Abel Wyatt
Ever since the advent of an online trading and investment platform, the overall methodology of price prediction has reached the next level of advancement. Especially for intraday traders who deal with a large volume of stocks and need to capitalise on even minute price movements, they should be equipped with tools and strategies to make quick and informed decisions. Predicting the price movements of stocks is the most challenging issue because of the many external factors on which the price pattern depends.
Internal factors directly related to stock market attributes are interest rates, liquidity, volatility, changing market dynamics, etc.; external factors are political conditions and economic growth. Since the potential of asset building is huge in this domain, it forms the basis of major financial decisions, and a little inaccuracy or shortfall may lead to inevitable losses. Generally, predictions are made based on two types of analysis: technical and fundamental. Technical analysis is focused more on historical data to identify the pattern of price movements. In contrast, fundamental analysis is based on unstructured data available on social media domains and online financial news portals.
Although researchers have believed for a long time that technical analysis is more favourable for predicting stock prices, statistics are changing now owing to the latest text mining techniques, which form the basis of fundamental analysis. In the technical approach, a prominent aspect of the bulk of unstructured data is ignored, through which useful information can be extracted and used to make informed decisions about investment in the stock market. Doing this manually can be a complex task, and to sort this out, developers have developed concepts like machine learning algorithms.
Curious to know the methodology of these technical tools? As to how they are useful in extracting the desired information from the pool of data, let’s dive in to decipher that.
- Collection of data: The process begins by sorting out the relevant historical data associated with the stocks, be it daily, weekly, or monthly prices, trading volumes, or other financial data listed on Nifty or any other reputed stock listing platform.
- Preprocessing of data: This step includes analysing the data to clean it from all sorts of discrepancies, inconsistencies, errors, and outliers and give it a uniform structure. The process also includes identifying the missing data.
- Features extraction: In this method, relevant data is extracted from the database, which can be moving averages, relative strength indexes, and Bollinger bands.
- Selection of the model: Here comes the choosing part as to which machine learning algorithm will suit the particular data and its issue. Generally, different types of algorithms like random forests, linear regression, and decision trees are used to perform such tasks related to data mining.
- Training of the model: Once the algorithm is finalised, the next step is setting the algorithm up in such a way that it can identify the relationship between the input data and the output data. For this, the algorithm has to be trained with the extracted features and preprocessed data.
- Evaluation of the model: It is the testing phase to determine whether the assigned algorithm model predicts the prices accurately by comparing the predicted and actual prices.
- Deployment: This is the final stage of implementation, and if the model delivers accurately, it will be the guiding source for traders and investors in deciding which stock should be bought, sold, or held for long positions.
Text mining and language processing are core techniques behind the methodology of machine learning algorithms, and these approaches have proved fruitful for forecasting future stock prices. Additional features of these trading platforms, like screener, watchlists, and portfolio trackers are beneficial for providing a seamless experience.