This paper seeks to develop minimum standards for backtesting in order to reduce curve-fitting and ensure strategies are picking up on market signals rather than market noise. Oftentimes, strategies will…
The purpose of this paper is to discover whether it is possible to train a machine-learning algorithm to behave as a risk-adverse investor by using a dynamic model involving transaction…
This paper introduces an intra-day price prediction and decision making model for the FX market using artificial neural networks and genetic algorithms taught to identify technical patterns. It is directed…
This paper considers the applications of machine learning when applied to high-frequency trading (HFT) using three studies to solve/optimize a specific trading problem involved with HFT. The cases look to…
Chakraborty and Kearns test the profitability of a market making algorithm as a security’s price changes over time using time series models, and find that market making strategies are generally…
This paper demonstrates the success of a series of mean-reversion, momentum and combination trading strategies originally designed for use in equities when applied to foreign exchange markets. Returns are measured…
Van den Hoek considers a number of existing studies which seek (but fail) to provide empirical evidence of mean reversion of stock prices. Many previous studies cite the random walk…
This purpose of this study is to analyze the impact of algorithmic trading on price discovery in financial markets. The authors use a time series of high-frequency data to model…
