Invesco Energy Fund Market Value
FSTEX Fund | USD 31.77 0.16 0.51% |
Symbol | Invesco |
Invesco Energy '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 Invesco Energy's mutual 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 Invesco Energy.
03/27/2024 |
| 04/26/2024 |
If you would invest 0.00 in Invesco Energy on March 27, 2024 and sell it all today you would earn a total of 0.00 from holding Invesco Energy Fund or generate 0.0% return on investment in Invesco Energy over 30 days. Invesco Energy is related to or competes with Invesco Municipal, Invesco Municipal, Invesco Municipal, Oppenheimer Rising, Invesco High, Oppenheimer Strategic, and Oppenheimer International. The fund invests, under normal circumstances, at least 80 percent of its net assets in securities of issuers engaged in ... More
Invesco Energy 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 Invesco Energy's mutual 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 Invesco Energy Fund upside and downside potential and time the market with a certain degree of confidence.
Downside Deviation | 0.8289 | |||
Information Ratio | 0.2206 | |||
Maximum Drawdown | 3.81 | |||
Value At Risk | (1.19) | |||
Potential Upside | 1.65 |
Invesco Energy Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Invesco Energy's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Invesco Energy's standard deviation. In reality, there are many statistical measures that can use Invesco Energy historical prices to predict the future Invesco Energy's volatility.Risk Adjusted Performance | 0.2032 | |||
Jensen Alpha | 0.2579 | |||
Total Risk Alpha | 0.159 | |||
Sortino Ratio | 0.2211 | |||
Treynor Ratio | (18.48) |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Invesco Energy'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.
Invesco Energy Backtested Returns
Invesco Energy appears to be very steady, given 3 months investment horizon. Invesco Energy holds Efficiency (Sharpe) Ratio of 0.28, which attests that the entity had a 0.28% return per unit of risk over the last 3 months. We have found twenty-seven technical indicators for Invesco Energy, which you can use to evaluate the volatility of the entity. Please utilize Invesco Energy's Market Risk Adjusted Performance of (18.47), downside deviation of 0.8289, and Risk Adjusted Performance of 0.2032 to validate if our risk estimates are consistent with your expectations. The fund retains a Market Volatility (i.e., Beta) of -0.0139, which attests to not very significant fluctuations relative to the market. As returns on the market increase, returns on owning Invesco Energy are expected to decrease at a much lower rate. During the bear market, Invesco Energy is likely to outperform the market.
Auto-correlation | -0.36 |
Poor reverse predictability
Invesco Energy Fund has poor reverse predictability. Overlapping area represents the amount of predictability between Invesco Energy time series from 27th of March 2024 to 11th of April 2024 and 11th of April 2024 to 26th 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 Invesco Energy price movement. The serial correlation of -0.36 indicates that just about 36.0% of current Invesco Energy price fluctuation can be explain by its past prices.
Correlation Coefficient | -0.36 | |
Spearman Rank Test | 0.09 | |
Residual Average | 0.0 | |
Price Variance | 0.12 |
Invesco Energy lagged returns against current returns
Autocorrelation, which is Invesco Energy mutual 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 Invesco Energy's mutual fund expected returns. We can calculate the autocorrelation of Invesco Energy returns to help us make a trade decision. For example, suppose you find that Invesco Energy has exhibited high autocorrelation historically, and you observe that the mutual 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 |
Invesco Energy 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 Invesco Energy mutual fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Invesco Energy mutual fund is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Invesco Energy mutual fund over time.
Current vs Lagged Prices |
Timeline |
Invesco Energy Lagged Returns
When evaluating Invesco Energy's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Invesco Energy mutual fund have on its future price. Invesco Energy 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, Invesco Energy autocorrelation shows the relationship between Invesco Energy mutual fund current value and its past values and can show if there is a momentum factor associated with investing in Invesco Energy Fund.
Regressed Prices |
Timeline |
Some investors attempt to determine whether the market's mood is bullish or bearish by monitoring changes in market sentiment. Unlike more traditional methods such as technical analysis, investor sentiment usually refers to the aggregate attitude towards Invesco Energy in the overall investment community. So, suppose investors can accurately measure the market's sentiment. In that case, they can use it for their benefit. For example, some tools to gauge market sentiment could be utilized using contrarian indexes, Invesco Energy's short interest history, or implied volatility extrapolated from Invesco Energy options trading.
Building efficient market-beating portfolios requires time, education, and a lot of computing power!
The Portfolio Architect is an AI-driven system that provides multiple benefits to our users by leveraging cutting-edge machine learning algorithms, statistical analysis, and predictive modeling to automate the process of asset selection and portfolio construction, saving time and reducing human error for individual and institutional investors.
Try AI Portfolio ArchitectCheck out Invesco Energy Correlation, Invesco Energy Volatility and Invesco Energy Alpha and Beta module to complement your research on Invesco Energy. Note that the Invesco Energy information on this page should be used as a complementary analysis to other Invesco Energy's statistical models used to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Portfolio Manager module to state of the art Portfolio Manager to monitor and improve performance of your invested capital.
Invesco Energy technical mutual 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.