Showing papers on "Market data published in 2024"
TL;DR: Market-GAN generates high-fidelity, context-aligned financial data with control, addressing challenges in existing frameworks due to lack of context labels, inadequate techniques for context-aware data generation, and difficulties in generating context-aligned data from noisy financial data.
Abstract: Financial simulators play an important role in enhancing forecasting accuracy, managing risks, and fostering strategic financial decision-making. Despite the development of financial market simulation methodologies, existing frameworks often struggle with adapting to specialized simulation context. We pinpoint the challenges as i) current financial datasets do not contain context labels; ii) current techniques are not designed to generate financial data with context as control, which demands greater precision compared to other modalities; iii) the inherent difficulties in generating context-aligned, high-fidelity data given the non-stationary, noisy nature of financial data. To address these challenges, our contributions are: i) we proposed the Contextual Market Dataset with market dynamics, stock ticker, and history state as context, leveraging a market dynamics modeling method that combines linear regression and clustering to extract market dynamics; ii) we present Market-GAN, a novel architecture incorporating a Generative Adversarial Networks (GAN) for the controllable generation with context, an autoencoder for learning low-dimension features, and supervisors for knowledge transfer; iii) we introduce a two-stage training scheme to ensure that Market-GAN captures the intrinsic market distribution with multiple objectives. In the pertaining stage, with the use of the autoencoder and supervisors, we prepare the generator with a better initialization for the adversarial training stage. We propose a set of holistic evaluation metrics that consider alignment, fidelity, data usability on downstream tasks, and market facts. We evaluate Market-GAN with the Dow Jones Industrial Average data from 2000 to 2023 and showcase superior performance in comparison to 4 state-of-the-art time-series generative models.
2 citations
TL;DR: This study employs generalized additive models to analyze the impact of heterogeneous data on financial market volatility, revealing significant nonlinear relationships between stock returns, interest rates, and market volatility, particularly during market downturns.
Abstract: Financial market volatility is driven by a complex interplay of factors, many of which exhibit nonlinear relationships with market behavior. Traditional linear models often struggle to adequately capture these intricate dynamics, particularly when analyzing heterogeneous data sources such as stock prices, interest rates, and macroeconomic indicators. In this paper, a nonlinear regression approach is proposed, specifically using generalized additive models, to better understand the impact of such diverse data on market volatility. By incorporating data from multiple sources, including historical stock prices and interest rates, significant nonlinear relationships between stock returns and interest rates with market volatility are revealed in this paper. The smooth terms for log returns show that volatility increases sharply during periods of negative returns, particularly during market downturns, which is consistent with the widely observed phenomenon of volatility clustering. By accounting for these nonlinear relationships, the model provides valuable insights into how varying economic conditions impact market stability, informing risk management strategies and policymaking.
TL;DR: This study explores the application and accuracy of big data in financial market forecasting, finding significant advantages in enhancing prediction accuracy through data integration, but also highlighting challenges in data quality, algorithm selection, and high costs.
Abstract: With the rapid development of information technology, the application of big data in financial markets has become increasingly widespread. Big data technology can process and analyze vast amounts of data to extract valuable information, providing new methods and tools for financial market forecasting. This paper explores the application and accuracy of big data technology in financial market forecasting, focusing on its specific applications in the stock market, bond market, and foreign exchange market. The study finds that big data technology has a significant advantage in enhancing the accuracy of financial market predictions by constructing more accurate forecasting models through the integration of various data sources. However, the paper also highlights challenges in practical applications, such as data quality, algorithm selection and optimization, and high costs. Nevertheless, with the ongoing development and improvement of technology, the application of big data in financial market forecasting remains promising, with the potential to further improve prediction accuracy and reliability, providing strong support for investors and financial institutions.
13 Oct 2024
TL;DR: This study explores the application of astro-finance in the Indian share market, blending traditional financial analysis with astrological principles to predict market trends, assess market volatility, and understand investor sentiment, using historical data and celestial events.
Abstract: This research paper explores the emerging field of astro-finance and its relevance to the Indian share market. Astro-finance blends traditional financial analysis with astrological principles, aiming to predict market trends based on planetary movements and cosmic patterns. While conventional financial theories emphasize macroeconomic factors, this study investigates how astrological insights might offer an additional perspective on market volatility, investor sentiment, and stock performance in India. By analyzing historical market data alongside astrological events, this paper seeks to provide a unique understanding of how celestial factors may influence market behavior. Through a critical lens, it assesses the potential of astro-finance as a supplementary tool in modern financial decision-making.
21 Oct 2024
Abstract: Abstract This study investigates the effects of financial development on trade openness in Sub-Saharan Africa using annual time series data which covered the period of 2000 to 2022, panel system generalized method of moment (GMM) as the baseline model as well as fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) as the models for robustness checks. Financial development was stratified into financial institutions and financial markets. Financial institutions were further explored via financial institution depths, financial institution access, financial institution efficiency and financial institution stability; while financial markets were measured via financial market depths, financial market access, financial market efficiency and financial market stability. Findings from system GM revealed that the results of the system GMM, it was discovered that financial development – financial institutions and financial markets have significant positive effects on trade openness in Sub-Saharan Africa. More specifically, this study found that financial institution depths, financial institution access, financial institution efficiency, financial institution stability, financial market depths, financial market access, financial market efficiency and financial market stability have significant positive effects on trade openness in Sub-Saharan Africa. This is also similar to findings made from the results of the robustness check – FMOLS and DOLS. From the results findings, we conclude that financial institution depths, financial institution access, financial institution efficiency, financial institution stability, financial market depths, financial market access, financial market efficiency and financial market stability have significant positive effects on trade openness in Sub-Saharan Africa. Based on these findings, this study recommends that financial development via financial institutions and financial markets as well as their depths, access, efficiency and stability should be improved by the authorities to improve the nature and quality of trade in Sub-Saharan Africa.
29 Dec 2024
TL;DR: This paper presents two generative model-based approaches to synthesize stock data for the A-share market, enhancing signal-to-noise ratio and addressing data scarcity through sector-based synthesis and recursive pattern recognition methods, validated on various datasets.
Abstract: The financial industry is increasingly seeking robust methods to address the challenges posed by data scarcity and low signal-to-noise ratios, which limit the application of deep learning techniques in stock market analysis. This paper presents two innovative generative model-based approaches to synthesize stock data, specifically tailored for different scenarios within the A-share market in China. The first method, a sector-based synthesis approach, enhances the signal-to-noise ratio of stock data by classifying the characteristics of stocks from various sectors in China's A-share market. This method employs an Approximate Non-Local Total Variation algorithm to smooth the generated data, a bandpass filtering method based on Fourier Transform to eliminate noise, and Denoising Diffusion Implicit Models to accelerate sampling speed. The second method, a recursive stock data synthesis approach based on pattern recognition, is designed to synthesize data for stocks with short listing periods and limited comparable companies. It leverages pattern recognition techniques and Markov models to learn and generate variable-length stock sequences, while introducing a sub-time-level data augmentation method to alleviate data scarcity issues.We validate the effectiveness of these methods through extensive experiments on various datasets, including those from the main board, STAR Market, Growth Enterprise Market Board, Beijing Stock Exchange, NASDAQ, NYSE, and AMEX. The results demonstrate that our synthesized data not only improve the performance of predictive models but also enhance the signal-to-noise ratio of individual stock signals in price trading strategies. Furthermore, the introduction of sub-time-level data significantly improves the quality of synthesized data.
20 Sep 2024
TL;DR: This paper proposes a data valuation framework for electricity market data, integrating cost and revenue approaches to evaluate data value from cost, quality, significance, application, and risk perspectives, validated with real electricity market data.
Abstract: In the context of the digital transformation of the electricity market, the electricity transaction data held by exchange centers have significant application potential. This paper proposes a comprehensive data valuation framework to assist power trading centers in extracting the value of electricity trading data. The framework integrates the cost and revenue approaches, evaluating electricity market data from the perspectives of data cost and relative value. Furthermore, accounting for the characteristics of electricity market data, our approach includes data quality, data significance, data application, and data risk within the relative value dimension. Using real electricity market data from the power exchange center, we validate that the proposed indicator system can comprehensively and dynamically reflect the value of electricity trading data.
TL;DR: This study examines the role of financial consulting in personal finance management, identifying its significance in household financial lives and its limited contribution to financial market development under unstable economic growth.
Abstract: The paper characterizes the theoretical foundations of financial consulting in personal finance management and its significance in the financial life of households in the country. Data on income and expenses of the population by deciles groups, as well as the structure of financial assets have been presented. A weak contribution of personal finances to the development processes of the financial market under conditions of unstable economic growth has been identified. The range of services for managing personal finances and conducting operations with financial instruments provided by financial institutions and independent financial consultants is outlined. The expediency of reorienting managers of financial institutions working in the field of personal finance management from a sales format to a financial consultant format is emphasized as a primary factor in the development of the financial market. The main directions for the development of financial consulting in personal finance management are formed-through the activation of consulting activities of financial institutions and the integration of joint efforts between financial institutions and independent financial consultants.