Price Prediction: Applying Machine Learning Approaches for Stock Market Analysis

2020 ◽  
Vol 14 (6) ◽  
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Market Analysis ◽  
Learning Approaches ◽  
Price Prediction

Extreme learning machine for stock price prediction

2021 ◽  
pp. 002072092098467
Author(s):  
Fangzhao Zhang
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Extreme Learning Machine ◽  
Stock Price ◽  
Market Performance ◽  
Market Analysis ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Stock Market Performance ◽  
Learning Machine

Stock market performance prediction has always been a hit research topic and is attractive due to its strong potential to generate financial profit. Being able to predict future stock price in a relatively accurate way forms a significant task of stock market analysis. Different mechanisms from fundamental analysis to statistical modeling have been deployed to study stock market performance and various factors from fundamental factors, technical factors to market sentiments are also incorporated in the stock price prediction task. However, due to the chaotic stock market performance, which is close to random walk, and the difficulty in discerning influential factors, predicting stock price faces a lot of challenges. In recent years, fast development in fields such as machine learning has offered new ways to look at this task. In this paper, we employ Extreme Learning Machine (ELM) algorithm, a recent modification of traditional feedforward neural network with single hidden layer, whose learning speed is greatly improved based on solid mathematical background and capability to circumvent problems such as local minimum is also enhanced, to construct an ELM combination model to study stock market performance and predict stock price. A comparison between the predicted output and the real data is carried out to test the feasibility of applying ELM model to stock market analysis. The result indicates that ELM model is desirable for predicting stock price variation trend while some inaccuracy exists in the prediction of peak values, which may require further model modification. Overall, by applying the machine learning model ELM to predict stock price and generating desirable outcome, this paper both contributes to offering a new way to investigate stock market performance and enlarging the field deployment of ELM model as well.

scholarly journals Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2717
Author(s):  
Nusrat Rouf ◽  
Majid Bashir Malik ◽  
Tasleem Arif ◽  
Sparsh Sharma ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Digital Libraries ◽  
Stock Price ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Stock Market Prediction ◽  
Research Areas ◽  
Recent Developments

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.

scholarly journals A Machine Learning View on Momentum and Reversal Trading

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 170 ◽  
Author(s):  
Zhixi Li ◽  
Vincent Tam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Stock Market ◽  
Short Term Memory ◽  
Predictive Ability ◽  
Trading Strategies ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Techniques

Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.

scholarly journals Advanced Markov-Based Machine Learning Framework for Making Adaptive Trading System

Computation ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 4 ◽  
Author(s):  
Francesco Rundo ◽  
Francesca Trenta ◽  
Agatino Luigi Di Stallo ◽  
Sebastiano Battiato
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Ad Hoc ◽  
Trend Prediction ◽  
Trading System ◽  
Trading Systems ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Historical Data Analysis

Stock market prediction and trading has attracted the effort of many researchers in several scientific areas because it is a challenging task due to the high complexity of the market. More investors put their effort to the development of a systematic approach, i.e., the so called “Trading System (TS)” for stocks pricing and trend prediction. The introduction of the Trading On-Line (TOL) has significantly improved the overall number of daily transactions on the stock market with the consequent increasing of the market complexity and liquidity. One of the most main consequence of the TOL is the “automatic trading”, i.e., an ad-hoc algorithmic robot able to automatically analyze a lot of financial data with target to open/close several trading operations in such reduced time for increasing the profitability of the trading system. When the number of such automatic operations increase significantly, the trading approach is known as High Frequency Trading (HFT). In this context, recently, the usage of machine learning has improved the robustness of the trading systems including HFT sector. The authors propose an innovative approach based on usage of ad-hoc machine learning approach, starting from historical data analysis, is able to perform careful stock price prediction. The stock price prediction accuracy is further improved by using adaptive correction based on the hypothesis that stock price formation is regulated by Markov stochastic propriety. The validation results applied to such shares and financial instruments confirms the robustness and effectiveness of the proposed automatic trading algorithm.

scholarly journals Support vector machine in the anticipation of currency markets

2018 ◽  
Vol 7 (2.7) ◽  
pp. 66
Author(s):  
V Lalithendra Nadh ◽  
G Syam Prasad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Stock Market ◽  
Support Vector ◽  
Learning Approaches ◽  
Currency Markets ◽  
Machine Learning Methods ◽  
Conventional Models

Various researchers have done an expansive research within the domain of stock market anticipation. The majority of the anticipated models is confronting some pivotal troubles because of the likelihood of the market. Numerous normal models are accurate when the data is linear. In any case, the expectation in view of nonlinear data could be a testing movement. From past twenty years with the progression of innovation and the artificial intelligence, including machine learning approaches like a Support Vector Machine it becomes conceivable to estimate in light of nonlinear data. Modern researchers are combining GA (Genetic Algorithm) with SVM to achieve highly precise outcomes. This analysis compares the SVM and ESVM with other conventional models and other machine learning methods in the domain of currency market prediction. Finally, the consequence of SVM when compared with different models it is demonstrated that SVM is the premier for foreseeing.

Machine Learning Based Stock Market Analysis: A Short Survey

2020 ◽  
pp. 12-26 ◽  
Author(s):  
Hrishikesh Vachhani ◽  
Mohammad S. Obiadat ◽  
Arkesh Thakkar ◽  
Vyom Shah ◽  
Raj Sojitra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Market Analysis ◽  
Short Survey
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