Filter Results
21 results
- Data for: Positional Momentum and Liquidity Management; A Bivariate Rank ApproachThis is the cleaned data, which lots of computation have been done for it. This data has been used to format the portfolios.
- Dataset
- Data for: Positional Momentum and Liquidity Management; A Bivariate Rank ApproachThis data is a cleaned data, that I computed from stock prices and trade volumes from Bloomberg.
- Dataset
- Data for: Interrelations in Market Fears of U.S. and European Equity MarketsVIX data for US and Europe
- Dataset
- Data for: Interrelations in Market Fears of U.S. and European Equity MarketsImplied volatility data from 2004-2019
- Dataset
- Data for: Returns, Volatility and Spillovers - A Paradigm Shift in India?Times series returns data for India.
- Dataset
- Data for: China’s capital account liberalization and asymmetric risk spillovers between Shanghai and Hong Kong stock marketsData for: China’s capital account liberalization and asymmetric risk spillovers between Shanghai and Hong Kong stock markets
- Dataset
- Data for: Vertical Separation of Transmission Control and Market Efficiency in the Wholesale Electricity MarketElectricity Industry; Deregulation; Vertical Separation; Transmission Control; Market Power
- Dataset
- Data for: Asymmetric volatility spillovers between international economic policy uncertainty and the U.S. stock marketBaker's EPU data and Stock Volatility
- Dataset
- Data for: The impact of China’s one belt one road initiative on international trade in the ASEAN regionSTATA FILES for "The impact of China’s One Belt One Road initiative on international trade in the ASEAN region"
- Dataset
- Data for: Information content of funds from operation and net income in real estate investment trustsData Description ‘raw.zip’ contains raw data which are used in the study. - ‘ff_factor’ is five factor return obtained from French’s website - ‘return_total’ is daily return of both REITs and non-REITs. - ‘used_reit_comp’ is raw COMPUSTAT data for REITs and ‘used_total_comp’ is raw COMPUSTAT data for non-REITs. ‘input.zip’ contains processed data. - ‘ann_ex_ret’ and ‘total_ann_ex_ret’ are three days cumulative abnormal return following earnings announcement for REITs and non-REITs, respectively. - ‘ann_raw_ret’ and ‘total_ann_raw_ret’ are three days cumulative raw return following earnings announcement for REITs and non-REITs, respectively. - ‘earnings’ and ‘total_earnings’ are earnings for REITs and non-REITs respectively. - ‘eps_ffo' and ‘total_eps_ffo' are ffo per share for REITs and non-REITs respectively. - ‘eps_ni' and ‘total_eps_ni' are ni per share for REITs and non-REITs respectively. - ‘ffo' and ‘total_ ffo' are ffo for REITs and non-REITs respectively. - ‘ni' and ‘total_ ni' are ni for REITs and non-REITs respectively. - ‘reit_atq_rank’ and ‘total_atq_rank’ are size rank based on total asset for REITs and non-REITs respectively. - ‘reit_rdq’ and ‘total_rdq’ are earnings announcement date for REITs and non-REITs respectively. - ‘sretq’ and ‘total_sret’ are gain or loss from sales of property for REITs and non-REITs respectively. - ‘depre’ and ‘total_depre’ are depreciation and amortization for REITs and non-REITs respectively. - ‘sue_ffo' and ‘total_sue_ffo' are standardized unexpected earnings for ffo for REITs and non-REITs respectively. - ‘sue_ni' and ‘total_sue_ni' are standardized unexpected earnings for ni for REITs and non-REITs respectively. - ‘sue_ffo_rank' and ‘total_sue_ffo_rank' are historical rank of standardized unexpected earnings for ffo for REITs and non-REITs respectively. - ‘sue_ni_rank' and ‘total_sue_ni_rank' are historical rank of standardized unexpected earnings for ffo for REITs and non-REITs respectively. ‘ziman.zip’ contains data from CRSP/Ziman database
- Dataset
1