LSTM in Algorithmic Investment Strategies on BTC and S&P500 Index [PDF]
Sensors, 2022 We use LSTM networks to forecast the value of the BTC and S&P500 index, using data from 2013 to the end of 2020, with the following frequencies: daily, 1 h, and 15 min data.
Jakub Michańków +2 more
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Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index [PDF]
Entropy, 2022 This research aims to compare the performance of ARIMA as a linear model with that of the combination of ARIMA and GARCH family models to forecast S&P500 log returns in order to construct algorithmic investment strategies on this index.
Nguyen Vo, Robert Ślepaczuk
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Artificial Intelligence Models for Predicting Stock Returns Using Fundamental, Technical, and Entropy-Based Strategies: A Semantic-Augmented Hybrid Approach [PDF]
EntropyThis study examines the effectiveness of combining semantic intelligence drawn from large language models (LLMs) such as ChatGPT-4o with traditional machine-learning (ML) algorithms to develop predictive portfolio strategies for NASDAQ-100 stocks over ...
Gil Cohen, Avishay Aiche, Ron Eichel
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Resource trading strategies with risk selection in collaborative training market. [PDF]
PLoS ONEThe rapid development of edge computing and artificial intelligence has brought growing interest in collaborative training. While prior research has addressed technical aspects of resource allocation, less attention has been paid to the underlying ...
Quyuan Wang +4 more
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Algorithmic Trading and Efficiency of the Stock Market in Poland
Finanse i Prawo Finansowe, 2021 The aim of the article is to investigate the impact of algorithmic trading on the returns obtained in the context of market efficiency theory. The research hypothesis is that algorithmic trading can contribute to a better rate of return than when using ...
Rafał Jóźwicki +2 more
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Using algorithmic trading to analyze short term profitability of Bitcoin [PDF]
PeerJ Computer Science, 2021 Cryptocurrencies such as Bitcoin (BTC) have seen a surge in value in the recent past and appeared as a useful investment opportunity for traders. However, their short term profitability using algorithmic trading strategies remains unanswered.
Iftikhar Ahmad +4 more
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Flexible Decision Support System for Algorithmic Trading: Empirical Application on Crude Oil Markets
IEEE Access, 2022 Generating reliable trading signals is a challenging task for financial market professionals. This research designs a novel decision-support system (DSS) for algorithmic trading and applies it empirically on two main crude oil markets.
Cristiana Tudor, Robert Sova
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LSTM-Based Deep Model for Investment Portfolio Assessment and Analysis
Applied Bionics and Biomechanics, 2022 In recent years, within the scope of financial quantification, quantitative investment models that support human-oriented algorithms have been proposed. These models attempt to characterize fiat-delayed series through intelligent acquaintance methods to ...
Haohua Yang
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Optimization of investment strategies through machine learning
Heliyon, 2023 The main objective of this research is to develop a sustainable stock quantitative investing model based on Machine Learning and Economic Value-Added techniques for optimizing investment strategies.
Jiaqi Li +4 more
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Additional Limit Conditions for Breakout Trading Strategies [PDF]
Informatică economică, 2019 One of the most popular trading methods used in financial markets is the Turtle strategy. Long time passed since the middle of 1983 when Richard Dennis and Bill Eckhardt disputed about whether great traders were born or made.
Cristian PAUNA
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