Stock Price Prediction and Portfolio Selection Using Artificial Intelligence

Patalay, Sandeep; Bandlamudib, Madhusudhan Rao

doi:10.14329/apjis.2020.30.1.31

Cited by 5 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the method was first proposed in the 1990s, it has received much attention from scholars. In recent years, the SVM algorithm has made great breakthroughs both theoretically and algorithmically, and has gradually become an effective means to solve problems such as "dimension disaster" and "over learning" (Patalay & Bandlamudi, 2020). This method has strong computing power and high computational efficiency, and has become a hot topic in machine learning research.…”

Section: Svm Algorithmmentioning

confidence: 99%

Exploration on Portfolio Selection and Risk Prediction in Financial Markets Based on SVM Algorithm

Han,

Yao

2023

International Journal of Information Technology and Web Engineering

View full text Add to dashboard Cite

In order to cope with the complex risk environment of the current financial market, achieve portfolio optimization and accurate risk prediction, this paper conducts effective research using SVM algorithm. This article uses stock data as a sample to empirically analyze the risk return and risk prediction performance of investment portfolio strategies based on SVM algorithm. Compared with traditional index fund investment strategies, the risk resistance of investment portfolio strategies is significantly improved, and the risk return is also stable at a high level. In addition, with the support of SVM algorithm, the risk prediction error level in the financial market remains within a relatively low range. From the perspective of practical applications, the financial market investment portfolio selection and risk prediction based on SVM algorithm has strong feasibility.

show abstract

Section: Svm Algorithmmentioning

confidence: 99%

Exploration on Portfolio Selection and Risk Prediction in Financial Markets Based on SVM Algorithm

Han,

Yao

2023

International Journal of Information Technology and Web Engineering

View full text Add to dashboard Cite

show abstract

“…The literature on the intra-day stock price prediction is scarce, which could be picked, by future researchers. Third, the majority of studies (Patalay and Bandlamudi 2020;Zhao et al 2021) concentrate on financial markets in developed economies, but recently, several papers have shown that predictability of return still exists in less developed financial markets (Chen et al 2003). Moreover, it was observed that the developed AI models function differently for developed markets (Cheng et al 2007;Dong and Zhou 2002) and developing markets (Dai et al 2012;Zhou et al 2019).…”

Section: Future Research Agendamentioning

confidence: 99%

“…Moreover, it was observed that the developed AI models function differently for developed markets (Cheng et al 2007;Dong and Zhou 2002) and developing markets (Dai et al 2012;Zhou et al 2019). Harvey (1995) examined that the degree of predictability in the emerging or developing markets is much greater than what is seen in developed Third, the majority of studies (Patalay and Bandlamudi 2020;Zhao et al 2021) concentrate on financial markets in developed economies, but recently, several papers have shown that predictability of return still exists in less developed financial markets (Chen et al 2003). Moreover, it was observed that the developed AI models function differently for developed markets (Cheng et al 2007;Dong and Zhou 2002) and developing markets (Dai et al 2012;Zhou et al 2019).…”

Section: Future Research Agendamentioning

confidence: 99%

Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

Chopra

Sharma

2021

JRFM

View full text Add to dashboard Cite

The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal and external environmental variables. Artificial intelligence (AI) techniques can detect such non-linearity, resulting in much-improved forecast results. This paper reviews 148 studies utilizing neural and hybrid-neuro techniques to predict stock markets, categorized based on 43 auto-coded themes obtained using NVivo 12 software. We group the surveyed articles based on two major categories, namely, study characteristics and model characteristics, where ‘study characteristics’ are further categorized as the stock market covered, input data, and nature of the study; and ‘model characteristics’ are classified as data pre-processing, artificial intelligence technique, training algorithm, and performance measure. Our findings highlight that AI techniques can be used successfully to study and analyze stock market activity. We conclude by establishing a research agenda for potential financial market analysts, artificial intelligence, and soft computing scholarship.

show abstract

“…For example of technical analysis, Rama Krishna and his team [4] used LSTM to forecast daily closing prices based on previous opening, closing, high, and low prices. Sandeep Patalay and his colleagues [5] utilized K-means clustering to predict stock prices and select the optimal portfolio fit. Similarly, in the realm of fundamental analysis, Shuo Han and Rung-Ching Chen [6] employed Support Vector Machine (SVM) to predict stock prices based on financial statements in comparison with the Outstanding Achievement Growth Rate.…”

Section: Introductionmentioning

confidence: 99%

Estimating stock price based on information from financial statement using machine learning approach

Kunathananon

View full text Add to dashboard Cite

This study introduces a new tool for stock market investors and institutions constructed from Long Short-Term Memory (LSTM) for predicting stock prices. By effectively analyzing financial statements and stock market data, LSTM provides a fast, unbiased, low-cost solution for stock price prediction, intending to increase profits for investors and reduce losses. The study results indicate that LSTM can maintain effectively captures complex relationships in the data and predicts stock prices. This research highlights the potential of LSTM as a valuable and innovative tool for investors and institutions in the stock market.

show abstract

Stock Price Prediction and Portfolio Selection Using Artificial Intelligence

Cited by 5 publications

References 0 publications

Exploration on Portfolio Selection and Risk Prediction in Financial Markets Based on SVM Algorithm

Exploration on Portfolio Selection and Risk Prediction in Financial Markets Based on SVM Algorithm

Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

Estimating stock price based on information from financial statement using machine learning approach

Contact Info

Product

Resources

About