Prediction of Stock Prices Using Statistical and Machine Learning Models: A Comparative Analysis

Abstract

With the advent of machine learning, numerous approaches have been proposed to forecast stock prices. Various models have been developed to date such as Recurrent Neural Networks, Long Short-Term Memory, Convolutional Neural Network sliding window, etc., but were not accurate enough. Here, the aim is to predict the price of a stock and compare the results obtained using three major algorithms namely Kalman filters, XGBoost and ARIMA. Kalman filters are recursive and use a feedback mechanism to perform error correction. This correction makes them best suited for making accurate predictions as they can factor in the market volatility, whereas XGBoost is a promising technique for datasets that are nonlinear and can gather knowledge by detecting patterns and relationships in the data. XGBoost is also capable of capturing the time dependency of features efficiently. ARIMA refers to an Auto Regressive Integrated Moving Average model that has become very popular in recent times. It is mostly used on time series data and works by eliminating its stationarity. Finally, a hybrid model combining Kalman filters and XGBoostis discussed and a comparison of the results of each of the four models, are made to provide a better clarity for making investments by forecasting the price of a stock.

An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction

“…Consequently, investors have the chance to minimize their losses while maximizing their earnings while dealing with the stock market [ 38 ]. Recent research suggests that statistical and machine learning are two distinct approaches [ 39 ].…”

“…Tiwari et al [ 72 ] proposed a hybrid model that combines the Markov model and a decision tree to forecast the BSE, India, with an accuracy of 92.1 percent. Prasad et al [ 39 ] investigated three algorithms, XGBoost, Kalman filters, and ARIMA, as well as two datasets, the NSE and NYSE. First, they looked at how well individual algorithms could predict and how well a hybrid model they made with Kalman filters and XGBoost worked.…”

A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators

“…Thus, there is a scope for investors to minimise the loss and maximise the profit when dealing with the stock market [50]. In recent studies, the financial market analysis and forecasting basically falls into two categories, i.e., statistical and machine learning [51].…”

“…The proposed model provides an accuracy level of 92.1%, and it was concluded that combined models provide better accuracy than any individual models. A comparative study conducted by Prasad et al [51] used three different algorithms, namely, XGBoost, Kalman filters, and ARIMA, and two different datasets taken, namely, NSE and NYSE. Their study was based on individual algorithm forecasting capability as well as a hybrid model also developed by them using Kalman filters and XGBoost.…”

Homo-Hetero Pairing Regression Model: An Econometric Predictive Model of Homo Paired Data

“…Another drawback is that artificial intelligence method such as neural network analysis, it not interpreted directly and deep neural analysis require more training time and dimensional reduction of the data (Seethalakshmi, 2018;Enke, Grauer and Mehdiyev, 2011;Guraray, Shriya and Ashwini, 2019). Moreover, Prasad, et al (2022) contended the application of the learning machine and artificial intelligence methods does not grant predictive accuracy, in comparison to the statistical methods.…”