Analytics (South Africa) Market Value
| 0P0000ZSMO | 9.82 0.11 1.11% |
| Symbol | Analytics |
Analytics '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 Analytics' fund 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 Analytics.
| 11/02/2024 |
| 12/02/2024 |
If you would invest 0.00 in Analytics on November 2, 2024 and sell it all today you would earn a total of 0.00 from holding Analytics Ci Balanced or generate 0.0% return on investment in Analytics over 30 days.
Analytics 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 Analytics' fund 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 Analytics Ci Balanced upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.3422 | |||
| Information Ratio | (0.27) | |||
| Maximum Drawdown | 1.74 | |||
| Value At Risk | (0.52) | |||
| Potential Upside | 0.6104 |
Analytics Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Analytics' investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Analytics' standard deviation. In reality, there are many statistical measures that can use Analytics historical prices to predict the future Analytics' volatility.| Risk Adjusted Performance | 0.0868 | |||
| Jensen Alpha | 0.0392 | |||
| Total Risk Alpha | (0.02) | |||
| Sortino Ratio | (0.27) | |||
| Treynor Ratio | (0.92) |
Analytics Ci Balanced Backtested Returns
At this stage we consider Analytics Fund to be very steady. Analytics Ci Balanced secures Sharpe Ratio (or Efficiency) of 0.0995, which signifies that the fund had a 0.0995% return per unit of standard deviation over the last 3 months. We have found twenty-seven technical indicators for Analytics Ci Balanced, which you can use to evaluate the volatility of the entity. Please confirm Analytics' mean deviation of 0.262, and Risk Adjusted Performance of 0.0868 to double-check if the risk estimate we provide is consistent with the expected return of 0.0384%. The fund shows a Beta (market volatility) of -0.0374, which signifies not very significant fluctuations relative to the market. As returns on the market increase, returns on owning Analytics are expected to decrease at a much lower rate. During the bear market, Analytics is likely to outperform the market.
Auto-correlation | 0.50 |
Modest predictability
Analytics Ci Balanced has modest predictability. Overlapping area represents the amount of predictability between Analytics time series from 2nd of November 2024 to 17th of November 2024 and 17th of November 2024 to 2nd of December 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 Analytics Ci Balanced price movement. The serial correlation of 0.5 indicates that about 50.0% of current Analytics price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.5 | |
| Spearman Rank Test | 0.41 | |
| Residual Average | 0.0 | |
| Price Variance | 0.0 |
Analytics Ci Balanced lagged returns against current returns
Autocorrelation, which is Analytics fund'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 Analytics' fund expected returns. We can calculate the autocorrelation of Analytics returns to help us make a trade decision. For example, suppose you find that Analytics has exhibited high autocorrelation historically, and you observe that the fund 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 |
Analytics 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 Analytics fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Analytics fund is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Analytics fund over time.
Current vs Lagged Prices |
| Timeline |
Analytics Lagged Returns
When evaluating Analytics' market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Analytics fund have on its future price. Analytics 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, Analytics autocorrelation shows the relationship between Analytics fund current value and its past values and can show if there is a momentum factor associated with investing in Analytics Ci Balanced.
Regressed Prices |
| Timeline |
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