# Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models

arXiv: https://arxiv.org/abs/2103.15096 · HTML: https://mlsanity.com/paper/2103.15096/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 28 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Autoregressive Convolutional Neural Networks for Asynchronous Time Series](https://mlsanity.com/paper/1703.04122/) (2017)
- [Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models](https://mlsanity.com/paper/2009.10819/) (2020)
- [Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models](https://mlsanity.com/paper/2010.13891/) (2020)

## Built on by (most cited first, 15 of 15)

- [Stock Portfolio Optimization Using a Deep Learning LSTM Model](https://mlsanity.com/paper/2111.04709/) (2021)
- [Design and Analysis of Robust Deep Learning Models for Stock Price Prediction](https://mlsanity.com/paper/2106.09664/) (2021)
- [Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model](https://mlsanity.com/paper/2204.01850/) (2022)
- [Continuous-time Autoencoders for Regular and Irregular Time Series Imputation](https://mlsanity.com/paper/2312.16581/) (2023)
- [Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models](https://mlsanity.com/paper/2111.01137/) (2021)
- [Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model](https://mlsanity.com/paper/2202.02723/) (2022)
- [Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model](https://mlsanity.com/paper/2111.04976/) (2021)
- [A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks](https://mlsanity.com/paper/2210.00984/) (2022)
- [Optimum Risk Portfolio and Eigen Portfolio: A Comparative Analysis Using Selected Stocks from the Indian Stock Market](https://mlsanity.com/paper/2107.11371/) (2021)
- [Machine Learning in Finance-Emerging Trends and Challenges](https://mlsanity.com/paper/2110.11999/) (2021)
- [A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market](https://mlsanity.com/paper/2305.17523/) (2023)
- [Portfolio Optimization: A Comparative Study](https://mlsanity.com/paper/2307.05048/) (2023)
- [Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stocks](https://mlsanity.com/paper/2309.13696/) (2023)
- [Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning](https://mlsanity.com/paper/2407.01572/) (2024)
- [Understanding the Impact of News Articles on the Movement of Market Index: A Case on Nifty 50](https://mlsanity.com/paper/2412.06794/) (2024)

## Abstract (arXiv)

Designing robust frameworks for precise prediction of future prices of stocks has always been considered a very challenging research problem. The advocates of the classical efficient market hypothesis affirm that it is impossible to accurately predict the future prices in an efficiently operating market due to the stochastic nature of the stock price variables. However, numerous propositions exist in the literature with varying degrees of sophistication and complexity that illustrate how algorithms and models can be designed for making efficient, accurate, and robust predictions of stock prices. We present a gamut of ten deep learning models of regression for precise and robust prediction of the future prices of the stock of a critical company in the auto sector of India. Using a very granular stock price collected at 5 minutes intervals, we train the models based on the records from 31st Dec, 2012 to 27th Dec, 2013. The testing of the models is done using records from 30th Dec, 2013 to 9th Jan 2015. We explain the design principles of the models and analyze the results of their performance based on accuracy in forecasting and speed of execution.