Z-Score is a statistical measure used to evaluate how far a particular data point is from the mean, expressed in terms of standard deviations. In the context of the stock market, a Z-Score helps investors identify whether a stock is overvalued or undervalued compared to its historical performance or peer group. It’s a valuable tool for traders and analysts seeking to make data-driven investment decisions.

The Z-Score formula is expressed as: Z = (X - _) / _ where X is the observed value, _ is the mean, and _ is the standard deviation. A Z-Score of 0 means the value is equal to the mean, while positive or negative scores indicate how many standard deviations the value lies above or below the mean. For instance, a Z-Score of +2 means the stock’s value is two standard deviations higher than its historical average, possibly suggesting overvaluation.

In market analysis, Z-Score is widely used in models like the Altman Z-Score to assess a company’s financial health and bankruptcy risk. A higher Altman Z-Score implies financial stability, whereas a lower score signals potential distress. It also assists in identifying mean reversion opportunities—where extreme deviations from the average could revert over time.

For investors and analysts, understanding Z-Score aids in risk management and portfolio evaluation. However, it should not be used in isolation; combining it with fundamental and technical analysis offers a more accurate assessment. As per SEBI guidelines, investors should exercise due diligence and avoid relying solely on quantitative indicators for making investment decisions.

In summary, Z-Score serves as an effective tool to measure statistical significance in market data, enabling investors to make informed, research-based financial choices.