The Value of Investor Data: An Experimental Approach using a Trading Simulation Platform

In the digital landscape, the adage "if you’re not paying for a product, then you are the product" rings true. This is particularly evident in daily-use services like Google, YouTube, Facebook, Twitter, and others, which monetize by providing personal data to third parties. However, this practice is not limited to social networks. It extends to financial firms as well. For instance, trading platforms, such as Robinhood, offer zero brokerage fees by selling customer data to other financial institutions via 'payment for order flow' (PFOF). Despite concerns about potential issues such as front-running and back-running, this practice has been legally ongoing in the United States for years.

With the rising prominence of data privacy rights and the enforcement of consumer data regulations around the world (i.e., the General Data Protection Regulation (GDPR) in the European Union in 2016, the California Consumer and Protection Act in the United States in 2020, and the Personal Information Protection Law of the People's Republic of China in 2021), there's a growing call to empower individuals with more control over their data usage and potentially compensate them for their data. Understanding how much individuals value their data becomes crucial to fairly compensate investors, especially as zero brokerage fees may underestimate the genuine worth of investor data. Balancing the advantages of information disclosure with privacy concerns necessitates quantifying both aspects using a unified metric for better decision-making in optimizing information sharing processes.

The scientific objectives of this research project revolve around investigating the value individuals place on their personal trading data in financial markets, particularly in the context of information sharing among market participants.

2024-05-01

2024-12-14

The project aims to achieve the following key objectives:

1. Quantifying Individual Concerns for Data Privacy: The research will measure the extent to which an individual investor cares about the sharing of their personal trading activity with other players in the financial market. This will be accomplished through a controlled experiment where a participant's trading behavior will be observed under scenarios where they are informed (or not) about the disclosure of their trading history to other market participants.

2. Evaluating Monetary Compensation for Data Privacy: The project will assess the implied monetary compensation associated with an individual's data privacy. We will analyze changes in trading behavior, such as alterations in trade timing, order size, and order type, between different information disclosure settings. Thus, we will infer the financial worth an individual investor places on maintaining their data privacy.

3. Analyzing Trading Profits of Individual Investors: The research will examine the trading profits obtained by individual investors within the context of different information-sharing regimes. Understanding how trading behaviors and profitability correlate with information disclosure can provide valuable insights into the potential impacts and implications of data sharing on investors' financial gains.

By undertaking these objectives, our project will provide empirical evidence and insights into the perceived value of data privacy among investors in financial markets. This understanding is crucial, especially amid ongoing discussions about fair compensation for user data and balancing the benefits of information disclosure, such as zero-brokerage fees, with the associated privacy risks. Our findings will contribute to optimizing information disclosure processes and provide a basis for estimating the utility gains associated with transactions involving personal data in financial settings.

The COVID-19 pandemic has witnessed a surge in female engagement with the stock market, attributed to job and savings insecurities during the pandemic, and increased usage of no-fee trading platforms facilitated by the time spent at home. For example, Robinhood saw a 369% increase in the number of women using its services during the pandemic and women now make up 30% of its customer base.

Despite this, a substantial body of academic literature underscores the heightened vulnerability of women within the stock market due to a historical gender disparity in financial literacy and participation. Women typically exhibit lower financial education levels and reduced involvement in investment activities (Guiso and Zaccaria, 2021 ), often remaining underbanked (Demirgurc ̧-Kunt et al., 2017 ), and exhibiting lower stock market participation levels (Ke, 2021 ). Furthermore, women frequently express higher privacy concerns while exhibiting limited adoption of protective behaviors in comparison to men (Armantier et al., 2021 , Hoy and Milne, 2010 , Sheehan and Hoy, 2000 ).

The prevailing digital landscape, characterized by the monetization of personal data by numerous online platforms, is notably relevant in the financial sector, whereby trading platforms such as Robinhood leverage customer data monetization as a means to offer commission-free services to individual investors. However, this data sharing practice raises concerns regarding investor vulnerability to welfare losses (Acquisti et al., 2015 ), especially concerning retail investors, including women, who may inadvertently consent to data sharing without a comprehensive understanding of its potential adverse impact on their trading outcomes.

Consequently, this study emphasizes the necessity to investigate whether women modify their risk attitudes and trading approaches in response to the awareness of their trading data being shared. The aim is to provide insights that guide policy-makers in tailoring adaptive measures that protect privacy, considering gender dynamics.

In our research, we extend beyond conventional survey-based approaches evaluating women's privacy preferences within the stock market, a prevailing practice in the existing literature. Traditional methods for assessing privacy value, like self-reported questionnaires, often lack direct relevance to real-world behavioral patterns. Our approach involves an experimental design rooted in revealed preference theory, seeking to comprehend shifts in an investor's trading behavior upon understanding the potential sharing of their past trading history with other market participants. Crucially, we meticulously elucidate the mechanisms through which trading data may be shared and its implications for investors. By doing so, we aim to offer an unbiased assessment of the value attributed to privacy for investors.

Moreover, we utilize this experiment to infer the monetary value of investor data by analyzing realized profits before and after the information treatment across diverse scenarios (totalling 60).

Solely considering zero-brokerage fees or the price improvement resulting from Payment for Order Flow (PFOF) provides an inadequate estimation of the genuine worth of investor data. Through this project, we directly quantify the value of investors' historical trading data by observing their actual trading behavior pre- and post-information treatment, offering a comprehensive understanding of the value of privacy, in particular but not only women’s privacy, in the context of investor data.

The first step involves developing a trading algorithm on a fully functioning trading simulation platform developed at ESSEC Business School, that provides direct access (“online”) trade execution and clearing services.

The algorithm will take into account three scenarios: (1) one in which retail investors are aware their trading data is shared with other market participants, (2) one in which they are aware the data is not shared and (one where nothing is mentioned regarding data-sharing, across three settings: (a) one in which participants receive good private information, (b) bad private information, or (c) no private information.

Thus, the experiment itself will have multiple versions: the control group will receive good or bad private information about the asset traded but will not be told anything about the functioning of their account. The treated group will receive good or bad private information and then be told that their account trading history will start being shared with other participants.

Demographic data collected will include the participants’ profile information (age, gender, experience with the stock market, etc.) and the experimental data will consist of participants’ trading activity during the trading simulations. More precisely, the trading activity consists of the orders (limit and market orders) sent by the participants during the simulation and the evolution of their position (cash and assets and profits) during the simulation.

The goal is to record how individuals react once they are made aware that their account trading data will be shared with others. This is virtually impossible to do in a non-experimental setting.

Not available

Randomization by a computer.

Individual subject.

Yes

2 schools

450 individual subjects

2 schools, 450 individual subjects at least, 150 control group, 150 privately informed that trading history info shared, 150 privately informed that trading history info not shared

10%, 5%, and 1%, that is 1.645, 1.96, and 2.575 t stats.