Documenting the Rise in Retail Trading
Why are retail trading volumes on the rise?
The rise in retail trading has been one of the standout trends in capital markets this decade.
Retail trading volumes reached a record high in 2023, accounting for roughly 23 per cent of global trading volumes in one week during Q1 2023. In this regard, the previous record for retail trading volumes was 22 per cent, set during the frenzy-fuelled pandemic era. In 2022, retail volumes accounted for roughly 15 per cent.
The rise in retail trading volumes have been driven by several factors.
In particular, the growing maturity of capital markets trading technology and platforms has driven greater retail trading interest. In particular, retail investors have benefitted from the increased ubiquity in fractional shares trading on mobile brokerages and integrations between banking and trading applications.
For example, global neobank Revolut has revolutionised the retail banking experience by integrating equities trading into an all-in one platform that removes accessibility barriers to equities trading.
In addition, the rise of socially driven communities have created hordes of investors creating ‘FOMO’ towards certain assets in the hope of generating capital gains. For example, in 2021, individual investors plotted the purchases of huge volumes of shares of video game retailer Gamestop in a perceived ‘David v Goliath’ battle with institutional professionals who expected the share price to fall. This led to significant price volatility and sizeable gains for some retail investors. This cult-like behaviour has come against a backdrop of macroeconomic headwinds, such as high inflation, as retail investors have sought new ways to generate additional income to make ends meet.
At the same time, the surging popularity of social media platforms and digital channels has undoubtedly transformed the investing landscape, providing retail investors with unprecedented access to new types of information and community-driven insights. According to Forbes, more than one third of retail investors have made an investment change due to social media content.
The new segment of retail investors generally falls into two categories. The first tends to be younger, first-time investors with limited disposable income. As novices, they are not yet as knowledgeable, and can be heavily influenced by various sources of information. The second category is comprised of savvier, more experienced investors with more money at their disposal, with social media playing an increasingly vital role in how information is perceived across both types of investor.
Retail and institutional investors can seize upon the growing influence of retail by undertaking sentiment analysis, which is a much bigger driver of investment decision making in financial markets compared to 20 years ago. This is because markets are becoming more sensitive to news developments.
This trend is otherwise known as information inelasticity, where it takes a smaller amount of Dollar-valued information (i.e., a piece of breaking news) to move a market by a given amount (in terms of price). The growing influence of retail is visible here, with retail investors tending to harbour more emotion (animal spirit) and reactiveness when it comes to making investment decisions in the wake of market developments.
As such, with markets becoming more retail-oriented, conducting sentiment analysis across novel news and social media platforms can provide credible forms of alpha for financial firms, trading platforms and retail investors in 2024.
However, to be conducted effectively, a more dynamic type of sentiment analysis, in comparison to traditional methods, is required.
Rule-based sentiment analysis is the traditional method of sentiment analysis. Around since the 2000s, this method typically uses NLP to identify whether a piece of text is negative, positive, or neutral. In contrast, automated sentiment analysis relies on machine learning (ML) techniques. In this case, a ML algorithm is trained to classify sentiment based on both the words and their order. The success of this approach depends on the quality of the training data set and the algorithm.
Specifically, automated sentiment analysis can gauge the interest across news and social media platforms, by ingesting and categorising data inputs and providing investment signals or actions based off of findings. Retail and institutional portfolios can then be benchmarked against the findings.
Increasingly, though, there is an opportunity for brokerages to capture greater market share by factoring in the preferences of a more retail-dominated market.
For example, as well as ensuring that usage of social media platforms is optimised, brokerages should seek to meet the preferences of a younger generation of retail traders. ‘Gen Z’ traders typically show strong interest in non-traditional investments, including meme stocks, alternative investments, and even cryptoassets. Brokerage platform Robinhood is a typical example, spearheading the provision of alternative investments for retail traders by letting retail investors bet on the forthcoming general election in the US and providing alternative data across market feeds that can assist decision making.
Collectively, adapting to a more retail-dominated capital market will, going forward, ensure brokerages improve their competitive positioning in a rapidly evolving industry.
For further information, please do not hesitate to contact us at london@greyspark.com with any questions or comments you may have. We are always happy to elaborate on the wider implications of these headlines from our unique capital markets consultative perspective