Models of evaluation of the effectiveness of hedge funds

Applying correlation condition in addition to Alpha and R-squared constraints, we say that correlations should be below average (-1.65%) and Alpha should be above average of strategy-model for corresponding fund group.

The results of this condition follow (Table 16).

Table 16

Outcomes after application of correlation criteria for efficiency

	MKTRF	MKTRF SMB HML	MKTRF SMB HML BAB	MKTRF SMB HML MOM	MKTRF SMB HML RMW CMA	MKTRF SMB HML RMW CMA QMJ
CTA	1	0	0	0	0	0
ED	0	0	0	0	0	0
EH	0	0	0	0	0	0
FID	0	0	0	0	0	0
FIRV	1	1	0	1	1	1
M	1	0	0	0	0	0
MS	0	0	0	0	0	0
NA	0	0	0	0	0	0

With the correlation constraint applied, it can be seen that CTA strategy have been eliminated from efficient for most of the models. FI Relative Value though remains efficient nevertheless, as well as Macro strategy according to CAPM model. The constraint significance is questionable since due to its negative value (-1.65%), so that it eliminates even hedge funds with close to zero positive correlations. It is not our case though since the funds eliminated were CTA, which had correlation value of almost 1 previously.

4.3 Analysis of regional focuses and asset pricing models

Then we perform similar analysis for different regional focuses by various models. Below are the Alphas (Table 17) and R-squares (Table 18).

Table 17

Average annual Alpha for different regional focuses and models

Alpha	E	G	P	NA	US	Average
MKTRF	-0.71%	0.16%	3.49%	1.41%	2.71%	1.41%
MKTRF SMB HML	-1.17%	0.19%	3.18%	1.19%	2.79%	1.24%
MKTRF SMB HML BAB	-1.28%	-0.31%	3.70%	0.76%	2.58%	1.09%
MKTRF SMB HML MOM	-0.78%	-0.08%	2.62%	0.88%	2.71%	1.07%
MKTRF SMB HML RMW CMA	0.47%	1.25%	3.70%	0.82%	3.14%	1.87%
MKTRF SMB HML RMW CMA QMJ	0.90%	1.28%	4.39%	3.37%	3.46%	2.68%
Average	-0.43%	0.42%	3.51%	1.41%	2.90%	1.56%

Table 18

R-squared	E	G	P	NA	US	Aver R-sq	Alpha 1-yr
MKTRF	12.66%	5.36%	14.99%	17.35%	8.26%	11.72%	1.41%
MKTRF SMB HML	14.46%	6.01%	17.07%	18.78%	9.57%	13.18%	1.24%
MKTRF SMB HML BAB	14.66%	6.10%	17.14%	19.53%	13.09%	14.10%	1.09%
MKTRF SMB HML MOM	14.90%	6.30%	15.03%	19.00%	10.01%	13.05%	1.07%
MKTRF SMB HML RMW CMA	15.47%	6.44%	17.70%	20.02%	10.46%	14.02%	1.87%
MKTRF SMB HML RMW CMA QMJ	15.89%	6.45%	18.02%	22.44%	10.73%	14.71%	2.68%
Aver R-sq	14.67%	6.11%	16.66%	19.52%	10.35%	13.46%	x
Alpha 1-yr	-0.43%	0.42%	3.51%	1.41%	2.90%	x	1.56%



Exposure of hedge funds' to asset pricing models across regions

It is observable that even though Alpha and R-squared value differ slightly across models, there is significant difference between them for regions. That said, it can be seen that in terms of R-squared North American and Pacific finds show greatest exposure of 16-19%, with the latter being among the most profitable locations for investing. Globally oriented funds tend to have lowest exposure, and at the same time smallest average Alpha.

We then apply the same constraints as before regarding Alpha and R-squared values. Efficient funds' Alphas should be above 1.56% and R-squares below 13.46% (Table 19).

Table 19

Outcomes after application of efficiency criteria for Alpha and R-squared

	E	G	P	NA	US
MKTRF	0	0	0	0	1
MKTRF SMB HML	0	0	0	0	1
MKTRF SMB HML BAB	0	0	0	0	1
MKTRF SMB HML MOM	0	0	0	0	1
MKTRF SMB HML RMW CMA	0	0	0	0	1
MKTRF SMB HML RMW CMA QMJ	0	0	0	0	1

There is no argument that hedge funds investing within United States are efficient according to the table above. American hedge fund industry proved not only to be among the most developed, but is efficient after all as it turned out.

We also try to find the differences in Alpha - R-squared relationship for different regions (Table 20).



Table 20

Correlation of funds' Alphas and R-squared for different regions and models

Correlation	E	G	P	NA	US	Average correlation
MKTRF	38.94%	-45.31%	-25.98%	-7.25%	-69.08%	-9.08%
MKTRF SMB HML	35.66%	-50.84%	-30.14%	-1.82%	-70.50%	-10.54%
MKTRF SMB HML BAB	36.17%	-48.85%	-29.89%	-0.27%	-68.90%	-11.14%
MKTRF SMB HML MOM	41.99%	-31.89%	-31.37%	13.17%	-64.91%	-8.27%
MKTRF SMB HML RMW CMA	23.24%	-24.25%	-36.18%	17.13%	-61.91%	-13.60%
MKTRF SMB HML RMW CMA QMJ	45.54%	-13.94%	-24.00%	25.33%	-55.38%	5.87%
Average correlation	37.76%	-32.56%	-26.34%	9.58%	-64.16%	-11.47%

For different regionally-focused funds we can say that on average, funds aimed at Europe tend to be more positively-correlated with the models under analysis, particularly to Momentum and Quality factors, which can be a sign for more conservatism among European investors and asset-managers. The U.S.-oriented funds, on the other hand, are showing significant negative correlation, which combined with a positive Alpha shows that these funds managed to capture inefficiencies in the stated models and profited on them. Funds focused on other parts of North America proved to be the most creative - the highest correlation is 25.33% and with positive Alpha we can say that North America-focused funds (most of which are actually offshore-based) tend to imply not typical investment strategies. However, since its Alpha is lower than our outlined benchmark (1.78%), we cannot name these funds to be entirely efficient. Considering that the average Alpha - R-squared correlation tends to be around -11%, we can also confirm our Hypothesis 2.1 about negative relationship between hedge funds' Alpha and exposure. Application of correlation constraint does not change the outcome - U.S. hedge funds remain efficient regardless of the model applied.



4.4 Analysis of time-related dependencies

Finally, of a much interest to us is how past exposure relates to future performance. We try to expand the findings of Titman, Tiu (2008) on a pool of different strategies and geographical focuses of hedge funds. We separated our sample on 132 months into two 66-months subsamples (breaking point is June 2010).

Table 21

Correlation of funds' future Alphas and past R-squared for two time-samples

	E	G	P	NA	US	Alpha <66	Alpha >66	Alpha-R-sq correlation
CTA	-81.43%	-16.34%	-59.92%	-	-	10.40%	-0.41%	65.80%
ED	-32.91%	75.71%	-36.38%	98.60%	-	-1.53%	-1.83%	94.58%
EH	35.74%	57.63%	39.34%	93.53%	57.15%	4.26%	-3.28%	41.64%
FID	38.24%	4.60%	60.66%	-	25.36%	7.21%	2.55%	12.63%
FIRV	-	3.83%	5.52%	-	45.86%	1.15%	4.25%	-35.92%
M	15.13%	-26.18%	21.76%	-	-1.79%	11.85%	-10.66%	51.56%
MS	53.55%	0.41%	-17.15%	61.42%	-32.09%	2.05%	5.10%	-63.36%
NA	95.12%	60.89%	68.87%	-	7.65%	-1.85%	-16.70%	-15.01%
Alpha <66	6.18%	2.41%	4.48%	3.25%	2.99%	x	x	x
Alpha >66	-8.42%	-3.16%	2.80%	-4.58%	-2.66%	x	xx	x
Alpha-R-sq correlation	18.06%	28.24%	11.62%	1.21%	-1.16%	x	x	12.37%

In Table 21 the correlations of first sample R-squares and second sample Alphas are presented. We can see that for some hedge funds, such as Event driven, Equity hedge North American funds, N.A. European funds or CTA European funds the correlations can be quite significant. Event driven funds tend to be the most correlated in terms of past exposure and future performance, which, given its low Alpha on both subsamples and high exposure, tells us poor performance will most likely persist in the future of these funds. For Macro and CTA funds, which we named efficient above, the correlation also tends to be quite significant, although lower especially for Macro funds. It can warn us since the increase of exposure for these funds has to lead to the Alpha increase, but given overall correlations to be low enough, Alphas can become lower in the future. The conclusion though should be addressed with caution though, since not much performance can be predicted for these funds via correlations and exposure. Multi-strategy and FI Relative Value funds show negative past R-squared - future Alpha correlations, and increase in Alpha with time. For these funds there is still room for exposures to drop lower and therefore for Alphas to increase even more.

There is also enough evidence to state that the industry of hedge funds experiences tough times now as compared to the years 2005-2010. The Alphas have dropped from around 4% annually to -3% for the second sample. Alphas have dropped across all regions of hedge funds interest, leaving only Asian and Pacific funds to add value nowadays.

From the two-sample analysis we can distinguish funds that provide positive return across time and eliminate funds that we previously named as efficient. This can be applied to CTA funds, which turned out to have positive Alpha solely due to the success of the strategy of earlier years. Since Alpha for CTA strategy dropped from 10.4% to -0.4% it is obvious there is no reason to name this funds group as efficient. Same can be applied to the Macro strategy, which we would call efficient 5 or 10 years ago, but not anymore. Fixed Income Relative Value funds and Multi-strategy funds though have shown an increase in Alphas ever since. FIRV funds have justified their investment strategy statement, proven able to find and exploit market inefficiencies and arbitrage opportunities, for which there were enough room during unstable recent years on financial markets. Multi-strategy funds bear their name for a reason as well, given close to zero Alpha - R-squared correlations. Multi-strategy Global funds also turned out to be efficient previously. Combined with positive growing Alpha funds of these type can be distinguished by us to be the most efficient. We can also say that even though some fund categories can experience low past exposure - high future Alpha relationship, the average correlation of about 12.37% makes us reject our Hypothesis 2.2 about negative relationship between these two factors.



5. Contribution, limitations and opportunities for further research

This paper adds value for the hedge funds research by producing the comparative analysis of hedge funds' profitability and exposure to the commonly used models for calculating required returns. Having divided our initial sample into groups of funds varied by investment strategy and regional focus, we applied asset pricing models to them and found out that there is no reason to invest in some categories of hedge funds given that the excess returns they provide from typical investment strategies rarely cover high management fees. Moreover, our findings allowed to distinguish hedge funds that have persistently high Alphas and low exposure to standard asset pricing models. Thereafter we adjusted the results in order to see if a fund group derives its profits from a particular return factor, which influenced the outcomes concerning truly efficient hedge funds. We created a framework, using which an investor can understand whether the commission a fund takes is justified or not. The research resulted in statement that nowadays only a few strategies can outperform a benchmark more demanding than S&P 500.

There are some problems that we face during the research. First, it is obvious that hedge fund databases lack information that would be vital for the research of any other investment vehicle. These problems were mentioned as drawbacks of any hedge fund research to date. Obtaining additional information would make the research much more representative and complete. Second, it is possible that we will not be able to find return factors for some hedge funds' strategies due to their specifics. The more complex analysis of hedge funds' returns decomposition is required in order to derive actual drivers of funds' returns.

There is still plenty of room for continuing this research. The opportunities vary from the usage of more and more complex asset pricing models and factors to dividing the sample into more specified strategy and precise location focus areas. One of the most promising frameworks of research would be a year-to-year comparison of hedge funds' performance and exposure, which can lead to interesting results given availability of monthly market and return factors data. Since the industry of hedge funds is transforming into high frequency algorithmic trading, it may be also necessary to include factors associated with this new field.



Conclusion

In this paper we performed comparative analysis of hedge funds efficiency and exposure. We analyzed 6 different asset pricing model specifications for a pool of 3668 hedge funds, divided into 8 strategies and 5 geographical focuses. We addressed efficiency of hedge funds in terms of their ability to generate returns above a benchmark, which we defined as Alpha, and their exposure to the asset pricing models, which we identified as regression R-squared. We assessed Alpha - R-squared relationship between different investment strategies, regional focuses, model specifications and timeframes.

We found out that absolute returns are on average 2% higher than the Alphas derived from the models. It is therefore reasonable to question efficiency of such funds, considering that investors usually pay 2% commission on their assets. Given that there is a necessity for a framework to distinguish efficient funds, we stated than a group of funds may be called efficient if its Alpha is above average, while its R-squared of asset pricing regression models should be below average. We managed to derive hedge fund strategies that suit this criteria, as well as regions where hedge funds tend to be creative as well as profitable. The employment of multiple asset pricing models allowed us to track the exposure to some of factors used in the models. multifactor research pricing asset

However, after dividing out sample into two timeframes, it turned out that most of hedge funds' positive Alphas were created 5-10 years ago. Not only it was possible to distinguish strategies that do not bring positive Alpha anymore, we saw evidence that the whole hedge funds industry is experiencing a downturn, supporting our research in pursuit of efficient hedge funds.



References

1. Agarwal, V. and Naik N. (1999), On Taking the `Alternative' Route: Risks, Rewards and Performance Persistence of Hedge Funds, London Business School, London

2. Agarwal V. and Naik N. (2000), Generalised Style Analysis of Hedge Funds, Journal of Asset Management, London

3. Agarwal, V. and Naik N. (2003), Risks and Portfolio Decisions Involving Hedge Funds, Georgia State University, Atlanta

4. Amman M., Huber O. and Schmid M. (2010), Hedge Fund Characteristics and Performance Persistence, University of St. Gallen, St.Gallen

5. Aragon G. (2004), Share Restrictions and Asset Pricing: Evidence from the Hedge Fund Industry, Carroll School of Management, Massachusetts

6. Asness C., Frazzini A. and Pedersen L. (2013), Quality Minus Junk, New York University, New York

7. Asness C., Krail R. and Liew J. (2001), Do Hedge Funds Hedge, AQR Capital Management, New York

8. Baquero G., Ter Horst J. and Verbeek M. (2002), Survival, Look-Ahead Bias and the Performance of Hedge Funds, European School of Management and Technology, Berlin

9. Boyson N. (2003), Do hedge fund exhibit performance persistence? A new approach, Purdue University, West Lafayette

10. Boyson N. (2003), Why do Experienced Hedge Fund Managers have Lower Returns, Purdue University, West Lafayette

11. Brown S., Goetzmann W. and Liang B. (2004), Fees On Fees In Fund Of Funds, NYU Stern School of Business, New York

12. Carhart M. (1997), On Persistence in Mutual Fund Performance, University of Southern California, Los Angeles

13. Fama E. and French K. (1992), The Cross-Section of Expected Stock Returns, University of Chicago Booth School of Business, Chicago

14. Fama E. and French K. (1993), Common risk factors in the returns on stocks and bonds, University of Chicago Booth School of Business, Chicago

15. Fama E. and French K. (2014), A Five-Factor Asset Pricing Model, University of Chicago Booth School of Business, Chicago

16. Frazzini A. and Pedersen L. (2013), Betting Against Beta, New York University, New York

17. Fung W. and Hsieh D. (2004), Hedge Fund Benchmarks: A Risk Based Approach, London Business School, London

18. Getmansky M., Lee P. and Lo A. (2015), Hedge Funds: A Dynamic Industry In Transition, Massachusetts Institute of Technology, Cambridge

19. Jensen M. (1968), The Performance of Mutual Funds in the Period 1945-1964, Harvard University, Cambridge

20. Kolokolova O. (2011), Life and Death of Hedge Funds: Introduction and Overview, University of Manchester, Manchester

21. Liang B. (1998), On the Performance of Hedge Funds, University of Massachusetts, Amherst

22. Lomax N., Wauchope J. and Fletcher J. (2013), The Cayman Islands: A Guide for Hedge Fund Managers, Mourant Ozannes, Cayman Islands

23. Marquering W. and Verbeek M. (2004), The Economic Value of Predicting Stock Index Returns and Volatility, Rotterdam School of Management, Rotterdam

24. Motaze S. (2009), Persistence analysis of hedge fund returns, HEC Montreal, Montreal

25. Pesaran H. and Timmermann A. (2000), A Recursive Modelling Approach to Predicting UK Stock Returns, University of Southern California, Los Angeles

26. Sharpe W. (1966), Mutual Fund Performance, Journal of Business

27. Titman S. and Tiu C. (2008), Do the Best Hedge Funds Hedge?, University of Texas, Austin

28. Zdorovenin V. (2009), Hedge Funds. Investment activity and its efficiency, Higher School of Economics, Moscow



Appendix 1

Cumulative return of CTA/Managed futures strategy and S&P 500 index

Cumulative return of Fixed Income Relative Value strategy and S&P 500 index

Cumulative return of Event Driven strategy and S&P 500 index

Cumulative return of not-available strategy funds and S&P 500 index

Cumulative return of Multi-strategy and S&P 500 index

Cumulative return of Macro strategy and S&P 500 index

Cumulative return of Equity Hedge strategy and S&P 500 index

Cumulative return of Fixed Income Directional strategy and S&P 500 index



Appendix 2

Descriptive statistics for CTA/Managed futures strategy returns for different regions

	CTAG	CTANA	CTAUS	CTAE	CTAP
Average	0.35%	0.51%	0.52%	0.23%	0.55%
Min	-4.43%	-40.93%	-10.48%	-5.05%	-18.54%
Max	7.30%	32.37%	3.29%	6.68%	21.14%
Std dev	2.30%	11.67%	2.55%	2.24%	5.67%

Descriptive statistics for Equity Hedge strategy returns for different regions

	EHG	EHNA	EHUS	EHE	EHP
Average	0.53%	0.49%	0.39%	0.54%	0.52%
Min	-10.09%	-10.93%	-12.94%	-9.88%	-12.04%
Max	8.61%	13.82%	9.39%	8.02%	9.66%
Std dev	2.99%	4.01%	3.23%	2.86%	3.14%

Descriptive statistics for Event Driven strategy returns for different regions

	EDG	EDNA	EDUS	EDE	EDP
Average	0.17%	0.13%	0.35%	0.62%	0.13%
Min	-8.89%	-37.19%	-14.48%	-8.55%	-11.71%
Max	4.38%	13.53%	23.77%	5.52%	7.27%
Std dev	1.95%	5.93%	3.74%	2.14%	2.52%

Descriptive statistics for FI Directional strategy returns for different regions

	FIDG	FIDNA	FIDUS	FIDE	FIDP
Average	0.37%	-0.29%	0.32%	0.32%	0.41%
Min	-8.44%	-2.41%	-8.89%	-7.40%	-8.92%
Max	3.59%	2.80%	5.95%	4.44%	4.13%
Std dev	1.55%	1.52%	1.76%	1.66%	1.58%

Descriptive statistics for FI Relative Value strategy returns for different regions

	FIRVG	FIRVNA	FIRVUS	FIRVE	FIRVP
Average	0.35%	0.84%	0.50%	0.00%	0.50%
Min	-10.86%	-21.44%	-8.79%	0.00%	-5.68%
Max	7.08%	22.19%	12.32%	0.00%	3.68%
Std dev	1.85%	7.03%	1.96%	0.00%	1.22%

Descriptive statistics for Macro strategy returns for different regions

	MG	MNA	MUS	ME	MP
Average	0.42%	0.55%	0.33%	0.16%	0.51%
Min	-3.91%	-8.02%	-7.12%	-16.96%	-8.29%
Max	5.09%	7.72%	8.83%	13.28%	5.81%
Std dev	1.70%	5.07%	3.08%	3.33%	2.33%

Descriptive statistics for Multi-strategy returns for different regions

	MSG	MSNA	MSUS	MSE	MSP
Average	0.42%	1.17%	-0.10%	0.47%	0.40%
Min	-6.32%	-9.96%	-22.34%	-10.29%	-4.73%
Max	9.45%	9.96%	44.89%	6.70%	5.53%
Std dev	1.93%	2.85%	8.31%	2.30%	1.58%

Descriptive statistics for not-available strategy funds' returns for different regions

	NAG	NANA	NAUS	NAE	NAP
Average	-0.48%	1.44%	0.04%	-0.15%	0.19%
Min	-22.36%	-7.37%	-9.60%	-9.11%	-20.43%
Max	13.87%	59.21%	11.14%	5.79%	7.93%
Std dev	4.41%	7.45%	3.24%	2.49%	3.04%



Appendix 3

Descriptive statistics for asset pricing models factors (Europe)

E	MKT-RF	SMB	HML	RMW	CMA	RF	MOM	BAB	QMJ
Average	0.47%	0.14%	-0.13%	0.40%	0.07%	0.11%	0.88%	0.52%	0.63%
Min	-22.14%	-4.65%	-4.60%	-5.25%	-3.66%	0.00%	-25.96%	-10.89%	-10.75%
Max	13.78%	4.85%	7.45%	3.77%	5.54%	0.44%	9.87%	7.90%	8.79%
Std dev	5.68%	1.93%	2.23%	1.40%	1.39%	0.15%	3.90%	2.82%	2.46%

Descriptive statistics for asset pricing models factors (Global)

G	MKT-RF	SMB	HML	RMW	CMA	RF	MOM	BAB	QMJ
Average	0.42%	0.03%	0.08%	0.29%	0.15%	0.11%	0.74%	0.70%	0.49%
Min	-21.14%	-4.67%	-4.01%	-2.84%	-5.59%	0.00%	-22.48%	-6.95%	-7.71%
Max	13.70%	4.54%	4.41%	2.88%	6.88%	0.44%	8.57%	7.08%	8.49%
Std dev	5.13%	1.79%	1.62%	1.05%	1.50%	0.15%	3.39%	2.15%	2.23%

Descriptive statistics for asset pricing models factors (North America)

NA	MKT-RF	SMB	HML	RMW	CMA	RF	MOM	BAB	QMJ
Average	0.57%	0.01%	-0.08%	0.31%	0.03%	0.11%	0.28%	0.57%	0.38%
Min	-18.23%	-4.33%	-7.47%	-3.48%	-3.49%	0.00%	-24.83%	-8.51%	-6.77%
Max	11.49%	5.64%	5.25%	5.58%	4.79%	0.44%	11.30%	11.40%	9.10%
Std dev	4.38%	2.14%	2.11%	1.49%	1.48%	0.15%	3.97%	2.69%	2.61%

Descriptive statistics for asset pricing models factors (United States)

US	MKT-RF	SMB	HML	RMW	CMA	RF	MOM	BAB	QMJ
Average	0.58%	0.08%	-0.04%	0.23%	0.03%	0.11%	0.19%	0.44%	0.32%
Min	-17.23%	-4.78%	-9.67%	-4.50%	-3.16%	0.00%	-34.58%	-9.25%	-7.44%
Max	11.35%	6.75%	7.65%	4.39%	3.44%	0.44%	12.45%	11.56%	8.86%
Std dev	4.31%	2.37%	2.35%	1.48%	1.31%	0.15%	4.78%	2.79%	2.65%



Descriptive statistics for asset pricing models factors (Asia and Pacific)

P	MKT-RF	SMB	HML	RMW	CMA	RF	MOM	BAB	QMJ
Average	0.71%	-0.05%	0.24%	0.02%	0.35%	0.11%	0.89%	1.20%	0.50%
Min	-26.06%	-10.91%	-6.51%	-9.31%	-7.77%	0.00%	-19.15%	-7.46%	-6.54%
Max	18.58%	10.42%	6.92%	6.62%	8.57%	0.44%	7.65%	7.94%	6.29%
Std dev	6.27%	3.01%	2.38%	2.42%	2.44%	0.15%	3.92%	2.59%	2.37%



Appendix 4

Regression results for CTA/Managed futures strategy funds

Asset pricing model specification	Regional focus	Strategy	Alpha (constant)	R-squared (overall)
MKTRF	E	CTA	0.001995	0.0071
MKTRF SMB HML	E	CTA	0.001904	0.0118
MKTRF SMB HML MOM	E	CTA	0.001892	0.0118
MKTRF SMB HML BAB	E	CTA	0.001824	0.0132
MKTRF SMB HML RMW CMA	E	CTA	0.003375	0.0148
MKTRF SMB HML RMW CMA QMJ	E	CTA	0.002155	0.0217
MKTRF	G	CTA	-4.8E-05	0.0049
MKTRF SMB HML	G	CTA	-0.00013	0.0103
MKTRF SMB HML MOM	G	CTA	-0.00186	0.0217
MKTRF SMB HML BAB	G	CTA	-0.00033	0.0103
MKTRF SMB HML RMW CMA	G	CTA	-0.00052	0.0139
MKTRF SMB HML RMW CMA QMJ	G	CTA	-0.00133	0.0146
MKTRF	P	CTA	0.009935	0.0039
MKTRF SMB HML	P	CTA	0.010714	0.0199
MKTRF SMB HML MOM	P	CTA	0.010632	0.02
MKTRF SMB HML BAB	P	CTA	0.011434	0.0218
MKTRF SMB HML RMW CMA	P	CTA	0.010991	0.0468
MKTRF SMB HML RMW CMA QMJ	P	CTA	0.011369	0.0471

Regression results for Event Driven strategy funds

Asset pricing model specification	Regional focus	Strategy	Alpha (constant)	R-squared (overall)
MKTRF	E	ED	-0.00938	0.0399
MKTRF SMB HML	E	ED	-0.01029	0.0721
MKTRF SMB HML MOM	E	ED	-0.01242	0.0825
MKTRF SMB HML BAB	E	ED	-0.0107	0.075
MKTRF SMB HML RMW CMA	E	ED	-0.00587	0.0837
MKTRF SMB HML RMW CMA QMJ	E	ED	-0.01	0.0956
MKTRF	G	ED	-0.00151	0.1062
MKTRF SMB HML	G	ED	-0.00153	0.1195
MKTRF SMB HML MOM	G	ED	-0.00194	0.1204
MKTRF SMB HML BAB	G	ED	-0.00255	0.1229
MKTRF SMB HML RMW CMA	G	ED	-0.00018	0.1244
MKTRF SMB HML RMW CMA QMJ	G	ED	-5.91E-06	0.1245
MKTRF	P	ED	0.004342	0.2598
MKTRF SMB HML	P	ED	0.00355	0.2961
MKTRF SMB HML MOM	P	ED	0.003115	0.3048
MKTRF SMB HML BAB	P	ED	0.002551	0.2965
MKTRF SMB HML RMW CMA	P	ED	0.002906	0.2991
MKTRF SMB HML RMW CMA QMJ	P	ED	0.002957	0.2992
MKTRF	NA	ED	-0.00178	0.1125
MKTRF SMB HML	NA	ED	-0.0015	0.1174
MKTRF SMB HML MOM	NA	ED	-0.00146	0.1175
MKTRF SMB HML BAB	NA	ED	-0.00194	0.1201
MKTRF SMB HML RMW CMA	NA	ED	-0.00152	0.1263
MKTRF SMB HML RMW CMA QMJ	NA	ED	-2.7E-05	0.1456
MKTRF	US	ED	0.000821	0.2019
MKTRF SMB HML	US	ED	0.00044	0.2299
MKTRF SMB HML MOM	US	ED	0.000146	0.2339
MKTRF SMB HML BAB	US	ED	-0.00115	0.4423
MKTRF SMB HML RMW CMA	US	ED	0.001404	0.2598
MKTRF SMB HML RMW CMA QMJ	US	ED	0.001836	0.2642

Regression results for Equity Hedge strategy funds

Asset pricing model specification	Regional focus	Strategy	Alpha (constant)	R-squared (overall)
MKTRF	E	EH	-0.0003176	0.1422
MKTRF SMB HML	E	EH	-0.0008963	0.1598
MKTRF SMB HML MOM	E	EH	-0.001071	0.1599
MKTRF SMB HML BAB	E	EH	-0.0008555	0.1598
MKTRF SMB HML RMW CMA	E	EH	-0.0003811	0.166
MKTRF SMB HML RMW CMA QMJ	E	EH	0.0006633	0.1671
MKTRF	G	EH	0.000466	0.0998
MKTRF SMB HML	G	EH	0.000458	0.1046
MKTRF SMB HML MOM	G	EH	0.0006263	0.1047
MKTRF SMB HML BAB	G	EH	0.0006273	0.1046
MKTRF SMB HML RMW CMA	G	EH	0.0018538	0.1072
MKTRF SMB HML RMW CMA QMJ	G	EH	0.0021829	0.1072
MKTRF	P	EH	0.0021063	0.2343
MKTRF SMB HML	P	EH	0.0017603	0.2592
MKTRF SMB HML MOM	P	EH	0.0012234	0.2605
MKTRF SMB HML BAB	P	EH	0.0033406	0.2615
MKTRF SMB HML RMW CMA	P	EH	0.0021296	0.2606
MKTRF SMB HML RMW CMA QMJ	P	EH	0.0039929	0.2652
MKTRF	NA	EH	-0.001917	0.1428
MKTRF SMB HML	NA	EH	-0.0023408	0.1558
MKTRF SMB HML MOM	NA	EH	-0.002327	0.1573
MKTRF SMB HML BAB	NA	EH	-0.0029135	0.1697
MKTRF SMB HML RMW CMA	NA	EH	-0.0019898	0.18
MKTRF SMB HML RMW CMA QMJ	NA	EH	0.000118	0.2019
MKTRF	US	EH	-0.0018443	0.2548
MKTRF SMB HML	US	EH	-0.0014958	0.2633
MKTRF SMB HML MOM	US	EH	-0.0014091	0.2644
MKTRF SMB HML BAB	US	EH	-0.0017376	0.2659
MKTRF SMB HML RMW CMA	US	EH	-0.0008328	0.2664
MKTRF SMB HML RMW CMA QMJ	US	EH	-0.0002982	0.27

Regression results for Fixed Income Directional strategy funds0

Asset pricing model specification	Regional focus	Strategy	Alpha (constant)	R-squared (overall)
MKTRF	E	FID	0.003983	0.1757
MKTRF SMB HML	E	FID	0.003444	0.2232
MKTRF SMB HML MOM	E	FID	0.0037914	0.2255
MKTRF SMB HML BAB	E	FID	0.0030867	0.2291
MKTRF SMB HML RMW CMA	E	FID	0.00376	0.2372
MKTRF SMB HML RMW CMA QMJ	E	FID	0.0038853	0.2373
MKTRF	G	FID	0.0011987	0.0742
MKTRF SMB HML	G	FID	0.0006559	0.0867
MKTRF SMB HML MOM	G	FID	0.000712	0.0867
MKTRF SMB HML BAB	G	FID	0.0004604	0.088
MKTRF SMB HML RMW CMA	G	FID	0.0013985	0.0907
MKTRF SMB HML RMW CMA QMJ	G	FID	0.0013003	0.0908
MKTRF	P	FID	0.0027031	0.2046
MKTRF SMB HML	P	FID	0.0020611	0.2143
MKTRF SMB HML MOM	P	FID	0.0029684	0.02177
MKTRF SMB HML BAB	P	FID	0.0020417	0.2143
MKTRF SMB HML RMW CMA	P	FID	0.002689	0.2165
MKTRF SMB HML RMW CMA QMJ	P	FID	0.0027879	0.2166
MKTRF	US	FID	0.0011629	0.0013
MKTRF SMB HML	US	FID	0.0019709	0.0018
MKTRF SMB HML MOM	US	FID	0.0020259	0.0039
MKTRF SMB HML BAB	US	FID	0.0020988	0.008
MKTRF SMB HML RMW CMA	US	FID	0.0028244	0.0063
MKTRF SMB HML RMW CMA QMJ	US	FID	0.0026745	0.0073

Regression results for Fixed Income Relative Value strategy funds0

Asset pricing model specification	Regional focus	Strategy	Alpha (constant)	R-squared (overall)
MKTRF	G	FIRV	0.0018747	0.0284
MKTRF SMB HML	G	FIRV	0.0020869	0.0338
MKTRF SMB HML MOM	G	FIRV	0.003735	0.0429
MKTRF SMB HML BAB	G	FIRV	0.001343	0.0351
MKTRF SMB HML RMW CMA	G	FIRV	0.0037434	0.0506
MKTRF SMB HML RMW CMA QMJ	G	FIRV	0.0039428	0.0506
MKTRF	P	FIRV	-0.0014194	0.0511
MKTRF SMB HML	P	FIRV	-0.0014949	0.0617
MKTRF SMB HML MOM	P	FIRV	-0.0018386	0.062
MKTRF SMB HML BAB	P	FIRV	-0.0018239	0.0618
MKTRF SMB HML RMW CMA	P	FIRV	0.0004182	0.0692
MKTRF SMB HML RMW CMA QMJ	P	FIRV	-0.0017387	0.0737
MKTRF	US	FIRV	0.0045554	0.0107
MKTRF SMB HML	US	FIRV	0.0043741	0.0309
MKTRF SMB HML MOM	US	FIRV	0.0043559	0.0315
MKTRF SMB HML BAB	US	FIRV	0.0043904	0.031
MKTRF SMB HML RMW CMA	US	FIRV	0.0039911	0.0341
MKTRF SMB HML RMW CMA QMJ	US	FIRV	0.0042391	0.0388

Regression results for Macro strategy funds

Asset pricing model specification	Regional focus	Strategy	Alpha (constant)	R-squared (overall)
MKTRF	E	M	0.003379	0.2031
MKTRF SMB HML	E	M	0.0028314	0.2134
MKTRF SMB HML MOM	E	M	0.0064218	0.2272
MKTRF SMB HML BAB	E	M	0.0034912	0.2157
MKTRF SMB HML RMW CMA	E	M	0.0039401	0.2186
MKTRF SMB HML RMW CMA QMJ	E	M	0.0087921	0.2267
MKTRF	G	M	0.0013919	0.029
MKTRF SMB HML	G	M	0.0013666	0.0309
MKTRF SMB HML MOM	G	M	0.0009492	0.0316
MKTRF SMB HML BAB	G	M	0.0014694	0.0309
MKTRF SMB HML RMW CMA	G	M	0.0018538	0.0314
MKTRF SMB HML RMW CMA QMJ	G	M	0.0019868	0.0314
MKTRF	P	M	0.0002501	0.1861
MKTRF SMB HML	P	M	3.72E-06	0.218
MKTRF SMB HML MOM	P	M	-0.0010336	0.2278
MKTRF SMB HML BAB	P	M	0.0011246	0.2201
MKTRF SMB HML RMW CMA	P	M	0.0007706	0.224
MKTRF SMB HML RMW CMA QMJ	P	M	0.0020931	0.2287
MKTRF	US	M	0.0071685	0.0051
MKTRF SMB HML	US	M	0.0071256	0.0111
MKTRF SMB HML MOM	US	M	0.0076324	0.0235
MKTRF SMB HML BAB	US	M	0.0071234	0.0118
MKTRF SMB HML RMW CMA	US	M	0.0074982	0.0217
MKTRF SMB HML RMW CMA QMJ	US	M	0.0079981	0.0253

Regression results for Multi-strategy funds

Asset pricing model specification	Regional focus	Strategy	Alpha (constant)	R-squared (overall)
MKTRF	E	MS	-0.000185	0.1471
MKTRF SMB HML	E	MS	-0.0005466	0.151
MKTRF SMB HML MOM	E	MS	0.0000693	0.1556
MKTRF SMB HML BAB	E	MS	-0.0006209	0.152
MKTRF SMB HML RMW CMA	E	MS	0.0010891	0.1797
MKTRF SMB HML RMW CMA QMJ	E	MS	0.0014244	0.1799
MKTRF	G	MS	0.0019575	0.015
MKTRF SMB HML	G	MS	0.0016691	0.017
MKTRF SMB HML MOM	G	MS	0.0012429	0.0175
MKTRF SMB HML BAB	G	MS	0.0011134	0.0174
MKTRF SMB HML RMW CMA	G	MS	0.0025471	0.0172
MKTRF SMB HML RMW CMA QMJ	G	MS	0.0025648	0.0172
MKTRF	P	MS	0.0063317	0.1272
MKTRF SMB HML	P	MS	0.0053884	0.1579
MKTRF SMB HML MOM	P	MS	0.0041273	0.1642
MKTRF SMB HML BAB	P	MS	0.006227	0.1566
MKTRF SMB HML RMW CMA	P	MS	0.0054533	0.1585
MKTRF SMB HML RMW CMA QMJ	P	MS	0.0066711	0.162
MKTRF	NA	MS	0.0018519	0.3625
MKTRF SMB HML	NA	MS	0.0019451	0.3964
MKTRF SMB HML MOM	NA	MS	0.0021769	0.3979
MKTRF SMB HML BAB	NA	MS	0.0017039	0.398
MKTRF SMB HML RMW CMA	NA	MS	0.0023224	0.404
MKTRF SMB HML RMW CMA QMJ	NA	MS	0.0048687	0.4441
MKTRF	US	MS	0.0045647	0.0272
MKTRF SMB HML	US	MS	0.004253	0.0532
MKTRF SMB HML MOM	US	MS	0.0044435	0.0587
MKTRF SMB HML BAB	US	MS	0.0047791	0.0745
MKTRF SMB HML RMW CMA	US	MS	0.0038049	0.063
MKTRF SMB HML RMW CMA QMJ	US	MS	0.003966	0.0648

Regression results for not-available strategy funds

Asset pricing model specification	Regional focus	Strategy	Alpha (constant)	R-squared (overall)
MKTRF	E	NA	-0.0043715	0.1711
MKTRF SMB HML	E	NA	-0.0040767	0.1807
MKTRF SMB HML MOM	E	NA	-0.0045177	0.1808
MKTRF SMB HML BAB	E	NA	-0.004532	0.1816
MKTRF SMB HML RMW CMA	E	NA	-0.0036408	0.1827
MKTRF SMB HML RMW CMA QMJ	E	NA	-0.002791	0.1839
MKTRF	G	NA	-0.0044743	0.0716
MKTRF SMB HML	G	NA	-0.0034488	0.0779
MKTRF SMB HML MOM	G	NA	-0.0042087	0.0781
MKTRF SMB HML BAB	G	NA	-0.0043614	0.0786
MKTRF SMB HML RMW CMA	G	NA	-0.0025806	0.08
MKTRF SMB HML RMW CMA QMJ	G	NA	-0.002304	0.08
MKTRF	P	NA	-0.0019868	0.1321
MKTRF SMB HML	P	NA	-0.001733	0.1382
MKTRF SMB HML MOM	P	NA	-0.0026162	0.141
MKTRF SMB HML BAB	P	NA	-0.0013493	0.1384
MKTRF SMB HML RMW CMA	P	NA	-0.0017048	0.141
MKTRF SMB HML RMW CMA QMJ	P	NA	-0.0000914	0.1491
MKTRF	NA	NA	0.006255	0.0762
MKTRF SMB HML	NA	NA	0.0056331	0.0815
MKTRF SMB HML MOM	NA	NA	0.0043827	0.0874
MKTRF SMB HML BAB	NA	NA	0.005435	0.0934
MKTRF SMB HML RMW CMA	NA	NA	0.0037786	0.0906
MKTRF SMB HML RMW CMA QMJ	NA	NA	0.0059353	0.1058
MKTRF	US	NA	-0.0012063	0.077
MKTRF SMB HML	US	NA	-0.0009054	0.0799
MKTRF SMB HML MOM	US	NA	-0.0019639	0.0848
MKTRF SMB HML BAB	US	NA	-0.0010048	0.083
MKTRF SMB HML RMW CMA	US	NA	-0.0009512	0.0807
MKTRF SMB HML RMW CMA QMJ	US	NA	-0.0008532	0.0807

Размещено на Allbest.ru