Wall Street Online

Over the last 30 years, the S&P 500 has generally outperformed most other major asset classes, delivering strong average annual returns, although with notable volatility.

Key points on performance:

• The S&P 500 has delivered approximately a 9% average annual return over the last 30 years, with inflation-adjusted returns around 6.3%. This return reflects dividends reinvested and price appreciation combined.
• Compared to other asset classes over similar periods, the S&P 500's returns have typically surpassed those of U.S. bonds, cash, and real estate. The U.S. housing market, for example, averaged about 5.5% annual growth over recent decades, which is less than the stock market returns.
• Certain alternative assets like real estate investment trusts (REITs) and gold have at times outperformed the S&P 500 from about 1999 through the early 2020s. Some REITs showed total returns in the mid-teens percentage annually, slightly beating the S&P 500’s roughly 13% total return in that timeframe.
• Over a longer historical context since 1928, stocks as represented by the S&P 500 have delivered average returns near 10%, compared to bonds averaging around 4.5%, cash about 3.3%, and real estate approximately 4.2% annually.
• The S&P 500 has endured significant ups and downs including bear markets, recessions, and financial crises during this 30-year span. Despite this, a hypothetical $100 invested 30 years ago in the S&P 500 would have grown substantially more than comparable investments in bonds, real estate, or cash, especially when dividends are reinvested and considering compound growth.

In summary, over the last 30 years, the S&P 500 has been the leading performer versus other major asset classes like bonds, real estate, cash, and gold in most periods, although certain segments like REITs and gold have occasionally outpaced it. Its average annual return around 9% to 10% (nominal) stands out when compared with the typical single-digit returns of alternative investments.

In the last hundred years, the S&P 500 index has had about 25 years with negative returns. This means the index was down for the year roughly 25 times since around 1928.

Additional details include:

• Negative years represent about 27% of all years since 1928, meaning roughly 73% of the years had positive returns.
• Among these down years, 11 experienced double-digit losses.
• Multiple consecutive years of negative returns have occurred but are rare. There have been about 8 instances of two consecutive down years and only three instances of three consecutive down years. Just once during the Great Depression, the index fell for four years in a row.
• Despite these down years, the S&P 500 has shown strong average growth over the long term, averaging about 10-11% annual growth including dividends over the past century.

This analysis is consistent with data showing annual historical returns from 1928 to 2024.

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AI can be helpful in predicting stock prices, but its predictions are not perfectly reliable; it often performs better than traditional models or random guessing but cannot guarantee consistent, substantial profits.

AI models leverage advanced algorithms, deep learning, and natural language processing to analyze vast amounts of financial data, news, and market sentiment more rapidly and objectively than humans. Studies show that certain sophisticated AI models, including deep neural networks, can identify complex patterns and trends in historical data and outperform traditional statistical methods for specific market tasks.

• Accuracy: In controlled academic and market studies, AI models have achieved prediction accuracy rates ranging between 52% and 95%, depending on the specific techniques, data used, and definition of prediction success. Most large language models average about 59% accuracy in predicting next-month stock direction—slightly better than chance, but not reliably so for trading.
• Human vs. AI performance: AI can sometimes outperform average human analysts and reduce human bias, but the top-performing human analysts still compete well with AI in certain scenarios.
• Best uses: AI excels at spotting statistical edges and uncovering hidden patterns across huge datasets, supporting decision-making, risk management, and high-frequency trading, but it doesn’t foresee unpredictable events or market shocks.
• Limitations: The stock market is affected by innumerable unpredictable factors—news, politics, sentiment—which remain difficult to model with high reliability. Even top AI systems cannot guarantee successful predictions nor eliminate risk, and they can occasionally underperform or be misled by rare, outlier events.

In summary, AI is a valuable tool for gaining insights and improving trading strategies, but it is not a guaranteed method for accurately predicting stock prices or ensuring financial success.

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