{"id":1360,"date":"2023-05-10T09:34:05","date_gmt":"2023-05-10T09:34:05","guid":{"rendered":"https:\/\/clinicamaddarena.com.br\/?p=1360"},"modified":"2023-12-05T11:07:36","modified_gmt":"2023-12-05T11:07:36","slug":"pdf-statistically-sound-machine-learning-for","status":"publish","type":"post","link":"https:\/\/clinicamaddarena.com.br\/blog\/pdf-statistically-sound-machine-learning-for\/","title":{"rendered":"PDF Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB"},"content":{"rendered":"

Stock prices fluctuate over time, affected by numerous factors, and the prediction of their changes is at the core of both long-term and short-term financial investing. The main objective of this analysis is to evaluate and compare the various classification statistically sound machine learning for algorithmic trading of financial instruments<\/a> algorithms for the automatic identification of favourable days for intraday trading using the Croatian stock index CROBEX data. Intra-day trading refers to the acquisition and sale of financial instruments on the same trading day.<\/p>\n

Time series prediction is a challenge for many complex systems, yet in finance predictions are hindered by the very nature of how financial markets work. In efficient markets, the opportunities for stock price predictions leading to profitable trades are supposed to rapidly disappear. In the growing industry of high-frequency trading, the competition over extracting predictions on stock prices from the increasing amount of available information for performing profitable trades is becoming more and more severe. With the development of big data analysis and advanced deep learning methodologies, traders hope to fruitfully analyse market information, e.g. price time series, through machine learning. Spot prices of stocks provide a simple snapshot representation of a financial market.<\/p>\n

Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments<\/h2>\n

The goal is to discover relation between selected financial indicators on a given day and the market situation on the following day i.e. to determine whether a day is favourable for day trading or not. The problem is modelled as a binary classification problem. The idea is to test different algorithms and to give greater attention to those that are more rarely used than traditional statistical methods. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. You can download the paper by clicking the button above. We are preparing your search results for download …<\/p>\n