Evaluating Machine Learning for Enhanced Investment Tracking

On reflection, the experiment was overly hopeful, with the end result being that a researcher could switch and combine pre-determined analytics into categories like fundamentals, behaviour, markets and occasions. However, following the implementation of LLM, the process is expected to be significantly enhanced as companies can utilise a licensed or private LLM to create these narratives. This experiment seeks to determine if machine learning can enhance the existing manual investment monitoring process. This could enable investment professionals to more easily absorb pertinent data pertaining to the investment portfolio and synthesize a cohesive account as to how a range of factors can influence the portfolio's performance. Investment practitioners must remain informed about the progress of the firms in the investment portfolio to gauge their influence on the present and future prospects of the investment portfolio. For example, a real estate investment portfolio that covers hospitality, residential real estate, logistic and consumer retail sectors will need to be aware of tourism trends, mortgage rate, supply chain and current distribution of footfall in various retail areas. When the portfolio expands internationally, further considerations are required, such as global macroeconomic outlook, country-specific economic indicators and/or geopolitical tension that may impact its investments. Tracking this ever-growing universe of potentially relevant information relating to the portfolio should be done on its own. When the portfolio expands to a certain level, further complexity arises as different elements of the portfolio start to displayed a correlation even though conceptually they are from different areas. This makes it harder to form a unified understanding of the portfolio's behavior from all the available data. research reports, regulatory filings, and digital media, so that they can identify the potential opportunities in the marketplace, remain competitive, and most importantly, maximize their investment returns. Investment practitioners often have to spend time and mental effort to collect and process data from multiple sources, including research reports, regulatory filings, and digital media, in order to recognize potential opportunities in the marketplace, retain competitive advantage, and maximize their investment returns. A, B, and C This is a laborious and time-intensive process that numerous solution providers have attempted to work out. A few of these solutions are A, B, and C. Despite these solutions attempting to bridge the data gap, investment practitioners still have to invest the time and resources into training their brains to quickly absorb information, sifting out irrelevant news, internalizing particular and universal factors, forming a comprehensive article, analyzing the effects on their portfolio, and expressing their outlook or faith in the applicable investment platform. Consequently, despite the abundance of data made accessible through digitization, investments still largely rely on manual monitoring by junior analysts, with senior risk takers at the top of the hierarchy receiving updates. The FinTech, in association with a FI owning more than 600 financial resources in its investment portfolio, thought that the process of investment managing can be improved by machine learning algorithms. These algorithms can be taught to go through the extensive data to put together a draft of a concise narrative with important points. Junior investment practitioners can then modify it to make a suitable narrative. In an effort to attain their objective, the FinTech broke down the necessary information concerning investment monitoring into four distinct divisions. If the experiment is successful, it could produce a market-ready solution that distills key information for investment monitoring and displays it in a way that reduces cognitive effort for investment practitioners, allowing them to quickly digest the news and develop an appropriate context for current events and their impact on the investment portfolio.

Source: https://medium.com/@notherArmchairEconomist/experiment-to-improve-investment-monitoring-process-with-machine-learning-c576ed83c26a?source=rss------fintech-5