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Investment Management in the Age of AI
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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: 31 August 2024 | Viewed by 3161

Special Issue Editor

Dr. Leo H. Chan

E-Mail Website
Guest Editor
Department of Finance and Economics, Woodbury School of Business, Utah Valley University, Orem, UT 84058, USA
Interests: investments; financial markets and institutions; options and futures; machine learning; artificial intelligence

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • algorithmic trading
  • neural network
  • natural language processing

Published Papers (3 papers)

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15 pages, 5827 KiB  
Article
Forecasting Agriculture Commodity Futures Prices with Convolutional Neural Networks with Application to Wheat Futures
by Avi Thaker, Leo H. Chan and Daniel Sonner
J. Risk Financial Manag. 2024, 17(4), 143; https://doi.org/10.3390/jrfm17040143 - 2 Apr 2024
Viewed by 1021
Abstract
In this paper, we utilize a machine learning model (the convolutional neural network) to analyze aerial images of winter hard red wheat planted areas and cloud coverage over the planted areas as a proxy for future yield forecasts. We trained our model to [...] Read more.
In this paper, we utilize a machine learning model (the convolutional neural network) to analyze aerial images of winter hard red wheat planted areas and cloud coverage over the planted areas as a proxy for future yield forecasts. We trained our model to forecast the futures price 20 days ahead and provide recommendations for either a long or short position on wheat futures. Our method shows that achieving positive alpha within a short time window is possible if the algorithm and data choice are unique. However, the model’s performance can deteriorate quickly if the input data become more easily available and/or the trading strategy becomes crowded, as was the case with the aerial imagery we utilized in this paper. Full article
(This article belongs to the Special Issue Investment Management in the Age of AI)
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16 pages, 744 KiB  
Article
Assessing Machine Learning Techniques for Predicting Banking Crises in India
by Sreenivasulu Puli, Nagaraju Thota and A. C. V. Subrahmanyam
J. Risk Financial Manag. 2024, 17(4), 141; https://doi.org/10.3390/jrfm17040141 - 30 Mar 2024
Viewed by 1043
Abstract
The historical prevalence of banking crises and their profound impact on global economies underscores the imperative for policy makers to refine their crisis forecasting frameworks. Against this backdrop, the present study endeavors to predict potential banking crises in India by leveraging a spectrum [...] Read more.
The historical prevalence of banking crises and their profound impact on global economies underscores the imperative for policy makers to refine their crisis forecasting frameworks. Against this backdrop, the present study endeavors to predict potential banking crises in India by leveraging a spectrum of artificial intelligence and machine learning techniques (AI-ML). These techniques encompass logistic regression, random forest, naïve Bayes, gradient boosting, support vector machine, neural networks, K-nearest neighbors, and decision trees. Initially, a banking fragility index was constructed utilizing monthly banking data spanning 2002 to 2023, demarcating the periods of crisis and stability. Subsequently, an extensive array of early warning indicators (EWIs) encompassing asset prices, macroeconomic factors, external influences, and credit-related variables were employed to forecast crisis periods. Our findings reveal that AI-ML models exhibit reasonable accuracy in predicting banking crises. Moreover, advanced model performance metrics highlight neural networks and random forest models as particularly effective in crisis prediction, surpassing other methodologies. Notably, among the EWIs, variables related to credit, interest rates, and liquidity emerge as possessing relatively higher information value in discerning fragilities within the Indian banking system. Importantly, the methodological framework presented herein can be extrapolated for banking crisis prediction in other economies. Full article
(This article belongs to the Special Issue Investment Management in the Age of AI)
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26 pages, 13560 KiB  
Article
Approximating Option Greeks in a Classical and Multi-Curve Framework Using Artificial Neural Networks
by Ryno du Plooy and Pierre J. Venter
J. Risk Financial Manag. 2024, 17(4), 140; https://doi.org/10.3390/jrfm17040140 - 29 Mar 2024
Viewed by 759
Abstract
In this paper, the use of artificial neural networks (ANNs) is proposed to approximate the option price sensitivities of Johannesburg Stock Exchange (JSE) Top 40 European call options in a classical and a modern multi-curve framework. The ANNs were trained on artificially generated [...] Read more.
In this paper, the use of artificial neural networks (ANNs) is proposed to approximate the option price sensitivities of Johannesburg Stock Exchange (JSE) Top 40 European call options in a classical and a modern multi-curve framework. The ANNs were trained on artificially generated option price data given the illiquid nature of the South African market, and the out-of-sample performance of the optimized ANNs was evaluated using an implied volatility surface constructed from published volatility skews. The results from this paper show that ANNs trained on artificially generated input data are able to accurately approximate the explicit solutions to the respective option price sensitivities of both a classical and a modern multi-curve framework in a real-world out-of-sample application to the South African market. Full article
(This article belongs to the Special Issue Investment Management in the Age of AI)
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