Nyse Composite Index Market Value
| NYA Index | 18,221 86.86 0.48% |
| Symbol | NYSE |
NYSE Composite '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 NYSE Composite's index 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 NYSE Composite.
| 02/15/2024 |
| 05/15/2024 |
If you would invest 0.00 in NYSE Composite on February 15, 2024 and sell it all today you would earn a total of 0.00 from holding NYSE Composite or generate 0.0% return on investment in NYSE Composite over 90 days.
NYSE Composite 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 NYSE Composite's index 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 NYSE Composite upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.6605 | |||
| Maximum Drawdown | 2.67 | |||
| Value At Risk | (1.23) | |||
| Potential Upside | 1.05 |
NYSE Composite Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for NYSE Composite's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as NYSE Composite's standard deviation. In reality, there are many statistical measures that can use NYSE Composite historical prices to predict the future NYSE Composite's volatility.Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of NYSE Composite'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.
Please note, it is not enough to conduct a financial or market analysis of a single entity such as NYSE Composite. Your research has to be compared to or analyzed against NYSE Composite's peers to derive any actionable benefits. When done correctly, NYSE Composite's competitive analysis will give you plenty of quantitative and qualitative data to validate your investment decisions or develop an entirely new strategy toward taking a position in NYSE Composite.
NYSE Composite Backtested Returns
NYSE Composite has Sharpe Ratio of 0.13, which conveys that the entity had a 0.13% return per unit of standard deviation over the last 3 months. We have found twenty-three technical indicators for NYSE Composite, which you can use to evaluate the volatility of the index. The index secures a Beta (Market Risk) of 0.0, which conveys not very significant fluctuations relative to the market. the returns on MARKET and NYSE Composite are completely uncorrelated.
Auto-correlation | -0.12 |
Insignificant reverse predictability
NYSE Composite has insignificant reverse predictability. Overlapping area represents the amount of predictability between NYSE Composite time series from 15th of February 2024 to 31st of March 2024 and 31st of March 2024 to 15th 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 NYSE Composite price movement. The serial correlation of -0.12 indicates that less than 12.0% of current NYSE Composite price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.12 | |
| Spearman Rank Test | -0.04 | |
| Residual Average | 0.0 | |
| Price Variance | 66.8 K |
NYSE Composite lagged returns against current returns
Autocorrelation, which is NYSE Composite index'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 NYSE Composite's index expected returns. We can calculate the autocorrelation of NYSE Composite returns to help us make a trade decision. For example, suppose you find that NYSE Composite has exhibited high autocorrelation historically, and you observe that the index 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 |
NYSE Composite 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 NYSE Composite index is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if NYSE Composite index is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in NYSE Composite index over time.
Current vs Lagged Prices |
| Timeline |
NYSE Composite Lagged Returns
When evaluating NYSE Composite's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of NYSE Composite index have on its future price. NYSE Composite 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, NYSE Composite autocorrelation shows the relationship between NYSE Composite index current value and its past values and can show if there is a momentum factor associated with investing in NYSE Composite.
Regressed Prices |
| Timeline |
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NYSE Composite technical index 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, index market cycles, or different charting patterns.