MCX ICOMDEX (India) Market Value
| MCIALUM Index | 12,030 83.40 0.70% |
| Symbol | MCX |
MCX ICOMDEX '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 MCX ICOMDEX's index 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 MCX ICOMDEX.
| 03/26/2024 |
| 04/25/2024 |
If you would invest 0.00 in MCX ICOMDEX on March 26, 2024 and sell it all today you would earn a total of 0.00 from holding MCX ICOMDEX ALUMINIUM or generate 0.0% return on investment in MCX ICOMDEX over 30 days.
MCX ICOMDEX 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 MCX ICOMDEX's index 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 MCX ICOMDEX ALUMINIUM upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.7234 | |||
| Information Ratio | 0.1591 | |||
| Maximum Drawdown | 3.83 | |||
| Value At Risk | (0.89) | |||
| Potential Upside | 2.36 |
MCX ICOMDEX Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for MCX ICOMDEX's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as MCX ICOMDEX's standard deviation. In reality, there are many statistical measures that can use MCX ICOMDEX historical prices to predict the future MCX ICOMDEX's volatility.| Risk Adjusted Performance | 0.1677 | |||
| Total Risk Alpha | 0.1055 | |||
| Sortino Ratio | 0.1999 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of MCX ICOMDEX's price to converge to an average value over time is called mean reversion. However, historically, high market prices usually discourage investors that believe in mean reversion to invest, while low prices are viewed as an opportunity to buy.
Please note, it is not enough to conduct a financial or market analysis of a single entity such as MCX ICOMDEX. Your research has to be compared to or analyzed against MCX ICOMDEX's peers to derive any actionable benefits. When done correctly, MCX ICOMDEX's competitive analysis will give you plenty of quantitative and qualitative data to validate your investment decisions or develop an entirely new strategy toward taking a position in MCX ICOMDEX ALUMINIUM.
MCX ICOMDEX ALUMINIUM Backtested Returns
MCX ICOMDEX ALUMINIUM has Sharpe Ratio of 0.24, which conveys that the index had a 0.24% return per unit of standard deviation over the last 3 months. We have found twenty-seven technical indicators for MCX ICOMDEX, which you can use to evaluate the volatility of the entity. The entity secures a Beta (Market Risk) of 0.0, which conveys not very significant fluctuations relative to the market. the returns on MARKET and MCX ICOMDEX are completely uncorrelated.
Auto-correlation | 0.92 |
Excellent predictability
MCX ICOMDEX ALUMINIUM has excellent predictability. Overlapping area represents the amount of predictability between MCX ICOMDEX time series from 26th of March 2024 to 10th of April 2024 and 10th of April 2024 to 25th of April 2024. The more autocorrelation exist between current time interval and its lagged values, the more accurately you can make projection about the future pattern of MCX ICOMDEX ALUMINIUM price movement. The serial correlation of 0.92 indicates that approximately 92.0% of current MCX ICOMDEX price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.92 | |
| Spearman Rank Test | 0.81 | |
| Residual Average | 0.0 | |
| Price Variance | 107.5 K |
MCX ICOMDEX ALUMINIUM lagged returns against current returns
Autocorrelation, which is MCX ICOMDEX index'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 MCX ICOMDEX's index expected returns. We can calculate the autocorrelation of MCX ICOMDEX returns to help us make a trade decision. For example, suppose you find that MCX ICOMDEX has exhibited high autocorrelation historically, and you observe that the index 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 |
MCX ICOMDEX 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 MCX ICOMDEX index is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if MCX ICOMDEX index is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in MCX ICOMDEX index over time.
Current vs Lagged Prices |
| Timeline |
MCX ICOMDEX Lagged Returns
When evaluating MCX ICOMDEX's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of MCX ICOMDEX index have on its future price. MCX ICOMDEX 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, MCX ICOMDEX autocorrelation shows the relationship between MCX ICOMDEX index current value and its past values and can show if there is a momentum factor associated with investing in MCX ICOMDEX ALUMINIUM.
Regressed Prices |
| Timeline |
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MCX ICOMDEX technical index analysis exercises models and trading practices based on price and volume transformations, such as the moving averages, relative strength index, regressions, price and return correlations, business cycles, index market cycles, or different charting patterns.