Empirical Study on Stock Market Prediction Using Machine Learning

Sable, Rachna; Goel, Shivani; Chatterjee, Pradeep

doi:10.1109/icac347590.2019.9036786

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…There are many previous studies on the financial applications of Machine Intelligence with equity investments using neural networks [17], [18], [21], [24]- [26] support vector machines [19], [20], or a genetic algorithm [27]- [32], [37]- [39]. Most of those price prediction problems, however, were not very successful in the real world, unlike the expectations [33]- [36]. For instance, even when the designed model recognized the earlier patterns well enough, we could not precisely estimate the actual prices in a different timeframe.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, unidentifiable noise in financial data also hinders the use of algorithmic trading. Unlike its prominent success in academic experiments, reallife investment and applications with many traditional machine learning in Finance [17]- [27], is known to be relatively less prevailing [33]- [36] compare to many successes in other fields of studies and works with traditional machine intelligence techniques. Even in the winning scenario of having 80% or 90% accuracy in the stock predictions, price pattern based forecasts may fail investors with large costs if they lose big in a single estimation with such approaches.…”

Section: Introductionmentioning

confidence: 99%

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A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations

2020

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Vulnerable nature of price forecasts, such as an unpredictability of future and numbers of socioeconomic factors that affect market stability, often makes investment risky. Earlier studies in Finance suggested that constructing a portfolio can promise risk-spread gains. While Fund Standardization improved the traditional theories by reducing the computational complexity and by associating every interaction in the portfolio, such a method still cannot become a winning strategy because it does not measure the current value or the relative price of each asset. Inspired by the works of finding returns per risk, we attempt to design an optimal portfolio by searching products that have potential to grow further. More specifically, we first analyze risk-adjusted returns in the previous periods and use their inertia as a momentum. However, because historic movements alone do not fully elucidate future changes nor guarantee positive returns, we scored the relative values of each stock to make more informed estimations. Using the Capital Asset Pricing Model, we measured the values of each stock and determined those undervalued. In this study, we applied a Genetic Algorithm to optimize portfolios while incorporating the momentum strategy and the asset valuations. The proposed GA model was tested in two separate markets, S&P500 and KOSPI200, and projected greater profits than that from both the previous method with momentum method and the market indexes. From the experimental results, the proposed CAPM+ method was found to be very effective in financial data analysis and to lay a groundwork for a sustainable investment execution.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations

2020

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“…The study presents an integrated strategy that uses LSTM for price prediction and real-time news analysis to capture investor sentiments, incorporating insights from behavioural finance and providing thorough recommendations for future investment decisions. Rachna Sable Dr. Shivani Goel , Dr. Pradeep Chatterjee [4] did Empirical Study on Stock Market Prediction Using Machine Learning .This paper aims to study the stock market prediction using multiple Traditional, Machine learning, and Deep learning algorithms.…”

Section: IImentioning

confidence: 99%

Stock Price Prediction and Alert System

Farde,

Deore,

Manyar

et al. 2024

IJRASET

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In the financial markets, accurate prediction of stock prices is important for investors seeking to optimize returns. This paper presents a project focused on Stock Price Prediction Using LSTM and Alert System. The Long Short-Term Memory (LSTM) models, known for their ability to capture intricate patterns in sequential data, our project aims to forecast stock prices with enhanced accuracy and reliability. Additionally, we introduce an alert system. This supplementary feature enables users to set alerts based on specific stock price, enhancing timely responses to market changes. The methodology involves rigorous data analysis using Yahoo Finance data, preprocessing techniques, and the implementation of LSTM models for predictive modeling. Through comprehensive evaluation and experimentation, we demonstrate the efficacy of LSTM models in generating accurate stock price predictions.

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Predict Stock Prices Using Supervised Learning Algorithms and Particle Swarm Optimization Algorithm

Bazrkar

Hosseini

2022

Comput Econ

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Empirical Study on Stock Market Prediction Using Machine Learning

Cited by 8 publications

References 16 publications

A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations

A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations

Stock Price Prediction and Alert System

Predict Stock Prices Using Supervised Learning Algorithms and Particle Swarm Optimization Algorithm

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