Financial Institutions Stock Market Value
| FISI Stock | USD 26.92 0.45 1.64% |
| Symbol | Financial |
Financial Institutions Price To Book Ratio
Is Regional Banks space expected to grow? Or is there an opportunity to expand the business' product line in the future? Factors like these will boost the valuation of Financial Institutions. If investors know Financial will grow in the future, the company's valuation will be higher. The financial industry is built on trying to define current growth potential and future valuation accurately. All the valuation information about Financial Institutions listed above have to be considered, but the key to understanding future value is determining which factors weigh more heavily than others.
Quarterly Earnings Growth (0.04) | Dividend Share 1.2 | Earnings Share 3.12 | Revenue Per Share 14.042 | Quarterly Revenue Growth (0.08) |
The market value of Financial Institutions is measured differently than its book value, which is the value of Financial that is recorded on the company's balance sheet. Investors also form their own opinion of Financial Institutions' value that differs from its market value or its book value, called intrinsic value, which is Financial Institutions' true underlying value. Investors use various methods to calculate intrinsic value and buy a stock when its market value falls below its intrinsic value. Because Financial Institutions' market value can be influenced by many factors that don't directly affect Financial Institutions' underlying business (such as a pandemic or basic market pessimism), market value can vary widely from intrinsic value.
Please note, there is a significant difference between Financial Institutions' value and its price as these two are different measures arrived at by different means. Investors typically determine if Financial Institutions is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Financial Institutions' price is the amount at which it trades on the open market and represents the number that a seller and buyer find agreeable to each party.
Financial Institutions '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 Financial Institutions' stock 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 Financial Institutions.
| 06/01/2024 |
| 11/28/2024 |
If you would invest 0.00 in Financial Institutions on June 1, 2024 and sell it all today you would earn a total of 0.00 from holding Financial Institutions or generate 0.0% return on investment in Financial Institutions over 180 days. Financial Institutions is related to or competes with First Community, Community West, First Financial, First Northwest, Home Federal, National Bank, and Kearny Financial. Financial Institutions, Inc. operates as a holding company for the Five Star Bank, a chartered bank that provides bankin... More
Financial Institutions 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 Financial Institutions' stock 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 Financial Institutions upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 2.06 | |||
| Information Ratio | 0.0034 | |||
| Maximum Drawdown | 18.3 | |||
| Value At Risk | (3.22) | |||
| Potential Upside | 3.83 |
Financial Institutions Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Financial Institutions' investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Financial Institutions' standard deviation. In reality, there are many statistical measures that can use Financial Institutions historical prices to predict the future Financial Institutions' volatility.| Risk Adjusted Performance | 0.0473 | |||
| Jensen Alpha | (0.15) | |||
| Total Risk Alpha | (0.26) | |||
| Sortino Ratio | 0.0042 | |||
| Treynor Ratio | 0.0531 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Financial Institutions' 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.
Financial Institutions Backtested Returns
Financial Institutions is not too volatile at the moment. Financial Institutions secures Sharpe Ratio (or Efficiency) of 0.0401, which denotes the company had a 0.0401% return per unit of risk over the last 3 months. We have found twenty-nine technical indicators for Financial Institutions, which you can use to evaluate the volatility of the firm. Please confirm Financial Institutions' Coefficient Of Variation of 1900.63, mean deviation of 1.71, and Downside Deviation of 2.06 to check if the risk estimate we provide is consistent with the expected return of 0.1%. Financial Institutions has a performance score of 3 on a scale of 0 to 100. The firm shows a Beta (market volatility) of 2.34, which means 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, Financial Institutions will likely underperform. Financial Institutions right now shows a risk of 2.58%. Please confirm Financial Institutions semi variance, and the relationship between the treynor ratio and daily balance of power , to decide if Financial Institutions will be following its price patterns.
Auto-correlation | 0.38 |
Below average predictability
Financial Institutions has below average predictability. Overlapping area represents the amount of predictability between Financial Institutions time series from 1st of June 2024 to 30th of August 2024 and 30th of August 2024 to 28th of November 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 Financial Institutions price movement. The serial correlation of 0.38 indicates that just about 38.0% of current Financial Institutions price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.38 | |
| Spearman Rank Test | 0.33 | |
| Residual Average | 0.0 | |
| Price Variance | 1.33 |
Financial Institutions lagged returns against current returns
Autocorrelation, which is Financial Institutions stock'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 Financial Institutions' stock expected returns. We can calculate the autocorrelation of Financial Institutions returns to help us make a trade decision. For example, suppose you find that Financial Institutions has exhibited high autocorrelation historically, and you observe that the stock 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 |
Financial Institutions 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 Financial Institutions stock is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Financial Institutions stock is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Financial Institutions stock over time.
Current vs Lagged Prices |
| Timeline |
Financial Institutions Lagged Returns
When evaluating Financial Institutions' market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Financial Institutions stock have on its future price. Financial Institutions 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, Financial Institutions autocorrelation shows the relationship between Financial Institutions stock current value and its past values and can show if there is a momentum factor associated with investing in Financial Institutions.
Regressed Prices |
| Timeline |
Currently Active Assets on Macroaxis
When determining whether Financial Institutions offers a strong return on investment in its stock, a comprehensive analysis is essential. The process typically begins with a thorough review of Financial Institutions' financial statements, including income statements, balance sheets, and cash flow statements, to assess its financial health. Key financial ratios are used to gauge profitability, efficiency, and growth potential of Financial Institutions Stock. Outlined below are crucial reports that will aid in making a well-informed decision on Financial Institutions Stock:Check out Financial Institutions Correlation, Financial Institutions Volatility and Financial Institutions Alpha and Beta module to complement your research on Financial Institutions. For more detail on how to invest in Financial Stock please use our How to Invest in Financial Institutions guide.You can also try the Competition Analyzer module to analyze and compare many basic indicators for a group of related or unrelated entities.
Financial Institutions technical stock 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, stock market cycles, or different charting patterns.