Alternative data and sentiment analysis: Prospecting non-standard data in machine learning-driven finance
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Alternative data and sentiment analysis : Prospecting non-standard data in machine learning-driven finance. / Hansen, Kristian Bondo; Borch, Christian.
In: Big Data & Society, Vol. 9, No. 1, 01.01.2022, p. 20539517211070701.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Alternative data and sentiment analysis
T2 - Prospecting non-standard data in machine learning-driven finance
AU - Hansen, Kristian Bondo
AU - Borch, Christian
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Social media commentary, satellite imagery and GPS data are a part of ‘alternative data’, that is, data that originate outside of the standard repertoire of market data but are considered useful for predicting stock prices, detecting different risk exposures and discovering new price movement indicators. With the availability of sophisticated machine-learning analytics tools, alternative data are gaining traction within the investment management and algorithmic trading industries. Drawing on interviews with people working in investment management and algorithmic trading firms utilizing alternative data, as well as firms providing and sourcing such data, we emphasize social media-based sentiment analytics as one manifestation of how alternative data are deployed for stock price prediction purposes. This demonstrates both how sentiment analytics are developed and subsequently utilized by investment management firms. We argue that ‘alternative data’ are an open-ended placeholder for every data source potentially relevant for investment management purposes and harnessing these disparate data sources requires certain standardization efforts by different market participants. Besides showing how market participants understand and use alternative data, we demonstrate that alternative data often undergo processes of (a) prospecting (i.e. rendering such data amenable to processing with the aid of analytics tools) and (b) assetization (i.e. the transformation of data into tradable assets). We further contend that the widespread embracement of alternative data in investment management and trading encourages a financialization process at the data level which raises new governance issues.
AB - Social media commentary, satellite imagery and GPS data are a part of ‘alternative data’, that is, data that originate outside of the standard repertoire of market data but are considered useful for predicting stock prices, detecting different risk exposures and discovering new price movement indicators. With the availability of sophisticated machine-learning analytics tools, alternative data are gaining traction within the investment management and algorithmic trading industries. Drawing on interviews with people working in investment management and algorithmic trading firms utilizing alternative data, as well as firms providing and sourcing such data, we emphasize social media-based sentiment analytics as one manifestation of how alternative data are deployed for stock price prediction purposes. This demonstrates both how sentiment analytics are developed and subsequently utilized by investment management firms. We argue that ‘alternative data’ are an open-ended placeholder for every data source potentially relevant for investment management purposes and harnessing these disparate data sources requires certain standardization efforts by different market participants. Besides showing how market participants understand and use alternative data, we demonstrate that alternative data often undergo processes of (a) prospecting (i.e. rendering such data amenable to processing with the aid of analytics tools) and (b) assetization (i.e. the transformation of data into tradable assets). We further contend that the widespread embracement of alternative data in investment management and trading encourages a financialization process at the data level which raises new governance issues.
KW - Faculty of Social Sciences
KW - Alternative data
KW - assetization
KW - financial markets
KW - investment management
KW - machine learning
U2 - 10.1177/20539517211070701
DO - 10.1177/20539517211070701
M3 - Journal article
VL - 9
SP - 20539517211070701
JO - Big Data & Society
JF - Big Data & Society
SN - 2053-9517
IS - 1
ER -
ID: 319888355