Kinea Renda (Brazil) Market Value
| KNRI11 Fund | BRL 164.00 1.05 0.64% |
| Symbol | Kinea |
Kinea Renda '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 Kinea Renda's 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 Kinea Renda.
| 03/08/2024 |
| 05/07/2024 |
If you would invest 0.00 in Kinea Renda on March 8, 2024 and sell it all today you would earn a total of 0.00 from holding Kinea Renda Imobiliria or generate 0.0% return on investment in Kinea Renda over 60 days. Kinea Renda is related to or competes with Kinea Indices, Kinea Rendimentos, and Kinea Fundo. More
Kinea Renda 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 Kinea Renda's 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 Kinea Renda Imobiliria upside and downside potential and time the market with a certain degree of confidence.
| Information Ratio | (0.18) | |||
| Maximum Drawdown | 2.39 | |||
| Value At Risk | (0.96) | |||
| Potential Upside | 0.694 |
Kinea Renda Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Kinea Renda's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Kinea Renda's standard deviation. In reality, there are many statistical measures that can use Kinea Renda historical prices to predict the future Kinea Renda's volatility.| Risk Adjusted Performance | (0.02) | |||
| Jensen Alpha | (0.03) | |||
| Total Risk Alpha | (0.08) | |||
| Treynor Ratio | (0.30) |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Kinea Renda'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.
Kinea Renda Imobiliria Backtested Returns
We consider Kinea Renda very steady. Kinea Renda Imobiliria has Sharpe Ratio of 0.0312, which conveys that the entity had a 0.0312% return per unit of risk over the last 3 months. We have found twenty-three technical indicators for Kinea Renda, which you can use to evaluate the volatility of the fund. Please verify Kinea Renda's Mean Deviation of 0.3746, risk adjusted performance of (0.02), and Standard Deviation of 0.508 to check out if the risk estimate we provide is consistent with the expected return of 0.0155%. The fund secures a Beta (Market Risk) of 0.0861, which conveys not very significant fluctuations relative to the market. As returns on the market increase, Kinea Renda's returns are expected to increase less than the market. However, during the bear market, the loss of holding Kinea Renda is expected to be smaller as well.
Auto-correlation | 0.01 |
Virtually no predictability
Kinea Renda Imobiliria has virtually no predictability. Overlapping area represents the amount of predictability between Kinea Renda time series from 8th of March 2024 to 7th of April 2024 and 7th of April 2024 to 7th of May 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 Kinea Renda Imobiliria price movement. The serial correlation of 0.01 indicates that just 1.0% of current Kinea Renda price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.01 | |
| Spearman Rank Test | 0.47 | |
| Residual Average | 0.0 | |
| Price Variance | 0.42 |
Kinea Renda Imobiliria lagged returns against current returns
Autocorrelation, which is Kinea Renda 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 Kinea Renda's fund expected returns. We can calculate the autocorrelation of Kinea Renda returns to help us make a trade decision. For example, suppose you find that Kinea Renda 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 |
Kinea Renda 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 Kinea Renda fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Kinea Renda fund is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Kinea Renda fund over time.
Current vs Lagged Prices |
| Timeline |
Kinea Renda Lagged Returns
When evaluating Kinea Renda's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Kinea Renda fund have on its future price. Kinea Renda 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, Kinea Renda autocorrelation shows the relationship between Kinea Renda fund current value and its past values and can show if there is a momentum factor associated with investing in Kinea Renda Imobiliria.
Regressed Prices |
| Timeline |
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Kinea Renda technical fund 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, fund market cycles, or different charting patterns.