Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model

Sen, Jaydip; Mehtab, Sidra; Dutta, Abhishek; Mondal, Saıkat

doi:10.1109/ocit53463.2021.00050

Cited by 14 publications

(10 citation statements)

References 12 publications

(11 reference statements)

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“…Several alternative methods and propositions to the classical mean-variance portfolio optimization also exist in the literature. The multiobjective optimization techniques [15], principal component analysis [16], deep learning LSTM models [17][18][19], future risk estimation methods [20], and swarm intelligence-based approaches [21][22] are some of the very popular portfolio optimization methods. Various other approaches such as the use of genetic algorithms [23], fuzzy sets [24], prospect theory [25], quantum evolutionary algorithms [26], and time series decomposition [27] for robust portfolio design are also proposed in the literature.…”

Section: Related Workmentioning

confidence: 99%

Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market

Sen¹,

Dutta²

2022

2022 International Conference on Decision Aid Sciences and Applications (DASA)

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Portfolio optimization is a challenging problem that has attracted considerable attention and effort from researchers. The optimization of stock portfolios is a particularly hard problem since the stock prices are volatile and estimation of their future volatilities and values, in most cases, is very difficult, if not impossible. This work uses three ratios, the Sharpe ratio, the Sortino ratio, and the Calmar ratio, for designing the mean-variance optimized portfolios for six important sectors listed in the National Stock Exchange (NSE) of India. Three portfolios are designed for each sector maximizing the ratios based on the historical prices of the ten most important stocks of each sector from Jan 1. 2017 to Dec 31. 2020. The evaluation of the portfolios is done based on their cumulative returns over the test period from Jan 1, 2021, to Dec 31, 2021. The ratio that yields the maximum cumulative returns for both the training and the test periods for the majority of the sectors is identified. Additionally, the sectors which exhibit the maximum cumulative returns for the same ratio are also identified. The results provide useful insights for the investors in the stock market in making their investment decisions based on the current return and risks associated with the six sectors and their stocks.

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Section: Related Workmentioning

confidence: 99%

Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market

Sen¹,

Dutta²

2022

2022 International Conference on Decision Aid Sciences and Applications (DASA)

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“…The literature [11] established for short-term forecasting of Karachi Stock Exchange index data, KSE100, combining Artificial Neural Net-works (ANN) model and ARIMA or ARCH/GARCH models. More and more scholars are using machine learning models for stock price prediction, the authors of literature [12] used Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (AN-FIS) for stock prediction based on fundamental analysis.Due to the superior performance, more and more scholars choose to use lstm and optimized lstm models for stock prediction, for example [13][14] [15] . The literature [16][17][18] used a model combining ARIMA and artificial neural networks with different optimization methods for forecasting the consumer index and exchange rate.…”

Section: Related Workmentioning

confidence: 99%

Back-propagation neural network and ARIMA algorithm for price trend analysis

Dong

Yuan²,

Guo³

2022

6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022)

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Forecasting models have a high value in the field of finance, we can better gain in investment and it provide a better basis for the national macroeconomic control strategy. In this paper we build three forecasting models based on a combination of linear ARIMA time series and nonlinear Back-Propagation neural networks to improve the accuracy of forecasting. The first model uses direct summation; the second uses ARIMA and Back-Propagation neural network forecasting results as independent variables and actual prices as dependent variables to build a multiple linear regression model; the third uses bp neural network to indirectly compensate for the residuals of ARIMA results. The more representative gold in the international market was selected as the forecasting object, and the final errors were respectively, proving that the new combination method can improve the accuracy.

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“…The classical mean-variance optimization approach is the most well-known method for portfolio optimization (Sen & Mehtab, 2022a;Sen et al, 2021e;Sen et al, 2021g;Sen et al, 2021h;. Several alternatives to the mean-variance approach to portfolio optimization have also been proposed by some researchers.…”

Section: Related Workmentioning

confidence: 99%

“…The classical mean-variance optimization method stands out as the most widely recognized approach for portfolio optimization (Sen & Mehtab, 2022a;Sen et al, 2021e;Sen et al, 2021g;Sen et al, 2021h;. Numerous researchers have put forward alternative methods for portfolio optimization, diverging from the traditional mean-variance approach.…”

Section: Related Workmentioning

confidence: 99%

Portfolio Optimization for the Indian Stock Market

Sen¹,

Dutta²

2022

Encyclopedia of Data Science and Machine Learning

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Portfolio optimization has been an area of research that has attracted a lot of attention from researchers and financial analysts. Designing an optimum portfolio is a complex task since it not only involves accurate forecasting of future stock returns and risks but also needs to optimize them. This article presents a systematic approach to portfolio design using two risk optimization approaches, the hierarchical risk parity algorithm and the hierarchical equal risk contribution algorithm on seven critical sectors and the NIFTY 50 stocks listed in the National Stock Exchange of India of the Indian stock market. The portfolios are built following the two approaches on historical stock prices from Jan 1, 2016 to Dec 31, 2020. The portfolio performances are evaluated on the test data from Jan 1, 2021 to Nov 1, 2021. The backtesting results of the portfolios indicate that the performance of the HRP portfolio is superior to that of the HERC portfolio on both training and the test data for the majority of the sectors studied in this work.

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Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model

Cited by 14 publications

References 12 publications

Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market

Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market

Back-propagation neural network and ARIMA algorithm for price trend analysis

Portfolio Optimization for the Indian Stock Market

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