LSTM Prediction and Portfolio Optimization for Artificial Intelligence Industry

Research Article
Open access

LSTM Prediction and Portfolio Optimization for Artificial Intelligence Industry

Xiaotian Jiang 1*
  • 1 University of Toronto    
  • *corresponding author geoorge.jiang@mail.utoronto.ca
Published on 10 November 2023 | https://doi.org/10.54254/2754-1169/38/20231912
AEMPS Vol.38
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-83558-097-4
ISBN (Online): 978-1-83558-098-1

Abstract

The launch of ChatGPT has overwhelmingly been revolutionizing the stock market. Of particular interests of stock traders and financial analysts, discovery about artificial intelligence stock market has become the main focus. The paper selected top worldwide artificial intelligence (AI) enterprises from Yahoo Finance and made future return forecasts with the long short-term memory networks (LSTM). The predicted information is employed in conducting portfolio optimization within the scope of mean-variance analysis to obtain an assessment of the portfolio’s performance. The outcomes illustrate that the utilization of the LSTM model exhibits aptness in forecasting the forthcoming returns of financial instruments. Furthermore, a favorable preference entails the inclusion of NVDIA and Microsoft stocks in the portfolio. These discoveries offer utility in proposing pioneering investment strategies and aligning with the prevailing tendencies of societal progression.

Keywords:

AI, LSTM, portfolio optimization

Jiang,X. (2023). LSTM Prediction and Portfolio Optimization for Artificial Intelligence Industry. Advances in Economics, Management and Political Sciences,38,192-197.
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References

[1]. Barberis, N., Thaler, R.: A survey of behavioral finance. Handbook of the Economics of Finance 1B, 1053-1128 (2003).

[2]. Lu, Z., Liang, M.: Integrated intellectual investment portfolio as an efficient instrument to manage personal financial investment. Journal of Business Research 96, 165-175 (2019).

[3]. Omisore, I., Yusuf, M., Christopher, N.: The modern portfolio theory as an investment decision tool. Journal of Accounting and Taxation 4(2), 19-28 (2012).

[4]. Smith, J.: Organizing Information: A Key Obstacle When Building an Investment Portfolio. Journal of Finance and Investment Analysis 10(2), 75-85 (2021).

[5]. Mustafa, M., Thomas, D., John, B.: The Construction of Efficient Portfolios: A Verification of Risk Models for Investment Making. The Journal of Finance and Data Science 6, 272-288 (2020).

[6]. Wang, W., Li, W., Zhang, N. Liu, K.: Portfolio formation with preselection using deep learning from long-term financial data. Expert Systems with Applications 143. 113042 (2019).

[7]. Birbil, Ş. İ., Frenk, J. B., Gürkan, G.: Asset allocation via decision trees. European Journal of Operational Research 180(1), 246-258 (2007).

[8]. Wu, Y., Wang, J., Liu, H.: Predicting portfolio performance by machine learning: Evidence from China. Asia-Pacific Journal of Financial Studies 49(1), 48-68 (2020).

[9]. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735-1780 (1997).

[10]. Markowitz, H.: Portfolio selection. The Journal of Finance 7(1), 77-91 (1952).

Cite this article

Jiang,X. (2023). LSTM Prediction and Portfolio Optimization for Artificial Intelligence Industry. Advances in Economics, Management and Political Sciences,38,192-197.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 7th International Conference on Economic Management and Green Development

ISBN：978-1-83558-097-4(Print) / 978-1-83558-098-1(Online)
Editor：Canh Thien Dang
Conference website: https://www.icemgd.org/
Conference date: 6 August 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.38
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Barberis, N., Thaler, R.: A survey of behavioral finance. Handbook of the Economics of Finance 1B, 1053-1128 (2003).

[2]. Lu, Z., Liang, M.: Integrated intellectual investment portfolio as an efficient instrument to manage personal financial investment. Journal of Business Research 96, 165-175 (2019).

[3]. Omisore, I., Yusuf, M., Christopher, N.: The modern portfolio theory as an investment decision tool. Journal of Accounting and Taxation 4(2), 19-28 (2012).

[4]. Smith, J.: Organizing Information: A Key Obstacle When Building an Investment Portfolio. Journal of Finance and Investment Analysis 10(2), 75-85 (2021).

[5]. Mustafa, M., Thomas, D., John, B.: The Construction of Efficient Portfolios: A Verification of Risk Models for Investment Making. The Journal of Finance and Data Science 6, 272-288 (2020).

[6]. Wang, W., Li, W., Zhang, N. Liu, K.: Portfolio formation with preselection using deep learning from long-term financial data. Expert Systems with Applications 143. 113042 (2019).

[7]. Birbil, Ş. İ., Frenk, J. B., Gürkan, G.: Asset allocation via decision trees. European Journal of Operational Research 180(1), 246-258 (2007).

[8]. Wu, Y., Wang, J., Liu, H.: Predicting portfolio performance by machine learning: Evidence from China. Asia-Pacific Journal of Financial Studies 49(1), 48-68 (2020).

[9]. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735-1780 (1997).

[10]. Markowitz, H.: Portfolio selection. The Journal of Finance 7(1), 77-91 (1952).

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