Public Mood–Driven Asset Allocation: the Importance of Financial Sentiment in Portfolio Management

Abstract

The study of the impact of investor sentiment on stock returns has gained increasing momentum in the past few years. It has been widely accepted that public mood is correlated with financial markets. However, only a few studies discussed how the public mood would affect one of the fundamental problems of computational finance: portfolio management. In this study, we use public financial sentiment and historical prices collected from the New York Stock Exchange (NYSE) to train multiple machine learning models for automatic wealth allocation across a set of assets. Unlike previous studies which set as target variable the asset prices in the portfolio, the variable to predict here is represented by the best asset allocation strategy ex post. Experiments performed on five portfolios show that long short-term memory networks are superior to multilayer perceptron and random forests producing, in the period under analysis, an average increase in the revenue across the portfolios ranging between 5% (without financial mood) and 19% (with financial mood) compared to the equal-weighted portfolio. Results show that our all-in-one and end-to-end approach for automatic portfolio selection outperforms the equalweighted portfolio. Moreover, when using long short-term memory networks, the employment of sentiment data in addition to lagged data leads to greater returns for all the five portfolios under evaluation. Finally, we find that among the employed machine learning algorithms, long short-term memory networks are better suited for learning the impact of public mood on financial time series.

Does Twitter Affect Stock Market Decisions? Financial Sentiment Analysis During Pandemics: A Comparative Study of the H1N1 and the COVID-19 Periods

“…Therefore, they suggested a fine-grained approach that returns a continuous score in the range of [− 1, + 1] to identify polarity. Malandri and colleagues [ 40 ] investigated whether public mood ascertained from social media and online news is correlated with or predictive of portfolio returns, introducing the framework of sentiment-driven portfolio allocation. This novel approach automatically produces an optimal online portfolio allocation strategy.…”

Comprehensive review of text-mining applications in finance

“…The models that incorporate technical indicators of the market with sentiments obtained from the aforementioned sources outperform those that rely on only one of the two (Li et al 2009). In a study pertaining to optimal portfolio allocation, Malandri et al (2018) used historical data of the New York Stock Exchange and combined it with sentiment data to get comparatively better returns for the portfolios taken under consideration.…”

An Efficient Word Embedding and Deep Learning Based Model to Forecast the Direction of Stock Exchange Market Using Twitter and Financial News Sites: A Case of Istanbul Stock Exchange (BIST 100)

“…In [25], a model for portfolio allocation is proposed. For this purpose, LSTM, multi-layer perceptron (MLP), and random forest classifier (RFC) are employed.…”