Category: Finance

Quantifying Trading Behavior in Financial Markets Using Google Trends

Finance / Trading

Quantifying Trading Behavior in Financial Markets Using Google Trends Preis, et al. Scientific Reports 2013 After a long hiatus from summarizing papers while I finished my thesis (and read countless more papers), I figure I’d start this year off by summarizing an interesting behavioural finance paper! Tobia Preis from the University of Warwick and his team worked to quantify the usefulness of Google Trends data when applied as a trading strategy. Although this research was …

A Simple Procedure To Test Neural Network Performance For Stock Trading Systems

Data Science / Deep Learning / Finance

An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework Sezer, et al. ArXiv 2017 Neural networks have come along way in the last five years in their performance as stock prediction engines. Although this paper is a few years old, it provides a nice introduction into how a simple neural network can be used to predict buy and sell signals with common technical indicators. The authors from TOBB University of …

Learning Real Estate Automated Valuation Models from Heterogeneous Data Sources

Data Science / Finance

Learning Real Estate Automated Valuation Models from Heterogeneous Data Sources Bergadano, et al. ArXiv 2019 The real estate market is often used as a testbed for new machine learning methods due to the vast diversity of the industry and the richness of the potential feature set. However, real estate valuations remain difficult to predict with consistent accuracy and the industry remains reliant on the services of professional appraisers. The team at the University of Turin, …

Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis

Data Science / Deep Learning / Finance

Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis Matsunaga, et al. ArXiv 2019 A theme that seems to proliferate in machine trading is to emulate the human trader while enhancing predictive capabilities. Since the world has become more interconnected, common technical and fundamental analysis techniques are often not capable of handling all the factors necessary to accurately model the financial world today. This paper takes an approach of integrating the vast …

Media Attention and the Volatility Effect

Data Science / Finance

Media Attention and the Volatility Effect Blitz, et al. SSRN 2019 The news coverage of companies undoubtedly has an effect on their stock price returns. With news travelling much faster since the widespread adoption of the internet, this effect has become an interesting point of study. The team at Robeco Institutional Management has recently taken swing at quantifying this effect using data from news sources, press releases, premium newswires, and over 19,000 web publications. It …

Trading via Image Classification

Data Science / Finance

Trading via Image Classification Cohen, et al. arXiv 2019 I’ve been reading many papers focusing on implementing machine learning methods into trading strategies lately. Most of these are naturally regression algorithms trying to calculate a continuous variable like price or volatility. I’ve come across a particularly interesting paper completed by the AI department at JP Morgan, who have been looking into how to utilize image classification of stock charts as an alternative to an algorithm …

Airbnb price prediction using machine learning and sentiment analysis

Data Science / Finance

Airbnb Price Prediction Using Machine Learning and Sentiment Analysis Kalehbasti, et al. arXiv 2019 The real estate market is no stranger to applied machine learning models trying to accurately predict future prices and trends based on the countless possible features. In this paper, the authors target Airbnb for their price prediction model and include an interesting and uncommon feature in the form of sentiment analysis. As most people are already familiar with how services like …

Comparing backtest and out-of-sample performance on a large cohort of trading algorithms

Data Science / Finance

All that glitters is not gold: Comparing backtest and out-of-sample performance on a large cohort of trading algorithms Wiecki et al., 2016 The quality and reliability of common algorithm performance metrics makes for an interesting area of research. The widespread application of data science to finance has enabled quants and traders to easily test and evaluate their strategies against previous market conditions. Backtesting on prior market data often forms the backbone of algorithm development, but …

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