# Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization

arXiv: https://arxiv.org/abs/2203.05673 · HTML: https://mlsanity.com/paper/2203.05673/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [LSTM Based Sentiment Analysis for Cryptocurrency Prediction](https://mlsanity.com/paper/2103.14804/) (2021)
- [Deep learning for Stock Market Prediction](https://mlsanity.com/paper/2004.01497/) (2020)
- [Machine Learning Optimization Algorithms & Portfolio Allocation](https://mlsanity.com/paper/1909.10233/) (2019)
- [ParDen: Surrogate Assisted Hyper-Parameter Optimisation for Portfolio Selection](https://mlsanity.com/paper/2107.02121/) (2021)
- [A Framework for Online Investment Algorithms](https://mlsanity.com/paper/2003.13360/) (2020)

## Built on by (most cited first, 2 of 2)

- [Volatility forecasting using Deep Learning and sentiment analysis](https://mlsanity.com/paper/2210.12464/) (2022)
- [Machine Learning for Socially Responsible Portfolio Optimisation](https://mlsanity.com/paper/2305.12364/) (2023)

## Abstract (arXiv)

The fusion of public sentiment data in the form of text with stock price prediction is a topic of increasing interest within the financial community. However, the research literature seldom explores the application of investor sentiment in the Portfolio Selection problem. This paper aims to unpack and develop an enhanced understanding of the sentiment aware portfolio selection problem. To this end, the study uses a Semantic Attention Model to predict sentiment towards an asset. We select the optimal portfolio through a sentiment-aware Long Short Term Memory (LSTM) recurrent neural network for price prediction and a mean-variance strategy. Our sentiment portfolio strategies achieved on average a significant increase in revenue above the non-sentiment aware models. However, the results show that our strategy does not outperform traditional portfolio allocation strategies from a stability perspective. We argue that an improved fusion of sentiment prediction with a combination of price prediction and portfolio optimization leads to an enhanced portfolio selection strategy.