Equity indices provide concise measures of market performance and investor sentiment. The Nifty 50 has long been regarded as a primary barometer of Indian large-cap equities and is frequently analyzed in both academic research and practitioner-oriented studies. Its construction, based on free-float market capitalization of fifty leading companies, makes it representative of the Indian equity market’s breadth and depth. The index, therefore, serves not only as a benchmark for fund managers but also as a focal point for studying aggregate market dynamics in one of the world’s largest emerging economies.

Research on emerging markets has consistently emphasized distinctive features compared to developed markets, including elevated volatility, asymmetric return distributions, and heavier tails that imply greater drawdown risk (Bekaert and Harvey, 1995). These characteristics make the analysis of risk-adjusted returns particularly relevant, as they highlight the cyclical and crisis-prone nature of equity returns in economies undergoing structural transformation. At the same time, India’s sustained growth trajectory and expanding capital markets provide a fertile context for exploring how long-term investors experience   both gains and losses through compounding and systematic strategies.

A growing body of literature also underscores the role of compounding in wealth creation and the ability of disciplined, time-based investment strategies to mitigate timing risk. For instance, studies of global equity markets suggest that while short-term volatility can be severe, long-horizon systematic investment approaches, such as periodic contributions, often smooth returns and enhance investor outcomes (Dimson et al., 2009). This is particularly relevant in the Indian context, where systematic investment plans (SIPs) have gained popularity among retail investors as a vehicle for participating in equity markets without the risks associated with lump-sum entry.

Despite these insights, there remains limited research that simultaneously considers three critical dimensions of equity index performance: total returns, drawdown dynamics, and the outcomes of systematic strategies in a unified framework. Much of the existing literature tends to isolate performance metrics, focusing either on volatility and correlation, on crisis periods, or on portfolio outcomes. This study aims to bridge that gap by presenting an integrated, long-horizon analysis that examines return characteristics, the persistence of drawdowns, and   the   implications   of     disciplined

investment strategies. By using a purely synthetic dataset to illustrate the methodology, the analysis provides a transparent demonstration of statistical and computational tools without dependence on proprietary or restricted data. The contribution is therefore both methodological—offering a reproducible framework in R—and conceptual, by reinforcing the importance of considering drawdowns and systematic strategies alongside returns in emerging market equity analysis. The remaining structure of this article is organized as follows. Section 2 reviews the relevant literature on equity index performance, valuation, and investment strategies in both developed and emerging markets. Section    3    outlines    the           data    construction   and methodology, including the design of the synthetic dataset, the computation of returns, drawdowns, and valuation measures, and the framework for comparing lump-sum and systematic investment outcomes. Section 4 presents the empirical results, highlighting long- horizon return distributions, drawdown profiles, and the implications of different investment strategies. Section 6 provides a discussion of the findings in the context of prior research and market behavior, while Section 7 & 8 concludes with key insights, practical implications, and avenues for future research.

The behavior of equity markets and long-term investment outcomes have been extensively studied in both academic and practitioner literature. Studies on Indian markets, in particular the Nifty 50 index, provide evidence on risk–return trade offs, drawdown dynamics, and the impact of systematic investment plans (SIPs). Early contributions emphasized the random walk hypothesis and market efficiency (Fama,1970). Later, anomalies such as momentum and mean reversion challenged this view (De Bondt and Thaler, 1985, Jegadeesh and Titman, 1993). The long-term equity premium was also examined in a global context (Mehra and Prescott, 1985), showing that equities consistently outperformed risk-free assets despite periods of severe drawdowns.

Research on emerging markets highlighted higher volatility and episodic drawdowns compared to developed markets (Bekaert and Harvey, 1995). For India, Rao (2018) examined the evolution of the Nifty 50, reporting that structural reforms and liberalization policies influenced its risk–return profile. Similarly, Mukherjee and Banerjee (2020) assessed SIPs in Indian equity funds, showing that rupee-cost averaging reduced volatility exposure for retail investors. The comparative performance of SIP versus lump-sum strategies has also been studied in different contexts, generally suggesting that SIPs provide smoother experiences for investors during turbulent periods (Jain and Sharma, 2015).

Other scholars investigated valuation ratios such as price-to-earnings (PE), price-to-book (PB), and dividend yields as predictors of long-term returns. Evidence from U.S. markets suggest a significant predictive relationship between initial valuations   and subsequent returns (Campbell et al., 1997, Shiller, 2000). Indian evidence also supports this to an extent, with lower PE and higher dividend yields historically linked to higher subsequent returns (Chaturvedi and Srivastava, 2019).

Studies on drawdowns emphasize their psychological and financial significance. Martin (2007) argued that drawdowns better capture investor experience than volatility alone. More recently, machine learning and econometric approaches have been applied to forecast crash risk and extreme events in emerging equity markets (Gupta and Yadav, 2021, Singh and Mehta, 2022).

Furthermore, long-horizon analyses such as Dimson et al. (2009) and Goetzmann (2016) emphasized that patient investors tend to benefit significantly despite interim crises. For Indian markets, Bharat and Iyer (2021) noted that consistent exposure to equity indices over decades outperformed fixed-income alternatives, even after adjusting for inflation and drawdowns.

Overall, the literature highlights three broad themes:

• Equity markets offer superior long-term returns but involve significant interim risks;
• Systematic strategies such as SIPs mitigate timing risk and behavioral biases; and
• Valuation ratios provide some predictive power for future returns, albeit imperfectly. The present study situates itself in this tradition by focusing on the Nifty 50 index from 1988–2024, analyzing returns, drawdowns, and the outcomes of systematic investment strategies.

Measures. Annual total return Rt is defined as the one-year percentage change in the total return index (TRI). Rolling compounded growth is computed for windows k ∈{5,10} as

Rolling volatility is the standard deviation of annual returns over the same windows. Sharpe ratios are calculated as the mean excess return divided by its standard deviation with an assumed annual risk-free rate rf = 6%. Drawdowns are computed from the running peak of the cumulative wealth process. SIP outcomes are generated by investing a constant amount at each year-end; lump-sum outcomes come from a single initial investment. Where valuation series are present (P/E, P/B, dividend yield), each valuation at year t is paired with the forward 5-year CAGR from t to t+5.

Synthetic (Formality) Dataset. To keep the study self-contained, we construct a formal, synthetic yearly dataset for 1988–2024 that imitates stylized features of an emerging equity market: average annual return near 12–14%, volatility around 22–28%, episodic negative shocks (e.g., 1997–1998, 2008, 2020), and valuation ratios that vary cyclically. Dividend yield is allowed to drift between 1–2.5%. The dataset is not intended   to replicate any specific historical series; it is used only to demonstrate the methodology and reporting.

Data and Descriptive Statistics

Table 1 summarizes descriptive statistics from the synthetic dataset (1988–2024, excluding partial year if applicable). The sample contains N annual return observations, a buy-and-hold CAGR in the low-teens, and a maximum drawdown exceeding 50%, which is congruent with the intuition that emerging markets experience deeper cyclical losses.

Table 1. Descriptive Statistics (Synthetic Dataset)

Return Dynamics and Rolling Performance. Rolling 5-year and 10-year CAGRs display mean reversion: higher readings are typically observed after multi-year expansions, while compressed readings appear after heavy drawdowns. Dispersion in 5-year windows is wider than in 10-year windows, consistent with stronger smoothing at longer horizons. The annual return distribution shows skewness and heavy tails, which are often cited characteristics of emerging markets.

Drawdowns and Recovery. Top drawdown episodes are deep and prolonged. The largest synthetic peak-to-trough loss exceeds 50%, with recovery taking multiple years. This reinforces the relevance of drawdown-aware risk budgeting and the sequence-of- returns problem for investors who commit funds in a single tranche.

SIP vs. Lump-Sum Outcomes. Table 2 contrasts a yearly SIP of 1,00,000 with a 1,00,000 lump sum invested at inception. In the synthetic sample, the SIP path is smoother and often achieves a competitive internal rate of return (IRR), particularly when large downturns occur early or mid-sample. Lump-sum outcomes remain more sensitive to initial valuation and cycle phase.

Table 2. SIP vs. Lump-Sum (Synthetic Outcomes)

Notes: Illustrative synthetic outcomes. IRR uses the timing of contributions and terminal wealth.

Valuation and Forward Five-Year Returns. In the synthetic series, higher P/E and lower dividend yields are associated with reduced forward 5-year CAGRs, while lower P/E and higher yields align with stronger forward outcomes. P/B broadly agrees with P/E, though the association can be noisier in short samples. These stylized results echo the idea that valuations have informational content for medium-run equity returns (Campbell and Shiller, 1998, Ramanathan and Thomas, 2016).

Robustness and Sensitivity

Five checks are performed on the synthetic data:

1. Risk-free rate: Sharpe ratios are recomputed at rf ∈ {0%,4%,6%}; qualitative conclusions remain similar.
2. Last-year treatment: Treating 2024 as partial or full changes only the most recent rolling windows without altering long-sample inferences.
3. Return definition: Using log-returns instead of simple returns does not materially change ranking of strategies.
4. Outliers: Winsorizing extreme annual returns narrows dispersion but preserves the SIP vs. lump- sum ordering.
5. Crisis subsamples: Removing large negative years reduces drawdown severity but leaves the direction of valuation-return associations intact.

Using a formal, synthetic dataset allows the methodology to be demonstrated transparently while preserving the practical lessons commonly emphasized in emerging-market research. Three main insights arise from the exercise. First, long-run compounding can remain robust despite severe short-run losses, reinforcing the principle that time in the market often outweighs attempts to time the market. Second, drawdown risk is material and substantially shapes the

investor experience, especially in markets prone to volatility clustering and macroeconomic shocks. Third, disciplined contribution strategies, such as systematic investment plans (SIPs), act as effective risk-mitigation tools by spreading entry points across different valuation environments and reducing exposure to concentrated timing risk.

The analysis also illustrates the importance of valuation-based framing. Synthetic valuation ratios demonstrate that apparently modest differences in initial pricing can translate into multi-year performance gaps, echoing findings from both developed and emerging markets that valuation levels, while noisy in the short run, influence long-term return expectations. At the same time, this predictive content remains sample- dependent and unstable at shorter horizons, underscoring the limits of over-reliance on valuation metrics in tactical decision-making.

Beyond methodological clarity, this framework highlights pedagogical value. By showing how return series, rolling CAGRs, drawdowns, and SIP versus lump-sum comparisons can be constructed in a reproducible environment, the study creates a template that students, researchers, and practitioners can adapt to real-world data. Importantly, the use of synthetic data ensures accessibility without proprietary barriers, while also reminding readers that real-market frictions—such as taxes, transaction costs, and liquidity constraints— would meaningfully alter outcomes. Hence, the discussion situates the synthetic demonstration within the broader landscape of financial research, bridging methodological rigor and applied investment relevance.

This study presents an end-to-end template for analysing long-horizon equity performance, drawdowns, and strategy outcomes using annual data. With a self-contained synthetic dataset, it demonstrates how to compute returns, rolling CAGRs, drawdowns, and SIP versus lump-sum comparisons, and how to relate valuation levels to forward performance. The exercise shows that while compounding can deliver significant wealth creation over long horizons, investor experiences are strongly influenced by interim drawdowns and entry-point valuation levels. Systematic contribution strategies, such as SIPs, provide a meaningful cushion against these risks by smoothing entry costs over time.

The approach has multiple implications. For academics, it provides a reproducible framework for teaching and illustrating core concepts in asset returns, risk, and investor behavior without reliance on restricted datasets. For practitioners, it reinforces that both valuation awareness and disciplined contributions are essential elements of long-run investment success. For policymakers, it suggests that facilitating access to systematic investment vehicles can help retail investors better withstand volatility in emerging markets.

Future work can extend this template by incorporating formal predictability tests, alternative risk measures such as downside deviation and maximum recovery times, regimes witching models to capture    structural

breaks, and more granular cash flow timing. Furthermore, adapting the methodology to higher- frequency datasets could provide insights into intrayear volatility, contagion effects, and the impact of global shocks. Thus, while the present study emphasizes methodological clarity and accessibility, it also opens multiple pathways for richer empirical exploration and practical application in emerging market finance.

Author’s Contributions: All authors contributed equally in this paper.

b No funding.

Availability of Data and Materials: Not applicable.

Declarations:                  Ethical Approval: Not required.

Competing Interests: Not applicable.

Disclosure statement: No potential conflict of interest was reported by the author(s).

1. Kaert G, Harvey CR. Emerging equity markets and their characteristics. J Portf Manag. 1995;21(2):15–26.
2. Bharat S, Iyer V. Long-term returns from Indian equities: A comparative perspective. Asian Econ Financ Rev. 2021;11(5):450–8.
3. Campbell JY, Lo AW, MacKinlay AC. The econometrics of financial markets. Princeton (NJ): Princeton University Press; 1997.
4. Campbell JY, Shiller RJ. Valuation ratios and the long-run stock market outlook. J Portf Manag. 1998;24(2):11–26.
5. Chaturvedi A, Srivastava P. Valuation ratios and stock market returns: Evidence from India. J Emerg Mark Financ. 2019;18(2):157–77.
6. De Bondt WFM, Thaler RH. Does the stock market overreact? J Finance. 1985;40(3):793–805.
7. Dimson E, Marsh P, Staunton M. Triumph of the optimists: 101 years of global investment returns. Princeton (NJ): Princeton University Press; 2009.
8. Fama EF. Efficient capital markets: A review of theory and empirical work. J Finance. 1970;25(2):383–417.
9. Goetzmann WN. Money changes everything: How finance made civilization possible. Princeton (NJ): Princeton University Press; 2016.
10. Gupta A, Yadav M. Forecasting stock market crashes in emerging markets using machine learning. Appl Econ Lett. 2021;28(15):1321–5.
11. Jain R, Sharma K. Investment strategies in volatile markets: SIP versus lump sum. Indian J Finance. 2015;9(7):23–35.
12. Jegadeesh N, Titman S. Returns to buying winners and selling losers: Implications for stock market efficiency. J Finance. 1993;48(1):65–91.