Exchange Rate Forecasting on a Napkin
This paper by Michele Ca’Zorzi and Michał Rubaszek claims to offer a formula for exchange rate forecasting so simple it can be calculated on the back of a napkin. The ‘back of…
Research
Beta’em Up: What is Market Beta in FX?
Beta is generally considered a measure of an instrument’s risk in comparison to the overall risk of an industry or portfolio. There are structural limitations of the FX market which…
Strategy Diversification: Combining momentum and carry strategies within a foreign exchange portfolio
The main research question of this article is whether there is value in combining technical and macro/fundamental strategies. Two strategies are tested individually, then three variations of the combined strategies are tested…
Model Calibration with Neural Networks
This article focuses on the effective calibration of a quantitative model to a specific data set using a novel method in the form of machine learning. It provides an alternative…
The Value of Volume in Foreign Exchange
This article proposes the primary research question: does FX volume contain information that is statistically and economically relevant for understanding future currency returns? The findings suggest that low volume tends…
Predicting Short-Term Bitcoin Price Fluctuations from Buy and Sell Orders
The purpose of this paper is to analyze whether order book (buy and sell) data can be used as a short-term predictor of Bitcoin (BTC) volatility against the US Dollar.…
Quant Strategies in the Cryptocurrency Space
This article from Factor Research discusses several traditional quantitative investment strategies applied to seven popular cryptocurrencies—Bitcoin, Ethereum, Ripple, Litecoin, Dash, NEM and Monero, which were selected because they each have…
Twitter Mood Predicts the Stock Market
This paper seeks to determine whether public sentiment, as measured in Twitter posts, can be used as a predictor of stock market performance. Two sources of public mood are used—OpinionFinder…
Extreme Value Theory and Backtest Overfitting in Finance
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…
Machine Learning for Trading
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…