Due to the affordable computing power and the importance of data in decision making Machine Learning in Finance is getting very useful to make financial and investment decisions with great accuracy. The use of Machine Learning in Finance is reshaping the financial industry to make as perfect decisions as never before.
There is a wide variety of Machine Learning Algorithms that fit perfectly in the financial data. Although, Machine Learning can be used in every field to make predictions for better decision making, but making accurate predictions about the financial returns is an art.
This is where machine learning plays a major role in Finance to guide financial returns by using historical data. In this article, I will take you through some highly used applications of Machine Learning in finance sectors that you should be perfect at if you have a good interest in Finance.
Useful Applications of Machine Learning in Finance
Now, let’s go through some very useful applications of Machine Learning in finance:
Candlestick Chart is a powerful way to visualize the trends and changes in the stock market and other financial instruments. Most people use a Candlestick chart to visualize the trading patterns. To visualize our data in the form of Candlesticks, we must be having data that comprises open price, high price, low price, and close price.
It is mainly used in financial analysis. Japanese commodity Traders created this technique to build this type of chart, and initially, they were known as the Japanese Candlesticks. You can learn the practical implementation of this chart from here.
Stock Price Prediction with Facebook Prophet Model
Stock Price Prediction means to determine the future value of the stocks or other financial instruments of an organisation. If you master the art to predict stock prices, you can earn a lot by investing and selling at the right time, and you can even earn by mentoring other people who want to explore trading.
Facebook Prophet is an algorithm developed by Facebook’s Core Data Science team. It is used in the applications of time series forecasting. It is very much used when there is a possibility of seasonal effects. The Time Series Forecasting is very much used in Stock Price Prediction. You can learn about its practical implementation from here.
Trading Strategy with Machine Learning
Trading Strategy with Machine Learning, which can be used to determine when you should buy stocks and when you should sell. Generally, it is not easy to predict the stock market, but building a Trading Strategy using Machine Learning in Finance can make it easy.
This strategy can be used to generate the signals conveying when to invest in the Stock market or when to sell. It uses the strategy of three moving averages – one fast/short, second middle/medium, one slow/long. You can learn its practical implementation from here.
Gross domestic product (GDP) at current prices is the GDP at the market value of goods and services produced in a country during a year. In other words, GDP measures the monetary value of final goods and services produced by a country/state in a given period of time.
GDP can be broadly divided into goods and services produced by three sectors: the primary sector (agriculture), the secondary sector (industry), and the tertiary sector (services). It is also known as nominal GDP. More technically, (real) GDP takes into account the price change that may have occurred due to inflation. This means that the real GDP is nominal GDP adjusted for inflation. You can learn to analyse GDP from here.
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