This paper studies a dynamic market microstructure model, in which a strategic market maker competes with an informed trader. We include the presence of noise traders and limit order traders in our setup. Our model is a N-period model. We give necessary and sufficient conditions for an...

We document that corporate insiders' trading behavior exhibits persistence over time. Classifying insiders as “Persistently Profitable (PP)” based on profits generated from historical trades, we find the current transactions of these insiders better predict firm performance. The relative...

This study sets up a contingent-claim framework incorporating the default risk of a mortgage insurer and the capital forbearance of regulators to value mortgage insurance (MI) contracts. We further investigate how critical policy parameters, such as capital requirements, prompt closure, and time...

We present an order flow model framework for limit order driven markets. Different from previous models we explicitly model a reference price process that “sweeps” the limit order book as it fluctuates up and down. Our framework allows us to use any stochastic process to model this reference...

We introduce Kinetic Component Analysis (KCA), a state-space application that extracts the signal from a series of noisy measurements by applying a Kalman Filter on a Taylor expansion of a stochastic process. We show that KCA presents several advantages compared to other popular noise-reduction...

Where can one systematically find research ideas? I propose a scheme to classify and broadly generalize the sources of inspiration for research topics, with emphasis on corporate finance. In the process, I propose 100 new research topics awaiting the brave and persistent

The papers included in this issue are selected from the 7th International Conference of the Financial Engineering and Banking Society (FEBS) organized by Strathclyde Business School during 1-3 June 2017. With circa 200 academics, practitioners and regulators participating as delegates from...

This paper presents evidence suggesting that artificial neural networks approach (ANNs) outperform traditional statistical methods and can forecast equity premiums reasonably well. The study replicates out-of-sample estimates of regression using ANN with economic fundamentals as inputs. The...

This paper contributes to the literature on cryptocurrencies, portfolio management and estimation risk by comparing the performance of naïve diversification, Markowitz diversification and the advanced Black-Litterman model with VBCs that controls for estimation errors in a portfolio of...

Using a global sample of 2,357 attempted initial coin offerings (ICOs), we examine whether ICO rating providers act as information intermediaries that may reduce information asymmetry and the associated adverse selection problem in the crypto-tokens market. We find that issuer-supplied voluntary...