In our World no advanced mathematical system can predict the actual future. But sophisticated mathematics can calculate the probability of events which succeeds in stock market, assisting traders to minimize the likelihood that some sad circumstances might happen before a certain date or other precursor. Similar to insurance: Actuarial tables cannot predict the date or cause of our death, but they can provide insurers a better general idea of the time frame or nature of our death.
Reduce loss frequently
Successful stock traders frequently give the impression that successful trading means 100 percent accuracy. But most successful traders are right only half the time at best. Simple mathematics demonstrates that “winning” on only three or four of every 10 trades can put a trader ahead, depending on how much was won against how much was lost. For example: earning $3,000 on three trades while losing $2,800 on the other four still brings us an inflow of $200. Mathematics, teamed with patience, builds stock market wealth more reliably than “big score” attempts.
Gaussian Laws and Power Laws
Gaussian mathematics (Gaussian function) evaluates random fluctuations of unrelated entities. This function is ideal for playing the undulating stock market, except that stock market transactions are all unrelated. Gaussian logic, therefore, cannot predict unexpected crashes. However Power law evaluates how changes in the value of one quantity affecting another quantity, for instance how a company’s value influences the stock prices in its industry. This assists evaluating standard deviations, which can assist traders effectively understand the potential risks and let them to buy or sell accordingly.
“Quants” are traders applying quantitative analysis on financial trades. Computer-based quantitative analysis, which investigates and analyses how amounts, or quantities, have reference to each other, is the most common mathematical model applied by trading houses. The field includes algorithms, which investigate and analyse patterns of behavior in entities such as the financial sector. These calculations can assist indicate potential risks ahead, but over reliance on quantitative models and algorithms can activate wild speculation, imprudent investing and “flash crashes.” Such happens when the market takes an unanticipated nosedive.
In linear algebra, Gaussian elimination (also known as row reduction) is an algorithm for solving systems of linear equations. It is usually understood as a sequence of operations performed on the corresponding matrix of coefficients. This method can also be used to find the rank of a matrix, to calculate the determinant of a matrix, and to calculate the inverse of an invertible square matrix. The method is named after Carl Friedrich Gauss (1777–1855), although it was known to Chinese mathematicians as early as 179 CE