# Keep It Simple: Why Basic Investment Strategies Might Be Your Best Bet
*By Kheelan Sarathee* · April 2025

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## 1. Introduction

In this project, I investigate whether sophisticated investment strategies outperform basic approaches anyone can implement. Using historical data from the S&P 500 index spanning from 2000 to early 2025, I compare three distinct investment strategies with varying levels of complexity: Dollar-Cost Averaging (simple), Buy-the-Dip (moderate), and SMA Crossover (complex).

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## 2. Data & Methods

### 2.1 Data Sources
- **Price data:** I used Python and the `yfinance` library to download historical daily price data for the S&P 500 index (^GSPC), covering January 2000 to December 2024. The S&P 500, comprising 500 of the largest publicly traded U.S. companies, serves as a widely recognised benchmark for assessing the overall performance of the U.S. equity market.

### 2.2 Tools & Reproducibility

- This analysis was conducted entirely using Python, allowing systematic backtesting of each investment strategy.
- For readers interested in exploring, replicating, or extending my results, all code and detailed documentation are available in this [**GitHub repository**](https://github.com/MrChicken90/empirical-project).

### 2.3 Performance Metrics
To compare strategies, I used these standard metrics:

| Metric | What it tells you | How to read it |
|--------|------------------|----------------|
| **CAGR (Compound Annual Growth Rate)** | The constant yearly growth rate needed to turn the starting balance into the ending balance. | Higher → faster compounding. |
| **Sharpe Ratio** | Return divided by volatility; shows how much return you earn for each unit of risk. | Above 1 is generally good; higher is better. |
| **Max Drawdown** | The deepest peak-to-trough fall along the equity curve. | Closer to zero (less-negative) means smaller worst-case loss. |

## 3. Strategy Overviews

### 3.1 Dollar-Cost Averaging (DCA)
**Complexity ★☆☆☆☆**

Dollar-Cost Averaging (DCA) is one of the simplest and most accessible investment strategies. It involves investing a fixed amount of money at regular intervals - regardless of market conditions, economic sentiment, or technical signals. In this analysis, I simulated investing **$1,000 per month** on a randomly selected day between 2000 and 2025.

<img
src="https://hackmd.io/_uploads/Bk01rHY1xx.png"
alt="Screenshot 2025-04-25 at 15.29.49"
width="900"
/>

*Figure 1: Portfolio value (blue) vs. total invested capital (red dashed line). The growing green area highlights the effect of compounding returns over time.*

As shown in Figure 1, the DCA portfolio grew steadily, significantly outperforming the amount invested. While downturns like the 2008 crash and the COVID-19 sell-off caused temporary dips, the portfolio quickly rebounded—demonstrating the strength of a long-term approach. The widening gap between investment and portfolio value illustrates the compounding effect, where reinvested gains accelerate growth over time.

DCA requires no financial analysis and delivers strong results—proving that simplicity can outperform more complex strategies.

**Key takeaways from the results:**

- 📈 **Consistent Growth:** The portfolio value steadily increased over time, significantly outperforming the total amount invested.
- 🔄 **Resilience to Volatility:** Despite downturns like the 2008 financial crisis and the COVID-19 crash in 2020, the strategy bounced back each time, highlighting the importance of staying invested.
- 💹 **Power of Compounding:** The gap between the invested amount and portfolio value widened over time, showing the strength of long-term compounding returns.

> This strategy’s success reinforces the core message of this project:
> 🧠 *You don’t need complexity to build wealth—just consistency.*

### 3.2 Buy-the-Dip
**Complexity ★★★☆☆**

The Buy-the-Dip strategy is built on a simple rule: invest when the market shows short-term weakness. Specifically, a "dip" is identified when the closing price is in the bottom **25%** of the day’s trading range. If this occurs **two days in a row**, the strategy invests **$1,000 at the next day's opening price**.

This rule-based approach avoids emotional timing by using clear, objective signals. It doesn’t chase bottoms—it waits for confirmation of weakness, then acts.

<img
src="https://hackmd.io/_uploads/SkRYTHYyle.png"
alt="Buy the Dip"
width="900"
/>

*Figure 2: Equity curve for the Buy-the-Dip strategy (2000–2025). Despite a max drawdown of 25.15%, the strategy delivers the highest final portfolio value.*

The results were impressive: the strategy ended with the **highest ROI** among all three approaches, despite a significant drawdown during the 2008 crisis. Its strength lies in deploying capital during volatility—capturing discounted prices that later rebound. However, **active monitoring** is required to spot dips, which may be a drawback for passive investors.

> 📌 *Buy-the-Dip outperformed—but the time commitment and need for real-time dip detection make it less hands-off than DCA.*

**Key observations:**

- 📊 **Highest ROI:** Final portfolio value of ~$1.15M, outperforming both DCA and SMA crossover.
- 🔻 **Drawdown Management:** Maximum drawdown of **25.15%** occurred during the 2008 crisis, but the portfolio quickly recovered and continued upward.
- 📈 **Consistent Upside:** The strategy grows slowly early on, but accelerates post-2010 as more capital is deployed during volatility.
- ⚠️ **Monitoring Required:** While rule-based and relatively simple, this strategy **does require actively tracking the market** to detect dips in real time.

> This strategy proves that basic value-aware rules can enhance returns—but the need for market monitoring means it may not be ideal for investors seeking a fully passive approach like DCA.

### 3.3 Simple Moving Average Crossover
**Complexity ★★★★☆**

This strategy uses two technical indicators—the 50-day and 200-day Simple Moving Averages (SMAs)—to generate trading signals. A **“golden cross”** (50-day SMA crossing above 200-day) triggers a long entry, while a **“death cross”** (50-day crossing below 200-day) signals a short entry. Positions are reversed at each crossover.

<img
src="https://hackmd.io/_uploads/rJIF9rFJxl.png"
alt="1*2d0XNPnmQl7KF-ImNg_REA"
width="900"
/>

*Figure 3: S&P 500 price with SMA-50/200 crossover signals. Red triangles mark long entries, yellow triangles mark long exits. Green triangles indicate short entries and grey triangles represent short exits.*

Over the 25-years, this approach underperformed the simpler alternatives. It generated **22 trades**, with less than half being profitable. The strategy was particularly vulnerable to **false signals** in choppy markets, resulting in **missed gains** and unnecessary trades.

While the SMA crossover is widely used in technical analysis, this backtest suggests that **timing the market with moving averages requires frequent attention and doesn’t always outperform staying invested**.

<img
src="https://hackmd.io/_uploads/ByZPpYtyge.png"
alt="1*2d0XNPnmQl7KF-ImNg_REA"
width="900"
/>

*Figure 4: Equity curve for the SMA crossover strategy (2000–2025), highlighting a maximum drawdown of 33.23%—worse than Buy-the-Dip or DCA.*

While the SMA strategy aimed to reduce risk by exiting during downtrends, it didn’t always succeed. As seen in Figure 4, the strategy suffered a drawdown of over **33%**, particularly during COVID-era volatility, despite being designed to sidestep major declines. This highlights one of the key issues with timing-based strategies: they can generate **false exits** that miss recoveries or worsen losses when re-entry is mistimed.

> ⚠️ *The SMA crossover strategy not only delivered the lowest returns, but also exposed the investor to deeper drawdowns—despite its reputation for defensive timing.*

**Performance overview:**

- 📉 **Mixed Results:** Over 25 years, the SMA strategy underperformed both DCA and Buy-the-Dip, returning ~196%.
- 🔁 **22 Trades Total:** The system entered/exited the market 22 times, producing just 10 profitable trades.
- ⚠️ **Drawbacks:** The strategy struggled with frequent **whipsaws**—false signals during sideways markets—leading to losses and missed opportunities.
- 🧠 **High Complexity:** Requires active monitoring and frequent switching between long and short positions, increasing effort without necessarily boosting returns.

> Despite its popularity in trading communities, the SMA crossover strategy didn’t outperform the simpler alternatives in this analysis—suggesting that added complexity doesn’t always lead to better outcomes.

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## 4. Head-to-Head Comparison

<img
src="https://hackmd.io/_uploads/HJpjl1kgeg.png"
alt="1*2d0XNPnmQl7KF-ImNg_REA"
width="900"
/>

*Figure 5 – Cumulative account value for the three strategies. Cyan = DCA · Yellow = Buy-the-Dip · Magenta = SMA Crossover. The red vertical line marks the March 2020 COVID-19 crash.*

| Strategy | Final Value (\$) | CAGR (%) | Sharpe | Max DD (%) |
|-------------------|-----------------:|---------:|-------:|-----------:|
| **Buy-the-Dip** | **1 151 062** | **6.03** | 1.15 | **-25.15** |
| **DCA** | 1 121 562 | 5.43 | 1.07 | -46.18 |
| **SMA Crossover** | 888 215 | 4.45 | **1.20** | -33.23 |

### How the Strategies Handled the COVID Crash

* **Buy-the-Dip** weathered the storm best, falling only about **-25 %**. By deploying capital at panic prices the strategy cushioned the blow and powered a rapid rebound.
* **SMA Crossover** exited late and re-entered late. The lagging trend signal locked in a deeper **-33 %** trough and surrendered early-recovery gains.
* **Dollar-Cost Averaging (DCA)** had no shock absorber: it rode the full decline, dropping roughly **-46 %**.

### At a glance

Three simple rules produced three very different journeys:

- **Buy-the-Dip** came out on top. By adding cash only after consecutive dips, it finished with the largest portfolio value of around $1.15 million - and the fastest compound growth, roughly 6 % per year. Its worst decline was -25 %, which was the lowest drawdownd out of the other strategies.

- **Dollar-Cost Averaging** was the quiet workhorse. Automatic monthly purchases pushed the account to nearly $1.12 million with a respectable 5.4 % CAGR. The trade-off for that effortlessness is pain during deep market swoons: drawdowns reached -46 %, the steepest of the trio, before the strategy ground its way back up.

- **SMA 50/200 Crossover** had the highest Sharpe ratio (1.20), which beat the others — yet it ended with the smallest balance, about $888,215 . Trend signals helped trim some losses, but frequent whipsaws and late re-entries left too much profit on the table to keep pace with the simpler rules.

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## 5. Discussion

| Strategy | Best For | Drawback | Overall Verdict |
|-----------------|-----------------------------|------------------------------------|-----------------------------------------------|
| **DCA** | Passive, long-term investors| Suffers big drawdowns | Simple & effective, but not optimally timed |
| **Buy-the-Dip** | Strategic capital deployment| Requires monitoring | Best return-risk balance |
| **SMA Crossover** | Technical traders | Whipsaw risk, underperformance | Needs refining—didn't deliver as hoped |

The table above provides a clear snapshot of each strategy's trade-off between return, volatility, and drawdown. However, a deeper look reveals important behavioural and practical considerations.

### Dollar-Cost Averaging (DCA)
DCA represents a fully passive investment approach: consistent monthly contributions regardless of market conditions. Over time, this built a strong portfolio that tripled the invested capital. While it endured the **largest drawdown** of all three strategies (−46.18%), it required **no market monitoring**, making it the most convenient and emotionally neutral choice. Its compounding benefits became increasingly clear after periods of market recovery.

### Buy-the-Dip
Buy-the-Dip achieved the **highest overall return** with a relatively modest drawdown (−25.15%). By deploying capital during periods of weakness, it avoided buying at market peaks and benefited from rebounds. It demands a small level of engagement to track dip signals, but the payoff was significant. This strategy strikes an effective balance between passive and active investing, offering **strong performance without requiring complex models** or frequent intervention.

### SMA Crossover
Despite a solid Sharpe ratio (1.20), the SMA Crossover strategy **underperformed** both in total return and capital growth. It was meant to sidestep downturns by reacting to trend changes, but in practice it suffered from **false signals** in sideways markets and was often late to re-enter during recoveries. It also required the most ongoing monitoring, yet delivered the lowest final value. This outcome highlights a key lesson: **more complexity does not guarantee better performance**—and in some cases, it introduces new risks.

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## 6. Conclusion

For anyone who doesn’t fancy watching charts, **Dollar-Cost Averaging is the clear winner**. It needs no market timing, yet still compounded capital three-fold. Yes, you must endure the deepest drawdowns, but the discipline of steady monthly buys ultimately pays off.

If you’re willing to keep an eye on price swings, **Buy-the-Dip** can boost returns and trim drawdowns. The **SMA Crossover**, meanwhile, shows that added complexity is no guarantee of better results; it lagged both simpler rules in absolute pounds earned.

> *Full code, data files, and instructions are on GitHub:*
> https://github.com/MrChicken90/empirical-project