Illuvium Market Value
| ILV Crypto | USD 98.35 0.41 0.42% |
| Symbol | Illuvium |
Illuvium '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 Illuvium's crypto coin 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 Illuvium.
| 02/24/2024 |
| 04/24/2024 |
If you would invest 0.00 in Illuvium on February 24, 2024 and sell it all today you would earn a total of 0.00 from holding Illuvium or generate 0.0% return on investment in Illuvium over 60 days. Illuvium is related to or competes with Solana, XRP, Staked Ether, Open Network, Avalanche, Chainlink, and Ethena. Illuvium is peer-to-peer digital currency powered by the Blockchain technology.
Illuvium 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 Illuvium's crypto coin 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 Illuvium upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 5.85 | |||
| Information Ratio | 0.0828 | |||
| Maximum Drawdown | 46.45 | |||
| Value At Risk | (9.46) | |||
| Potential Upside | 12.87 |
Illuvium Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Illuvium's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Illuvium's standard deviation. In reality, there are many statistical measures that can use Illuvium historical prices to predict the future Illuvium's volatility.| Risk Adjusted Performance | 0.0687 | |||
| Jensen Alpha | 0.6329 | |||
| Total Risk Alpha | (0.32) | |||
| Sortino Ratio | 0.1068 | |||
| Treynor Ratio | 0.7824 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Illuvium'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.
Illuvium Backtested Returns
Illuvium appears to be unreasonably risky, given 3 months investment horizon. Illuvium holds Efficiency (Sharpe) Ratio of 0.1, which attests that digital coin had a 0.1% return per unit of risk over the last 3 months. By evaluating Illuvium's technical indicators, you can evaluate if the expected return of 0.79% is justified by implied risk. Please utilize Illuvium's Risk Adjusted Performance of 0.0687, downside deviation of 5.85, and Market Risk Adjusted Performance of 0.7924 to validate if our risk estimates are consistent with your expectations. The crypto retains a Market Volatility (i.e., Beta) of 0.91, which attests to possible diversification benefits within a given portfolio. Illuvium returns are very sensitive to returns on the market. As the market goes up or down, Illuvium is expected to follow.
Auto-correlation | -0.24 |
Weak reverse predictability
Illuvium has weak reverse predictability. Overlapping area represents the amount of predictability between Illuvium time series from 24th of February 2024 to 25th of March 2024 and 25th of March 2024 to 24th 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 Illuvium price movement. The serial correlation of -0.24 indicates that over 24.0% of current Illuvium price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.24 | |
| Spearman Rank Test | -0.47 | |
| Residual Average | 0.0 | |
| Price Variance | 332.75 |
Illuvium lagged returns against current returns
Autocorrelation, which is Illuvium crypto coin'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 Illuvium's crypto coin expected returns. We can calculate the autocorrelation of Illuvium returns to help us make a trade decision. For example, suppose you find that Illuvium has exhibited high autocorrelation historically, and you observe that the crypto coin 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 |
Illuvium 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 Illuvium crypto coin is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Illuvium crypto coin is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Illuvium crypto coin over time.
Current vs Lagged Prices |
| Timeline |
Illuvium Lagged Returns
When evaluating Illuvium's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Illuvium crypto coin have on its future price. Illuvium 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, Illuvium autocorrelation shows the relationship between Illuvium crypto coin current value and its past values and can show if there is a momentum factor associated with investing in Illuvium.
Regressed Prices |
| Timeline |
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Illuvium technical crypto coin 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, crypto market cycles, or different charting patterns.