Market Prediction Research Articles

Stock market prediction based on sentiment analysis using deep long short-term memory optimized with namib beetle henry optimization

Stock market prediction based on sentiment analysis using deep long short-term memory optimized with namib beetle henry optimization

A Systematic Study of Stock Markets Using Analytical and AI Techniques

Predicting stock market patterns is seen as a crucial and highly productive activity. Therefore, if investors make wise choices, stock prices will result in significant gains. Investors face a lot of difficulty making predictions about the stock market because of the noisy and stagnating data. As a result, making accurate stock market predictions is difficult for investors who want to put their money to work for them. Predictions of the stock market are made using mathematical techniques and study aids. Out of 30 research papers advocating approaches, this study offers a thorough analysis of each, including computational methodologies, AI algorithms( machine learning and deep learning), performance evaluation parameters, and chosen publications. Research questions are used to choose studies.
 As a result, these chosen studies contribute to the discovery of ML methods and their corresponding data set for predicting security markets. The majority of Artificial Neural Network and Neural Network techniques are employed for producing precise stock market forecasts. The most recent stock market-related prediction system has significant limitations despite the substantial amount of work that has gone into it. In this survey, one may infer that the stock price forecasting procedure is a comprehensive affair and it is very necessary to look more closely at the typical parameters for the stock market prediction.

Adaptive Market Hypothesis and Predictability: Evidence in Latin American Stock Indices

AbstractThis article examines the adaptive market hypothesis in the five most important Latin American stock indices. To that end, we apply three versions of the variance ratio test, as well as the Brock-Dechert-Scheinkman test for nonlinear predictability. Additionally, we perform the Dominguez-Lobato and generalized spectral tests to evaluate the Martingale difference hypothesis. Moreover, we consider salient news related to the plausible market inefficiencies detected by these four tests. Finally, we apply a GARCH-M model to assess the risk-return relationship through time. Our results suggest that the predictability of stock returns varies over time. Furthermore, the efficiency in each market behaves differently over time. All in all, the analyzed emerging market indices satisfy the adaptive market hypothesis, given the switching behavior between periods of efficiencies and inefficiencies, since the adaptive market hypothesis suggests that market efficiency and market anomalies might coexist in capital markets.

Stock Market Prediction and Portfolio Management with AI

Stock market forecast is an important task and is essential for predicting stock prices which can lead to profits and also making proper decisions. But at the same time the task is very complex in nature. Stock market forecast is a major challenge owing to non-stationary, blaring, and chaotic data, and thus, the prediction becomes challenging among the investors to invest the money for making profits. There are several techniques devised to predict the stock market trends such as Bayesian model, Artificial Neural Networks (ANN), Support Vector Machine (SVM) classifier, Neural Network (NN), Machine Learning Methods, and time series models like Auto Regression Integrated Moving Average (ARIMA), Seasonal Auto Regression Integrated Moving Average (SARIMA), Auto Regression Fractional Integrated Moving Average (ARFIMA). The stock market prediction is a very complex task, anddifferent factors should be considered for predicting the future of the market more accurately and efficiently. Forecast models help traders to reduce investment risk andselect the most profitable stocks. The goal of this paper is to analyse different set of models which uses different prediction and clustering techniques and present the results after comparing various approaches. This can help the researchers to upgrade the future works.

The impact of European law on national legislation and the need to harmonize contract law of Ukraine

The practical implementation of the harmonization of Ukrainian legislation with European law includes harmonization of legislative acts, implementation of European standards, institutional reforms, information campaign and education, international cooperation, creation of specialized bodies, strengthening of legal education and training, monitoring and evaluation of implementation. The practical implementation of harmonization contributes to the creation of a consistent and modern legal system, ensures the protection of citizens’ rights, promotes economic development and promotes international cooperation.In general, the harmonization of contract law of Ukraine with European law is an urgent task that contributes to the development of the economy, increased protection of the rights and interests of citizens and enterprises, improvement of the legal system and international cooperation. The relevance of harmonizing Ukrainian contract law with European law arises for several key reasons, this is: integration with the European Union, promotion of economic development, protection of rights and interests of citizens and enterprises, promotion of international cooperation.Harmonization of contract law of Ukraine with European law contributes to the improvement of cooperation with European countries and other countries that adopt European standards. This opens up new opportunities for trade, investment and cultural exchange, etc.Harmonization of contract law refers to the process of alignment and unification of norms and principles relating to the conclusion, performance and termination of contracts, with the aim of ensuring their mutual compatibility and the implementation of generally accepted standards in this area.Harmonization of contract law is usually aimed at creating a single legal space where different countries or regions have common norms and rules governing contractual relations. This allows for equal conditions for all market participants, increases transparency and predictability, reduces risks and increases trust between parties.Harmonization can be achieved by harmonizing and unifying national laws and regulations, or by adopting international treaties, conventions or recommendations that become binding on participating countries.Harmonization of contract law contributes to the creation of a favorable environment for business and international trade, simplifies the crossing of borders and the implementation of international transactions. It also helps to avoid disagreements and contradictions in legal regulation, ensuring stability and predictability of legal relations.

Machine learning-based approaches for financial market prediction: A comprehensive review

This research paper investigates the use of machine learning techniques in financial markets. The paper provides a comprehensive literature review of recent research on machine learning applications in finance, including stock price prediction, financial time series forecasting, and portfolio optimization. Various machine learning techniques, such as regression analysis, decision trees, support vector machines, and deep learning, are discussed in detail, with a focus on their strengths, weaknesses, and potential applications. The paper also highlights the challenges associated with machine learning in finance, such as data quality, model interpretability, and ethical considerations. Overall, the paper demonstrates that machine learning has significant potential in finance but calls for further research to address these challenges and fully explore its potential in financial markets.

Stock Price Forecasting with LSTM: A Brief Analysis of Mathematics Behind LSTM

Stock marketing prediction is really important in this digital world and for this reason, its forecasting tools are highly desired. This research examines the influence of Long Short-Term Memory (LSTM) in stock market prediction. The basic genetic algorithm behind the time series analysis tool is described with the help of a structure diagram, and its mathematical structure is explained. We also described the application of LSTM in the field of finance. LSTM is playing a vital role in prediction in several applications. Experiments on the proposed method have been conducted on stock market data. The results show that LSTM predicts the price quite fairly accurately. It outperformed other prediction models due to long-term dependency and due to its accuracy. The research demonstrates the LSTM tool’s impact on finance, particularly stock market prediction.

INE oil futures volatility prediction: Exchange rates or international oil futures volatility?

This study examines the predictive performances of volatility information from international oil futures volatility and exchange rates for Shanghai International Energy Exchange (INE) oil futures volatility. Various empirical findings show that the predictive performance of international oil futures volatility is superior to that of the exchange rate. In addition, we examine whether regime switching is efficient, and the results show that time-varying regime switching plays a satisfactory role. Moreover, these results are robust even during some special periods, including the COVID-19 pandemic, the Russia–Ukraine conflict, and different business cycles. This study provides new insights into the volatility prediction of the INE oil futures market.

Dynamic Operads, Dynamic Categories: From Deep Learning to Prediction Markets

Dynamic Operads, Dynamic Categories: From Deep Learning to Prediction Markets

Forecasting of Taiwan's weighted stock Price index based on machine learning

AbstractThis study proposes a stack framework of light gradient boosting machine (LGBM) for Taiwan stock market index prediction. Stock market predictions have been regarded as a challenging task, as the market is affected by several factors such as political events, general economic conditions, institutional investors' choices, movement of the global market, psychology of investors. We construct a rich feature set to capture the impacts of global markets, institutional investors' choices, and the psychology of investors. A feature selection algorithm is proposed to choose important feature subset and enhance the training performance. To further improve the prediction accuracy, we employ stacking strategy to combine multiple classifiers together. A 10‐year period of the Taiwan stock exchange capitalization weighted stock index (TAIEX) is used to verify the performance of the proposed model. The experimental results suggest that our prediction model as well as the feature selection method can achieve good prediction performance.

Opinion Dynamics Explain Price Formation in Prediction Markets.

Prediction markets are heralded as powerful forecasting tools, but models that describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong to a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable of replicating the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, which is an exchange platform whose data have never been used before to validate models of opinion diffusion.

Efficient Error Detection for Matrix Multiplication With Systolic Arrays on FPGAs

Matrix multiplication has always been a cornerstone in computer science. In fact, linear algebra tools permeate a wide variety of applications: from weather forecasting, to financial market prediction, radio signal processing, computer vision, and more. Since many of the aforementioned applications typically impose strict performance and/or fault tolerance constraints, the demand for fast and reliable matrix multiplication (MxM) is at an all-time high. Typically, increased reliability is achieved through redundancy. However, coarse-grain duplication incurs an often prohibitive overhead, higher than 100%. Thanks to the peculiar characteristics of the MxM algorithm, more efficient algorithm-based hardening solutions have been designed to detect (and even correct) some types of errors with lower overhead. We show that, despite being more efficient, current solutions are still sub-optimal in certain scenarios, particularly when considering persistent faults in Field-Programmable Gate-Arrays (FPGAs). Based on a thorough analysis of the fault model, we propose an error detection technique for MxM that decreases both algorithmic and architectural costs by over a polynomial degree, when compared to existing algorithm-based strategies. Furthermore, we report arithmetic overheads at the application level to be under 1% for three state-of-the-art Convolutional Neural Networks (CNNs).

Cryptocurrency Price Prediction and Forecasting Market momentum Using Machine Learning Techniques

Abstract: Cryptocurrency price prediction is a challenging task due to the high volatility and uncertainty of the market. Machine learning techniques can provide useful insights and forecasts for investors and traders. In this paper, we propose a novel approach for cryptocurrency price prediction using machine learning models and sentiment analysis. We collect historical price data of Bitcoin from yahoo business. We then apply various machine learning models, such as LSTM for the price prediction of the cryptocurrency using the past data. LSTM is a type of recurrent neural network that can manage long-term dependencies and sequential data. LSTM has three gates: forget gate, input gate, and output gate, which control the flow of information in and out of the memory cell. LSTM can be implemented in Python using the Keras and TensorFlow library. In this paper, we use LSTM as one of the machine learning models for cryptocurrency price prediction. We then use the average of the next 5 days of the predicted data to implement a buy-sell call strategy that aims to maximize the profit and minimize the risk. We evaluate our framework on a popular cryptocurrency Bitcoin.

Stock Market Analysis and Prediction using Machine Learning Algorithm

Abstract: The prediction of a stock market direction serves as an unseasonable recommendation of system for shortterm investors and before time financial distress warning system for long-term shareholders. Forecasting accuracy is the most important part in selecting any forecasting technique. Research efforts in upgrading the accuracy of forecasting models are increasing from the last decade. The appropriate stock selections those are fit for investment is a challenging task. The key factor for each investor is to be paid maximum profits on their investments. In this paper Support Vector Machine Algorithm (SVM) is handed down. SVM is a very specific kind of learning algorithms characterized by the volume control of the decision function, the work of the kernel functions and the scarcity of the compound. In this paper, we investigate the predictability of economic movement with SVM

Integrating AI-Driven Techniques in Big Data Analytics: Enhancing Decision-Making in Financial Markets

The growing complexity and volume of financial data, driven by globalization and advancements in digital technologies, have significantly transformed decision-making processes in financial markets. This paper explores the integration of Artificial Intelligence (AI)-driven techniques in Big Data Analytics to enhance decision-making capabilities in the financial sector. AI techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), are reshaping the landscape of data analytics by providing more accurate predictions, uncovering market trends, and automating complex trading decisions. The study focuses on three core areas where AI-driven techniques have been effectively applied: predictive analytics, sentiment analysis, and algorithmic trading. Predictive models, such as support vector machines and neural networks, are employed to forecast market trends by analyzing vast amounts of historical and real-time financial data. Sentiment analysis, powered by NLP, is used to assess market sentiment from textual data, such as news articles and social media posts, providing additional context to market movements. Lastly, algorithmic trading utilizes AI algorithms to automate and optimize trading decisions based on predefined criteria, enhancing speed and precision in trade execution. Through a quantitative methodology, historical financial data from major stock exchanges were analyzed using AI models. The results demonstrate that AI-driven models, particularly neural networks and sentiment analysis tools, significantly improve prediction accuracy and market timing compared to traditional methods. The findings suggest that integrating AI into Big Data Analytics can lead to more effective decision-making, allowing financial institutions to better manage risks, seize opportunities, and maintain a competitive edge in increasingly volatile markets. Despite the benefits, the paper also addresses the challenges associated with the adoption of AI in financial markets, including issues related to data privacy, model interpretability, and regulatory compliance. As AI technologies continue to evolve, their role in financial markets will likely expand, but careful consideration of these challenges will be critical for their sustainable implementation.

Stock market prediction based on deep hybrid RNN model and sentiment analysis

Stock market movements, stocks, and exchange rates are the primary subjects and active areas of research for analysts and researchers. The stock prices is being influenced by financial news, which has been demonstrated to be an important element in fluctuating stock prices. Furthermore, previous research mostly evaluated shallow characteristics and ignored functional relationships between words in a sentence. Many studies have attempted to analyse the sentiment of investors’ reactions to corresponding news occurrences. In this paper, we proposed a unique methodology for predicting the stock prices trend by using both stock features and financial news. The proposed methodology is the hybrid Recurrent Neural-Network (HyRNN) architecture. This design includes Bidirectional Long Short-Term Memory (Bi-LSTM) on top of the Gated Recurrent Unit (GRU) and stacked Long Short-Term Memory (sLSTM). The performance of HyRNN for forecasting stock price can be considerably improved by mixing the sentiments of financial news with the features of stock as an input to the model. In comparison to earlier statistical models, the suggested model increases the analysing capability of GRU, LSTM, RNN, and proposed models independently. The findings of this study shows the deep learning (DL) approach has high potential for predicting stock price changes.

Stock Market Prediction Based On Political Impact Using Zasl-Ecc And Tlsaaf-Bilstm

Stock Market Prediction Based On Political Impact Using Zasl-Ecc And Tlsaaf-Bilstm

Stock market prediction with time series data and news headlines: a stacking ensemble approach

Stock market prediction with time series data and news headlines: a stacking ensemble approach

Stock Market Prediction using RNN-based Models with Random and Tuned Hyperparameters

Stock Market Prediction using RNN-based Models with Random and Tuned Hyperparameters

Herding Behavior in Prediction Markets: Evidence from UK Financial Spread-Trading Markets

We contrast the degree (strong vs. weak), nature (interaction between more and less informed traders; MI and LI, respectively), and patterns of herding behavior (via their feedback strategies) among MI and LI traders and their speed of reaction to shifts in trading by these groups. This is achieved by analyzing individual investment records of 1,943 traders in UK spread-trading markets (2010–2012). We find that herding is far more prevalent than previous studies suggest, particularly among LI; herding activities of MI and LI are related, and the means used to distinguish MI and LI needs to be considered carefully.