Data Patterns (India) Market Value

DATAPATTNS   2,758  138.10  4.77%   
Data Patterns' market value is the price at which a share of Data Patterns trades on a public exchange. It measures the collective expectations of Data Patterns Limited investors about its performance. Data Patterns is selling for under 2757.50 as of the 19th of July 2025; that is 4.77 percent decrease since the beginning of the trading day. The stock's last reported lowest price was 2735.8.
With this module, you can estimate the performance of a buy and hold strategy of Data Patterns Limited and determine expected loss or profit from investing in Data Patterns over a given investment horizon. Check out Data Patterns Correlation, Data Patterns Volatility and Data Patterns Alpha and Beta module to complement your research on Data Patterns.
Symbol

Please note, there is a significant difference between Data Patterns' value and its price as these two are different measures arrived at by different means. Investors typically determine if Data Patterns is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Data Patterns' price is the amount at which it trades on the open market and represents the number that a seller and buyer find agreeable to each party.

Data Patterns 'What if' Analysis

In the world of financial modeling, what-if analysis is part of sensitivity analysis performed to test how changes in assumptions impact individual outputs in a model. When applied to Data Patterns' stock what-if analysis refers to the analyzing how the change in your past investing horizon will affect the profitability against the current market value of Data Patterns.
0.00
04/20/2025
No Change 0.00  0.0 
In 3 months and 1 day
07/19/2025
0.00
If you would invest  0.00  in Data Patterns on April 20, 2025 and sell it all today you would earn a total of 0.00 from holding Data Patterns Limited or generate 0.0% return on investment in Data Patterns over 90 days. Data Patterns is related to or competes with Cartrade Tech, Manaksia Steels, Akme Fintrade, Steelcast, Aarti Drugs, V Mart, and Visa Steel. Data Patterns is entity of India. It is traded as Stock on NSE exchange. More

Data Patterns Upside/Downside Indicators

Understanding different market momentum indicators often help investors to time their next move. Potential upside and downside technical ratios enable traders to measure Data Patterns' stock current market value against overall market sentiment and can be a good tool during both bulling and bearish trends. Here we outline some of the essential indicators to assess Data Patterns Limited upside and downside potential and time the market with a certain degree of confidence.

Data Patterns Market Risk Indicators

Today, many novice investors tend to focus exclusively on investment returns with little concern for Data Patterns' investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Data Patterns' standard deviation. In reality, there are many statistical measures that can use Data Patterns historical prices to predict the future Data Patterns' volatility.
Hype
Prediction
LowEstimatedHigh
2,7202,7243,033
Details
Intrinsic
Valuation
LowRealHigh
2,2512,2553,033
Details
Naive
Forecast
LowNextHigh
2,7272,7312,735
Details
Earnings
Estimates (0)
LowProjected EPSHigh
11.9512.2912.95
Details

Data Patterns Limited Backtested Returns

Data Patterns appears to be very steady, given 3 months investment horizon. Data Patterns Limited secures Sharpe Ratio (or Efficiency) of 0.16, which denotes the company had a 0.16 % return per unit of risk over the last 3 months. By reviewing Data Patterns' technical indicators, you can evaluate if the expected return of 0.6% is justified by implied risk. Please utilize Data Patterns' Coefficient Of Variation of 575.66, mean deviation of 2.65, and Downside Deviation of 3.16 to check if our risk estimates are consistent with your expectations. On a scale of 0 to 100, Data Patterns holds a performance score of 12. The firm shows a Beta (market volatility) of -0.51, which means possible diversification benefits within a given portfolio. As returns on the market increase, returns on owning Data Patterns are expected to decrease at a much lower rate. During the bear market, Data Patterns is likely to outperform the market. Please check Data Patterns' skewness, and the relationship between the value at risk and day median price , to make a quick decision on whether Data Patterns' price patterns will revert.

Auto-correlation

    
  -0.54  

Good reverse predictability

Data Patterns Limited has good reverse predictability. Overlapping area represents the amount of predictability between Data Patterns time series from 20th of April 2025 to 4th of June 2025 and 4th of June 2025 to 19th of July 2025. The more autocorrelation exist between current time interval and its lagged values, the more accurately you can make projection about the future pattern of Data Patterns Limited price movement. The serial correlation of -0.54 indicates that about 54.0% of current Data Patterns price fluctuation can be explain by its past prices.
Correlation Coefficient-0.54
Spearman Rank Test-0.46
Residual Average0.0
Price Variance6352.71

Data Patterns Limited lagged returns against current returns

Autocorrelation, which is Data Patterns stock's lagged correlation, explains the relationship between observations of its time series of returns over different periods of time. The observations are said to be independent if autocorrelation is zero. Autocorrelation is calculated as a function of mean and variance and can have practical application in predicting Data Patterns' stock expected returns. We can calculate the autocorrelation of Data Patterns returns to help us make a trade decision. For example, suppose you find that Data Patterns has exhibited high autocorrelation historically, and you observe that the stock is moving up for the past few days. In that case, you can expect the price movement to match the lagging time series.
   Current and Lagged Values   
       Timeline  

Data Patterns regressed lagged prices vs. current prices

Serial correlation can be approximated by using the Durbin-Watson (DW) test. The correlation can be either positive or negative. If Data Patterns stock is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Data Patterns stock is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Data Patterns stock over time.
   Current vs Lagged Prices   
       Timeline  

Data Patterns Lagged Returns

When evaluating Data Patterns' market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Data Patterns stock have on its future price. Data Patterns autocorrelation represents the degree of similarity between a given time horizon and a lagged version of the same horizon over the previous time interval. In other words, Data Patterns autocorrelation shows the relationship between Data Patterns stock current value and its past values and can show if there is a momentum factor associated with investing in Data Patterns Limited.
   Regressed Prices   
       Timeline  

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Additional Tools for Data Stock Analysis

When running Data Patterns' price analysis, check to measure Data Patterns' market volatility, profitability, liquidity, solvency, efficiency, growth potential, financial leverage, and other vital indicators. We have many different tools that can be utilized to determine how healthy Data Patterns is operating at the current time. Most of Data Patterns' value examination focuses on studying past and present price action to predict the probability of Data Patterns' future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Data Patterns' price. Additionally, you may evaluate how the addition of Data Patterns to your portfolios can decrease your overall portfolio volatility.