There is strong evidence of individuals and institutions divesting from so-called “sin stocks” or funds – those with dubious ethical provenance that investors are reluctant to highlight within their portfolios. There is a demand for emphasis on ethical investing.
The investment industry continues to undergo a sustained transformation as industry challenges intensify. Inadequate organic growth, volatile capital market returns and fee/margin compression have created a more challenging context. Furthermore, investors are demanding to find ethical and sustainable investments.
Machine learning will reshape quantitative investing over the next few years. Applying non-financial factors in investing is going to be vitally important. In this shifting paradigm, the investment industry is facing significant challenges to profitability. The value of investment management has become shakier in a market where returns are narrowing, and data is growing exponentially, making it impossible for humans to extract corroborated insights. There is a need to create an integrated target-state vision for data and investment intelligence.
The industry has a lot to gain from artificial and augmented intelligence. AI can facilitate automated research, include ESG and impact investment metrics, and provide predictive analytics on growing information in real-time. There is an imperative to seamlessly integrate ESG, sentiment, and metadata directly into your quantitative and traditional models.
We have seen a rise in robo quants on trading floors – this operationalises trading but is not capable of looking beyond numbers. Nor does it cater to the needs of passive, mid-to-long-term investments, let alone ethical investing. It is necessary to employ nonlinear metrics to identify ethical investment opportunities. Ethical or impact investing needs to look beyond traditional analysis to find sustainable investment opportunities. This is required to couple ESG and alternate data with generally detached fundamental, quant, macro and technical data, to Intuitively derive insights from usually detached investment styles. AI is the catalyst here – it can help us find hidden nuggets of information in massive unstructured data, which would normally not be possible for humans to see in real-time.
It is time to arm research analysts with bots that can appropriate real-time data for market and financial analysis and device reports, recommendations and signals. The current research and analysis process is incomplete, fragmented and expensive, and in need of modernisation. We can make it more efficient and accurate by combining it with an AI element, while enriching the research with alternate or digital data.
The investment industry is looking to transform and adapt. A synthesised approach to investment strategies is the future for asset and wealth management. It is possible to find investments that are good for the planet while providing users with a further edge that will intensify the returns over a long period.
Laborious and expensive analysis can be transformed with machine learning and natural language processing algorithms, while also considering the environmental, social and ethical impact of the investment. All investment research and decisions can be AI-driven, leaving asset and wealth managers free to focus more on portfolio management, client relationships and business strategy.
AI is accelerating and reshaping the asset and wealth management industry. The devil is in the detail, and AI can provide an edge. AI can analyse magnitudes of data, foretell corrections in supply and demand imbalances and forecast market drift to help make better tactical decisions. Strategies are the byproduct of crunching billions of data points and learning how to adjust to markets in real-time – explaining how returns are generated is pushing the boundaries of human comprehension.
There is a significant rise in the number of conscious investments. It is imperative to build ethics into AI. It’s time to find positive profitability.
by Hamsa Bharadwaj, Founder and CEO, Pecten