English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cryptocurrency trading represents a nascent field of research, with growingadoption in industry. Aided by its decentralised nature, many metricsdescribing cryptocurrencies are accessible with a simple Google search andupdate frequently, usually at least on a daily basis. This presents a promisingopportunity for data-driven systematic trading research, where limitedhistorical data can be augmented with additional features, such as hashrate orGoogle Trends. However, one question naturally arises: how to effectivelyselect and process these features? In this paper, we introduce Multi-FactorInception Networks (MFIN), an end-to-end framework for systematic trading withmultiple assets and factors. MFINs extend Deep Inception Networks (DIN) tooperate in a multi-factor context. Similar to DINs, MFIN models automaticallylearn features from returns data and output position sizes that optimiseportfolio Sharpe ratio. Compared to a range of rule-based momentum andreversion strategies, MFINs learn an uncorrelated, higher-Sharpe strategy thatis not captured by traditional, hand-crafted factors. In particular, MFINmodels continue to achieve consistent returns over the most recent years(2022-2023), where traditional strategies and the wider cryptocurrency markethave underperformed.

Multi-Factor Inception: What to Do with All of These Features?