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Record W6908437460 · doi:10.25904/1912/4904

Economic Uncertainty and Cross-sectional Asset Pricing

2022· other· en· W6908437460 on OpenAlexaboutno aff

Bibliographic record

VenueGriffith Research Online (Griffith University, Queensland, Australia) · 2022
Typeother
Languageen
FieldArts and Humanities
TopicLibraries and Information Services
Canadian institutionsnot available
Fundersnot available
KeywordsCapital asset pricing modelStock (firearms)Consumption-based capital asset pricing modelEconomic riskInvestment (military)Risk premiumRisk–return spectrumStock marketConsumption (sociology)Asset (computer security)

Abstract

fetched live from OpenAlex

One of the most important challenges in asset pricing is to explain cross-sectional variation in returns across different assets. As returns represent compensation for bearing risk, assets with different risk exposures should earn dissimilar returns. Consequently, investors and researchers have long attempted to specify factors capturing the risk that can drive stock returns by investigating the relationship between the potential risk factors and the future returns. Factors that are closely related to future returns are widely accepted candidates for better explaining return spreads. However, there is no consensus on the predictive power of these potentially competing factors. The intertemporal capital asset pricing model (ICAPM), for example, assumes that investors tend to hedge against unfavorable risk by adjusting their consumption and investment using shifts in future economic conditions and investment opportunities over the long run (Merton, 1973). As such hedging needs affect investor behavior, macroeconomic variables that predict future macroeconomic fundamentals and investment conditions are widely accepted return predictors. Among these macroeconomic factors is economic uncertainty, which measures the turbulence of general economic conditions. This is important as it heightens risks, adversely affects investment, and even triggers global financial crises and worldwide economic downturns. Many studies find that economic uncertainty, being an unfavorable shift in the economic and financial environment, undermines macroeconomic outcomes and eventually drags down market returns at the aggregate level. In their focus on individual stock returns, Bali et al. (2017, 2019a) conclude that domestic uncertainty exposure indeed predicts cross-sectional returns in the US. Motivated by the significant role of uncertainty in affecting macroeconomic conditions and stock market returns, this thesis investigates the relationship between exposure to economic uncertainty and future individual stock returns over multiple trading horizons in non-US markets. It extends the US-based findings of Bali et al. (2017, 2019a) by examining the predictive role of domestic uncertainty exposure for individual stock returns in Australia. In addition to domestic uncertainty, the research considers the spillover effects of uncertainty risk from other markets. Two underlying trends in global economic development serve as background for this analysis. The first is the ongoing integration of the global economy and the comovements existing among financial markets. Motivated by the ongoing integration of the global economy, this thesis then investigates the relationship between risk exposure to global uncertainty and future individual stock returns in the top-five non-US developed markets, comprising the Japanese, UK, Hong Kong, Euronext, and Canadian stock markets. A second trend is the continuing integration of economies in geographically close areas. Motivated by the growing economic power of China in the Asian regional economy, the thesis also investigates the relationship between exposure to Chinese uncertainty and future individual stock returns in the five leading Asian markets, namely, Japan, Hong Kong, India, South Korea, and Taiwan. [...]

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.134
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0930.000

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 head0.151
GPT teacher head0.353
Teacher spread0.203 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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