Historical Market Data Research Articles

A Study On The Effectiveness Of Moving Average Convergence And Divergence (MACD)

In the digitalized word, it becomes easier to trade in stock but earning profit requires some analysis before investing. Analysis in stock can be categorized into fundamental and technical. Fundamental analysis is based on the performance, financials and future prospects of the company whereas technical analysis is based on the historical market data of stock mainly on stock price and volume. Technical analysis is done through charts and statistical indicators. One of the most popular technical analysis tools is Moving Average Convergence and Divergence shortly called as MACD. MACD can be interpreted in two ways i.e. through Centreline Crossover Strategy and Signal Line Crossover Strategy. The objective of this study is to analyse the effectiveness of the MACD with reference to select stocks in Information Technology Sector being traded in BSE. For this purpose, a comparative study on profitability is made among two strategies of MACD and with Buy and Hold strategy. In the Buy and Hold strategy itself, analysis is done for two variations i.e. buy based on MACD centreline crossover and then hold and Random buy and hold. Extended Internal Rate of Return (XIRR) has been used to measure profitability. Based on the sample data for the population taken for analysis, Centre Line Crossover Strategy is same or more profitable than Signal Line Crossover Strategy and Random Buy and Hold strategy.

Stock Market Price Prediction

Abstract: Investing in the stock market can be a convoluted and refined method of conducting business. Stock prediction is an extremely difficult and complex endeavor since stock values can fluctuate abruptly owing to a variety of reasons, making the stock market incredibly unpredictable.This paper explores predictive models for the stock market, aiming to forecast stock prices using machine learning algorithms. By analyzing historical market data and employing various predictive techniques, thestudy aims to enhance accuracy in predicting future stock movements. this paper contributes understanding into the potential of LSTM models for enhancing stock market prediction accuracy and reliability.

Stock Market Prediction Model using LSTM

Abstract: The stock market is a complex and dynamic financial system that constantly undergoes fluctuations driven by various economic, political, and social factors. Accurate prediction of stock prices is a challenging yet essential task for investors, traders, and financial analysts. The objective of this project is to design and implement an LSTM-based stock market prediction system that leverages historical market data to forecast future stock prices. The methodology involves the creation of a deep learning model leveraging LSTM layers for temporal dependency modeling, Dense layers for feature extraction, Dropout layers for regularization, and a Sequential architecture for seamless integration. The model is trained on historical stock data, incorporating essential features such as past prices, trading volumes, and market indicators. The model's performance is rigorously evaluated through back testing and out-of-sample testing using historical stock data. The results indicate the model's ability to make predictions that outperform traditional buy-and-hold strategies. This research contributes to the ongoing efforts to harness the power of machine learning and artificial intelligence in predicting stock market trends and demonstrates the potential for improved decision-making in the financial industry. It is hoped that this model will serve as a valuable resource for market participants seeking to navigate the complexities of the stock market.

Stockdiary

STOCKDAIRY, a pioneering stock market tracking platform, represents a novel integration of cutting-edge technologies for financial analysis. This research paper meticulously documents the design, development, and implementation journey of STOCKDAIRY, focusing specifically on its utilization of the React.js framework. Employing a comprehensive approach, STOCKDAIRY integrates diverse data sources, predictive models, and methodologies, providing a holistic solution for stock market predictions. At its core, STOCKDAIRY relies on React.js, renowned for its modular and component-based architecture, ensuring seamless integration and real-time data updates. Trained on a combination of standard patterns and customized models using historical stock market data, STOCKDAIRY delivers a dynamic and user- centric experience, allowing users to tailor predictions and conduct thorough risk analysis, meeting the evolving demands of stock market investors. However, the developmental journey of STOCKDAIRY is not without challenges. Bulk data handling, precise insights filtration, and meticulous verification and validation of each module pose critical obstacles. The research paper transparently details the rigorous testing methodologies employed, highlighting the complexities faced and overcome in the development cycle. Despite these challenges, STOCKDAIRY emerges as a robust and reliable market prediction tool, underscoring its paramount importance in the ever-dynamic financial landscape. The paper emphasizes the need for a standard reference platform like STOCKDAIRY to successfully navigate the intricacies of stock market investments. The platform's guiding motto, "To minimize risk and maximize profit," encapsulates its overarching objective and commitment to delivering valuable insights to users in the realm of stock market predictions.

A novel randomized weighted fuzzy AHP by using modified normalization with the TOPSIS for optimal stock portfolio selection model integrated with an effective sensitive analysis

Building an ideal stock portfolio is a difficult and important undertaking in the world of financial decision-making. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique and the Randomised Weighted Fuzzy Analytic Hierarchy Process (AHP) are combined in this research to create a powerful model for selecting the best stocks for a portfolio. To improve the portfolio's robustness under various market scenarios, our model also incorporates sensitivity analysis. Given the inherent uncertainty in financial markets, the suggested approach starts by using modified fuzzy AHP based on introducing the randomized weights and the novel normalization to get expert views and assign suitable weights to the underlying selection criteria. Based on the established criteria and associated fuzzy scores, the TOPSIS approach is then used to rank and choose the best stocks. Our model also includes sensitivity analysis to evaluate the performance of the portfolio under various market conditions. Extensive empirical tests are performed utilizing historical stock market data to assess the effectiveness of our strategy. The outcomes show the suggested model's superiority in building stock portfolios that beat conventional approaches in terms of risk-adjusted returns.

Comparative Analysis of LSTM, GRU, and ARIMA Models for Stock Market Price Prediction

This study delves into the efficacy of various machine learning and statistical models that have captured the attention of financial analysts. Two of them, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are variations of Recurrent Neural Networks while the Autoregressive Integrated Moving Average (ARIMA) is a statistical model. These models will be used to forecast stock market data across different economic sectors. In the dynamic landscape of financial markets, accurate forecasting is crucial. This research paper contributes to quantitative finance by conducting a comprehensive comparative analysis of these models on historical stock market data from three sectors: extraction, manufacturing, and service (which are considered the primary, secondary, and tertiary sectors of the economy respectively). The models' performances are evaluated using mean squared error (MSE) on six selected stocks representing these sectors. Results reveal the power of recurrent neural networks in capturing intricate patterns. Moreover, the results will explore whether or not the efficacy of each model is impacted by the sector of the economy that it is forecasting data for.

EVALUATION OF SUPPORT VECTOR MACHINE BASED STOCK PRICE PREDICTION


 
 
 In recent years with the advent of computational power, Machine Learning has become a popular approach in financial forecasting, particularly for stock price analysis. In this paper, the authors develop a non-recurrent active trading algorithm based on stock price prediction, using Support Vector Machines on high frequency data, and compare its risk adjusted performance to the returns of a statistical portfolio predicted by the Capital Asset Pricing Model. The authors selected the three highest volume securities from a pool of 100 initially selected stock dataset to investigate the algorithmic trading strategy. The abnormal return estimates are significant and positive, and the systematic risk is lower than unity in all cases, suggesting lower risk compared to the market. Moreover, the estimated beta values for all stocks were close to zero, indicating a market independent process. The correlation analysis revealed weak correlations among the processes, supporting the potential for risk reduction and volatility mitigation through portfolio diversification. The authors tested an equally weighted portfolio of the selected three assets and demonstrated a remarkable return of 1348% during the evaluation period from July 1st, 2020, to January 1st, 2023. The results suggest that the weak form of market efficiency can be questioned, as the algorithmic trading strategy, employing a Support Vector Machine binary classification model, has consistently generated statistically significant and substantial abnormal returns using historical market data.
 
 

Sentiment-Driven Cryptocurrency Price Prediction: A Machine Learning Approach Utilizing Historical Data and Social Media Sentiment Analysis

This research explores the impact of social media sentiments on predicting Bitcoin prices using machine learning models, integrating on-chain data, and applying a Multi Modal Fusion Model. Historical crypto market, on-chain, and Twitter data from 2014 to 2022 were used to train models including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting, and Multi Modal Fusion. Performance was compared with and without Twitter sentiment data which was analysed using the Twitter-roBERTa and VADAR models. Inclusion of sentiment data enhanced model performance, with Twitter-roBERTa-based models achieving an average accuracy score of 0.81. The best performing model was an optimised Multi Modal Fusion model using Twitter-roBERTa, with an accuracy score of 0.90. This research underscores the value of integrating social media sentiment analysis and onchain data in financial forecasting, providing a robust tool for informed decision-making in cryptocurrency trading.

On a Data-Driven Optimization Approach to the PID-Based Algorithmic Trading

This paper proposes an optimal trading algorithm based on a novel application of conventional control engineering (CE). We consider a fundamental CE concept, namely, the feedback control, and apply it to algorithmic trading (AT). The concrete feedback control strategy is designed in a form of the celebrated proportional–integral–derivative (PID) model. The highly fluctuating nature of the modern financial markets has led to the adoption of a model-free realization of the generic PID framework. The control theoretical methodology we propose is combined with the advanced statistics for the historical market data. We obtain a specific log-normal probability distribution function (pdf) associated with the specific quantities associated with the available stock data. The empirical log-normal pdf mentioned above enables the necessary PID gains optimization. For this aim, we apply the data-driven optimization approaches and consider the corresponding Monte Carlo solution procedure. The optimized PID trading algorithm we propose is also studied in the Fourier analysis framework. This equivalent frequency domain representation involves a new concept in financial engineering, namely, the “stock market energy” concept. For the evaluation, we implement the proposed PID optimal trading algorithm and develop a Python-based prototype software. We finally apply the corresponding prototype software to a data set from the Binance BTC/USDT (Bitcoin/Tether) stock market. The experimental result illustrates the implementability of the proposed optimal PID trading scheme and also shows the effectiveness of the proposed CE methods in the modern AT.

Development of a machine learning framework to determine optimal alloy composition based on steel hardenability prediction

Mechanical property prediction plays a crucial role in the steel industry, enabling better materials selection and enhancing production efficiency. In this study, we propose a novel framework that facilitates optimal materials selection for steel’s alloying elements, based on accurate predictions of the Jominy hardness curve. Leveraging Gaussian Process (GP) regression, we provide probabilistic predictions of steel hardenability characteristics from alloy element composition. Taking it a step further, our framework incorporates these accurate predictions into a constrained optimization process, yielding optimal compositions that reduce overall spending while meeting performance specifications. Through data obtained from 1080 steel samples, our GP regression model exhibits high accuracy, achieving an RMSE of 1.37 and showcasing significant improvements in the field. Moreover, our constrained optimization utilizing the GP model and historical market data reveals an average cost reduction of 18% on alloying element expenses, highlighting the tangible cost-saving potential of this approach. By leveraging Gaussian Process (GP) regression, we not only achieve accurate predictions of the Jominy hardness curve based on alloy element composition, but we also introduce a crucial element of uncertainty quantification. This empowers us to place trust in the results of our optimization process, ensuring robust and reliable materials selection. The integration of GP regression and optimization provides a powerful tool for achieving cost-effective materials selection and marks a significant advancement compared to existing studies. This research underscores the promise of machine learning in the steel industry, demonstrating its ability to yield substantial cost savings and enhance decision-making in materials selection.

THEORETICAL METHODOLOGICAL JUSTIFICATION IN TECHNICAL ANALYSIS OF FINANCIAL MARKETS

We live in strange times times of change, crises, pandemics, conflicts and even wars. Technology is developing at an incredible speed, and our ability to predict or react is narrowed by rapidly changing trends, market changes and more. Throughout millennia of written and unwritten history, humanity has learned to change, evolve and survive. Economists and marketers have long recognised the cyclical nature of events and their repeatability, thus creating theories and tools. In our attempt to predict future events, we often refer to past experience and historical lessons. In the field of finance, technical analysis is a methodology for analysing financial markets, the purpose of which is to predict likely price commute based on regularities deploy on similar price changes in the past under similar circumstances. For this purpose, historical market data such as prices and volume are used (Kirkpatrick, Dahlquist, 2006). At its core, technical analysis has many tools: time charts, support and resistance charts, Fibonacci analysis, Japanese candlestick chart and many other theoretical methods and methodologies with greater or lesser practical success. But while there are many instruments, the indicators that help correctly start the future investment or withdrawal are a few looking for changes in time and matching trends by comparing the lengths of their intervals: there are indicators reporting the involvement of the instrument in the trend Indicator ADL, McClellan Oscillator, the Index of McClellan Sherman (McClellan Summation Index) and indicators follow the change in prices Indicator %C (Percent Contraction Index), Indicator ADX (Average directional index), Index of the Commodity Channel (Commodity Channel Index), Indicator MACD (Moving Average Convergence / Divergence), Relative strength index (RSI), Relative vigor index (RVI), Stochastic oscillator and indicators based on market volume Accumulation/distribution index, Cash flow and Balance sheet volume. The purpose of the research is to draw attention to a theoretical hypothesis for the beginning of the trend wave itself, the beginning of the considered period supporting future investment or withdrawal from a given industry, the development of a trending product or service. The chosen methodology and corresponding tool formula are entirely author’s and would help to visualise the time intervals for investment or withdrawal from the market.

House Price Prediction and Recommendation

Abstract: Determining how much a house will sell for in a city is still a challenging and time-consuming task. This article's goal is to make predictions about the coherence of non-housing prices. A crucial method to ease the challenging design is to use machine learning, which can intelligently optimize the best pipeline fit for a task or dataset. For individuals who will be residing in a home for an extended period of time but not permanently, it is essential to predict the selling price. Real estate forecasting is a crucial part of the industry. From historical real estate market data, the literature seeks to extract pertinent information. Land price bubbles grow as a result of real estate prices, which leads to macroeconomic instability. The government should look into the variables that drive up real estate prices so that it can use them as a guide to assist stabilize the area. There are many economic circumstances that are in play at the time also have an impact on the selling price of a home.

An Economic Model of Spatial and Temporal Water Trade in the Australian Southern Murray‐Darling Basin

AbstractWithin the Australian southern Murray‐Darling Basin (sMDB) water markets have become a key institutional response to water scarcity and environmental problems. These markets facilitate trading of water among many users and across multiple connected regions. Increasingly, these markets also support intertemporal transfers via the carryover of user water allocations within public water storages. While trading can improve the spatial allocation of water, carryover can improve the temporal allocation by ensuring storage reserves reflect prevailing user preferences. This paper presents an economic model of water markets and irrigated agriculture in the sMDB. A reduced‐form statistical model of water demand is estimated using historical irrigation and water market data. This statistical model is placed within an economic partial‐equilibrium framework accounting for both interregional trade and interyear carryover. The model is solved numerically for a dynamic market equilibrium given assumptions over future stochastic water supplies. Out‐of‐sample tests show the model can realistically simulate water market prices and irrigation activity across the basin. The model is applied to estimate the economic benefits of interregional water trading and interyear carryover. Results show around a third of the economic benefits of water markets in the region can be attributed to interyear carryover.

The value of forecasts for PV power plants operating in the past, present and future Scandinavian energy markets

When participating in the Scandinavian wholesale energy markets with intermittent PV power, imbalances between market-committed and actual energy generation need to be economically accounted for in the imbalance settlement. The market-committed energy is based on forecasts issued at specific timestamps defined by the market. The economic value of such forecasts can be quantified for the past, present and future energy market regulations by comparing the achieved income using perfect forecasts to the income with imperfect forecasts. Differences between these regulations include changing from 60 min to 15 min market intervals, and from a dual-price to a single-price imbalance settlement structure.PV power production data were modelled from meteorological measurements carried out at 11 locations in each of the Nord Pool bidding areas. Forecasts generated from a smart persistence model and a more sophisticated numerical weather prediction (NWP) model were compared for these locations, and the value of these forecasts was estimated from historical market data for each of the 11 bidding areas. The results show that the estimated income, and income losses, are dependent on the electricity mix of the bidding areas. In addition, the transition from the dual-price to the single-price imbalance settlement structure reduces the value of providing accurate forecasts. Moreover, the added incentive to provide accurate forecasts when settling the markets and imbalances on 15 min instead of 60 min intervals is negligible with the single-price imbalance settlement. Finally, the results show that intraday trading is generally beneficial for PV power plants in Scandinavia.

Option Pricing Based on Black-Scholes Model, Monte Carlo Method and Binomial Tree Model

Contemporarily, option is one of the widely implemented underlying assets to hedge the risk of portfolio and satisfy certain trading motivation for investors. To obtain extra return based on investment, it is crucial to utilize and apply an accuracy and suitable tool to price option. On this basis, this study focused on comparing and applying different model to achieve the goal of option pricing. To be specific, the Python code was used to help make calculations and list the results, with three functions to price options with three diverse methods. This paper chose Alphabet Inc., Amazon, Meta Platforms, Spotify, Sunrun, and Tesla, Inc. and priced the options using the historical market data from July 20, 2022, to October 20, 2022. Reasonable results for each option are achieved with differences between each method, offering price ranges for the options. These results offer a guideline for applying different methods to option pricing and compare the effectiveness of the three methods.

Data fusion with factored quantization for stock trend prediction using neural networks

As compared to the continuous temporal distributions, discrete data representations may be desired for simplified and faster data analysis and forecasting. Data compression can introduce one of the efficient ways to reduce continuous historical stock market data and present them in discrete forms; while predicting stock trend, a primary concern is towards up and down directions of the price movement and thus, data discretization for a focused approach can be beneficial. In this article, we propose a quantization-based data fusion approach with a primary motivation to reduce data complexity and hence, enhance the prediction ability of a model. Here, the continuous time-series values are transformed into discrete quantum values prior to applying them to a prediction model. We extend the proposed approach and factorize quantization by integrating different quantization step sizes. Such fused data can reduce the data to mainly concentrate on the stock price movement direction. To empirically evaluate the proposed approach for stock trend prediction, we adopt long short-term memory, deep neural network, and backpropagation neural network models and compare our prediction results with five existing approaches on several datasets using ten performance metrics. We analyze the impact of specific quantization factors and determine the individual best as well as overall best factor sizes; the results indicate a consistent performance enhancement in stock trend prediction accuracy as compared to the considered baseline methods with an improvement up to 7%. To evaluate the impact of quantization-based data fusion, we analyze time required to execute the experiments along with percentage reduction in the number of unique numeric terms. Further, these results are statistically evaluated using Wilcoxon signed-rank test. We discuss the superiority and applicability of factored quantization-based data fusion approach and conclude our work with potential future research directions.

Neural network systems with an integrated coefficient of variation-based feature selection for stock price and trend prediction

Stock market forecasting has been a subject of interest for many researchers; the essential market analyses can be integrated with historical stock market data to derive a set of features. It is crucial to select features with useful information about the specific aspect. In this article, we propose coefficient of variation (CV)-based feature selection for stock prediction. The unitless statistical method, CV, is widely used to obtain variability among data distributions. We calculate CV for each feature and integrate an existing method, k-means algorithm, as well as proposed methods, median range and top-M, to select a set of features with specific characteristics such as features belonging to the largest cluster, the defined range, and with the highest CV values, respectively. We apply the set of selected features to models such as backpropagation neural network (BPNN), long short-term memory (LSTM), gated recurrent unit (GRU), and convolutional neural network (CNN) for stock price and trend prediction. We demonstrate the applicability of our proposed approach using five of the existing feature selection methods, namely, correlation coefficient, Chi2, mutual information, principal component analysis, and variance threshold; comparison indicates remarkable performance enhancement using several accuracy-based, as well as error-based, metrics and the same is statistically supported using Wilcoxon signed-rank test.

Enhancing Stock Market Predictability: A Comparative Analysis of RNN And LSTM Models for Retail Investors

The stock markets are important components of the global financial system and have a considerable impact on an economy's growth and stability. This research article uses algorithms, notably deep learning, to increase the prediction of stock values. The efficacy and precision of long short-term memory (LSTM) and recurrent neural networks (RNN) algorithms to estimate stock prices are compared in this study. The paper investigates the potential of deep learning algorithms in creating a more predictable and trustworthy environment for the stock market. The study utilizes historical market data obtained from the Alpha Vault API and evaluates the performance of the RNN and LSTM models in forecasting stock prices. The results indicate that LSTM exhibits superior precision and is better suited for stock price prediction, while RNN faces certain challenges. Overall, this research contributes to the understanding of the application of deep learning algorithms in stock market analysis, to make informed investment decisions, thereby reducing risks and maximizing returns.

Stock Price Prediction: An Incremental Learning Approach Model of Multiple Regression

The endeavour of predicting stock prices using different mathematical and technological methods and tools is not new. But the recent advancements and curiosity regarding big data and machine learning have added a new dimension to it. In this research study, we investigated the feasibility and performance of the multiple regression method in the prediction of stock prices. Here, multiple regression was used on the basis of the incremental machine learning setting. The study conducted an experiment to predict the closing price of stocks of six different organizations enlisted in the Dhaka Stock Exchange (DSE). Three years of historical stock market data (2017-2019) of these organizations have been used. Here, the Multiple Regression, Squared Loss Function, and Stochastic Gradient Descent (SGD) algorithms are used as a predictor, loss function, and optimizer respectively. The model incrementally learned from the data of several stock-related attributes and predicted the closing price of the next day. The performance of prediction was then analysed and assessed on the basis of the rolling Mean Absolute Error (MAE) metric. The rolling MAE scores found in the experiment are quite promising.

Energy recovery strategies for batteries providing frequency containment reserve in the Nordic power system

Batteries are key components of future power systems with high shares of renewables. Their fast response enables them to contribute to one of the most crucial ancillary services — frequency containment reserve. However, longer periods with biased frequency pose challenges for batteries due to their limited energy capacity. This is particularly crucial in the Nordic synchronous area where 100% certainty of delivery is required. The present study uses a simulation model with three years of historical frequency, market, and tariff data, to compare three energy management strategies that comply with the Nordic regulatory framework. The first two strategies both utilize an hourly adjustment of the reference power to, on average, recover the energy level to a desired value. In the first strategy, this energy is traded on the intraday market, while in the second strategy it is settled via the balancing market. The third strategy is based on an exemption agreement with the Danish transmission system operator. Since capacity payment is the strongest factor that determines the profit, the study derives maximum regulating power and annual profit as a function of the energy/power capacity ratio and the efficiency of the storage system. The highest regulating power and profit can be achieved when applying the exemption agreement. Where this agreement is not available, the battery operator should apply the second strategy and settle the energy costs through the balancing market. Trading the energy on the intraday market slightly reduces the energy costs, but allows for significantly lower regulating power and revenues.