Insurance Portfolio Insurance Fund Market Value
| FSPCX Fund | USD 81.51 0.46 0.57% |
| Symbol | Insurance |
Insurance Portfolio '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 Insurance Portfolio'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 Insurance Portfolio.
| 03/31/2024 |
| 04/30/2024 |
If you would invest 0.00 in Insurance Portfolio on March 31, 2024 and sell it all today you would earn a total of 0.00 from holding Insurance Portfolio Insurance or generate 0.0% return on investment in Insurance Portfolio over 30 days. Insurance Portfolio is related to or competes with Fidelity Puritan, Fidelity Pennsylvania, Fidelity Freedom, Fidelity Freedom, Fidelity Income, Fidelity Real, and Fidelity Real. The fund normally invests at least 80 percent of assets in securities of companies principally engaged in underwriting, ... More
Insurance Portfolio 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 Insurance Portfolio'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 Insurance Portfolio Insurance upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 1.04 | |||
| Information Ratio | (0) | |||
| Maximum Drawdown | 6.08 | |||
| Value At Risk | (1.28) | |||
| Potential Upside | 1.26 |
Insurance Portfolio Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Insurance Portfolio's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Insurance Portfolio's standard deviation. In reality, there are many statistical measures that can use Insurance Portfolio historical prices to predict the future Insurance Portfolio's volatility.| Risk Adjusted Performance | 0.0432 | |||
| Jensen Alpha | 0.0259 | |||
| Total Risk Alpha | (0.02) | |||
| Sortino Ratio | (0) | |||
| Treynor Ratio | 0.1111 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Insurance Portfolio'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.
Insurance Portfolio Backtested Returns
We consider Insurance Portfolio very steady. Insurance Portfolio holds Efficiency (Sharpe) Ratio of 0.0632, which attests that the entity had a 0.0632% return per unit of risk over the last 3 months. We have found twenty-eight technical indicators for Insurance Portfolio, which you can use to evaluate the volatility of the entity. Please check out Insurance Portfolio's Risk Adjusted Performance of 0.0432, downside deviation of 1.04, and Market Risk Adjusted Performance of 0.1211 to validate if the risk estimate we provide is consistent with the expected return of 0.0584%. The fund retains a Market Volatility (i.e., Beta) of 0.42, which attests to possible diversification benefits within a given portfolio. As returns on the market increase, Insurance Portfolio's returns are expected to increase less than the market. However, during the bear market, the loss of holding Insurance Portfolio is expected to be smaller as well.
Auto-correlation | 0.03 |
Virtually no predictability
Insurance Portfolio Insurance has virtually no predictability. Overlapping area represents the amount of predictability between Insurance Portfolio time series from 31st of March 2024 to 15th of April 2024 and 15th of April 2024 to 30th 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 Insurance Portfolio price movement. The serial correlation of 0.03 indicates that only 3.0% of current Insurance Portfolio price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.03 | |
| Spearman Rank Test | -0.65 | |
| Residual Average | 0.0 | |
| Price Variance | 0.76 |
Insurance Portfolio lagged returns against current returns
Autocorrelation, which is Insurance Portfolio 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 Insurance Portfolio's mutual fund expected returns. We can calculate the autocorrelation of Insurance Portfolio returns to help us make a trade decision. For example, suppose you find that Insurance Portfolio 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 |
Insurance Portfolio 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 Insurance Portfolio mutual fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Insurance Portfolio mutual fund is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Insurance Portfolio mutual fund over time.
Current vs Lagged Prices |
| Timeline |
Insurance Portfolio Lagged Returns
When evaluating Insurance Portfolio's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Insurance Portfolio mutual fund have on its future price. Insurance Portfolio 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, Insurance Portfolio autocorrelation shows the relationship between Insurance Portfolio mutual fund current value and its past values and can show if there is a momentum factor associated with investing in Insurance Portfolio Insurance.
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 Insurance Portfolio 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, Insurance Portfolio's short interest history, or implied volatility extrapolated from Insurance Portfolio options trading.
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Try AI Portfolio ArchitectCheck out Insurance Portfolio Correlation, Insurance Portfolio Volatility and Insurance Portfolio Alpha and Beta module to complement your research on Insurance Portfolio. Note that the Insurance Portfolio information on this page should be used as a complementary analysis to other Insurance Portfolio'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 Instant Ratings module to determine any equity ratings based on digital recommendations. Macroaxis instant equity ratings are based on combination of fundamental analysis and risk-adjusted market performance.
Insurance Portfolio 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.