This paper combines daily non-public data on buy and sell volume by institutions from 2003 through 2005 for NYSE-listed stocks with all news announcements from Reuters. Natural language processing categorizes the sentiment associated with each news story. We use institutional order flow (buy...

Traditional liquidity measures can provide a false impression of the liquidity and stability of financial market trading. Using data on auctions (bids wanted in competition; BWICs) from the collateralized loan obligation (CLO) market, we show that a standard measure of liquidity, the effective...

We develop a dynamic model of corporate investment and financing decisions in which corporate insiders have superior information about the firm's growth prospects. We show that firms with positive private information can credibly signal their type to outside investors using the timing of...

We develop a dynamic tradeoff model to examine the importance of manager-shareholder conflicts in capital structure choice. Using panel data on leverage choices and the model's predictions for different statistical moments of leverage, we show that while refinancing costs help explain the...

Build America Bonds (BABs) were issued by states and municipalities for twenty months as an alternative to tax-exempt bonds. The program was part of the 2009 fi scal stimulus package. The bonds are taxable to the holder, but the federal Treasury rebates 35% of the coupon payment to the issuer....

We use a dynamic model of financing decisions to measure agency conflicts for a large panel of 12,652 firms from 14 countries. Our estimates show that agency conflicts are large and vary significantly across firms and countries. Differences in agency conflicts are largely due to differences in...

The effective functioning of the municipal bond market is crucial for the provision of public services, as it is the largest capital market for state and municipal issuers. Prior research has documented tax, credit, liquidity, and segmentation effects in municipal bonds. Recent regulatory...

While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function from observing an expert’s behavior. It is well known that, in general, various reward functions can lead to the...