Bank of America (Brazil) Market Value
| BOAC34 Stock | BRL 53.50 0.39 0.73% |
| Symbol | Bank |
Bank of America '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 Bank of America's 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 Bank of America.
| 03/14/2024 |
| 06/12/2024 |
If you would invest 0.00 in Bank of America on March 14, 2024 and sell it all today you would earn a total of 0.00 from holding Bank of America or generate 0.0% return on investment in Bank of America over 90 days. Bank of America is related to or competes with BTG Pactual, Plano Plano, Companhia Habitasul, FirstEnergy Corp, British American, and Suzano SA. Bank of America Corporation, through its subsidiaries, provides banking and financial products and services for individu... More
Bank of America 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 Bank of America's 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 Bank of America upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.9989 | |||
| Information Ratio | 0.2396 | |||
| Maximum Drawdown | 4.12 | |||
| Value At Risk | (1.66) | |||
| Potential Upside | 2.29 |
Bank of America Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Bank of America's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Bank of America's standard deviation. In reality, there are many statistical measures that can use Bank of America historical prices to predict the future Bank of America's volatility.| Risk Adjusted Performance | 0.1618 | |||
| Jensen Alpha | 0.2795 | |||
| Total Risk Alpha | 0.2749 | |||
| Sortino Ratio | 0.2786 | |||
| Treynor Ratio | 0.4087 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Bank of America'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.
Bank of America Backtested Returns
Bank of America appears to be very steady, given 3 months investment horizon. Bank of America secures Sharpe Ratio (or Efficiency) of 0.24, which signifies that the company had a 0.24% return per unit of risk over the last 3 months. We have found twenty-nine technical indicators for Bank of America, which you can use to evaluate the volatility of the firm. Please makes use of Bank of America's Risk Adjusted Performance of 0.1618, mean deviation of 0.9299, and Downside Deviation of 0.9989 to double-check if our risk estimates are consistent with your expectations. On a scale of 0 to 100, Bank of America holds a performance score of 18. The firm shows a Beta (market volatility) of 0.69, which signifies possible diversification benefits within a given portfolio. As returns on the market increase, Bank of America's returns are expected to increase less than the market. However, during the bear market, the loss of holding Bank of America is expected to be smaller as well. Please check Bank of America's standard deviation, total risk alpha, treynor ratio, as well as the relationship between the jensen alpha and sortino ratio , to make a quick decision on whether Bank of America's price patterns will revert.
Auto-correlation | 0.73 |
Good predictability
Bank of America has good predictability. Overlapping area represents the amount of predictability between Bank of America time series from 14th of March 2024 to 28th of April 2024 and 28th of April 2024 to 12th of June 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 Bank of America price movement. The serial correlation of 0.73 indicates that around 73.0% of current Bank of America price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.73 | |
| Spearman Rank Test | 0.66 | |
| Residual Average | 0.0 | |
| Price Variance | 3.57 |
Bank of America lagged returns against current returns
Autocorrelation, which is Bank of America 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 Bank of America's stock expected returns. We can calculate the autocorrelation of Bank of America returns to help us make a trade decision. For example, suppose you find that Bank of America 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 |
Bank of America 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 Bank of America stock is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Bank of America stock is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Bank of America stock over time.
Current vs Lagged Prices |
| Timeline |
Bank of America Lagged Returns
When evaluating Bank of America's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Bank of America stock have on its future price. Bank of America 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, Bank of America autocorrelation shows the relationship between Bank of America stock current value and its past values and can show if there is a momentum factor associated with investing in Bank of America.
Regressed Prices |
| Timeline |
Also Currently Popular
Analyzing currently trending equities could be an opportunity to develop a better portfolio based on different market momentums that they can trigger. Utilizing the top trending stocks is also useful when creating a market-neutral strategy or pair trading technique involving a short or a long position in a currently trending equity.Additional Information and Resources on Investing in Bank Stock
When determining whether Bank of America offers a strong return on investment in its stock, a comprehensive analysis is essential. The process typically begins with a thorough review of Bank of America's 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 Bank Of America Stock. Outlined below are crucial reports that will aid in making a well-informed decision on Bank Of America Stock:Check out Bank of America Correlation, Bank of America Volatility and Bank of America Alpha and Beta module to complement your research on Bank of America. For information on how to trade Bank Stock refer to our How to Trade Bank Stock guide.You can also try the Balance Of Power module to check stock momentum by analyzing Balance Of Power indicator and other technical ratios.
Bank of America 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.