Sentiment-Driven Speculation in Financial Markets with Heterogeneous Beliefs: A Machine Learning Approach

32 Pages Posted: 1 May 2023

See all articles by Tommaso Di Francesco

Tommaso Di Francesco

University of Amsterdam - CeNDEF; Università Ca' Foscari Venezia - Department of Economics

Cars H. Hommes

Government of Canada - Bank of Canada; CeNDEF, Amsterdam School of Economics, University of Amsterdam; Tinbergen Institute

Date Written: April 26, 2023

Abstract

This paper proposes an heterogenous asset pricing model in which different classes of investors coexist and evolve, switching among strategies over time according to a fitness measure. In the presence of boundedly rational agents, with biased forecasts and trend following rules, rational or fundamentalist expectations do not coincide with perfect foresight ones which are not analytically obtainable. The first contribution of this paper is to propose the use of a Long-Short Term Memory Model (LSTM) to approximate the non linear and unknown functional form imposed by the presence of heterogenous investors. It is shown that when speculators use LSTM in their forecast, instead of being fundamentalists, they can reduce volatility at the cost of pushing prices further away from the fundamental price.

The second contribution of the paper is empirical. Although the presence of so called noise traders in financial markets has been intensely studied, few attempts have been made in measuring their bias. Focusing on the Bitcoin market, we propose to capture the bounded rationality of noise traders by constructing an index of their bias based on textual data from Twitter. Using a dataset of more than ten million tweets containing the word “Bitcoin” we construct the Bitcoin Twitter Sentiment Index (BiTSI) through sentiment analysis in the form of the Valence Aware Dictionary and sEntiment Reasoner (VADER). The BiTSI is shown to be uncorrelated with the main factors capturing expected cryptocurrency returns identified in the literature. This suggests that the index is capturing a unique dimension of the Bitcoin market that is not accounted for in traditional financial models.

Finally the heterogenous asset pricing model is estimated on daily prices by non-linear least squares, and the results confirm the switching among forecasting rules and the presence of boundedly rational investors. The model captures a significant proportion of the variation in daily returns of the cryptocurrency.

Keywords: Bounded Rationality, Heterogenous Beliefs, Machine Learning, Financial Markets

JEL Classification: C63, D84, E32, E44, G12

Suggested Citation

Di Francesco, Tommaso and Hommes, Cars H., Sentiment-Driven Speculation in Financial Markets with Heterogeneous Beliefs: A Machine Learning Approach (April 26, 2023). Available at SSRN: https://ssrn.com/abstract=4429858 or http://dx.doi.org/10.2139/ssrn.4429858

Tommaso Di Francesco (Contact Author)

University of Amsterdam - CeNDEF ( email )

Roetersstraat 11
Amsterdam, NL-1018WB
Netherlands

Università Ca' Foscari Venezia - Department of Economics ( email )

Venice
Italy

Cars H. Hommes

Government of Canada - Bank of Canada ( email )

234 Wellington Street
Ontario, Ottawa K1A 0G9
Canada

CeNDEF, Amsterdam School of Economics, University of Amsterdam ( email )

Roetersstraat 11
Amsterdam, NL-1018WB
Netherlands

HOME PAGE: http://https://www.uva.nl/en/profile/h/o/c.h.hommes/c.h.hommes.html

Tinbergen Institute ( email )

Burg. Oudlaan 50
Rotterdam, 3062 PA
Netherlands

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
85
Abstract Views
365
Rank
542,449
PlumX Metrics