Bo Hee Min
Biographic Data
| ID | 355449 |
|---|---|
| NAME | Bo Hee Min |
| GIVEN NAMES | Bo Hee |
| FAMILY NAME | Min |
| SIGNATURE | MIN B H |
| AFFILIATIONS | Bo Hee Min, Copenhagen Business School, Frederiksberg, Denmark. |
| ORCID | 0000-0003-2800-7370 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 29 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 3 |
Machine learning and social action in markets
Machine learning (ML) models are gaining traction in securities trading because of their ability to recognize and predict patterns. This study examines how ML is transforming automated trading. Drawing on 213 interviews with market participants (including 94 with people working at ML-employing firms) as well as ethnographic observations of a trading firm specializing in ML-based automated trading, we argue that ML-based (‘second-generation’) auto…
Toward a sociology of machine learning explainability
Machine learning systems are making considerable inroads in society owing to their ability to recognize and predict patterns. However, the decision-making logic of some widely used machine learning models, such as deep neural networks, is characterized by opacity, thereby rendering them exceedingly difficult for humans to understand and explain and, as a result, potentially risky to use. Considering the importance of addressing this opacity, this…
Systemic failures and organizational risk management in algorithmic trading
This article examines algorithmic trading and some key failures and risks associated with it, including so-called algorithmic 'flash crashes'. Drawing on documentary sources, 189 interviews with market participants, and fieldwork conducted at an algorithmic trading firm, we argue that automated markets are characterized by tight coupling and complex interactions, which render them prone to large-scale technological accidents, according to Perrow'…
Machine learning and social action in markets
Machine learning (ML) models are gaining traction in securities trading because of their ability to recognize and predict patterns. This study examines how ML is transforming automated trading. Drawing on 213 interviews with market participants (including 94 with people working at ML-employing firms) as well as ethnographic observations of a trading firm specializing in ML-based automated trading, we argue that ML-based (‘second-generation’) auto…
Toward a sociology of machine learning explainability
Machine learning systems are making considerable inroads in society owing to their ability to recognize and predict patterns. However, the decision-making logic of some widely used machine learning models, such as deep neural networks, is characterized by opacity, thereby rendering them exceedingly difficult for humans to understand and explain and, as a result, potentially risky to use. Considering the importance of addressing this opacity, this…
Systemic failures and organizational risk management in algorithmic trading
This article examines algorithmic trading and some key failures and risks associated with it, including so-called algorithmic 'flash crashes'. Drawing on documentary sources, 189 interviews with market participants, and fieldwork conducted at an algorithmic trading firm, we argue that automated markets are characterized by tight coupling and complex interactions, which render them prone to large-scale technological accidents, according to Perrow'…
Systemic failures and organizational risk management in algorithmic trading
This article examines algorithmic trading and some key failures and risks associated with it, including so-called algorithmic 'flash crashes'. Drawing on documentary sources, 189 interviews with market participants, and fieldwork conducted at an algorithmic trading firm, we argue that automated markets are characterized by tight coupling and complex interactions, which render them prone to large-scale technological accidents, according to Perrow'…
Toward a sociology of machine learning explainability
Machine learning systems are making considerable inroads in society owing to their ability to recognize and predict patterns. However, the decision-making logic of some widely used machine learning models, such as deep neural networks, is characterized by opacity, thereby rendering them exceedingly difficult for humans to understand and explain and, as a result, potentially risky to use. Considering the importance of addressing this opacity, this…
Machine learning and social action in markets
Machine learning (ML) models are gaining traction in securities trading because of their ability to recognize and predict patterns. This study examines how ML is transforming automated trading. Drawing on 213 interviews with market participants (including 94 with people working at ML-employing firms) as well as ethnographic observations of a trading firm specializing in ML-based automated trading, we argue that ML-based (‘second-generation’) auto…
Blockchain Technology Applications and Security (2 works) · Computer Science (2 works) · Economics (2 works) · Finance (2 works) · Financial market (2 works) · Financial Markets and Investment Strategies (2 works) · Action (physics (1 work) · Actuarial science (1 work) · Algorithmic trading (1 work) · Alternative trading system (1 work)