Passive Investing

A comprehensive review and analysis of different modeling approaches for financial index tracking problem ArXiv ID: 2601.03927 “View on arXiv” Authors: Vrinda Dhingra, Amita Sharma, Anubha Goel Abstract Index tracking, also known as passive investing, has gained significant traction in financial markets due to its cost-effective and efficient approach to replicating the performance of a specific market index. This review paper provides a comprehensive overview of the various modeling approaches and strategies developed for index tracking, highlighting the strengths and limitations of each approach. We categorize the index tracking models into three broad frameworks: optimization-based models, statistical-based models and machine learning based data-driven approach. A comprehensive empirical study conducted on the S&P 500 dataset demonstrates that the tracking error volatility model under the optimization-based framework delivers the most precise index tracking, the convex co-integration model, under the statistical-based framework achieves the strongest return-risk balance, and the deep neural network with fixed noise model within the data-driven framework provides a competitive performance with notably low turnover and high computational efficiency. By combining a critical review of the existing literature with comparative empirical analysis, this paper aims to provide insights into the evolving landscape of index tracking and its practical implications for investors and fund managers. ...

On the hidden costs of passive investing ArXiv ID: 2506.21775 “View on arXiv” Authors: Iro Tasitsiomi Abstract Passive investing has gained immense popularity due to its low fees and the perceived simplicity of focusing on zero tracking error, rather than security selection. However, our analysis shows that the passive (zero tracking error) approach of waiting until the market close on the day of index reconstitution to purchase a stock (that was announced days earlier as an upcoming addition) results in costs amounting to hundreds of basis points compared to strategies that involve gradually acquiring a small portion of the required shares in advance with minimal additional tracking errors. In addition, we show that under all scenarios analyzed, a trader who builds a small inventory post-announcement and provides liquidity at the reconstitution event can consistently earn several hundreds of basis points in profit and often much more, assuming minimal risk. ...

Risk Analysis of Passive Portfolios ArXiv ID: 2407.08332 “View on arXiv” Authors: Unknown Abstract In this work, we present an alternative passive investment strategy. The passive investment philosophy comes from the Efficient Market Hypothesis (EMH), and its adoption is widespread. If EMH is true, one cannot outperform market by actively managing their portfolio for a long time. Also, it requires little to no intervention. People can buy an exchange-traded fund (ETF) with a long-term perspective. As the economy grows over time, one expects the ETF to grow. For example, in India, one can invest in NETF, which suppose to mimic the Nifty50 return. However, the weights of the Nifty 50 index are based on market capitalisation. These weights are not necessarily optimal for the investor. In this work, we present that volatility risk and extreme risk measures of the Nifty50 portfolio are uniformly larger than Markowitz’s optimal portfolio. However, common people can’t create an optimised portfolio. So we proposed an alternative passive investment strategy of an equal-weight portfolio. We show that if one pushes the maximum weight of the portfolio towards equal weight, the idiosyncratic risk of the portfolio would be minimal. The empirical evidence indicates that the risk profile of an equal-weight portfolio is similar to that of Markowitz’s optimal portfolio. Hence instead of buying Nifty50 ETFs, one should equally invest in the stocks of Nifty50 to achieve a uniformly better risk profile than the Nifty 50 ETF portfolio. We also present an analysis of how portfolios perform to idiosyncratic events like the Russian invasion of Ukraine. We found that the equal weight portfolio has a uniformly lower risk than the Nifty 50 portfolio before and during the Russia-Ukraine war. All codes are available on GitHub (\url{“https://github.com/sourish-cmi/quant/tree/main/Chap_Risk_Anal_of_Passive_Portfolio"}). ...

Fundamental Indexation ArXiv ID: ssrn-713865 “View on arXiv” Authors: Unknown Abstract A trillion-dollar industry is based on investing in or benchmarking to capitalization-weighted indexes, even though the finance literature rejects the mean-vari Keywords: capitalization-weighted indexes, mean-variance, passive investing, benchmarking, portfolio optimization, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper presents a straightforward, intuitive strategy (fundamental indexing) with minimal mathematical derivations, but heavily relies on empirical backtests, real-world benchmark comparisons, and data analysis to challenge capitalization-weighted norms. flowchart TD A["Research Goal:<br/>Test if capitalization-weighted indexes<br/>are truly optimal"] --> B["Methodology:<br/>Compare Cap-Weighted vs.<br/>Fundamental Indexation"] B --> C["Data: Equities &<br/>Fundamental Metrics"] C --> D["Computation:<br/>Mean-Variance Optimization<br/>& Portfolio Simulation"] D --> E["Key Finding:<br/>Fundamental Indexation<br/>Outperforms Cap-Weighting"] E --> F["Outcome:<br/>Rejection of passive indexing<br/>as mean-variance efficient"]

Fundamental Indexation ArXiv ID: ssrn-604842 “View on arXiv” Authors: Unknown Abstract A trillion-dollar industry is based on investing in or benchmarking to capitalization-weighted indexes, even though the finance literature rejects the mean-vari Keywords: capitalization-weighted indexes, mean-variance, passive investing, benchmarking, portfolio optimization, Equities Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper involves moderate mathematical finance concepts like portfolio optimization and benchmark analysis, but it is heavily data-driven, featuring extensive backtesting, real-world index performance comparisons, and discussion of implementation for a trillion-dollar industry. flowchart TD A["Research Goal<br>Test: Does capitalization weighting<br>violate mean-variance efficiency?"] --> B["Methodology<br>Constrained Optimization<br>vs. Capitalization Weighting"] B --> C["Input: Historical Returns<br>U.S. Large Cap Equities"] C --> D["Computational Process<br>Maximize Sharpe Ratio<br>Under Optimization Constraints"] D --> E{"Key Finding 1: Efficiency<br>Optimal Portfolio Sharpe Ratio<br>> Cap-Weighted Portfolio?"} E -- Yes --> F["Outcome: Cap-weighting is<br>Mean-Variance Inefficient"] E -- No --> G["Outcome: Cap-weighting is<br>Mean-Variance Efficient"] F --> H["Key Finding 2: Performance<br>Fundamental Indexation<br>Outperforms Cap-Weighting"] G --> H H --> I["Key Takeaway<br>Trillion-dollar cap-weighted industry<br>is suboptimal vs. optimized portfolios"]