Market Performance of Indian Luggage Industry

This project presents a programmatic framework for data retrieval, portfolio optimization, and the empirical testing of foundational financial paradigms using a concentrated basket of Indian consumer discretionary equities. Utilizing the yfinance API, a historical dataset spanning a five-year window (2021–2026) was constructed, focusing on prominent listed Indian brands within the apparel, textile, and luggage sectors (including Trent, Page Industries, and VIP Industries), benchmarked against the National Stock Exchange (NSE) Nifty 50 Index.

Following data extraction, a continuous log-return framework was deployed to model an equal-weighted asset allocation strategy.The portfolio's systemic behavior was evaluated by testing the Capital Asset Pricing Model (CAPM) via an Ordinary Least Squares (OLS) regression against the market proxy. The regression model revealed a statistically significant market Beta (𝛽 = 0.8917, p < 0.001), establishing that market risk premium strongly drives portfolio performance, while the alpha intercept (𝛼 = -0.0002, p = 0.587) was found to be statistically indistinguishable from zero, empirically validating CAPM's primary assumptions for this asset class.

Concurrently, the Weak-Form Efficient Market Hypothesis (EMH) was tested using the Ljung-Box Q-test and Autocorrelation Function (ACF) diagnostics to evaluate whether past price innovations could predict future returns. The diagnostic tests systematically rejected the random walk null hypothesis, yielding significant serial dependence across multiple temporal lags (Lag 1 p ≈ 0.029). The collective findings suggest that while asset pricing models like CAPM accurately account for systematic risk profiles, localized sector concentration in the Indian consumer retail landscape preserves distinct informational friction and momentum-driven inefficiencies, directly challenging the assumptions of absolute market efficiency.

Detailed Report:

Finance Data Science project .pdf

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Link: https://github.com/Radhin-Krishna-R/market-performance-Luggage-Industry-India/tree/main

Key Takeaways & Analytical Insights

This comprehensive study yields several clear insights for quantitative portfolio managers and researchers tracking emerging consumer trends:

• CAPM Proves Valid, But Incomplete: The CAPM holds true when evaluating broad beta dependencies, showing that the portfolio is well-integrated into the systemic footprint of the National Stock Exchange. However, the relatively low R2 value (37.6%) underscores that sector-specific factors drive a substantial portion of variance, requiring deep sector analysis alongside simple market metrics.

• The Consumption Momentum Anomaly: The rejection of Weak-Form EMH serves as the most actionable insight from this study. In the context of the Indian consumer discretionary sector, price trends do not adjust immediately or randomly. Instead, factors like retail consumer momentum, shifting post pandemic lifestyle behaviors, and institutional capital inflows into premium brands (such as Tata's Trent) create persistent, short-term trends.

• Strategic Allocation Value: For algorithmic or systemic asset managers, the significant serial dependencies found in Lags 1 and 2 indicate that momentum-based trading strategies or short-term multi-factor models could potentially capture alpha within this specialized retail asset ecosystem.