Bny Mellon Small Fund Market Value
MISCX Fund | USD 17.43 0.05 0.29% |
Symbol | Bny |
Bny Mellon '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 Bny Mellon'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 Bny Mellon.
03/20/2024 |
| 04/19/2024 |
If you would invest 0.00 in Bny Mellon on March 20, 2024 and sell it all today you would earn a total of 0.00 from holding Bny Mellon Small or generate 0.0% return on investment in Bny Mellon over 30 days. Bny Mellon is related to or competes with Bny Mellon, Bny Mellon, Bny Mellon, Bny Mellon, Bny Mellon, Bny Mellon, and Bny Mellon. The fund normally invests at least 80 percent of its net assets, plus any borrowings for investment purposes, in equity ... More
Bny Mellon 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 Bny Mellon'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 Bny Mellon Small upside and downside potential and time the market with a certain degree of confidence.
Information Ratio | (0.08) | |||
Maximum Drawdown | 5.5 | |||
Value At Risk | (2.08) | |||
Potential Upside | 1.63 |
Bny Mellon Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Bny Mellon's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Bny Mellon's standard deviation. In reality, there are many statistical measures that can use Bny Mellon historical prices to predict the future Bny Mellon's volatility.Risk Adjusted Performance | (0) | |||
Jensen Alpha | (0.11) | |||
Total Risk Alpha | (0.13) | |||
Treynor Ratio | (0.02) |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Bny Mellon'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.
Bny Mellon Small Backtested Returns
Bny Mellon Small secures Sharpe Ratio (or Efficiency) of -0.0509, which signifies that the fund had a -0.0509% return per unit of risk over the last 3 months. Bny Mellon Small exposes twenty-one different technical indicators, which can help you to evaluate volatility embedded in its price movement. Please confirm Bny Mellon's Risk Adjusted Performance of (0), mean deviation of 0.8443, and Standard Deviation of 1.12 to double-check the risk estimate we provide. The fund shows a Beta (market volatility) of 1.43, which signifies a somewhat significant risk relative to the market. As the market goes up, the company is expected to outperform it. However, if the market returns are negative, Bny Mellon will likely underperform.
Auto-correlation | 0.20 |
Weak predictability
Bny Mellon Small has weak predictability. Overlapping area represents the amount of predictability between Bny Mellon time series from 20th of March 2024 to 4th of April 2024 and 4th of April 2024 to 19th 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 Bny Mellon Small price movement. The serial correlation of 0.2 indicates that over 20.0% of current Bny Mellon price fluctuation can be explain by its past prices.
Correlation Coefficient | 0.2 | |
Spearman Rank Test | 0.17 | |
Residual Average | 0.0 | |
Price Variance | 0.21 |
Bny Mellon Small lagged returns against current returns
Autocorrelation, which is Bny Mellon 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 Bny Mellon's mutual fund expected returns. We can calculate the autocorrelation of Bny Mellon returns to help us make a trade decision. For example, suppose you find that Bny Mellon 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 |
Bny Mellon 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 Bny Mellon mutual fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Bny Mellon mutual fund is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Bny Mellon mutual fund over time.
Current vs Lagged Prices |
Timeline |
Bny Mellon Lagged Returns
When evaluating Bny Mellon's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Bny Mellon mutual fund have on its future price. Bny Mellon 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, Bny Mellon autocorrelation shows the relationship between Bny Mellon mutual fund current value and its past values and can show if there is a momentum factor associated with investing in Bny Mellon Small.
Regressed Prices |
Timeline |
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Bny Mellon 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.