Robo-advisors and the financialization of lay investors

Tan, Gordon

doi:10.1016/j.geoforum.2020.09.004

Cited by 28 publications

(13 citation statements)

References 40 publications

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“…Recent scholarly work has primarily studied the assetization of data relating to how big tech firms such as Apple, Amazon, Facebook, Google and Microsoft exploit big data, when they assetize personal data in advertisementdriven surveillance capitalism (Zuboff, 2019), or when they turn user metrics into assets that might be positively valued by investors (Birch et al, 2021). Where Zuboff's analysis, in particular, is surprisingly silent on financial markets and the role they play in data capitalism, we demonstrate that the turn to alternative data, and thus its prospecting and assetization, amounts to the financialization (Krippner, 2005) of otherwise non-financial data within the circuit of finance (Aitken, 2017;Bernards, 2019;Tan, 2020). This entails an important change in the relevant actor composition when it comes to understanding the connection between data and capitalism.…”

Section: Introductionmentioning

confidence: 75%

Alternative data and sentiment analysis: Prospecting non-standard data in machine learning-driven finance

Hansen

Borch

2022

Big Data & Society

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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.

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Section: Introductionmentioning

confidence: 75%

Alternative data and sentiment analysis: Prospecting non-standard data in machine learning-driven finance

Hansen

Borch

2022

Big Data & Society

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“…Robo-advisors are financial innovations that provide low-cost and convenient financial services for retail investors (Tan, 2020). Using statistical rules and machine learning algorithms, robo-advisors can automatically analyze the risk level of financial products and provide investment portfolio suggestions to investors.…”

Section: Investors' Willingness To Use Robo-advisors 2857mentioning

confidence: 99%

Exploring investors' willingness to use robo-advisors: mediating role of emotional response

Xia,

Zhang,

Zhang

et al. 2023

IMDS

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PurposeThis paper aims to investigate investors' willingness to use robo-advisors from customers' perspectives and analyzes the factors that drive them to use robo-advisors, including perceived usefulness and emotional response.Design/methodology/approachThe authors extend the Cognition-Affect-Conation (CAC) framework to the behavioral domain of robo-advisor users on financial technology platforms and conduct an empirical study based on 248 valid questionnaires.FindingsThe authors find two types of factors driving the willingness to use robo-advisors: perceived usefulness, trust and perceived risk as external driving forces and investor sentiment as an internal driving force. Trust has a significant positive effect on willingness to use, and arousal in emotional response plays a mediating role between perceived usefulness and willingness to use.Originality/valueThis research provides valuable insights for financial institutions to engage in robo-advisor innovation from customers' perspectives.

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“…E platformok a befektetési lehetőségek értékelése során óriási mennyiségű adatot dolgoznak fel, mely folyamán jellemzően mesterséges intelligenciaalapú megoldásokra építenek (Tao et al, 2020). A robottanácsadók a befektetők kezdeti értékelése során felmérik többek között a befektetési célokat, a befektetni kívánt összeg nagyságát, a kockázatéhséget és a termékpreferenciákat (Tan, 2020).…”

Section: Zöld Robottanácsadásunclassified

FinTech és blockklánc alapú megoldások alkalmazási lehetőségei a zöld pénzügyekben

Horváth

2022

Veztud

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A klímavédelmi törekvésekhez kapcsolódóan napjainkban egyre nagyobb figyelmet kapnak az ún. zöld pénzügyek, amelyek elsődleges célja a fenntarthatósággal összefüggő célkitűzések, projektek finanszírozása. A zöld pénzügyek elterjedését azonban számos tényező gátolhatja. Az egyre intenzívebb digitális transzformáció és az új technológiák nagymértékben támogathatják a zöld pénzügyek fejlődését. Jelen cikkben a szerző két fintech – zöld robottanácsadás, zöld közösségi finanszírozás – és két blokklánc alapú megoldást – felhasználók közötti finanszírozási és befektetési, valamint felhasználók közötti kereskedési platformok – mutat be, amelyek segíthetik a zöld finanszírozás minél szélesebb körben történő elterjedését. A vizsgált megoldásokról összességében elmondható, hogy megkönnyítik a pénzügyi forrásokhoz való hozzáférést, bővíthetik a befektetői bázist – beleértve a kisbefektetőket és a magántőkét –, valamint új finanszírozási mechanizmusokat kínálhatnak a zöld projektek ötletgazdái számára. A cikk feltárja azt is, hogy az ismertetett megoldások a zöld pénzügyek elterjedését gátló tényezők közül főként a pénzügyi akadályok mérséklésében nyújthatnak segítséget.

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Robo-advisors and the financialization of lay investors

Cited by 28 publications

References 40 publications

Alternative data and sentiment analysis: Prospecting non-standard data in machine learning-driven finance

Alternative data and sentiment analysis: Prospecting non-standard data in machine learning-driven finance

Exploring investors' willingness to use robo-advisors: mediating role of emotional response

FinTech és blockklánc alapú megoldások alkalmazási lehetőségei a zöld pénzügyekben

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