Use Case

Global Market Efficiency Impact

C. Tiu, D. Rösch, B. Wolfe (University at Buffalo) with S. Titman, K. Brown (University at Texas Austin), U. Yoeli (University of Texas), M. Carlson (University of British Columbia), A. Reed (TIAA), A. Subrahmanyam, R. Roll (UCLA), M. van Dijk, D. Bongaerts (Erasmus University)

What?

  • Measure market efficiencies with high volumes of historical and current intraday market data

Why Aristotle?

  • Bursting to integrate new data
  • Tens of TBs moved to available processing resources
  • Sharing data and pipelines around the world for collaboration and reproducibility

Accomplishments

  • Developed the capability to migrate OpenNebula images to the cloud.
  • Setup the framework on a VM and began to import and successfully analyze 2TBs of international tick-by-tick financial data from the Thomson Reuters Tick History (TRTH) database.
  • Imported 25TBs Trade and Quote (TAQ) data up until 2017 for intraday trades and quote transactions for all US exchange-listed stocks, and computed 9 measures of efficiency.
  • Created VMs in collaboration with Varun Chandola and Jialiang Jiang (UB) that share the same image and hold all the data; they are executing well.
  • Demonstrated the framework to PhD students so they can analyze the data independently on Aristotle.
  • Launched new investigations, i.e., how price deviations (market inefficiencies) affect liquidity (the ease at which you can buy or sell), how the recent increase in tick-size (the minimum price movement of US stocks) affects liquidity, etc.
  • Gave a guest lecture at Cornell introducing the financial framework and data hosted on Aristotle.
  • Working with UB's CCR to make the framework easily accessible to researchers at UB and, at a later stage, to researchers at other universities.

Plans

  • Use Aristotle and the financial framework to investigate whether human trades still matter at a time when trading is dominated by computers. Also, employ a LASSO regression to predict short-term stock returns using the whole cross-section of international stocks and investigate CumEx trading (labeled as the "Biggest Tax Heist Ever" by the NYTimes). All projects require large datasets and computational power. Collaborators include UCLA and the University of Utah.
  • Work with a UB student to automate part of the financial data framework hosted on Aristotle and investigate the possibility of integrating it with CCR OnDemand, OSC's Open OnDemand interactive HPC via the web.
  • Continue to mentor PhD student researchers.
  • Revise and resubmit "Financial market frictions and learning from the stock price" to the Journal of Financial Markets.

Products



Time-variation in global and local market efficiency studies need large data storage
Time-variation in global and local market efficiency studies need large data storage

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