I undertake a data-driven approach to identify the sense of luxury vis-a-vis residential properties and also investigate the market microstructure from the dynamics of Bid-Ask spread and Years on Market for these properties. I identify Luxury properties using 1,174proprietary features of owners,...

This paper attempts to assess whether money can generate persistent economic" fluctuations in dynamic general equilibrium models of the business cycle. We show that a small" nominal friction in the goods market can make the response of output to monetary shocks large" and persistent if it is...

I consider limit order book events that are likely to contribute to price discovery in the foreign exchange market. These events involve transactions and limit orders. The variance decomposition shows that improving order and worsening cancel, these are limit order events, substantially...

We consider loans being marked to market to constitute new information that is only immediately available to large institutional traders, so-called qualified institutional buyers (QIBs). Smaller investors (non-QIBs) do not have instant access to such information. Investigating the effects of...

Five of the ten largest stablecoins are backed by fiat assets. Four hold digital assets; all ten are collateralized. Seven of the ten are on the Ethereum network. Failure rates of stablecoin projects are almost as high as other digital assets. The 2015 and 2018 vintages of stablecoins have...

We study the link between informed trading and co-movement in illiquidity. We argue that investors concerned with liquidity and fire-sale shocks respond to an increase in informed trading by shifting their portfolios away from stocks with high information asymmetry. This rebalancing causes a...

Deciding between the use of market orders and limit orders is an important question in practical optimal trading problems. A key ingredient in making this decision is understanding the uncertainty of the execution of a limit order, that is, the fill probability or the probability that an order...

We employ deep learning in forecasting high-frequency returns at multiple horizons for 115 stocks traded on Nasdaq using order book information at the most granular level. While raw order book states can be used as input to the forecasting models, we achieve state-of-the-art predictive accuracy...

This paper examines how the introduction of a dark pool impacts price discovery, market quality, and aggregate welfare of traders. I use a four-period model where rational and risk-neutral agents choose the order type and the venue and obtain the equilibrium numerically. The comparative statics...

This paper builds the clustering model of measures of market microstructure features which are popular in predicting the stock returns. In a 10-second time frequency, we study the clustering structure of different measures to find out the best ones for predicting. In this way, we can predict...