Betas from return regressions are commonly used to measure systematic financial market risks. "Good" beta measurements are essential for a range of empirical inquiries in finance and macroeconomics. We introduce a novel econometric framework for the nonparametric estimation of time-varying betas...

We build statistical models to describe how market participants choose the direction, price, and volume of orders. Our dataset, which spans sixteen weeks for four shares traded in Euronext Amsterdam, contains all messages sent to the exchange and includes algorithm identification and member...

This paper develops the necessary methodology for high frequency ANOVA, which includes the estimations of idiosyncratic volatility and realized R-Squared. Because the residual process is latent in the high frequency regression, the estimation of idiosyncratic volatility is notoriously difficult...

Time-series data, which exhibit a low signal-to-noise ratio, non-stationarity, and non-linearity, are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies in prices and trading on them quickly and in...

We argue that owing to traders' inability to fully express their preferences over the execution times of their orders, contemporary stock market designs are prone to latency arbitrage. In turn, we propose a new order type, which allows traders to specify the time at which their orders are...

For this research, we implemented a trading system based on the Turtle rules and examined its efficiency when trading selected assets from the Forex, Metals, Commodities, Energy and Cryptocurrency Markets using historical data. Afterwards, we enhanced our Turtle-based trading system with...