In this thesis, I follow Hasbrouck and Seppi (2001)’s work and use reduced-rank regression to model the commonality in Chapter Two. The literature on the study of return commonality generally attributes its source to the order flow. But I find that return and order flows are endogenous and use the new exogenous Twitter sentiment dataset to show that return commonality may be due to sentiment and attention. Furthermore, I observe the non-linear (linear) relationship between sentiment (attention) and return commonality. Finally, I may export the non-linear relationship using the same reduced-rank regression framework in future research.
I also follow Korajczyk and Sadka (2008)’s work in Chapter Three. They use PCA to extract a systematic liquidity factor from eight liquidity measures and show that it is a priced factor. Previous studies conceptually state that liquidity has multiple dimensions (three to five dimensions). Still, few studies discuss how to extract one or a set of systematic liquidity factors from various liquidity measures. I fill this gap and empirically confirm that the systematic liquidity factor is multi-dimensional and priced multi-dimensionally using the multi-linear PCA (MPCA) method on daily-level data. MPCA allows me to provide a nice explanation of the factor loadings of the systematic liquidity factor. In future work, I want to apply the MPCA method to asset pricing with large dimensions of firm characteristics.
","abstract_html":"<p>In this thesis, I follow Hasbrouck and Seppi (2001)’s work and use reduced-rank regression to model the commonality in Chapter Two. The literature on the study of return commonality generally attributes its source to the order flow. But I find that return and order flows are endogenous and use the new exogenous Twitter sentiment dataset to show that return commonality may be due to sentiment and attention. Furthermore, I observe the non-linear (linear) relationship between sentiment (attention) and return commonality. Finally, I may export the non-linear relationship using the same reduced-rank regression framework in future research.</p> <p>I also follow Korajczyk and Sadka (2008)’s work in Chapter Three. They use PCA to extract a systematic liquidity factor from eight liquidity measures and show that it is a priced factor. Previous studies conceptually state that liquidity has multiple dimensions (three to five dimensions). Still, few studies discuss how to extract one or a set of systematic liquidity factors from various liquidity measures. I fill this gap and empirically confirm that the systematic liquidity factor is multi-dimensional and priced multi-dimensionally using the multi-linear PCA (MPCA) method on daily-level data. MPCA allows me to provide a nice explanation of the factor loadings of the systematic liquidity factor. In future work, I want to apply the MPCA method to asset pricing with large dimensions of firm characteristics.</p>","abstract_has_math":false,"creators":["Zhou, Zhaoque"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Finance","degree_department":null,"school":null,"contributors":["Raja Velu","Lai Xu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08-26T07:00:00Z","date_published":"2022-08-26T07:00:00Z","updated_at":"2026-07-24T04:56:23Z","subjects":["Commonality","High-frequency","Liquidity","Multi-dimension","Business","Finance and Financial Management"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://surface.syr.edu/etd/1623","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Raja Velu","Lai Xu"]},{"key":"dc:creator","label":"Author","values":["Zhou, Zhaoque"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Finance"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Commonality","High-frequency","Liquidity","Multi-dimension","Business","Finance and Financial Management"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://surface.syr.edu/etd/1623"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis, I follow Hasbrouck and Seppi (2001)’s work and use reduced-rank regression to model the commonality in Chapter Two. The literature on the study of return commonality generally attributes its source to the order flow. But I find that return and order flows are endogenous and use the new exogenous Twitter sentiment dataset to show that return commonality may be due to sentiment and attention. Furthermore, I observe the non-linear (linear) relationship between sentiment (attention) and return commonality. Finally, I may export the non-linear relationship using the same reduced-rank regression framework in future research.
I also follow Korajczyk and Sadka (2008)’s work in Chapter Three. They use PCA to extract a systematic liquidity factor from eight liquidity measures and show that it is a priced factor. Previous studies conceptually state that liquidity has multiple dimensions (three to five dimensions). Still, few studies discuss how to extract one or a set of systematic liquidity factors from various liquidity measures. I fill this gap and empirically confirm that the systematic liquidity factor is multi-dimensional and priced multi-dimensionally using the multi-linear PCA (MPCA) method on daily-level data. MPCA allows me to provide a nice explanation of the factor loadings of the systematic liquidity factor. In future work, I want to apply the MPCA method to asset pricing with large dimensions of firm characteristics.
"]},{"key":"dc:title","label":"Title","values":["Commonality in Two-Dimensions: An Empirical Investigation"]}]}],"canonical_facts":{"dc:contributor":["Raja Velu","Lai Xu"],"dc:creator":["Zhou, Zhaoque"],"dc:description.abstract":["In this thesis, I follow Hasbrouck and Seppi (2001)’s work and use reduced-rank regression to model the commonality in Chapter Two. The literature on the study of return commonality generally attributes its source to the order flow. But I find that return and order flows are endogenous and use the new exogenous Twitter sentiment dataset to show that return commonality may be due to sentiment and attention. Furthermore, I observe the non-linear (linear) relationship between sentiment (attention) and return commonality. Finally, I may export the non-linear relationship using the same reduced-rank regression framework in future research.
I also follow Korajczyk and Sadka (2008)’s work in Chapter Three. They use PCA to extract a systematic liquidity factor from eight liquidity measures and show that it is a priced factor. Previous studies conceptually state that liquidity has multiple dimensions (three to five dimensions). Still, few studies discuss how to extract one or a set of systematic liquidity factors from various liquidity measures. I fill this gap and empirically confirm that the systematic liquidity factor is multi-dimensional and priced multi-dimensionally using the multi-linear PCA (MPCA) method on daily-level data. MPCA allows me to provide a nice explanation of the factor loadings of the systematic liquidity factor. In future work, I want to apply the MPCA method to asset pricing with large dimensions of firm characteristics.
"],"dc:identifier":["https://surface.syr.edu/etd/1623"],"dc:subject":["Commonality","High-frequency","Liquidity","Multi-dimension","Business","Finance and Financial Management"],"dc:title":["Commonality in Two-Dimensions: An Empirical Investigation"],"thesis:degree_discipline":["Finance"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T04:56:23Z"}