Investment Management in the Age of AI
A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Markets".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 10758
Special Issue Editor
Interests: investments; financial markets and institutions; options and futures; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) and machine learning (ML) are rapidly transforming the investment management industry. AI-driven algorithmic trading is becoming increasingly widespread, and ML algorithms are being used to forecast stock prices, identify investment opportunities, and manage risk.
However, there are also some potential risks associated with the use of AI and ML in investment management. For example, the misuse of AI-driven algorithmic trading could contribute to higher market volatility. Additionally, some machine learning algorithms are being applied incorrectly in stock price forecasting, which could lead to inaccurate investment decisions.
Despite these risks, AI and ML have the potential to significantly improve the efficiency and effectiveness of investment management. By better understanding how AI and ML are affecting investment management, we can help to mitigate the risks and maximize the benefits of these technologies.
In this Special Issue, we aim to publish papers that explore the impact of AI and ML on investment management. Papers should address the following topics:
- The potential misuse of AI-driven algorithmic trading.
- The incorrect application of ML algorithms in stock price forecasting and the potentials for DNN.
- The use of NLP for understanding market sentiments.
Potential Misuse of AI-Driven Algorithmic Trading
AI-driven algorithmic trading has become increasingly popular in recent years, as it can allow traders to execute trades more quickly and efficiently than when using traditional methods. However, there is a potential for AI-driven algorithmic trading to contribute to higher market volatility. This is because AI algorithms can be programmed to trade in a way that amplifies market movements. For example, if an AI algorithm detects that a particular stock is starting to rise/fall, it may trigger a large number of buy/sell orders, which could cause the stock price volatility to rise even further. We aim to publish papers that address how algorithmic trading can impact market volatility.
Incorrect Application of ML Algorithms in Asset Price Forecasting and the Potentials for DNN
Machine learning algorithms have been used to forecast asset prices for many years. Recent advancements in computing power and DNN (deep neural network) architectures have made the implementation of these techniques cheaper and more efficient. However, there is a risk that these algorithms may be applied incorrectly. For example, some algorithms may be trained on data that are not representative of the current market conditions (out of sample distribution). This could lead to inaccurate forecasts, which could result in poor investment decisions. On the other hand, DNN could have the potential to uncover hidden features that drive a specific asset price. We aim to publish papers that address either side of the application of these techniques.
NLP for Understanding Market Sentiments
NLP can be used to analyze text data, such as news articles, social media posts, and financial reports. These data can be used to understand market volatility, as these can provide insights into investor sentiment and market trends. For example, NLP can be used to track the frequency of certain words or phrases in news articles. This can be used to gauge investor sentiment, as positive words are often associated with rising stock prices, while negative words are often associated with falling stock prices. Recent advancements in NLP architectures, such as transformers, have made the task of sentiment analysis relatively easy to perform and far more efficient than when using older NLP models. We aim to publish papers that address this issue.
The deadline for submission is December 31, 2023. We look forward to receiving your submissions.
Dr. Leo H. Chan
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence
- machine learning
- algorithmic trading
- neural network
- natural language processing
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