |
|5- year |8.1% |3.4% |
|Alternative Strategies |
| | |Replacement |
|Year |Study |Portfolio |
| | | |
|2013 |7.3% |8.1% |
|2014 |14.2% |-0.8% |
|2015 |-2.1% |-9.0% |
|2016 |7.1% |10.1% |
|2017 |9.8% |5.0% |
|5- year |7.1% |2.5% |
|Short-term Securities/Cash |
| | |Replacement |
|Year |Study |Portfolio |
| | | |
|2013 |0.1% |0.5% |
|2014 |0.4% |0.2% |
|2015 |0.1% |-0.2% |
|2016 |1.2% |0.2% |
|2017 |0.8% |-0.6% |
|5- year |0.5% |0.0% |
The returns in US Equities and Short-term Securities/Cash was fairly close. The larger differentials were in Fixed Income (5.4%), Non-US Equities (4.7%) and Alternative Strategies (4.6%). It should not be overlooked that the replacement portfolio was an entirely naïve portfolio – all ETFs selected for the portfolio were done without reference to past returns, and once selected the ETF remained in the portfolio unless a future asset allocation required the removal of an ETF in a particular allocation. The ETF to be removed was the last one listed, not the worst performer. It should also be pointed out the ETFs that were selected were based on a higher Morningstar rating and/or being designated an “all-star,” so the extent to which this represents a better performing ETF then the replacement portfolio may not have been so naïve.
The take-away is that from the data in this study, passive investing produces comparable returns to active investing for allocations to US Equities and Short-term Securities/Cash, and active investing outperforms passive for allocations to Fixed Income, Non-US Equities and Alternative Strategies.
References
Council on Foundations-Commonfund, “2013 Study of Investments for Private Foundations.”
Council on Foundations-Commonfund, “2014 Study of Investment of Endowments for Private and Community Foundations.”
Council on Foundations-Commonfund, “2015 Study of Investment of Endowments for Private and Community Foundations.”
Council on Foundations-Commonfund, “2016 Study of Investment of Endowments for Private and Community Foundations.”
Council on Foundations-Commonfund, “2017 Study of Investment of Endowments for Private and Community Foundations.”
Screening Leaders for Success in Turbulent Environments
Phillip L. Hunsaker, School of Business, University of San Diego, 5998 Alcala Park, San Diego, CA 92110, Phone: (619 985-8600, philmail@sandiego.edu
Abstract
The success of task-oriented organizations is highly dependent on the individuals selected to assume responsibility for leadership. Because of the high costs involved in leadership training, and the costs related to future consequences, it is important to ensure that individuals who can profit from training and perform successfully in the criterion environment are selected as candidates. The purpose of the present study was to test the efficacy of a unique personality variable, the General Incongruity Adaptation Level, as a predictor of success in OCS leadership training. The results of the study confirmed that a higher proportion of high GIAL candidates successfully complete the OCS program, which provides support for the basic GIAL hypothesis concerning the relationship between GIAL and environmental turbulence. Exposure to the tremendous turbulence in the OCS program resulted in a significant increase of the mean GIAL score of candidates completing the program. Low GIAL candidates also reacted more strongly to environmental turbulence than high GIAL candidates, emphasizing the importance of controlling for individual differences when investigating the effects of exposure to incongruent environments. Implications for OCS programs of this nature (i.e., producing turbulent-field conditions) include that the GIAL Self- Description Inventory appears to have high potential as a screening device, and that this type of program is instrumental in increasing the adaptation levels of low GIAL candidates.
Introduction
It has been established for some time that the success of task-oriented organizations is highly dependent on the individuals selected to assume responsibility for leadership (Williams, and Leavitt, 1947). Because of the high costs involved in leadership training, and the costs related to future consequences, it is important to ensure that individuals who can profit from training and perform successfully in the criterion environment are selected as candidates. Consequently, the determination of effective selection devices is highly desirable.
This need is especially acute in the Army Officer Candidate School (OCS) where over one-third of the entering class does not graduate, despite an initial screening examination which eliminates approximately 75 percent of all enlisted personnel from OCS consideration (Lippitt and Petersen, 1967), When examining personality characteristics as possible screening criteria, studies have found few significant correlations related to success in OCS leadership training, (Richardson, 1969; Williams and Leavitt, 1947) Although Petersen and Lippitt (1968) found that some OCS candidates have a greater propensity to successfully complete training programs than others, their results were confounded by a variety of design problems making their conclusions only tentative.
Theoretical Framework, Purpose and Hypotheses
The purpose of the present study was to test the efficacy of a unique personality variable as a predictor of success in OCS leadership training. The General Incongruity Adaptation Level (GIAL) has been proposed by Driver and Streufert (1965) as an important predictor of responses to turbulent situations (i.e., constantly changing, highly uncertain and ambiguous). Basically, the GIAL is an average expectation of all types of incongruity (e.g., stress, conflict, failure and ambiguity, etc.). Individuals differ in GIAL depending upon their previous experience with incongruity, i.e., the more, incongruity experienced in one's past, the higher his G IAL. Environments that provide too little or too much incongruity (i.e., very high or low degree of turbulence) will be disliked, and the individual will attempt to maintain the desired level of environmental turbulence within the range of his GIAL via physical or psychological
avoidance, changing the nature of his environment, or the use of other internal defense mechanisms.
Since the OCS leadership training program is designed to expose candidates to
turbulence similar to that encountered in actual combat, they are constantly subjected
to mental, physical, and emotional stress (Petersen and Lippitt, 1968). Within this environment, the following relationships with the GIAL concepts were investigated:
Hypothesis 1: A greater proportion of high GIAL candidates than low GIAL candidates will successfully complete OCS (Hunsaker, 1975).
Hypothesis 2: Experience in OCS will increase candidates' expectations of incongruence.
Hypothesis 3: The OCS experience will elicit greater increases in the incongruity expectations of low G IA L candidates than high GIAL candidates.
Hypothesis 4: High GIAL candidates will be more effective leaders than low GIAL candidates in the OCS environment.
Method
Eighty-five cadets of the Wisconsin Army National Guard and Army Reserve completed the GIAL Self-Description Inventory (Driver and Streufert, 1967), immediately prior to, and immediately after, the two-week OCS training program conducted at the Wisconsin
Military Academy. For comparison, a (nonequivalent) control group consisting of 29
undergraduate students enrolled in the Administrative Organization course at the
University of Wisconsin-Milwaukee completed the GIAL inventory on the same dates. No
significant differences existed between the mean scores of the control group and experimental group on the pre-test administration of the GIAL inventory. Comparisons of before and after scores provided evidence of the effects of differences in environmental turbulence on both groups’ GIALs. Quartile comparisons provided estimates of the variation of these effects between low and high GIAL subjects.
Rosters of candidates withdrawing from the training program, and the reasons for these withdrawals, were obtained from the OCS administrative officers. The proportions of high GIAL candidates (i.e., scores above the mean) and low GIAL candidates dropping out was determined after eliminating withdrawals due to extraneous reasons such as physical injury. Leadership scores, based on observations of the candidates' ability to accomplish assigned missions, were obtained from peer rankings and evaluations by the Tactical Department Officers [Tac officers) who made certain that each candidate was given ample opportunity to exercise leadership skills
in turbulent environments. Leadership ranks were correlated with GIAL scores to determine the relationship of GIAL level to leadership effectiveness.
Results
The proportion of high GIAL candidates dropping out of the OCS program was .09, while the proportion of low GIAL candidates dropping out was .18. The difference between these proportions was significant (Z = l.76, p .04), resulting in acceptance of the first hypothesis that the proportion of high GIAL candidates successfully completing the program is greater than the proportion of low GIAL candidates completing the program.
The mean GIAL score of the OCS candidates was 44.87 before exposure to the two-week training program, and 48.12 after completion. This 3.25-point difference represents a significant increase (t = 4.12, df = 65, p < .001) in the mean GIAL score. The before and after difference between mean GIAL scores for the control group was not significant, and the second hypothesis that subjection to the highly turbulent environment of OCS would result in increases in incongruity expectations was accepted.
Quartile comparisons revealed significant differences in the changes of incongruity expectations for low and high candidates in OCS, but not in the control group. Although OCS candidates in the first (top) quartile and second quartile manifest no significant changes, the mean GIAL scores for candidates in the third quartile increased significantly (t = 2.98, df = 15, p < .01) as did those for candidates in the fourth quartile (t = 6.59, df = 16, p < .001). Because of these differences the third hypothesis that the incongruity expectations of low G IAL candidates would increase by a greater degree than those of high GIAL candidates was accepted.
Pearson product-moment correlations between GIAL scores and leadership rankings by peers did not yield significant results. Correlations between GIAL scores and Tac officers’ leadership rankings also failed to be significant. Consequently, the hypotheses suggesting a positive relationship between GIAL scores and leadership in the OCS environment were rejected. A significant, negative correlation was found between the leadership rankings of peers and Tac officers (r = .59, Z = 4.72, p < .0001). Since the numerical values in ranking schemes for peers and Tac officers were reversed, the significance of this correlation indicates that both types of judges agreed on candidates' relative leadership capabilities.
Discussion and Conclusions
The positive results confirming the first hypothesis, that a higher proportion" of high GIAL candidates than low GIAL candidates would successfully complete the OCS program, provides support for the basic GIAL hypothesis concerning the relationship between GIAL and environmental turbulence. The proposition is that whenever the environment provides either too much or too little turbulence relative to the individual's GIAL, the negative effect associated with this incongruence will motivate the individual to change or avoid it. Since an OCS candidate can do little to modify the nature of his environment, an active response alternative for overloaded
individuals is to withdraw from the program. Consequently, low GIAL candidates behave in accordance with traditional dissonance theory and choose to sacrifice the future rewards of becoming an officer in order to avoid the surplus of immediate dissonance relative to their expectations. High GIAL candidates, on the other hand, find less discrepancy between this turbulent environment and their expectations. Consequently, they have little difficulty enduring the dissonant occurrences and successfully completing the program.
Support of the second hypothesis suggests an addition to the GIAL model. Exposure to the tremendous turbulence in the OCS program resulted in a significant increase of the mean GIAL score of candidates completing the program. Thus, when subjected to a situation where they can neither significantly alter the nature of dissonant inputs, nor escape from the situation without considerable cost, it appears that the successful candidates experience at least temporary increases in their incongruity expectations, allowing them to endure the situation, Research is currently in process to determine whether these shifts in expectations arc temporary or
permanent.
The results supporting the third hypothesis that low CIAL candidates react more strongly to environmental turbulence than high GIAL candidates, emphasizes the importance of controlling for individual differences when investigating the effects of exposure to incongruent environments. These results also substantiate the GIAL hypothesis that low GIAL individuals will be disturbed by much less turbulence than high GIAL individuals, who may actually seek more incongruence at the same level of environmental turbulence that causes low GIAL individuals to avoid it.
In terms of the resulting increases in adaptation levels, the largest increase occurred for candidates in the fourth quartile (i.e., lowest CIAL scores), and the second largest for candidates in the third quartile. No significant changes occurred for candidates in the top two quartiles (a slight decrease was noted for candidates in the first quartile and a slight increase was noted for candidates in the second quartile). These results suggest that the low GIAL candidates were encountering a degree of environmental incongruity exceeding their adaptation levels, and since withdrawal from the OCS program may have been even more costly (in terms of dissonance experienced) than enduring it, the outcome was an increase in their incongruity expectations.
High GIAL candidates, on the other hand, may have found the dissonance of OCS training to be congruent with their expectations and, therefore, had no need to adapt. Had the level of environmental turbulence been even greater, so that the resulting incongruity exceeded the expectations of both high and low GIAL candidates, the result could have been an increase in the expectations of candidates in all quartiles.
The lack of significant results regarding the fourth hypothesis indicates that differences in GIAL's are not enough by themselves to predict leadership success rankings in OCS environments. Since a significant correlation was found between the leadership rankings of peers and experienced officers, it seems that this is another case, similar to that reported by Williams and Leavitt (1947), where the cadet's fellow candidates are better predictors of leadership effectiveness than personality tests. Further research to determine the criteria utilized by these raters, controlling for their own personality make-up, is needed to suggest other personality variables related to leadership success in OCS.
Implications for OCS programs of this nature (i.e., producing turbulent-field conditions) include the following: (1) the GIAL Self- Description Inventory appears to have high potential as a screening device. (2) this type of program is instrumental in increasing the adaptation levels of low GIAL candidates (at least temporarily), (3) although common leadership rankings are produced by peer groups and superior officers, more research is needed to determine the personality and behavioral characteristics contributing to leadership effectiveness.
References
Driver. M. and S. Streufert (1965), The General Incongruity Adaption Level (GIAL) Hypothesis: An Analysis and Integration of Cognitive Approaches to Motivation (W. Lafayette, lnd: Purdue
University Institute for Research in the Behavioral. Economic and Management Sciences.
Driver. M. and S. Streufert (1967), Purdue-Rutgers Prior Experience Inventory II (GIAL Self-Description Test, Purdue University.
Hunsaker, P.L. (1975), "Incongruity Adaptation Capability and Risk Performance in Turbulent Decision-Making Environments," Organizational Behavior and Human Performance, Vol. 14, No. 2, pp. 173-185.
Hunsaker, P.L. (1972). "The Effects of Environmental Incongruity and General Incongruity Adaptation Level on Risk Perception and Risk Preference," Proceedings of the 1972 Annual Convention of the American Psychological Association.
Hunsaker, P.L., Mudgett, W.C. and Wynne, B.E. (1975), "Assessing and Developing Administrators for Turbulent Environments," Administration and Society, Vol. 17, No. 3, pp. 312-327.
Hunsaker, P.L., Wynne, B.E. and Mudgett, W.C. (1974), "A Preliminary Model for Developing Managerial Capabilities for Coping with Environmental Turbulence," Proceedings, Midwest Division of the Academy of Management, pp. 217-234.
Lippitt, G. and P. Petersen (1967), "Development of a Behavioral Style in Leadership Training." Training and Development Journal, pp. 9-17.
Petersen, P. and G. Lippitt (1968), "Comparison of Behavioral Styles Between Entering and Graduating Students in Officer Candidate School." Journal of Applied Psychology, Vol. 52, No.1, pp. 66-70.
Richardson, J. (1969), "The Relationship of Some Measures of Candidate Personality and Selection by OTU Board," Australian Military Forces Research Report, Vol. 69, pp. 1- 26.
Tannenbaum, R. I., Weschler, R. I and F. Massarik, Leadership and Organization: A Behavioral Science Approach (New York: McGraw-Hill, 1961).
Williams, S., and H. Leavitt (1947), "Group Opinion as a Predictor of Military Leadership," Journal of Consulting Psychology, Vol. II, pp. 283-291.
Use of Alternative Data in Consumer Lending Models: The Case of “Upstart”
Naveen Gudigantala, Robert B. Pamplin School of Business Administration, The University of Portland, 5000 N. Willamette Blvd., Portland, OR 97203, Phone: (503) 943-8457
gudigant@up.edu
Abstract
This work discusses the case of a fin-tech company called Upstart, which specializes in using AI/ML based platform to provide credit to traditionally underserved populations. Upstart’s AI platform uses alternative data in addition to the traditional FICO scores in its algorithms. This alternative data includes borrowers’ educational data and occupational data. Upstart’s data shows that a majority of traditionally underserved populations was able to obtain more credit and at better terms using their credit scoring system.
Introduction
Issues surrounding the fairness of algorithms are attracting much attention from the researchers (Saxena et al., 2019). The goal of this case study is to discuss the opportunities and challenges in using alternative data for credit scoring modeling. The case study uses a fin-tech company “Upstart Network, Inc.” (called “Upstart” from here on) and an analysis of Upstart’s AI practices in lending to address the questions of algorithmic fairness in consumer lending. In specific, this work will look at how do different approaches to the development of machine learning models can either help or hinder fairness in consumer lending.
This case study is intended for researchers in AI and financial services, students learning analytics/AI, and for practitioners doing AI/Data science work. The issues discussed in this case will help students better evaluate the implications of models they learn to create as part of analytics curriculum; for the researchers to continue investigating the problems raised in this study; and for data science practitioners to reflect on issues of algorithmic fairness.
Consumer Lending and Problems Addressed by Upstart
Upstart is an online lending platform, launched by ex-Google employees in 2014, with an aim to provide credit to people with limited credit or work history. Consumers in need of credit approach Upstart, a website embedded with Artificial Intelligence (AI)/ Machine Learning (ML) technologies, to inquire and create a loan application. Upstart automated the underwriting technology for credit scoring, meaning, given the information provided by the consumer, a model will decide whether to give or reject loan and loan terms. The use of a model – as opposed to a human – for decision-making refers to AI and the model itself may be developed using one or many machine learning (ML) algorithms. The Upstart’s website is cloud-based, meaning the consumer data and underwriting technologies operate on the Internet by providing online services to the consumers.
Upstart learned from an early study that although 83% of Americans have never actually defaulted on a loan, only 45% have access to bank-quality credit. Upstart notes this “45% vs. 83% gap” as unfair and sets out to create an AI platform that can make ingenious use of alternative data in expanding credit to underserved groups (Girouard, 2019). Girouard (2019), the CEO of Upstart, suggests that FICO score - a measure of credit risk available through credit reporting agencies such as Equifax, Experian, and Transunion – is limited in its predictive ability of consumer risk because it focuses exclusively on a consumer’s past credit history. Therefore, traditional lenders who rely almost exclusively on FICO score and traditional modeling techniques ignore some important predictive information about potential borrowers. This is one of the reasons contributing to the “45% vs. 83% gap” (Girourad, 2019).
To overcome this problem, Upstart’s underwriting model, in addition to using FICO scores, uses alternative data for borrowers, such as educational attainment and work history as predictors. Using the model with alternative data, Upstart claimed that 27% more loans are approved which also lowered interest rates by an average of 3.57% (Girourad, 2019). Although Upstart used education and work history as alternative data, other pieces of data such as payment history concerning rent, electricity, gas and telecom bills, repayments to payday lenders can be considered as alternative data. The major U.S. credit reporting agencies are initiating attempts to include alternative data in their credit scoring system, but they face several hurdles in capturing this information fully (Malik, 2019). Therefore, opportunities emerge for companies such as Upstart to ascertain creditworthy individuals with near prime FICO scores and create a business model around such customers.
In credit risk modeling, an important and universally used predictor is FICO score/credit score. The FICO scores range from 300 to 850. Many lenders consider borrowers with FICO scores of at least 720 to be “prime”. The next classification, “near-prime” generally falls in an interval of mid-to-high 600s to the low 700s. The third classification of “sub-prime” includes borrowers whose scores fall below 620 (Andriotis, 2016). The FICO score distribution of U.S. population as of April 2018 is shown in Table 1. As per this data, the individuals with credit scores between 300-600 don’t qualify for bank-quality credit (Dornhelm, 2018). Individuals with credit scores above 700 (58.2% of population) usually qualify for best possible terms. The “near prime” from this table can be loosely categorized as the percentage of people between scores 600 and 700, and they stand at 22.6% of U.S. population. This segment of population can be considered as “traditionally underserved” in terms of credit.
|FICO Score |Percentage of U.S. population |
|300-600 |19.1% |
|600-649 |9.6% |
|650-699 |13% |
|700-749 |16.2% |
|750 and above |42% |
Table 1. Distribution of FICO scores across U.S. population as of April 2018 (Dornhelm, 2018)
What is the problem with FICO score as an important predictor? The FICO credit scores are unduly impacted by the length of credit history of an individual. Even the other components that go into the calculation of FICO scores such as payment history, new credit, credit mix, and credit utilization also favor individuals with longer credit history. In conclusion, FICO scores inherently create bias against younger borrowers or recent immigrants with fewer accounts (called “thin files”), lower credit limits, and fewer years of making payments. These types of borrowers have substantially lower credit scores compared to older borrowers. Another interesting point noted by Upstart is that a majority of traditional lenders use the length and breadth of borrowers’ credit files as independent criteria in making determination of loans (Upstart, 2017). Please see the data in table 2 and a scatterplot in Figure 1 showing the positive linear relationship between Age and FICO score (Dornhelm, 2018).
|Age Range of U.S. Individual |Average FICO Score |
|18-29 |659 |
|30-39 |677 |
|40-49 |690 |
|50-59 |713 |
|60+ |747 |
Table 2. Data of Age and Credit Score as of April 2018 (Reference: Dornhelm, 2018)
[pic]
Figure 1. Relationship between Age and Credit Score as of April 2018 (Reference: Dornhelm, 2018)
So what happens if a model predominantly uses FICO score to assess creditworthiness of an individual? Upstart conducted a study in September 2016 with a random sample of their borrowers during the years 2014-16. It used two models to do the comparison: a limited model with no alternative data (used FICO score and length of credit history) and an Upstart’s model with alternative data. The results are presented in Table 3 and show that the use of alternative data in credit modeling results in better credit terms and also improves the predictive accuracy of the model.
|Limited Model (No alternative data; use of |Upstart Model (traditional variables plus |
|traditional variables) |alternative data) |
|Model recommended average APR of 23.5% |Model recommended average APR of 16.7% |
|Model has lower R^2 for predicting default rate |Model has higher R^2 for predicting default rate |
Table 3. Results from comparison of credit models by Upstart (Upstart, 2017)
Benefits of Upstart’s AI models for underwriting
Dave Girouard (2019), CEO of Upstart, presented the following benefits of using AI system to the House Committee taskforce:
1. Upstart’s models approved 27% more consumers and lowered interest by average of 3.57% compared to traditional models.
2. For a near-prime consumers (620-660 FICO), Upstart’s models approved 95% more consumers and reduced interest rates by an average of 5.42% compared to traditional models.
3. Upstart’s model provided higher approval rates and lower interest rates for every traditionally underserved demographic.
Upstart reported to have facilitated 80,000 loans totaling over $1 billion. The loans typically fall in the range of $1,000 to $50,000 with repayment periods between 3 and 5 years. The average age of the borrower is 28 years with the APR rates ranging between 4% and 25.9% (Upstart, 2017).
An interesting aspect of these statistics is that the average age of borrower for Upstart’s services is 28 years. What do Upstart’s consumers do with this money? A majority of Upstart’s borrowers paydown higher interest credit card balances, use them to consolidate payday loans, reduce student loans, or to pay tuition for graduate education (Upstart, 2017).
Limitations of Upstart’s AI models for underwriting
Any AI model that is developed within ‘certain constraints’ will not work well outside of that specific environment. In this instance, Upstart appears to focus on relatively young borrowers with limited credit history but good educational background and work history. Looking at it from another perspective, Upstart’s model focuses more on future financial potential of their borrowers – mostly appearing to be students and recent graduates – than the traditional models which look at the past credit history of borrowers. Therefore, Upstart (2017) acknowledges that their underwriting model may not be equally predictive across all demographic groups, meaning that the benefits similar to those offered by Upstart to their “thin file” consumers may not be as attractive to older borrowers.
Summary and Conclusions
In conclusion, the use of alternative data offers much promise in our efforts offer bank quality credit to millions of underserved Americans. Such promise is possible because of Big Data and the use of AI and ML technologies. However, there is also a great danger that lurks in the corner if companies don’t exercise due diligence in employing this new generation of tools and technologies. This work attempts to show the efforts of an innovative company, Upstart, in making strides in the use of AI/ML to expand credit, and also given the challenges concerning this nascent phenomenon, calls for further research.
References
Andriotis, Annamaria (2016). Banks Have a New Phrase for Risky Customers: ‘Near Prime’, Wall Street Journal Blogs. Retrieved from (Current August 15, 2019).
CFPB consumer laws (2013). Equal Credit Opportunity Act (ECOA). Retrieved from current August 15, 2019.
Dornhelm, Ethan (2018). Average U.S. FICO Score Hits New High. FICO/Blog. Retrieved from (Current August 15, 2019).
Girouard (2019). Examining the Use of Alternative Data in Underwriting and Credit Scoring to Expand Credit Access. Testimony of Dave Girouard, CEO and Co-Founder, upstart Nework, Inc. Before the Taskforce on Fintech, United States House Committee on Financial Services, Retrieved from: (Current December 19, 2019).
Hayashi, Yuka (2019). Where You Went to College May Matter on Your Loan Application. The Wall Street Journal. Retrieved from: (current August 15, 2019).
Malik, Sanjay (2019). Alternative Data: The Great Equalizer To Lending Inequalities? Forbes. Retrieved from: (Current August 15, 2019).
Saxena, N. A., Huang, K., DeFilippis, E., Radanovic, G., Parkes, D. C., and Liu, Y. (2019). How Do Fairness Definitions Fare?: Examining Public Attitudes Towards Algorithmic Definitions of Fairness. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 99-106.
Upstart (2017). Request for No-action letter, Consumer Financial Protection Bureau. Retrieved from: (Current December 19, 2019).
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[1] Haber, Jeffry, “The Failure of Equity Managers to Beat Their Benchmark: Lord, Is It I (Or Is It the Benchmark)?” Journal of International Business Management & Research, Volume 4, Issue 11, pp 122-129
[2] Haber, Jeffry, “Resolving the Dichotomy between Investors and Managers About Whether Active Management Beats the Index,” Journal of Business and Economics, Volume 4, Number 10, October 2013, pp 1033-1037
[3] Haber, Jeffry, “Can Active Management Outperform a Benchmark: Let’s Stop the Madness – The Benchmark is an Unattractive Investment,” American Journal of Management, Volume 15(1) 2015, pp 101-110
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