1
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Shi ZL, Li XP, Leung CS, So HC. Cardinality Constrained Portfolio Optimization via Alternating Direction Method of Multipliers. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:2901-2909. [PMID: 35895648 DOI: 10.1109/tnnls.2022.3192065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Inspired by sparse learning, the Markowitz mean-variance model with a sparse regularization term is popularly used in sparse portfolio optimization. However, in penalty-based portfolio optimization algorithms, the cardinality level of the resultant portfolio relies on the choice of the regularization parameter. This brief formulates the mean-variance model as a cardinality ( l0 -norm) constrained nonconvex optimization problem, in which we can explicitly specify the number of assets in the portfolio. We then use the alternating direction method of multipliers (ADMMs) concept to develop an algorithm to solve the constrained nonconvex problem. Unlike some existing algorithms, the proposed algorithm can explicitly control the portfolio cardinality. In addition, the dynamic behavior of the proposed algorithm is derived. Numerical results on four real-world datasets demonstrate the superiority of our approach over several state-of-the-art algorithms.
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2
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Vuong PH, Phu LH, Van Nguyen TH, Duy LN, Bao PT, Trinh TD. A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach. Sci Prog 2024; 107:368504241236557. [PMID: 38490223 PMCID: PMC10943735 DOI: 10.1177/00368504241236557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models.
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Affiliation(s)
- Pham Hoang Vuong
- Information Science Faculty, Sai Gon University, Ho Chi Minh City, Vietnam
- Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Lam Hung Phu
- Information Science Faculty, Sai Gon University, Ho Chi Minh City, Vietnam
| | - Tran Hong Van Nguyen
- Faculty of Finance and Banking, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Le Nhat Duy
- Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Pham The Bao
- Information Science Faculty, Sai Gon University, Ho Chi Minh City, Vietnam
| | - Tan Dat Trinh
- Information Science Faculty, Sai Gon University, Ho Chi Minh City, Vietnam
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3
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Chen Y, Liu J, Gao Y, He W, Li H, Zhang G, Wei H. A new stock market analysis method based on evidential reasoning and hierarchical belief rule base to support investment decision making. Front Psychol 2023; 14:1123578. [PMID: 36844262 PMCID: PMC9949897 DOI: 10.3389/fpsyg.2023.1123578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 01/16/2023] [Indexed: 02/11/2023] Open
Abstract
Stock market analysis is helpful for investors to make reasonable decisions and maintain market stability, and it usually involves not only quantitative data but also qualitative information, so the analysis method needs to have the ability to deal with both types of information comprehensively. In addition, due to the inherent risk of stock investment, it is necessary to ensure that the analysis results can be traced and interpreted. To solve the above problems, a stock market analysis method based on evidential reasoning (ER) and hierarchical belief rule base (HBRB) is proposed in this paper. First, an evaluation model is constructed based on expert knowledge and ER to evaluate stock market sentiment. Then, a stock market decision model based on HBRB is constructed to support investment decision making, such as buying and selling stocks and holding positions. Finally, the Shanghai Stock Index from 2010 to 2019 is used as an example to verify the applicability and effectiveness of the proposed stock market analysis method for investment decision support. Experimental research demonstrates that the proposed method can help analyze the stock market comprehensively and support investors to make investment decisions effectively.
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Affiliation(s)
- Yujia Chen
- School of Computer Science and Information Engineering, Harbin Normal University, Harbin, China
| | - Jiangdan Liu
- School of Economics, Beijing International Studies University, Beijing, China
| | - Yanzi Gao
- Institute of Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, China
| | - Wei He
- School of Computer Science and Information Engineering, Harbin Normal University, Harbin, China,*Correspondence: Wei He, ✉
| | - Hongyu Li
- School of Computer Science and Information Engineering, Harbin Normal University, Harbin, China,Hongyu Li, ✉
| | - Guangling Zhang
- School of Computer Science and Information Engineering, Harbin Normal University, Harbin, China
| | - Hongwei Wei
- School of Computer Science and Information Engineering, Harbin Normal University, Harbin, China
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4
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Pattewar T, Jain D. Stock prediction analysis by customers opinion in Twitter data using an optimized intelligent model. SOCIAL NETWORK ANALYSIS AND MINING 2022. [DOI: 10.1007/s13278-022-00979-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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5
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Dezhkam A, Manzuri MT, Aghapour A, Karimi A, Rabiee A, Shalmani SM. A Bayesian-based classification framework for financial time series trend prediction. THE JOURNAL OF SUPERCOMPUTING 2022; 79:4622-4659. [PMID: 36196451 PMCID: PMC9521884 DOI: 10.1007/s11227-022-04834-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
Financial time series have been extensively studied within the past decades; however, the advent of machine learning and deep neural networks opened new horizons to apply supercomputing techniques to extract more insights from the underlying patterns of price data. This paper presents a tri-state labeling approach to classify the underlying patterns in price data into up, down and no-action classes. The introduction of a no-action state in our novel approach alleviates the burden of denoising the dataset as a preprocessing task. The performance of our labeling algorithm is experimented with using machine learning and deep learning models. The framework is augmented by applying the Bayesian optimization technique for the selection of the best tuning values of the hyperparameters. The price trend prediction module generates the required trading signals. The results show that the average annualized Sharpe ratio as the trading performance metric is about 2.823, indicating the framework produces excellent cumulative returns.
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Affiliation(s)
- Arsalan Dezhkam
- Computer Engineering Department, Sharif University of Technology, Tehran, Iran
| | | | - Ahmad Aghapour
- Computer Engineering Department, Sharif University of Technology, Tehran, Iran
| | - Afshin Karimi
- Computer Engineering Department, Sharif University of Technology, Tehran, Iran
| | - Ali Rabiee
- Computer Engineering Department, Sharif University of Technology, Tehran, Iran
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6
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Guo G, Xiao Y, Yao C. Multi-period uncertain portfolio selection model with prospect utility function. PLoS One 2022; 17:e0274625. [PMID: 36103564 PMCID: PMC9473444 DOI: 10.1371/journal.pone.0274625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 08/31/2022] [Indexed: 11/19/2022] Open
Abstract
In this paper, we discuss a multi-period portfolio optimization problem based on uncertainty theory and prospect theory. We propose an uncertain multi-period portfolio selection model, in which the return utility and risk of investment are measured by prospect theory utility function and uncertain semivariance. More realistically, the influence of transaction costs and bankruptcy of investor are also considered. Moreover, to solve the portfolio model, this paper designs a new artificial bee colony algorithm by combining sine cosine search method. Finally, a numerical experiment is presented to demonstrate the proposed model and the effectiveness of the designed algorithm.
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Affiliation(s)
- Gaohuizi Guo
- School of Management and Engineering, Capital University of Economics and Business, Beijing, China
- * E-mail:
| | - Yao Xiao
- School of Management and Engineering, Capital University of Economics and Business, Beijing, China
| | - Cuiyou Yao
- School of Management and Engineering, Capital University of Economics and Business, Beijing, China
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7
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Chen Y, Zhao P, Zhang Z, Bai J, Guo Y. A Stock Price Forecasting Model Integrating Complementary Ensemble Empirical Mode Decomposition and Independent Component Analysis. INT J COMPUT INT SYS 2022. [DOI: 10.1007/s44196-022-00140-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
AbstractIn recent years, due to the non-stationary behavior of data samples, modeling and forecasting the stock price has been challenging for the business community and researchers. In order to address these mentioned issues, enhanced machine learning algorithms can be employed to establish stock forecasting algorithms. Accordingly, introducing the idea of “decomposition and ensemble” and the theory of “granular computing”, a hybrid model in this paper is established by incorporating the complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SE), independent component analysis (ICA), particle swarm optimization (PSO), and long short-term memory (LSTM). First, aiming at reducing the complexity of the original data of stock price, the CEEMD approach decomposes the data into different intrinsic mode functions (IMFs). To alleviate the cumulative error of IMFs, SE is performed to restructure the IMFs. Second, the ICA technique separates IMFs, describing the internal foundation structure. Finally, the LSTM model is adopted for forecasting the stock price results, in which the LSTM hyperparameters are optimized by synchronously utilizing the PSO algorithm. The experimental results on four stock prices from China stock market reveal the accuracy and robustness of the established model from the aspect of statistical efficiency measures. In theory, a useful attempt is made by integrating the idea of “granular computing” with “decomposition and ensemble” to construct the forecasting model of non-stationary data. In practice, the research results will provide scientific reference for the business community and researchers.
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8
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Wu D, Wang X, Wu S. Construction of stock portfolios based on k-means clustering of continuous trend features. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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9
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Outperformance of the pharmaceutical sector during the COVID-19 pandemic: Global time-varying screening rule development. Inf Sci (N Y) 2022; 609:1181-1203. [PMID: 35915847 PMCID: PMC9330866 DOI: 10.1016/j.ins.2022.07.146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 07/24/2022] [Accepted: 07/24/2022] [Indexed: 01/26/2023]
Abstract
This study demonstrates the major role played by the healthcare and pharmaceutical industries during the COVID-19 pandemic. For this purpose, it provides evidence of a better risk-return relationship in these sectors through a multivariate study of monthly frequency. A global and dynamic ratio is developed to summarize different investor profiles according to their attitude toward risk and to consider the dynamic and changing nature of the economy and financial markets. This global ratio synthesizes the information from different orders of Kappa indices and Farinelli-Tibiletti ratios into a single measure. Additionally, we verify that Principal Component Analysis allows summarizing the information contained in the initial variables from the first new component. Our findings confirm that a filtering asset screening rule strategy is relevant and necessary. In this respect, passive management of midterm equal-weighted pharmaceutical portfolios outperforms both the pure financial and the healthcare investment strategies used during the pandemic.
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10
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Stock Portfolio Optimization Using a Combined Approach of Multi Objective Grey Wolf Optimizer and Machine Learning Preselection Methods. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5974842. [PMID: 36072718 PMCID: PMC9444365 DOI: 10.1155/2022/5974842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/29/2022] [Accepted: 07/10/2022] [Indexed: 11/18/2022]
Abstract
The present paper deals with optimizing the stock portfolio of active companies listed on the Tehran Stock Exchange based on the forecast price. This paper is based on a combination of different filtering methods such as optimization of trading rules based on technical analysis (ROC, SMA, EMA, WMA, and MACD at six levels—Very Very Weak (VVW), Very Weak (VW), Weak (W), Strong (S), Very Strong (VS), and Very Very Strong (VVS)), Markov Chains, and Machine Learning (Random Forest and Support Vector Machine) Filter stock exchanges and provide buy signals between 2011 and 2020. In proportion to each combination of filtering methods, a buy signal is issued and based on the mean-variance (M-V) model, the stock portfolio is optimized based on increasing the portfolio return and minimizing the stock portfolio risk. Based on this, out of 480 companies listed on the Tehran Stock Exchange, 85 active companies have been selected and stock portfolio optimization is based on two algorithms, MOGWO and NSGA II. The analysis results show that the use of SVM learning machine leads to minor correlation error than the random forest method. Therefore, this method was used to predict stock prices. Based on the results, it was observed that if the shares of companies are filtered, the risk of transactions decreases, and the return on the stock portfolio increases. Also, if two filtering methods are applied simultaneously, the stock portfolio returns slightly and the risk increases. In the analysis, MOGWO algorithm has obtained 133.13% stock return rate with a risk of 3.346%, while the stock portfolio returns in NSGA II algorithm 107.73, with a risk of 1.459%. Comparison of solution methods shows that the MOGWO algorithm has high efficiency in stock portfolio optimization.
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11
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Shen KY, Lo HW, Tzeng GH. Interactive portfolio optimization model based on rough fundamental analysis and rational fuzzy constraints. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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12
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Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm. MATHEMATICS 2022. [DOI: 10.3390/math10132234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
With the development of the Internet and big data, more and more consumer behavior data are used in different forecasting problems, which greatly improve the performance of prediction. As the main travel tool, the sales of automobiles will change with the variations of the market and the external environment. Accurate prediction of automobile sales can not only help the dealers adjust their marketing plans dynamically but can also help the economy and the transportation sector make policy decisions. The automobile is a product with high value and high involvement, and its purchase decision can be affected by its own attributes, economy, policy and other factors. Furthermore, the sample data have the characteristics of various sources, great complexity and large volatility. Therefore, this paper uses the Support Vector Regression (SVR) model, which has global optimization, a simple structure, and strong generalization abilities and is suitable for multi-dimensional, small sample data to predict the monthly sales of automobiles. In addition, the parameters are optimized by the Grey Wolf Optimizer (GWO) algorithm to improve the prediction accuracy. First, the grey correlation analysis method is used to analyze and determine the factors that affect automobile sales. Second, it is used to build the GWO-SVR automobile sales prediction model. Third, the experimental analysis is carried out by using the data from Suteng and Kaluola in the Chinese car segment, and the proposed model is compared with the other four commonly used methods. The results show that the GWO-SVR model has the best performance of mean absolute percentage error (MAPE) and root mean square error (RMSE). Finally, some management implications are put forward for reference.
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13
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High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method. FUTURE INTERNET 2022. [DOI: 10.3390/fi14060180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Futures price-movement-direction forecasting has always been a significant and challenging subject in the financial market. In this paper, we propose a combination approach that integrates the XGBoost (eXtreme Gradient Boosting), SMOTE (Synthetic Minority Oversampling Technique), and NSGA-II (Non-dominated Sorting Genetic Algorithm-II) methods. We applied the proposed approach on the direction prediction and simulation trading of rebar futures, which are traded on the Shanghai Futures Exchange. Firstly, the minority classes of the high-frequency rebar futures price change magnitudes are oversampled using the SMOTE algorithm to overcome the imbalance problem of the class data. Then, XGBoost is adopted to construct a multiclassification model for the price-movement-direction prediction. Next, the proposed approach employs NSGA-II to optimize the parameters of the pre-designed trading rule for trading simulation. Finally, the price-movement direction is predicted, and we conducted the high-frequency trading based on the optimized XGBoost model and the trading rule, with the classification and trading performances empirically evaluated by four metrics over four testing periods. Meanwhile, the LIME (Local Interpretable Model-agnostic Explanations) is applied as a model explanation approach to quantify the prediction contributions of features to the forecasting samples. From the experimental results, we found that the proposed approach performed best in terms of direction prediction accuracy, profitability, and return–risk ratio. The proposed approach could be beneficial for decision-making of the rebar traders and related companies engaged in rebar futures trading.
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14
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Exploration of Stock Portfolio Investment Construction Using Deep Learning Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7957097. [PMID: 35592717 PMCID: PMC9113895 DOI: 10.1155/2022/7957097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/06/2022] [Accepted: 04/16/2022] [Indexed: 11/17/2022]
Abstract
To study the intelligent and efficient stock portfolio in China's financial market, based on the relevant theories such as deep learning (DL) neural network (NN) and stock portfolio, this study selects 111 stable stocks from the constituent stocks of the China Security Index (CSI) 300 from January 1, 2018, to December 31, 2021, as the research samples. Then, it analyzes these research samples and imports the relevant data of 111 stocks into the DL NN model. The corresponding prediction results of stock prices are obtained. Finally, the stock portfolio model based on DL NN is compared with the data results of the Shanghai Stock Exchange (SSE) 50 Index and CSI 500 Index. The results show that the closing prices of the selected 111 stocks are relatively stable and fluctuate up and down around the horizontal axis, and the positive and negative returns are relatively balanced, roughly between −5% and 5%. There is a phenomenon of fluctuation aggregation to a certain extent. Comparing the prediction results of different models reveals that the prediction results of model c are closest to the actual stock price trend. Comparing the relevant returns of the proposed stock portfolio with other stocks uncovers that the annualized return of the stock portfolio based on the DL NN model is 47.44%. The sharp ratio is 1.52, the maximum pullback is 18.15%, the monthly excess return is 3.11%, and the information ratio is 0.82. Compared with other indexes, the proposed stock portfolio shows the best results. Therefore, the proposal of the stock portfolio based on DL NN provides a theoretical basis for the development of the financial field in the future.
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15
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Taguchi R, Watanabe H, Sakaji H, Izumi K, Hiramatsu K. Constructing Equity Investment Strategies Using Analyst Reports and Regime Switching Models. Front Artif Intell 2022; 5:865950. [PMID: 35664507 PMCID: PMC9157435 DOI: 10.3389/frai.2022.865950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
This study demonstrates whether analysts' sentiments toward individual stocks are useful for stock investment strategies. This is achieved by using natural language processing to create a polarity index from textual information in analyst reports. In this study, we performed time series forecasting for the created polarity index using deep learning, and clustered the forecasted values by volatility using a regime switching model. In addition, we constructed a portfolio from stock data and rebalanced it at each change point of the regime. Consequently, the investment strategy proposed in this study outperforms the benchmark portfolio in terms of returns. This suggests that the polarity index is useful for constructing stock investment strategies.
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Affiliation(s)
- Rei Taguchi
- School of Engineering, The University of Tokyo, Tokyo, Japan
- *Correspondence: Rei Taguchi
| | - Hikaru Watanabe
- Faculty of Engineering, The University of Tokyo, Tokyo, Japan
| | - Hiroki Sakaji
- School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Kiyoshi Izumi
- School of Engineering, The University of Tokyo, Tokyo, Japan
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16
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Cheng W, Feng J, Wang Y, Peng Z, Cheng H, Ren X, Shuai Y, Zang S, Liu H, Pu X, Yang J, Wu J. High precision reconstruction of silicon photonics chaos with stacked CNN-LSTM neural networks. CHAOS (WOODBURY, N.Y.) 2022; 32:053112. [PMID: 35649979 DOI: 10.1063/5.0082993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 04/20/2022] [Indexed: 06/15/2023]
Abstract
Silicon-based optical chaos has many advantages, such as compatibility with complementary metal oxide semiconductor (CMOS) integration processes, ultra-small size, and high bandwidth. Generally, it is challenging to reconstruct chaos accurately because of its initial sensitivity and high complexity. Here, a stacked convolutional neural network (CNN)-long short-term memory (LSTM) neural network model is proposed to reconstruct optical chaos with high accuracy. Our network model combines the advantages of both CNN and LSTM modules. Further, a theoretical model of integrated silicon photonics micro-cavity is introduced to generate chaotic time series for use in chaotic reconstruction experiments. Accordingly, we reconstructed the one-dimensional, two-dimensional, and three-dimensional chaos. The experimental results show that our model outperforms the LSTM, gated recurrent unit (GRU), and CNN models in terms of MSE, MAE, and R-squared metrics. For example, the proposed model has the best value of this metric, with a maximum improvement of 83.29% and 49.66%. Furthermore, 1D, 2D, and 3D chaos were all significantly improved with the reconstruction tasks.
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Affiliation(s)
- Wei Cheng
- College of Artificial Intelligence, Southwest University, Chongqing 400715, China
| | - Junbo Feng
- United Microelectronics Center Co., Ltd, Chongqing 401332, China
| | - Yan Wang
- College of Artificial Intelligence, Southwest University, Chongqing 400715, China
| | - Zheng Peng
- College of Artificial Intelligence, Southwest University, Chongqing 400715, China
| | - Hao Cheng
- College of Artificial Intelligence, Southwest University, Chongqing 400715, China
| | - Xiaodong Ren
- College of Artificial Intelligence, Southwest University, Chongqing 400715, China
| | - Yubei Shuai
- College of Artificial Intelligence, Southwest University, Chongqing 400715, China
| | - Shengyin Zang
- College of Artificial Intelligence, Southwest University, Chongqing 400715, China
| | - Hao Liu
- College of Artificial Intelligence, Southwest University, Chongqing 400715, China
| | - Xun Pu
- College of Computer & Information Science, Southwest University, Chongqing 400715, China
| | - Junbo Yang
- Center of Material Science, National University of Defense Technology, Changsha 410073, China
| | - Jiagui Wu
- School of Physical Science and Technology, Southwest University, Chongqing 400715, China
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17
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l1-Regularization in Portfolio Selection with Machine Learning. MATHEMATICS 2022. [DOI: 10.3390/math10040540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this work, we investigate the application of Deep Learning in Portfolio selection in a Markowitz mean-variance framework. We refer to a l1 regularized multi-period model; the choice of the l1 norm aims at producing sparse solutions. A crucial issue is the choice of the regularization parameter, which must realize a trade-off between fidelity to data and regularization. We propose an algorithm based on neural networks for the automatic selection of the regularization parameter. Once the neural network training is completed, an estimate of the regularization parameter can be computed via forward propagation. Numerical experiments and comparisons performed on real data validate the approach.
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18
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A deep learning method DCWR with HANet for stock market prediction using news articles. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00658-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
AbstractStock movement prediction is a challenging problem to analyze in both academic and financial research areas. The advancement of deep learning (DL) techniques has grasped the attention of researchers to employ them for predicting the stock market’s future trends. Few frameworks can understand the financial terms in literature, and the volatile nature of stock markets further complicates this process. This paper has tried to overcome the existing challenges by introducing a DL-based framework using financial news articles to forecast the stock market. After performing preprocessing step, the deep contextualized word representation (DCWR) approach is applied for feature extraction. In the next step, the independent component analysis (ICA) method is used for feature reduction. Finally, the resultant features train the hierarchical attention networks (HANet) classifier to predict the stock movements. The proposed scheme is evaluated over the 7 years of data from a publicly available dataset gathered from the Reuter’s website and attained an average prediction accuracy of 92.5% which shows our framework’s robustness.
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19
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Multi-period portfolio selection under the coherent fuzzy environment with dynamic risk-tolerance and expected-return levels. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108104] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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20
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Bai J, Sun B, Chu X, Wang T, Li H, Huang Q. Neighborhood rough set-based multi-attribute prediction approach and its application of gout patients. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108127] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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21
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Min L, Dong J, Liu J, Gong X. Robust mean-risk portfolio optimization using machine learning-based trade-off parameter. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107948] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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22
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Deng S, Huang X, Qin Z, Fu Z, Yang T. A novel hybrid method for direction forecasting and trading of Apple Futures. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107734] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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23
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Improved multiobjective bat algorithm for the credibilistic multiperiod mean-VaR portfolio optimization problem. Soft comput 2021. [DOI: 10.1007/s00500-021-05638-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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