Uncovering the Geometry of the Indian Stock Market through Scale-free Network Analysis

Concetti Chiave

The core message of this article is that the hyperbolic geometry of complex networks provides a more accurate representation of the Indian stock market compared to Euclidean embeddings, enabling improved detection of market stability/volatility, early identification of market changes, and natural clustering of market sectors.

Sintesi

The article presents an analysis of the Indian stock market using a method based on embedding the network in a hyperbolic space using machine learning techniques. The key highlights and insights are:

The hyperbolic clusters resemble the topological network communities more closely than the Euclidean clusters, as demonstrated by higher Normalized Mutual Information (NMI) and Adjusted Mutual Information (AMI) scores.

The hyperbolic distance (HD) and hyperbolic shortest path distance (HSD) of the embedded network can effectively distinguish between periods of market stability and volatility, outperforming traditional network measures.

The modularity of the embedded network in the hyperbolic space can detect significant market changes earlier than the modularity of the original network, serving as an early indicator of market trends.

The coalescent embedding algorithm is able to naturally segregate different market sectors, underscoring its ability to capture the inherent clustering structure of the stock market.

The authors leverage complex network theory, hyperbolic geometry, and machine learning techniques to provide novel insights into the dynamics of the Indian stock market.

Statistiche

The daily closing prices of all 500 stocks listed on the CNX500 index of the National Stock Exchange (NSE) India from January 01, 2017, to December 31, 2021, were used in the analysis.
The authors removed the records of 117 stocks due to missing data, leaving a total of 383 stocks with complete data.
The daily logarithmic returns were calculated for the 383 stocks, resulting in 939 values of logarithmic returns.

Citazioni

"It is demonstrated that the hyperbolic clusters resemble the topological network communities more closely than the Euclidean clusters."
"We are able to clearly distinguish between periods of market stability and volatility through a statistical analysis of hyperbolic distance and hyperbolic shortest path distance corresponding to the embedded network."
"Using the modularity of the embedded network, significant market changes can be spotted early."
"The coalescent embedding is able to segregate the certain market sectors thereby underscoring its natural clustering ability."

Approfondimenti chiave tratti da

Explaining Indian Stock Market through Geometry of Scale free Networks

by Pawanesh Yad... alle arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04710.pdf

Domande più approfondite

How can the insights from the hyperbolic embedding of the stock market network be leveraged to develop new portfolio optimization strategies or trading algorithms

The insights gained from hyperbolic embedding of the stock market network can be instrumental in developing innovative portfolio optimization strategies and trading algorithms. By leveraging the natural clustering ability of hyperbolic spaces, investors can create diversified portfolios that take into account the inherent structure and dynamics of the market. The hyperbolic clusters can help identify sectors or groups of stocks that exhibit similar behavior, allowing for the construction of more resilient and balanced portfolios. Additionally, the early detection of market changes through hyperbolic modularity can be utilized to adjust portfolio allocations proactively, optimizing returns and managing risks effectively.

What are the potential limitations or drawbacks of the hyperbolic embedding approach, and how can they be addressed in future research

While hyperbolic embedding offers valuable insights into the structure of the stock market network, there are potential limitations and drawbacks that need to be considered. One limitation could be the complexity of interpreting the hyperbolic space and its impact on traditional financial analysis methods. Additionally, the computational resources required for hyperbolic embedding may be significant, especially for large datasets. To address these limitations, future research could focus on developing user-friendly tools and algorithms that simplify the interpretation of hyperbolic structures and optimize the computational efficiency of the embedding process. Moreover, conducting robust sensitivity analyses and validation studies can help ensure the reliability and accuracy of the hyperbolic embedding results.

Given the ability of the hyperbolic modularity to detect market changes earlier, how can this metric be integrated into existing market monitoring and risk management frameworks

The hyperbolic modularity, with its ability to detect market changes earlier, can be integrated into existing market monitoring and risk management frameworks to enhance decision-making processes. By incorporating hyperbolic modularity as a leading indicator of market shifts, financial institutions and investors can proactively adjust their strategies and positions in response to changing market conditions. This metric can serve as an early warning system for potential market disruptions or opportunities, enabling stakeholders to take timely and informed actions to mitigate risks and capitalize on emerging trends. Integrating hyperbolic modularity into risk management frameworks can improve the resilience and adaptability of portfolios, enhancing overall performance and stability.