Analyzing Complex Interdependencies in Financial Markets: A Neural Network-Based Approach for News Impact Assessment
Primary Area: general machine learning (i.e., none of the above)
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Keywords: Stock Market Trend Prediction, Market Volatility, LSTM Sentiment Analysis, Demand & Supply Dependency tree, Multi Layer Neural Networks, Learning Statistics, Regressions, Depth-First-Search, Advance Web Scraping, Balance Sheet
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TL;DR: This research explores the intricate connections between companies in financial markets and how external news affects them.
Abstract: Analyzing Complex Interdependencies in Financial Markets: A Neural Network-Based Approach for News Impact Assessment
In the ever-evolving landscape of financial markets, the intricate web of interdependencies among companies, driven by supply chain intricacies and competitive dynamics, has become a central concern for investors and analysts alike. Our research endeavors to shed light on these intricate relationships and their susceptibility to external news events.
In this study, we examine a hypothetical scenario where Company A relies on Companies B and C, Company B depends on Company D, and Company C's fortunes are intertwined with those of Companies E and F, all while these companies are directly reliant on finite natural resources. We use this scenario to illustrate the profound impact of news pertaining to any one of these companies, be it Company A, B, C, or their competitors, on the entire ecosystem. The ripple effect extends through supply chains and demand chains, with repercussions resonating both directly and indirectly. Of importance, we show how emerging ML techniques can model and predict such effects.
To navigate this complex terrain, we introduce a novel approach based on constructing dependency graphs for each company using a suitable methodology akin to BFS. This method involves expanding the nodes in the graph to represent companies, scrutinizing their lists of competitors, suppliers, and clients, with terminal nodes denoting natural resources often owned by government entities.
Our research harnesses the wealth of sentiment and dependency information extracted from news articles covering a diverse array of companies. These companies are integrated as nodes into our data model. Through the aggregation of stock values for these nodes during successive news intervals, coupled with a meticulous analysis of news sentiment's influence on each node and the deduction of intricate relationships among them, we present a comprehensive view of the interplay between news events and the financial market landscape.
The culmination of our efforts culminates in the integration of this analysis into a neural network-based stock trend prediction model. The objective is to assess the effectiveness of our approach in gauging the impact of news on associated companies, providing investors and analysts with a powerful tool to navigate the complex and interconnected world of financial markets. This research not only contributes to a deeper understanding of market dynamics but also offers practical insights for informed decision-making in an increasingly volatile financial landscape.
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Submission Number: 8617
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