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PROJECT Q-REIT

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PROJECT Q-REIT
PRELIMINARY QUANTITATIVE ANALYSIS
OF
CERTAIN INTERNATIONAL REITS
(WIP)
By
Background
 A dataset consisting of monthly closing prices of certain international REITs – was made available in January, 2019 on a personal basis
 Certain information about those REITs included the following:
 The closing price data were for the period from January, 2010 until end of December, 2018 in US$ on monthly basis on “as is” basis
 Altogether, the total number of 113 unique REITs were included in the dataset from many different countries / markets viz.
USA (57)
Australia (19 )
UK (19)
Singapore (9)
Japan (4)
Hongkong (2)
Canada (1)
Mexico (1)
Spain (1)
 An attempt has been made to analyze the above mentioned data in a quantitative manner using CRAN R Language Project (version 3.6.1 Dated 5th
July, 2019; 64 Bit) and RStudio (version 1.2.5019 dated 24th October, 2019; 64 Bit) to arrive at an optimal portfolio consisting of these REITs that provides
/ fulfills the following:
 No shorting was allowed; and only long positions are to be considered,
 The potential / candidate portfolios were to be fully invested at all times (meaning, cash holdings),
 Certain constraints such as the maximum number of REITs in an optimized portfolio and transaction costs were also considered, wherever applicable,
 Some of the key objectives for the portfolio optimization process were minimization of risk; and maximization of returns and Sharpe Ratios etc.,
 Another set of portfolio returns were considered when the portfolios consisted of all the qualified REITs (meaning those ones that had their price data available for
the entire period under consideration) on a buy-and-hold basis as well as when they are rebalanced to the original equally weights every quarter, and every year
 The following slides present an overview of the results of the preliminary quantitative analysis of the qualified REITs (total of 75 out of 113) in order to
find a set of REITs and their weights such that either the returns of such a portfolio are maximised and/or the risks are reduced to the optimal, and then
a comparisons was made for the different approaches undertaken
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REIT Monthly Prices…peculiarities can be observed better…
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REIT Monthly Returns…volatilities provide insights…
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REIT Monthly Returns…equally-weighted portfolio of qualified REITs…
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Equal Weighted Portfolio of REITs…rebalancing makes not much difference…
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Equal Weighted Portfolio of REITs…even for drawdowns…
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Optimally Weighted Portfolio of REITs…maximizing return while minimizing standard
deviation…results in relatively higher return at lower risk…
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Optimally Weighted Portfolio of REITs…with much fewer number of REITs…
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Optimally Weighted Portfolio of REITs…maximizing return, minimizing standard
deviation while maximizing Sharpe Ratio…also results in relatively higher return at lower risk…
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Optimally Weighted Portfolio of REITs…again with much fewer number of REITs…
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Optimally Weighted Portfolio of REITs…comparison…the choice is obvious…optimally
weighted portfolio that maximizes returns while minimizing standard deviation!
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Certain Remarks
 Methodology
 It may be noted that this analysis was carried out based on the data on prices of certain REITs provided on “as is” basis, and no attempt was made to verify the
same independently;
 The maximum weight of any single REIT was restricted to 15% of the total value of the portfolio at all times; and the maximum possible number of REITs included in
the portfolios was restricted to 25 (out of total of 75 numbers of qualified REITs);
 No “shorting” was allowed at any time, and the portfolio was assumed to be invested almost 100% at all times;
 The analysis and optimization of REIT portfolios was carried out on a laptop computer using the software as mentioned earlier in this document; and
 The main software packages used under the CRAN R Language were the latest publicly available versions of “PortfolioAnalytics” and “PerformanceAnalytics” as of
03rd December, 2019; and “DEoptim” was the main method used to carry out the optimization.
 Conclusions
 The equal weighted portfolios of all the 75 REITs performed almost equally well in terms of Sharpe Ratio while considering a “buy-and-hold” and “periodic
rebalancing” strategies; while the optimized portfolios generally provided higher returns with relatively lower standard deviations and maximum drawdowns;
 The optimized portfolios using “DEoptim” method generally performed better for the time period under consideration as a whole as compared to the equal weighted
portfolios including the rebalanced ones as well, though their results varied somewhat; the final results depicted in this document are one of the typical results;
 Total transaction costs were assumed to be 0.25% for the optimized portfolios; while no such costs were considered for all the equally weighted portfolios,
thereby, showing that the optimized portfolio results are much better relatively as they are net of costs; and
 Based on the analysis contained in this document, perhaps better investment outcomes may be obtained while investing in much fewer number of REITs.
 Potential Further Analysis
 More and higher quality data may be provided in terms of longer duration as well as in terms of recency;
 Optimized portfolios may be considered with different types of periodic rebalancing to ascertain whether such portfolios provide better results along with reduced
risks and reduced maximum drawdowns;
 Other enhanced methods may be undertaken to include certain moments of REIT factors such as prices while running different optimization methods; and
 Suitable economic, sector, and REIT-specific fundamental and technical factors / indicators may be incorporated to finetune further the optimized portfolios.
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THANK YOU
Contact:
SHAHAB HAIDER
[email protected]
[email protected]
00971 – 50 149 8590
0091 – 983 900 8615
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