We summarise the key findings from a recent research paper that examines the dynamic relationship and volatility spillover effect between the Chinese stock market and Chinese commodity markets.

Given some of the limitations of in-situ biodiversity data, the authors explore using satellite data to derive biodiversity-related physiological proxies. We summarise the data sources, advantages and limitations discussed in the research, and highlight potentially relevant satellite data providers.

We summarise a 2022 paper that examines whether Bitcoin and the aggregate cryptocurrency market acts as a diversifier, hedge or safe haven to US stocks, bonds, US dollar, commodities, real estate and gold.

Published research suggests the securities lending market is opaque and subject to non-competitive features. We summarise the findings of this research and examine the suitability of IHS Markit’s Securities Finance product in assessing securities lending market activity.

We summarise a recent paper based on a high-frequency trading firm that adopts deep neural network (DNN) machine learning systems for trading functions. The paper considers how we can begin to explain the decisions of DNNs, despite their opacity.

With current concerns over rising inflation across major economies, we summarise two studies that anticipate official CPI figures ahead of their release. These studies use high-frequency alternative data sources, which are found to improve the forecast accuracy of inflation models and provide timely insights into global pricing dynamics.

Recent research from 2022 suggests that incorporating transactional and POS data can replicate or outperform the Bank of Japan’s current Consumption Activity Index released since 2016. We summarise the findings of this research and highlight several Neudata-listed data providers that may be leveraged for such use cases.

We summarise the key findings from a recent research paper that examines the features of the Chinese stock market through various machine learning models and analyses the predictability of different machine learning models for Chinese stock returns.

We summarise a paper that questions if various sources of ‘consumer big data’ can predict company revenue and stock returns. We highlight several Neudata-listed clickstream, geolocation and brand sentiment providers that may be applicable for this use case.

We summarise a research paper which showcases how equity investors can use machine-readable news data to create systematic trading strategies. The researcher was able to do so by deriving sentiment indicators from a large point-in-time dataset of news articles.