Loomis Sayles High Fund Momentum Indicators Stochastic
| LSIOX Fund | USD 9.00 0.02 0.22% |
| Symbol |
The output start index for this execution was eight with a total number of output elements of fifty-three. The Stochastic indicator compares Loomis Sayles closing price to its price range over a given period of time.
Loomis Sayles Technical Analysis Modules
Most technical analysis of Loomis Sayles help investors determine whether a current trend will continue and, if not, when it will shift. We provide a combination of tools to recognize potential entry and exit points for Loomis from various momentum indicators to cycle indicators. When you analyze Loomis charts, please remember that the event formation may indicate an entry point for a short seller, and look at other indicators across different periods to confirm that a breakdown or reversion is likely to occur.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
About Loomis Sayles Predictive Technical Analysis
Predictive technical analysis modules help investors to analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of Loomis Sayles High. We use our internally-developed statistical techniques to arrive at the intrinsic value of Loomis Sayles High based on widely used predictive technical indicators. In general, we focus on analyzing Loomis Mutual Fund price patterns and their correlations with different microeconomic environment and drivers. We also apply predictive analytics to build Loomis Sayles's daily price indicators and compare them against related drivers, such as momentum indicators and various other types of predictive indicators. Using this methodology combined with a more conventional technical analysis and fundamental analysis, we attempt to find the most accurate representation of Loomis Sayles's intrinsic value. In addition to deriving basic predictive indicators for Loomis Sayles, we also check how macroeconomic factors affect Loomis Sayles price patterns. Please read more on our technical analysis page or use our predictive modules below to complement your research.
Learn to be your own money manager
As an individual investor, you need to find a reliable way to track all your investment portfolios' performance accurately. However, your requirements will often be based on how much of the process you decide to do yourself. In addition to allowing you full analytical transparency into your positions, our tools can tell you how much better you can do without increasing your risk or reducing expected return.Did you try this?
Run Fundamentals Comparison Now
Fundamentals ComparisonCompare fundamentals across multiple equities to find investing opportunities |
| All Next | Launch Module |
Loomis Sayles High pair trading
One of the main advantages of trading using pair correlations is that every trade hedges away some risk. Because there are two separate transactions required, even if Loomis Sayles position performs unexpectedly, the other equity can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Loomis Sayles will appreciate offsetting losses from the drop in the long position's value.Loomis Sayles Pair Trading
Loomis Sayles High Pair Trading Analysis
The ability to find closely correlated positions to Loomis Sayles could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Loomis Sayles when you sell it. If you don't do this, your portfolio allocation will be skewed against your target asset allocation. So, investors can't just sell and buy back Loomis Sayles - that would be a violation of the tax code under the "wash sale" rule, and this is why you need to find a similar enough asset and use the proceeds from selling Loomis Sayles High to buy it.
The correlation of Loomis Sayles is a statistical measure of how it moves in relation to other instruments. This measure is expressed in what is known as the correlation coefficient, which ranges between -1 and +1. A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as Loomis Sayles moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Loomis Sayles High moves in either direction, the perfectly negatively correlated security will move in the opposite direction. If the correlation is 0, the equities are not correlated; they are entirely random. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally considered weak.
Correlation analysis and pair trading evaluation for Loomis Sayles can also be used as hedging techniques within a particular sector or industry or even over random equities to generate a better risk-adjusted return on your portfolios.Other Information on Investing in Loomis Mutual Fund
Loomis Sayles financial ratios help investors to determine whether Loomis Mutual Fund is cheap or expensive when compared to a particular measure, such as profits or enterprise value. In other words, they help investors to determine the cost of investment in Loomis with respect to the benefits of owning Loomis Sayles security.
| Volatility Analysis Get historical volatility and risk analysis based on latest market data | |
| Pattern Recognition Use different Pattern Recognition models to time the market across multiple global exchanges | |
| Positions Ratings Determine portfolio positions ratings based on digital equity recommendations. Macroaxis instant position ratings are based on combination of fundamental analysis and risk-adjusted market performance | |
| My Watchlist Analysis Analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like |