Predictive Analytics in Finance: Leveraging Machine Learning for Market Trends and Investment Decisions
Year: 2026
Type: conference-paper
Abstract: Academically, there is a current paradigm change in machine learning-based predictive analytics, as a technology to be adopted in the contemporary finance market, the speed in its adoption, and its success, in large part, is because it can assist in market forecasting and data-driven investment decision-making. It is against this background of the increasing complexity and apparently endless flow of information in financial markets of this day that even some of the most gifted financial analysts are dismally unprepared to completely decipher what lies within the inseparable region that is inherently in a state of chaos. Here highly sophisticated data -processing algorithms are regularly used to estimate non - linear dependencies between variables, through machine - learning algorithms. This essay will discuss the financial implications of predictive analytics and narrow it down to how machine learning may be utilized to identify new trends in the market movement and, consequently, how to invest in the best possible manner. The usefulness of supervised methods such as regression analysis and classification and unsupervised methods such as, clustering and dimensionality reduction have been found very helpful in the prediction of financial future. One way of supervised learning is used by the organization to determine asset organizing expenses in the marketplace, detect marketplace anomalies and estimate credit risk by driving a model using prior financial facts. An example would be some financial measures, as input to a regression problem which answers the query of what most likely will become of asset prices as a consequence of different combinations of factors; or learning algorithms can be trained to search through the market environment, and categorize and predict most probably the direction the future environment will take.
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7203682344