MétaCan
Menu
Back to cohort
Record W3121394337 · doi:10.5555/1083-5547-21.1.21

Trend Following and Momentum Strategies for Global REITs

2015· article· en· W3121394337 on OpenAlexaff
Andrew Clare, James Seaton

Bibliographic record

VenueCity Research Online (City University London) · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsSharpe ratioReal estate investment trustPortfolioTrend followingMomentum (technical analysis)Volatility (finance)EconometricsEconomicsFinancial economicsAsset (computer security)Index (typography)Project portfolio managementComputer scienceFinanceReal estate

Abstract

fetched live from OpenAlex

In this study, we investi- gate whether the risk-Adjusted returns of a global REIT portfolio would be enhanced by adopting a trend following global strategy (which is an abso- lute concept sometimes known as absolute mo- mentum), a momentum-based strategy (which is a relative concept and requires individual country al- locations), or indeed a combination of the two. e examine the results in terms of both a dedicated global REIT exposure, and the impact on a multi- asset portfolio. We find that the main improve- ments arise when the broad index is replaced with one of the four trend following strategies. The port- folios deliver similar returns but volatility is re- duced by up to a quarter to the 8%-9% range, the Sharpe ratios increase by 0.1 to 0.5 with the main benefit being the reduction in the maximum draw- down to under 30% compared to 43% when the broad index was used. We thus find that a com- bined momentum and trend following a global REIT strategy can be beneficial for both a dedicated REIT portfolio and adding REITs to a multi-Asset portfolio.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.162
GPT teacher head0.321
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCity Research Online (City University London)Same topicFinancial Markets and Investment StrategiesFrench-language works237,207