Market Price Prediction Research Articles

Machine learning techniques and methodology analysis for stock market price prediction

Prediction of market movements is a very emerging topic in research nowadays. The one handsome efficient hypothesis says that accurate stock prediction is impossible but, in another hand, if appropriate techniques and algorithms can be used then it can be predicted with high accuracy. Moreover, predicting profitable and accurate value is a challenging task for investors because price prediction is volatile in nature. It could be affected by various factors like the global economy, politics, disasters, etc. A lot of literature survey on technical analysis is available which identify the movement patterns of the market. Various hybrid machine learning approaches in combination have been used to forecast the beginning values of the market trends and also for the future long- term values.This paper gives a comparative assessment of 40 research papers that recommend strategies such as calculating methodologies, stock prediction algorithms, Datasets, results, and more which leads to improved accuracy and lowers the error percentage of stock market prediction.

Stock Price Prediction Using Long Short Term Memory

Stock market price prediction is difficult and complex task. Prediction in stock market is very complex and unstable Process. Stock Price are most of the time tend to follow patterns those are more or less regular in stock price curve. Machine Learning techniques use different predictive models and algorithms to predict and automate things to reduce human effort. This research paper focuses on the use of Long Short Term Memory (LSTM) to predict the future stock market company price of stock using each day closing price analysis. LSTM is very helpful in sequential data models. In this paper LSTM algorithm has been used to train and forecast the future stock prices.

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information is examined using the Spearman correlation coefficient to eliminate characteristics that have a weak link with the price of power. Secondly, a similar day-screening model with weighted grey correlation degree is constructed based on the grey correlation theory (GRA) to exclude superfluous samples. Thirdly, the regularized limit learning machine (RELM) is tuned using the Marine Predators Algorithm (MPA) to increase RELM parameter accuracy. Finally, the proposed forecasting model is applied to the Shanxi spot market, and other forecasting models and error computation methodologies are compared. The results demonstrate that the model suggested in this paper has a specific forecasting effect for power price forecasting technology.

Stock Price Prediction Using Python in Machine Learning

The process of anticipating the stock market is one that is both difficult and time-consuming. On the other hand, advancements in stock market projection have begun to incorporate these methods of evaluating stock market data since the introduction of Machine Learning and its various algorithms. This has occurred since the beginning of the 21st century. We found that the Long-Short Term Memory (LSTM) technique was the most effective when predicting stock values by using historical data. This was determined by analyzing the performance of the various algorithms in this endeavor. Because the algorithm has been taught using a massive accumulation of historical data and has been selected after being tested on a sample of data, it is going to be an excellent instrument for dealers and purchasers to utilize when they are investing in the stock market. According to the findings of this research, the machine learning model is superior to other machine learning models in terms of its ability to effectively predict market price.

Performance Analysis of Stock Price Prediction Model using LSTM and BiLSTM

Abstract: In the world of market price prediction, price prediction plays an important role in terms of providing investors with an opportunity to reduce risk while making a profit. A successful prediction model has the potential to have a significant impact on industry. For prediction, researchers use a variety of methods. We used two recurrent neural network models in our research: long short term memory and bidirectional long short term memory network model. This model can assist in determining the first accurate stock price with high accuracy. Keywords: LSTM, Bi-LSTM, RNN, Deep learning, Stock price prediction

Investigating the informativeness of technical indicators and news sentiment in financial market price prediction

Real-time market prediction tool tracking public opinion in specialized newsgroups and informative market data persuades investors of financial markets. Previous works mainly used lexicon-based sentiment analysis for financial markets prediction, while recently proposed transformer-based sentiment analysis promise good results for cross-domain sentiment analysis. This work considers temporal relationships between consecutive snapshots of informative market data and mood time series for market price prediction. We calculate the sentiment mood time series via the probability distribution of news embedding generated through a BERT-based transformer language model fine-tuned for financial domain sentiment analysis. We then use a deep recurrent neural network for feature extraction followed by a dense layer for price regression. We implemented our approach as an open-source API for real-time price regression. We build a corpus of financial news related to currency pairs in foreign exchange and Cryptocurrency markets. We further augment our model with informative technical indicators and news sentiment scores aligned based on news release timestamp. Results of our experiments show significant error reduction compared to the baselines. Our Financial News and Financial Sentiment Analysis RESTFul APIs are available for public use.

Prediction Markets for Probabilistic Forecasting of Renewable Energy Sources

This paper demonstrates how a binary prediction market is capable of achieving a probabilistic renewable energy forecast. In prediction markets, participants trade shares associated with the outcome of unknown future events (here, the renewable production, as a random variable), and the instantaneous price of shares represents the probability of the outcome. The focus of this study is to exploit this informational value of the prediction market price in renewable energy forecasting. To this end, in this paper three different methods of renewable probabilistic forecasting have been considered as the trading agents in a binary prediction market, the aggregated probability of the renewable output is elicited from the equilibrium price in this market and finally, the full cumulative distribution function of possible renewable output is extracted through regression analysis. The proposed method is applied to the test cases of three onshore wind farms in Australia. The simulation results suggest that the performance of the proposed method is superior to the individual models and forecasting is improved in terms of reduction in the electricity market imbalance costs.

A Survey on Stock Market Price Prediction System using Machine Learning Techniques

Abstract: Prediction of stock prices is one of the most researched topics and gathers interest from academia and the industry alike. In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market using machine learning is Python. In this paper we propose a Machine Learning (ML) approach that will be trained from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. The paper focuses on the use of Linear Regression, Moving Average, K-Nearest Neighbours, Auto ARIMA, Prophet, and LSTM based Machine learning techniques to predict stock values. Factors considered are open, close, low, high and volume. The models are evaluated using standard strategic indicators: RMSE and MAPE. The low values of these two indicators show that the models are efficient in predicting stock closing price. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for stock market price prediction. This work contributes to the stock analysis research community both in the financial and technical domains. Keywords: Stock Market, Machine Learning, Prediction, LSTM, Python, Analysis, Linear Regression, Feature Engineering.

Carbon sink price prediction based on radial basis kernel function support vector machine regression model

Abstract At present, the main pricing methods for carbon sink trading are marginal cost pricing, shadow price pricing and physical option pricing. The most serious defect of these three methods is that their theoretical basis is the extremely idealized effective market hypothesis, which obviously does not conform to the fractal and chaotic behavior characteristics of the actual carbon sink trading market. It is particularly important to study new pricing technologies and methods for a complex, nonlinear and dissipative system. Therefore, this paper constructs a carbon sink price prediction method based on the radial basis kernel function support vector machine (RBF-SVM) model. The main findings are the following: • Compared with the build nonlinear support vector machine (SVM) models by the three kernel functions: linear kernel function (LF), radial basis function (RBF) and sigmoid kernel function, the RBF-SVM has the highest prediction accuracy. • The adaptability of the RBF-SVM model is tested by using the price fluctuation data of EU allowance (EUA). It is proved that the adaptability of the RBF-SVM model for the price predictions of heterogeneous carbon products is also excellent. • The forecast results of the RBF-SVM model for carbon sink price for the period of September 2021 to August 2022 show that the lowest carbon sink price of 20 euros is expected to occur around November 2021 and the highest price of 38.5 euros around August 2022. Therefore, the established RBF-SVM model can be used for carbon sink trading market price prediction, which is more scientific and applicable than the previous three methods.

Prediction and Analysis of Wuxi Stainless Steel Market Price Based on ARIMA-BP Neural Network Combination Model

Prediction and Analysis of Wuxi Stainless Steel Market Price Based on ARIMA-BP Neural Network Combination Model

A Review of the Fractal Market Hypothesis for Trading and Market Price Prediction

This paper provides a review of the Fractal Market Hypothesis (FMH) focusing on financial times series analysis. In order to put the FMH into a broader perspective, the Random Walk and Efficient Market Hypotheses are considered together with the basic principles of fractal geometry. After exploring the historical developments associated with different financial hypotheses, an overview of the basic mathematical modelling is provided. The principal goal of this paper is to consider the intrinsic scaling properties that are characteristic for each hypothesis. In regard to the FMH, it is explained why a financial time series can be taken to be characterised by a 1/t1−1/γ scaling law, where γ>0 is the Lévy index, which is able to quantify the likelihood of extreme changes in price differences occurring (or otherwise). In this context, the paper explores how the Lévy index, coupled with other metrics, such as the Lyapunov Exponent and the Volatility, can be combined to provide long-term forecasts. Using these forecasts as a quantification for risk assessment, short-term price predictions are considered using a machine learning approach to evolve a nonlinear formula that simulates price values. A short case study is presented which reports on the use of this approach to forecast Bitcoin exchange rate values.

Prediction Strategies of Stock Market Data Using Deep Learning Algorithm

Background: Predictive analytics has a multiplicity of statistical schemes from predictive modelling, data mining, machine learning. It scrutinizes present and chronological data to make predictions about expectations or if not unexplained measures. Most predictive models are used for business analytics to overcome loses and profit gaining. Predictive analytics is used to exploit the pattern in old and historical data. Objective: People used to follow some strategies for predicting stock value to invest in the more profit-gaining stocks and those strategies to search the stock market prices which are incorporated in some intelligent methods and tools. Such strategies will increase the investor’s profits and also minimize their risks. So prediction plays a vital role in stock market gaining and is also a very intricate and challenging process. Method: The proposed optimized strategies are the Deep Neural Network with Stochastic Gradient for stock prediction. The Neural Network is trained using Back-propagation neural networks algorithm and stochastic gradient descent algorithm as optimal strategies. Results: The experiment is conducted for stock market price prediction using python language with the visual package. In this experiment RELIANCE.NS, TATAMOTORS.NS, and TATAGLOBAL. NS dataset are taken as input dataset and it is downloaded from National Stock Exchange site. The artificial neural network component including Deep Learning model is most effective for more than 100,000 data points to train this model. This proposed model is developed on daily prices of stock market price to understand how to build model with better performance than existing national exchange method.

A new carbon price prediction model

The excessive emission of carbon is one of the important factors causing environmental pollution, and the prediction of carbon trading market price is an important mean of emission reduction. In order to accurately predict the carbon price, a new carbon price prediction model is proposed in this paper. Firstly, the data is decomposed into multiple intrinsic mode functions (IMFs) by optimized variational mode decomposition (OVMD). Secondly, the complexity of IMFs is analyzed by spatial-dependence recurrence sample entropy (SdrSampEn). Thirdly, the IMFs with higher complexity are integrated and decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to get high complexity IMFs. Then, particle swarm optimized extreme learning machine (PSOELM) is used to predict the high complexity IMFs, and extreme learning machine (ELM) is used to predict other. Finally, the predicted value is reconstructed to complete the prediction. In this paper, OVMD is proposed to solve the selection of decomposition layers K by variational mode decomposition (VMD) from the perspective of variance contribution rate. Through the experimental results, the effectiveness of the proposed model is verified, and it can be used to predict the supply and demand of carbon market and evaluate the effectiveness of current carbon trading policies.

Analysis of Stock Market Price Prediction of Indian Finance Companies using Artificial Neural Network Approaches

Organizations get their capital by giving portions of stock; these are the units into which partnerships partition their possession. Shares are commonly called stocks. A solitary portion of the stock addresses fragmentary responsibility for enterprise in relation to the absolute number of offers. Preference shares are the shares that promise the holder a fixed dividend, whose payment takes priority over that of the ordinary share dividends. The stock market prediction has attracted much attention from academia as well as business. However, it is a challenging research topic in which many advanced computational methods have been proposed but they have not yet been attained a desirable and reliable performance. In this paper, stock market prediction analysis done specially on Indian finance companies’ market. Analysis accomplished using three approaches Simple RNN, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).

Stock market index prediction based on reservoir computing models

Prediction of the financial market price is critical for financial decision-making and market policy-making. Recently, various machine learning and deep learning methods have been adopted to predict financial markets’ movements using historical time series of prices. However, accurate prediction of financial prices is still a long-standing challenge that always calls for new approaches. In this study, a novel machine learning model of reservoir computing is developed to predict stock market indices. The performance of the proposed new model is systematically evaluated using the time series of daily closing prices of seven major international stock market indices including S&P500 Index, New York Stock Exchange Composite, Dow Jones Industrial Average, Nasdaq Composite Index, Financial Times Stock Exchange 100 Index, Nikkei 225 Index, and Shanghai Stock Exchange Index between January 4, 2010, and December 31, 2018 covering 2,272 trading days. The results show that our model outperforms the widely used deep learning methods of long short-term memory and recurrent neural network in most cases. To further evaluate the predictive capability of our model, we compare our model to the other two newly reported deep learning methods in recent studies. Comparative results also show that our model is competitive to those deep learning methods in predicting stock market indices. Our study contributes to the literature by developing novel reservoir computing models for financial market predictions. Meanwhile, our results also provide practical implications for financial practitioners of potential financial applications of reservoir computing in financial time series analysis and predictions.

Predicting replicability-Analysis of survey and prediction market data from large-scale forecasting projects.

The reproducibility of published research has become an important topic in science policy. A number of large-scale replication projects have been conducted to gauge the overall reproducibility in specific academic fields. Here, we present an analysis of data from four studies which sought to forecast the outcomes of replication projects in the social and behavioural sciences, using human experts who participated in prediction markets and answered surveys. Because the number of findings replicated and predicted in each individual study was small, pooling the data offers an opportunity to evaluate hypotheses regarding the performance of prediction markets and surveys at a higher power. In total, peer beliefs were elicited for the replication outcomes of 103 published findings. We find there is information within the scientific community about the replicability of scientific findings, and that both surveys and prediction markets can be used to elicit and aggregate this information. Our results show prediction markets can determine the outcomes of direct replications with 73% accuracy (n = 103). Both the prediction market prices, and the average survey responses are correlated with outcomes (0.581 and 0.564 respectively, both p < .001). We also found a significant relationship between p-values of the original findings and replication outcomes. The dataset is made available through the R package “pooledmaRket” and can be used to further study community beliefs towards replications outcomes as elicited in the surveys and prediction markets.

A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

An Implementation of IoT and Data Analytics in Smart Agricultural System – A Systematic Literature Review

India is a country that depends on agriculture, where about half the population relies heavily on agriculture for their livelihood. However, most of the practices undertaken in the agricultural process are not for profit and yield favorable. It should upgrade with current technologies to boost seed quality, check soil infertility, check the water level, environmental changes, and market price prediction, and achieve in agriculture sensitivity of faults and background understanding. The advancement in technology and developments is seen as a significant aspect in their financial development and agricultural production growth. The Internet of Things (IoT), Wireless Sensor Networks (WSN), and data analytics accomplish these upgrades. These technologies help in providing solutions to agricultural issues such as resource optimization, agricultural land monitoring, and decision-making support, awareness of the crop, land, weather, and market conditions for farmers. Smart agriculture is based on data from sensors, data from cloud platform storage and data from databases, all three concepts need to be implemented. The data are collected from different sensors and stored in a cloud-based back end support, which is then analyzed using proper analytics techniques, and then the relevant information is transferred to a user interface, which naturally supported the decision to conclude. The IoT applications mainly use sensors to monitor the situation, which collects a large size of data every time, so in the case of the Internet of Things (IoT) application, sensors contribute more. Data analytics requires data storage, data aggregation, data processing and data extraction. To retrieve data and information from database, we must use data mining techniques. It acts a significant position in the selection-making process on several agricultural issues. The eventual objective of data mining is to acquire information form data transform it for some advanced use into a unique human-comprehensible format. Big data's role in Agriculture affords prospect to increase the farmers' economic gain by undergoing a digital revolution in this aspect that we examine with precision. This paper includes reviewing a summary of some of the conference papers, journals, and books that have been going in favor of smart agriculture. The type of data required for smart farming system are analyzed and the architecture and schematic diagram of a proposed intelligent farming system are included. It also involves implementing different components of the smart farming system and integrating IoT and data analytics in the smart farming system. Based on the review, research gap, research agendas to carry out further research are identified.

Comparative study of Ohlson and cash flow discounting models in the prediction of the stock price

Two models derived from the dividend discount model attracted the attention of researchers: the residual income model (RIM) and the Ohlson model. These models are said to be dualistic since they combine both aspects of the economic and accounting vision. We propose, in our study, to test the performance of the dualistic evaluation model and to show the importance of accounting information. To do this, we will calculate the value of a listed company according to the actuarial valuation model, namely: the available cash flow discounting model (DCF) and the Ohlson model as a dualistic model. Then, we will determine, based on the expectation and the variance of the signed prediction error (SPE), the model that comes closest to the market price in the case of a Tunisian listed company. The results found in the Tunisian context show the superiority of the Ohlson model in the prediction of stock market prices. This model underlies the traditional belief that the company value is compounded of two main parts: the net value of the investment made in it (book value) and the present value of the period benefits (earnings) that together bring the “clean surplus” concept of the shareholders’ equity value. Specifically, Ohlson (1995) motivates the adoption of the historical price model in value relevance studies, which expresses value as a function of earnings and book values

Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

The outbreak of COVID-19 has brought the world to an unprecedented position where financial and mental resources are drying up. Livelihoods are being lost, and it is becoming tough to save lives. These are the times to think of unprecedented solutions to the financial challenges being faced. Artificial intelligence (AI) has provided a fresh approach to finance through its implementation in the prediction of financial market prices by promising more generalizable results for stock market forecasting. Immense literature has attempted to apply AI and machine learning for predicting stock market returns and volatilities. The research on the applications of AI in finance lacks a consolidated overview of different research directions, findings, methodological approaches, and contributions. Therefore, there is a need to consolidate the extant literature in this upcoming field to consolidate the findings, identify the research gaps in the existing literature, and set a research agenda for future researchers. This paper addresses this need by synthesizing the extant literature in the form of a systematic review for addressing the use of AI in stock market predictions and interpreting the results in a narrative review. The gap formed through this article is the use of a combination of AI as a subject with the neural network as another area and stock market forecasting as another theme, and it will pave the way for future research studies. The analyses help highlight four important gaps in the existing literature on the subject.