Historical Market Data Research Articles

Enhancing Agricultural Commodity Price Forecasting Using Generative Models: A Deep Learning Approach

Predicting stock market prices is a critical yet challenging task in finance. Technical analysis, a widely used methodology in investment theory, involves forecasting price movements by analyzing historical market data. Recently, deep learning has gained prominence for its exceptional ability to process complex data, making it a popular tool for financial applications such as stock prediction, portfolio optimization, financial information analysis, and trade execution strategies. In this study, we propose a novel deep learning architecture that integrates a Generative Adversarial Network (GAN) with a Convolutional Neural Network (CNN) as the discriminator and Gated Recurrent Units (GRU) as the generator. This framework generates distributions of daily stock prices through adversarial learning to predict stock closing prices. Using daily trading data from Ruchi Soya Industries Limited, an empirical analysis was performed across a broad time frame. Results show that the proposed GAN-based architecture significantly outperforms traditional deep learning models, including GRU, LSTM, and Bi-LSTM, in predicting stock closing prices. These findings demonstrate the potential of this novel approach for improving stock price forecasting accuracy.

Integration of Modified Heston Model (Jump and Sentiment Factor) with Artificial Neural Networks (ANN) for Volatility Forecasting in Financial Markets

This study examined a new methodology for predicting volatility in financial markets by combining the modified Heston model (JSF) with Artificial Neural Networks (ANN). Forecasting volatility is essential in various financial contexts, such as risk management, option pricing and portfolio optimization. Although the traditional Heston model is effective but it does not account for additional elements like jumps and investor sentiment factors, which impact asset price dynamics. To address this limitation, the extended Heston model with jump and sentiment variables helped in enhancing its ability to capture complex market trends. We utilized ANN's predictive capabilities on the modified Heston model to improve volatility predictions using historical random market data from NASDAQ Composite. The ANN was trained on simulated data from the modified Heston model and assessed on its effectiveness in forecasting volatility. This approach helped in developing a forecasting hybrid semi-parametric model known as the modified-Heston-ANN model. Our findings revealed that this integrated approach outperformed the individual model, offering more precise and robust volatility predictions

Comparative Analysis of ETF Arbitrage Strategy in Efficient and Inefficient Markets

This paper analyzes the different performance of an ETF arbitrage strategy in efficient and inefficient markets, the markets of the United States and Russia. It would initiate from the ETFs and their importance to the global financial market. Then, the paper introduced what the arbitrage strategy is, and how it works. Then, by comparing the key factors, it classifies efficient market, the U.S. market, and the inefficient market, the Russian market. In the paper, the S&P 500 ETF represents the U.S. market, and the VanEck Russia ETF (RSX) represents the Russian Market. The paper analyzes the performance in these two markets from 2016 to 2021 and ignores the transaction costs. The paper concluded that the ETF arbitrage strategy performed better in the inefficient market (Russia) compared to the efficient market (U.S.). Limitations are acknowledged in the paper, such as the effect of transaction costs, which would reduce the actual profits and returns, and that the historical market data might not strong enough in predicting the future performance of the market. These would lead to future research and further analysis to enhance the overall effectiveness of the project and make it aligns more closely with the real-world conditions.

Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks

In the ambitiously evolving construction industry of Kuwait, characterised by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. The pressing need for advanced analytical frameworks in the Kuwait Construction Market arises from the necessity to identify inefficiencies, predict market trends, and enhance decision-making processes. For instance, these frameworks can be used to detect anomalies in investment patterns, forecast the impact of economic changes on project timelines, and optimise resource allocation by analysing labour and material supply data. By leveraging deep learning techniques, such as autoencoder neural networks, stakeholders can gain deeper insights into the market’s complexities and improve strategic planning and operational efficiency. This research paper introduces a deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector’s significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This analysis involves training the autoencoder on historical market data to learn the normal patterns and subsequently using it to identify deviations from these learned patterns. This allows for the detection of anomalies that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning’s pivotal role in construction and building management. Empirically, the model detected anomalies in transaction volumes of lands and houses, highlighting unusual spikes that correlate with specific market activities. These findings demonstrate the autoencoder’s effectiveness in anomaly detection, emphasising its importance in enhancing operational efficiency and strategic planning in the construction industry.

Exploring Different Dynamics of Recurrent Neural Network Methods for Stock Market Prediction - A Comparative Study

ABSTRACT The intricate and unpredictable nature of stock markets underscores the importance of precise forecasting for timely detection of downturns and subsequent rebounds. Various factors, including news, rumors surrounding events or companies, market sentiments, and governmental policies, can significantly impact stock prices. Nevertheless, the precision of current methods remained insufficient until the adoption of artificial neural network architectures like long short-term memory (LSTM). The aim of this study is to create a precise AI-driven platform tailored for both the Indian and international stock markets. This platform is designed to assist retail investors in navigating digital environments by employing various LSTM algorithms. Its primary goals include predicting stock price fluctuations, pinpointing potential investment prospects, and refining trading strategies. The application aims to leverage advanced LSTM algorithms to analyze historical market data, recognize patterns, and provide real-time insights. It will take past price and process it through LSTM algorithms to take a logical decision. In the quest to broaden retail participation in the capital markets, the effort is to develop an application for novice investors who either have no time in research or are the victims of financial mis-selling and enable them to leverage the technology to their advantage.

Real Time-Cutting Algorithmic Trading

a. Brief overview of algorithmic trading. Algorithmic trading, also known as algo trading or automated trading, refers to the use of computer algorithms and mathematical models to execute trading orders in financial markets. The primary goal of algorithmic trading is to achieve efficient and optimized execution of trading strategies, leveraging the speed and precision of computer systems. Here is a brief overview of algorithmic trading: 1. **Automation:** Algorithmic trading involves automating the process of buying or selling financial instruments, such as stocks, bonds, currencies, or commodities. The trading decisions are based on predefined rules and algorithms, eliminating the need for manual intervention. 2. **Speed:** One of the key advantages of algorithmic trading is its ability to execute trades at extremely high speeds. Algorithms can analyze market conditions, make decisions, and execute trades in a fraction of a second, responding to market changes much faster than human traders. 3. **Quantitative Analysis:** Algorithmic trading relies heavily on quantitative analysis and statistical models. Traders develop algorithms that are based on mathematical formulas, historical price data, technical indicators, and other relevant factors to identify trading opportunities. 4. **Market Making:** Some algorithmic trading strategies are designed for market making, where traders provide liquidity to the market by quoting bid and ask prices. Market-making algorithms aim to profit from the spread between the buying and selling prices. 5. **Arbitrage:** Algorithmic trading is often employed for arbitrage strategies, which involve exploiting price discrepancies between different markets or instruments. These discrepancies may arise due to inefficiencies, delays in information dissemination, or other market imperfections. 6. **Risk Management:** Algo trading systems typically incorporate risk management measures to control exposure and mitigate potential losses. These measures may include setting stop-loss orders, position sizing algorithms, and other risk controls. 7. **Backtesting:** Before deploying algorithms in live markets, traders often perform extensive backtesting. This involves running historical market data through the algorithm to simulate how it would have performed in the past. This helps refine and optimize the algorithm for real-world conditions. 8. **Machine Learning:** Some advanced algorithmic trading strategies incorporate machine learning techniques to adapt and learn from changing market conditions. Machine learning algorithms can analyze vast amounts of data to identify patterns and make predictions. 9. **Regulatory Considerations:** Algorithmic trading is subject to various regulations to ensure fair and orderly markets. Regulatory bodies may impose rules regarding algorithmic trading practices, transparency, and risk management to prevent market manipulation and protect investors. Algorithmic trading has become increasingly prevalent in financial markets, with institutional investors, hedge funds, and proprietary trading firms utilizing sophisticated algorithms to execute trades efficiently and capitalize on market opportunities.

Integrating Technical Indicators and Ensemble Learning for Predicting the Opening Stock Price

Accurately predicting stock prices poses a significant challenge due to the dynamic and complex nature of financial markets. This paper introduces a novel method that combines technical indicators with ensemble learning techniques to effectively forecast opening stock prices. Technical indicators offer valuable insights into market trends and patterns, while ensemble learning methods merge multiple models to enhance predictive precision. The study utilizes various technical indicators such as moving averages, Relative Strength Index (RSI), and Bollinger Bands to capture diverse aspects of market behaviour. Ensemble learning techniques like Random Forest, Gradient Boosting, Support Vector Regressor, and ARIMA model are then employed to consolidate the forecasts from these indicators. The proposed framework is assessed using historical stock market data, and extensive experiments showcase its superior performance compared to individual indicators and traditional forecasting approaches. The findings reveal that integrating technical indicators with ensemble learning leads to a significant improvement in accuracy, with a success rate of 91.45% in predicting opening stock prices, thus providing valuable insights for investors and financial analysts.

Hybrid machine learning for stock price prediction in the Moroccan banking sector

Analyzing historical stock market data using machine-learning techniques is crucial for data scientists and researchers to optimize stock price prediction models. This study uses machine learning regression algorithms and feature selection methods to optimize a simulated stock price prediction model using real historical data from Bank of Africa, a Moroccan bank. The approach compares multiple supervised regression algorithms, such as linear regression, extreme gradient boosting, ordinary least squared, random forest regressor, a linear least-squares L2-regularized, epsilon-support vector regression, and linear support vector regression. Each of these algorithms is associated with different feature selection algorithms to improve the performance of the prediction model. The analysis results revealed that hybridizing algorithms between the highest score percentiles, univariate linear regression, and linear support vector regression perform better according to the root mean squared error and R2-Score measures. This approach overcomes the problems associated with high-dimensional data by reducing the number of features and improving prediction accuracy.

Stock Market Prediction

The prediction of stock market trends is a challenging yet critical task in the financial sector, given its significant implications for investors, traders, and financial institutions. This research leverages the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN), to develop a robust model for forecasting stock prices. The study utilizes historical stock market data sourced from Yahoo Finance, accessed via the yfinance package in Python. The primary objectives are to preprocess the data, implement the LSTM model, and evaluate its performance against traditional models such as Random Forest and Linear Regression. Data preprocessing involved handling missing values, normalizing the dataset, and transforming it into sequences suitable for LSTM training. The model's architecture includes multiple LSTM layers designed to capture temporal dependencies in the data. The study evaluates the model's performance using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and prediction accuracy. Comparative analysis shows that the LSTM model outperforms both Random Forest and Linear Regression models, with lower MSE and RMSE values and higher accuracy in predicting stock prices. This research discovered that LSTM's ability to retain long-term dependencies makes it particularly effective for stock market prediction, where historical trends and patterns significantly influence future prices. The results indicate that the LSTM model provides more reliable and precise predictions, which can enhance decision-making in trading and investment. This research highlights the potential of advanced neural network architectures in financial forecasting, offering a valuable tool for investors aiming to optimize their strategies and mitigate risks. The significance of this study lies in its practical application in the financial industry, demonstrating that machine learning models, particularly LSTM, can substantially improve the accuracy of stock market predictions. Future research could explore the integration of additional features, such as macroeconomic indicators and sentiment analysis, to further enhance model performance. This study underscores the importance of continuous innovation and the adoption of sophisticated algorithms to navigate the complexities of financial markets.

Unveiling influential factors in the capital asset pricing model: A novel approach utilizing Taguchi method

This study uses a Design of Experiment (DOE) approach and the Taguchi method to examine how different factors affect the expected return within the framework of the CAPM. The Capital Asset Pricing Model (CAPM) is a financial framework that forecasts an investment’s expected return by taking into account its systematic risk. This model factors elements like the risk-free rate, market risk premium, and beta to ascertain the expected return for a given asset. Typically, historical market data and financial analysis provide the inputs for CAPM. This method, frequently used in manufacturing and engineering, is modified for use in the financial domain to evaluate the importance of variables like beta, market risk premium, and the risk-free rate. The research identifies the factors that influence investment performance and shows how much each factor contributes to the expected returns. It is possible to ascertain each factor's percentage contribution to the expected return through statistical analysis. This work improves knowledge of CAPM and creates opportunities for improving investment decision-making processes by bridging the gap between financial theory and experimental design. Financial practitioners can achieve more reliable and accurate investment return forecasts by incorporating DOE techniques into their existing analytical toolkit.

Loan Approval Prediction System Using Machina Learning

With the growth of the banking sector, the identification of reliable borrowers that must maintain the natural core income and asset accumulation becomes a key issue. Despite all security measures, reliability of customers remains an unclear question. To tackle this barrier, banking management which is directed towards customer loan repayment consistency is required. Credit approval is a significant aspect of economy since it determines the allocation of credit-linked funds. Today, machine learning is known for its power to automate and scale up the processing of application for loans. This project will begin with data collection which will consist of data on historical (regarding the past) loan applications and the borrower profiles. The dataset has features of the credit score, income, previous work experience, debt-to-income ratio, and loan repayment record. This way, the models learn through the strengths to find good features and the reasons for accepting a loan. They are the experts in these areas and can forecast the potential patterns and connections of the data. Within the scope of this work, the supervised algorithms used are logistic regression, decision trees, random forests, and support vector machines. These algorithms are applied to the dataset available to often produce results like binary classification and regression. The adoption of machine learning among financial institutions is intended for a faster processing of loans which is their benefit. What is credit scoring, it is a tool which automate manual loan application review thereby increases efficiency. The machine-learning algorithms that analyse applications for loans could cut down on the possibilities of human biases and mistakes which are an inherent part of the process. Also, ML uses the model to recognize borrowers who may default and subsequently lower the likelihood of default. Part of the task involve utilizing historical credit market data and implementing ML algorithms to develop a highly accurate and reliable loan approving system based on trained-data, random forests, the stream of loans and reliable clients.

Deep Reinforcement Learning Based Optimization and Risk Control of Trading Strategies

Deep reinforcement learning (DRL) based optimization leverages advanced machine learning techniques to solve complex decision-making problems in various domains. Deep learning in trading involves the application of sophisticated neural network architectures to analyze financial data and make predictions in stock markets, forex, commodities, and other trading domains. Deep learning algorithms, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), traders can extract valuable insights from vast amounts of historical market data, including price movements, trading volumes, and market sentiment. These models can learn complex patterns and relationships within the data, enabling them to forecast future market trends, identify potential trading opportunities, and manage risks more effectively. Deep learning in trading has shown promise in improving decision-making processes, enhancing trading strategies, and achieving higher returns, although challenges such as data scarcity, model interpretability, and overfitting remain areas of ongoing research and development. In this paper evaluated the stock market to achieve the financial gain to achieve the significant improvement in prediction with the stock trading. The data related to the stock market are optimized with stock trend data of 7,000 stocks situated in the United States. The estimated stock trade data were computed and processed with the Multiagent Q-learning model for the data detection and classification. A deep Q-network is developed to adaptively pick distinct action subsets and train the market making agent based on the inventory states. The experimental findings demonstrate the effectiveness of our suggested methodology in feature learning and superiority in accuracy improvement when compared to deep learning-based modelling of frequency trading patterns and standard signal processing approaches for stock trend prediction.

Cryptocurrency Price Prediction for Next 30 Days with Machine Learning

The goal of this project is to create a model from machine learning that will predict cryptocurrency prices toward the following 30 days. The main goal is to build a prediction model that is able to predict future swings in prices for selected cryptocurrencies by using historical market data that encompasses price patterns, trading volumes, and appropriate indicators. The key objective of the model is to empower traders and investors functioning in the fluctuating and unpredictable the digital currency market to make well-informed decisions. Throughout every moment of the next 30 days, the model aims to give traders an advantage of strategy in maintaining the cryptocurrency ecosystem through provision of practical information. Key Words: Cryptocurrency, Prediction, LTSM, Normalization, Features Extraction, Pediction Date

Investment Decision Using Technical Analysis: A Study on Selected Stocks in Indian Stock Market

Technical analysis is a methodology in financial markets that asserts its capability to predict future stock directions by scrutinizing historical market data, patterns in price movements, and correlations between price and volume. Traders widely utilize these data in their decision- making processes. The approach also takes into account the real-time behavior of market prices and volumes, operating under the assumption that these are the most influential factors in determining a stock’s or market’s future trajectory. Investors often grapple with decisions regarding which stocks to buy, the optimal purchase quantities, and when to execute selling or holding actions to maximize profits in the trading period. This research delves into these considerations, offering insights for short-term traders by analyzing the stock price trends of twelve companies across three distinct sectors over the 2020-2022 period.

Research on Financial Risk Intelligent Monitoring and Early Warning Model Based on LSTM, Transformer, and Deep Learning

As global financial markets continue to evolve and change, financial risk monitoring and early warning have become increasingly important. However, the complexity and diversity of financial markets have led to the emergence of multidimensional and multimodal data. Traditional risk monitoring methods face difficulties in handling such diverse data and adapting to the monitoring and early warning needs of emerging risk types. To address these issues, this article proposes a financial risk intelligent monitoring and early warning model that integrates deep learning to better cope with uncertainty and risk in the financial market. Firstly, the authors introduce an LSTM model in the initial approach, trained on historical financial market data, to capture long-term dependencies and trends in the data, enabling effective monitoring of financial risk. They also optimize the model architecture to improve its performance and prediction accuracy. Secondly, the authors further introduce a transformer model with self-attention mechanism to better handle sequential data.

Innovative entrepreneurial market trend prediction model based on deep learning: Case study and performance evaluation.

In the current economic landscape, the growing importance of innovation and entrepreneurship underscores an urgent need for accurate market trend prediction. Addressing this challenge, our study introduces an innovative entrepreneurial market trend prediction model based on deep learning principles. Through detailed case studies and performance evaluations, this paper demonstrates the model's effectiveness and its potential to enhance decision-making capabilities in a competitive business environment. Accurate market trend prediction is crucial in the fields of innovation and entrepreneurship, and our approach meets this demand. Our model leverages the power of deep learning technology, combining historical market data with diverse market indicators, including sentiment analysis derived from social media, to create an advanced predictive model that surpasses traditional methods. By analyzing data from multiple channels, our model exhibits exceptional accuracy in forecasting future market trends. The case study provides strong evidence of our model's performance and precision, showcasing its significant support for innovators and entrepreneurs navigating complex market trends. Furthermore, this study highlights the vast potential of deep learning technology in the economic sector. We emphasize the importance of developing innovative entrepreneurial market trend prediction models and foresee an increase in project success rates for innovators and entrepreneurs by enhancing decision quality through the adoption of deep learning.

Market Behaviors and Economic Crisis: Insights from Financial Economics

The research abstract encapsulates a comprehensive exploration of market behaviors during economic crises within financial economics. The study aims to elucidate the intricate dynamics of market behaviors amidst crises and their implications for theory and practice in financial economics. Employing a mixed-methods approach, the research integrates theoretical frameworks with empirical evidence to offer insights into the complexities of market dynamics. The study design thoroughly examines theoretical constructs and empirical investigations, drawing upon established frameworks such as the Efficient Market Hypothesis and behavioral finance theories. The research unravels the interplay between rational and irrational factors driving market outcomes during crises through empirical studies, including analysis of historical market data and examination of investor behavior. The findings underscore the need for a nuanced understanding of market behaviors, emphasizing the integration of rational and behavioral perspectives in financial economics theory and practice. The study's implications extend to policymakers, investors, and market participants, providing actionable insights to navigate turbulent economic environments effectively and foster stability in global financial markets.

Performance Analysis of Share Market by Using ML Technique

This research paper delves into the intricate world of stock market analysis, exploring the application of machine learning techniques to enhance performance evaluation. The study adopts multifaceted approach, combining historical stock market data, advanced machine learning algorithms, and rigorous back testing to derive insights into market dynamics. The findings aim to contribute to the growing body of literature on financial analytics, providing valuable implications for investors and financial professionals.

Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.

Managing extreme cryptocurrency volatility in algorithmic trading: EGARCH via genetic algorithms and neural networks

<abstract> <p>The blockchain ecosystem has seen a huge growth since 2009, with the introduction of Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In this article, we contribute a comparative analysis encompassing deep learning and quantum methods within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In this study, we evaluated how well Neural Networks and Genetic Algorithms predict "buy" or "sell" decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This research provides insights for investors, regulators, and developers in the cryptocurrency market. Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural network models for enhanced analysis.</p> </abstract>