Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications

Luigi Troiano · E. Mejuto-Villa · Vincenzo Loia

91 citations0/0 files storedIEEE Transactions on Industrial Informatics

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Financial Markets and Investment Strategies Hydrological Forecasting Using AI

Abstract

This paper investigates the possibility of learning a trading rule looking at the relationship between market indicators and decisions undertaken regarding entering or quitting a position. As means to achieve this objective, we employ a long short-term memory machine, due its capability to relate past and recent events. Our solution is a first step in the direction of building a model-free robot, based on deep learning, able to identify the logic that links the market mood given by technical indicators to the undertaken investment decisions. Although preliminary, experimental results show that the proposed solution is viable and promising.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 91 citations exist upstream.

References

2016Deep Learning for Finance: Deep Portfolios 2015A LSTM-based method for stock returns prediction: A case study of China stock market 2017Classification-based financial markets prediction using deep neural networks 2016High-Frequency Trading Strategy Based on Deep Neural Networks

Cited by

2020Financial market prediction under deep learning framework using auto encoder and kernel extreme learning machine 2022A Novel Ensemble Learning Approach for Stock Market Prediction Based on Sentiment Analysis and the Sliding Window Method 2022Implementation of Long Short-Term Memory and Gated Recurrent Units on grouped time-series data to predict stock prices accurately 2022Deep learning for volatility forecasting in asset management 2022A Deep Reinforcement Learning-Based Decision Support System for Automated Stock Market Trading 2023Neural Networks for Portfolio Analysis in High-Frequency Trading 2021Revealing Pairs-trading opportunities with long short-term memory networks 2021Building Intelligent Moving Average-Based Stock Trading System Using Metaheuristic Algorithms

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.