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on Big Data |
| By: | Zuo Xiangtai (Xiamen University) |
| Abstract: | With the development of the times and the advancement of technology, general statistical data has been widely used. At the same time, unstructured data in the form of text is gradually becoming the backbone of the empirical |
| Date: | 2024–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:boc:chin24:14 |
| By: | Kaushal Attaluri; Mukesh Tripathi; Srinithi Reddy; Shivendra |
| Abstract: | Forecasting stock market prices remains a complex challenge for traders, analysts, and engineers due to the multitude of factors that influence price movements. Recent advancements in artificial intelligence (AI) and natural language processing (NLP) have significantly enhanced stock price prediction capabilities. AI's ability to process vast and intricate data sets has led to more sophisticated forecasts. However, achieving consistently high accuracy in stock price forecasting remains elusive. In this paper, we leverage 30 years of historical data from national banks in India, sourced from the National Stock Exchange, to forecast stock prices. Our approach utilizes state-of-the-art deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM optimized through Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). We further integrate sentiment analysis from tweets and reliable financial sources such as Business Standard and Reuters, acknowledging their crucial influence on stock price fluctuations. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05788 |
| By: | Qianggang Ding; Haochen Shi; Bang Liu |
| Abstract: | The integration of Artificial Intelligence (AI) in the financial domain has opened new avenues for quantitative trading, particularly through the use of Large Language Models (LLMs). However, the challenge of effectively synthesizing insights from diverse data sources and integrating both structured and unstructured data persists. This paper presents TradeExpert, a novel framework that employs a mix of experts (MoE) approach, using four specialized LLMs, each analyzing distinct sources of financial data, including news articles, market data, alpha factors, and fundamental data. The insights of these expert LLMs are further synthesized by a General Expert LLM to make a final prediction or decision. With specific prompts, TradeExpert can be switched between the prediction mode and the ranking mode for stock movement prediction and quantitative stock trading, respectively. In addition to existing benchmarks, we also release a large-scale financial dataset to comprehensively evaluate TradeExpert's effectiveness. Our experimental results demonstrate TradeExpert's superior performance across all trading scenarios. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.00782 |
| By: | Kassiani Papasotiriou; Srijan Sood; Shayleen Reynolds; Tucker Balch |
| Abstract: | Investment Analysis is a cornerstone of the Financial Services industry. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), offers opportunities to enhance the equity rating process. This paper explores the application of LLMs to generate multi-horizon stock ratings by ingesting diverse datasets. Traditional stock rating methods rely heavily on the expertise of financial analysts, and face several challenges such as data overload, inconsistencies in filings, and delayed reactions to market events. Our study addresses these issues by leveraging LLMs to improve the accuracy and consistency of stock ratings. Additionally, we assess the efficacy of using different data modalities with LLMs for the financial domain. We utilize varied datasets comprising fundamental financial, market, and news data from January 2022 to June 2024, along with GPT-4-32k (v0613) (with a training cutoff in Sep. 2021 to prevent information leakage). Our results show that our benchmark method outperforms traditional stock rating methods when assessed by forward returns, specially when incorporating financial fundamentals. While integrating news data improves short-term performance, substituting detailed news summaries with sentiment scores reduces token use without loss of performance. In many cases, omitting news data entirely enhances performance by reducing bias. Our research shows that LLMs can be leveraged to effectively utilize large amounts of multimodal financial data, as showcased by their effectiveness at the stock rating prediction task. Our work provides a reproducible and efficient framework for generating accurate stock ratings, serving as a cost-effective alternative to traditional methods. Future work will extend to longer timeframes, incorporate diverse data, and utilize newer models for enhanced insights. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.00856 |
| By: | Felix Divo; Eric Endress; Kevin Endler; Kristian Kersting; Devendra Singh Dhami |
| Abstract: | Company fundamentals are key to assessing companies' financial and overall success and stability. Forecasting them is important in multiple fields, including investing and econometrics. While statistical and contemporary machine learning methods have been applied to many time series tasks, there is a lack of comparison of these approaches on this particularly challenging data regime. To this end, we try to bridge this gap and thoroughly evaluate the theoretical properties and practical performance of 22 deterministic and probabilistic company fundamentals forecasting models on real company data. We observe that deep learning models provide superior forcasting performance to classical models, in particular when considering uncertainty estimation. To validate the findings, we compare them to human analyst expectations and find that their accuracy is comparable to the automatic forecasts. We further show how these high-quality forecasts can benefit automated stock allocation. We close by presenting possible ways of integrating domain experts to further improve performance and increase reliability. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05791 |
| By: | Juite Wang (Graduate Institute of Technology Management, National Chung Hsing University) |
| Abstract: | Innovation ecosystems have become an indispensable element in the growth strategy of firms in various industries. In the birth stage of innovation ecosystem, it is important for firms to assess technological positions of various actors in the innovation ecosystem to support decisions on external R&D collaboration. This research integrates semantic analysis and bibliometric analysis for predicting evolving collaboration patterns and predict collaboration potential. Semantic analysis applies the context-aware deep learning framework based on BERT [14] to analyze unstructured patent data and evaluate technological similarity between individual firms. In addition, biblio-metric analysis uses patent indicators related to technological capabilities and potential technology synergy of individual firms. Then, the deep neural network (DNN) approach is used to learn the relationships between descriptive features and collaboration potentials as target feature. Our findings suggest that the metaverse innovation ecosystem remains in its nascent stages, with the collaborative network still being sparse. The illustrative example reveals that recommended candidate partners often align with or resemble past partners from prior periods. This suggests that the pro-posed deep learning approach is capable of predicting collaborative relationships between various firms. |
| Keywords: | Innovation ecosystems, Deep learning, Collaboration network, Natural language processing |
| URL: | https://d.repec.org/n?u=RePEc:sek:iefpro:14716418 |
| By: | Anika Tahsin Meem; Mst. Shapna Akter; Deponker Sarker Depto; M. R. C. Mahdy |
| Abstract: | In modern times, the cryptocurrency market is one of the world's most rapidly rising financial markets. The cryptocurrency market is regarded to be more volatile and illiquid than traditional markets such as equities, foreign exchange, and commodities. The risk of this market creates an uncertain condition among the investors. The purpose of this research is to predict the magnitude of the risk factor of the cryptocurrency market. Risk factor is also called volatility. Our approach will assist people who invest in the cryptocurrency market by overcoming the problems and difficulties they experience. Our approach starts with calculating the risk factor of the cryptocurrency market from the existing parameters. In twenty elements of the cryptocurrency market, the risk factor has been predicted using different machine learning algorithms such as CNN, LSTM, BiLSTM, and GRU. All of the models have been applied to the calculated risk factor parameter. A new model has been developed to predict better than the existing models. Our proposed model gives the highest RMSE value of 1.3229 and the lowest RMSE value of 0.0089. Following our model, it will be easier for investors to trade in complicated and challenging financial assets like bitcoin, Ethereum, dogecoin, etc. Where the other existing models, the highest RMSE was 14.5092, and the lower was 0.02769. So, the proposed model performs much better than models with proper generalization. Using our approach, it will be easier for investors to trade in complicated and challenging financial assets like Bitcoin, Ethereum, and Dogecoin. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13615 |
| By: | Aleksandr Simonyan |
| Abstract: | This paper introduces BreakGPT, a novel large language model (LLM) architecture adapted specifically for time series forecasting and the prediction of sharp upward movements in asset prices. By leveraging both the capabilities of LLMs and Transformer-based models, this study evaluates BreakGPT and other Transformer-based models for their ability to address the unique challenges posed by highly volatile financial markets. The primary contribution of this work lies in demonstrating the effectiveness of combining time series representation learning with LLM prediction frameworks. We showcase BreakGPT as a promising solution for financial forecasting with minimal training and as a strong competitor for capturing both local and global temporal dependencies. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.06076 |
| By: | Shuoling Liu; Gaoguo Jia; Yuhang Jiang; Liyuan Chen; Qiang Yang |
| Abstract: | Large Language Models (LLMs) have achieved remarkable success recently, displaying exceptional capabilities in creating understandable and organized text. These LLMs have been utilized in diverse fields, such as clinical research, where domain-specific models like Med-Palm have achieved human-level performance. Recently, researchers have employed advanced prompt engineering to enhance the general reasoning ability of LLMs. Despite the remarkable success of zero-shot Chain-of-Thoughts (CoT) in solving general reasoning tasks, the potential of these methods still remains paid limited attention in the financial reasoning task.To address this issue, we explore multiple prompt strategies and incorporated semantic news information to improve LLMs' performance on financial reasoning tasks.To the best of our knowledge, we are the first to explore this important issue by applying ChatGPT to the gold investment.In this work, our aim is to investigate the financial reasoning capabilities of LLMs and their capacity to generate logical and persuasive investment opinions. We will use ChatGPT, one of the most powerful LLMs recently, and prompt engineering to achieve this goal. Our research will focus on understanding the ability of LLMs in sophisticated analysis and reasoning within the context of investment decision-making. Our study finds that ChatGPT with CoT prompt can provide more explainable predictions and overcome behavioral biases, which is crucial in finance-related tasks and can achieve higher investment returns. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13599 |
| By: | Godwill Chimamiwa |
| Abstract: | This paper presents a critical analysis of current cyber risk management practices in light of new and evolving Artificial Intelligence (AI) and Machine Learning driven adversarial attacks. Many enterprises are constantly grappling with cybersecurity risks and increased threats from phishing, ransomware, and other forms of cyber-attacks, often resulting in substantial financial losses when risks are not adequately addressed. With the advent of AI and ML, such cyber-attacks and incidents are expected to become more prevalent and potentially more devastating to businesses of all sizes. With AI and ML tools at their disposal, cybercriminals can significantly reduce technical barriers to launching cyberattacks. They can easily develop more sophisticated social engineering tactics and "deep fakes" that are challenging to identify, thereby increasing the risks of unauthorized data disclosure. Drawing on a literature review analysis, this research explores current and emerging AI- and ML-driven cyber threats faced by enterprises, assesses the effectiveness of current cyber mitigation measures, and discusses future management practices to enhance the security posture of enterprises. The study evaluates both technical and non-technical cyber risk management and mitigation measures and frameworks. The findings from this study aim to inform enterprise cyber risk managers and practitioners about the enormity of AI- and ML-driven cyber risks and present emerging best practices to adequately mitigate those risks. This study contributes to the growing body of research on how threat actors leverage AI and ML to expand cyber threats and how enterprises and organizations should respond to these ever-evolving cyber risks. |
| Keywords: | cyber risk management, AI-driven, ML-driven, adversarial attacks, cyber risk frameworks |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:bfv:sbsrec:006 |
| By: | Harris Borman; Anna Leontjeva; Luiz Pizzato; Max Kun Jiang; Dan Jermyn |
| Abstract: | Large Language Models (LLMs) have demonstrated the ability to adopt a personality and behave in a human-like manner. There is a large body of research that investigates the behavioural impacts of personality in less obvious areas such as investment attitudes or creative decision making. In this study, we investigated whether an LLM persona with a specific Big Five personality profile would perform an investment task similarly to a human with the same personality traits. We used a simulated investment task to determine if these results could be generalised into actual behaviours. In this simulated environment, our results show these personas produced meaningful behavioural differences in all assessed categories, with these behaviours generally being consistent with expectations derived from human research. We found that LLMs are able to generalise traits into expected behaviours in three areas: learning style, impulsivity and risk appetite while environmental attitudes could not be accurately represented. In addition, we showed that LLMs produce behaviour that is more reflective of human behaviour in a simulation environment compared to a survey environment. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05801 |
| By: | Li Zhanfeng (Zhongnan University of Economics and Law) |
| Abstract: | Mediation models with censored outcomes play a crucial role in social and medical sciences. However, the inherent censoring characteristics of the data often lead existing models to rely on assumptions of linearity, homogeneity, and normality for estimation. Unfortunately, these assumptions may not align with the complexities of real-world problems, limiting the persuasiveness of causal analyses. In this study, I investigate causal mediation analysis within a counterfactual framework by framing it as a neural style transfer problem commonly encountered in image processing. Acknowledging the impressive capabilities of generative adversarial networks (GANs) in handling neural style transfer, I propose a novel GAN-based model named generative adversarial censored mediation network to address mediation issues under my concern. My model employs recti |
| Date: | 2024–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:boc:chin24:08 |
| By: | Krishna Neupane; Igor Griva |
| Abstract: | According to The Exchange Act, 1934 unlawful insider trading is the abuse of access to privileged corporate information. While a blurred line between "routine" the "opportunistic" insider trading exists, detection of strategies that insiders mold to maneuver fair market prices to their advantage is an uphill battle for hand-engineered approaches. In the context of detailed high-dimensional financial and trade data that are structurally built by multiple covariates, in this study, we explore, implement and provide detailed comparison to the existing study (Deng et al. (2019)) and independently implement automated end-to-end state-of-art methods by integrating principal component analysis to the random forest (PCA-RF) followed by a standalone random forest (RF) with 320 and 3984 randomly selected, semi-manually labeled and normalized transactions from multiple industry. The settings successfully uncover latent structures and detect unlawful insider trading. Among the multiple scenarios, our best-performing model accurately classified 96.43 percent of transactions. Among all transactions the models find 95.47 lawful as lawful and $98.00$ unlawful as unlawful percent. Besides, the model makes very few mistakes in classifying lawful as unlawful by missing only 2.00 percent. In addition to the classification task, model generated Gini Impurity based features ranking, our analysis show ownership and governance related features based on permutation values play important roles. In summary, a simple yet powerful automated end-to-end method relieves labor-intensive activities to redirect resources to enhance rule-making and tracking the uncaptured unlawful insider trading transactions. We emphasize that developed financial and trading features are capable of uncovering fraudulent behaviors. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13564 |
| By: | Lidia Cano Pecharroman; Melissa O. Tier; Elke U. Weber |
| Abstract: | Efforts are needed to identify and measure both communities' exposure to climate hazards and the social vulnerabilities that interact with these hazards, but the science of validating hazard vulnerability indicators is still in its infancy. Progress is needed to improve: 1) the selection of variables that are used as proxies to represent hazard vulnerability; 2) the applicability and scale for which these indicators are intended, including their transnational applicability. We administered an international urban survey in Buenos Aires, Argentina; Johannesburg, South Africa; London, United Kingdom; New York City, United States; and Seoul, South Korea in order to collect data on exposure to various types of extreme weather events, socioeconomic characteristics commonly used as proxies for vulnerability (i.e., income, education level, gender, and age), and additional characteristics not often included in existing composite indices (i.e., queer identity, disability identity, non-dominant primary language, and self-perceptions of both discrimination and vulnerability to flood risk). We then use feature importance analysis with gradient-boosted decision trees to measure the importance that these variables have in predicting exposure to various types of extreme weather events. Our results show that non-traditional variables were more relevant to self-reported exposure to extreme weather events than traditionally employed variables such as income or age. Furthermore, differences in variable relevance across different types of hazards and across urban contexts suggest that vulnerability indicators need to be fit to context and should not be used in a one-size-fits-all fashion. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.10628 |
| By: | Bargain, Olivier B. (Université de Bordeaux); Vincent, Rose Camille (Utrecht School of Economics); Caldeira, Emilie (CERDI, Université Clermont Auvergne) |
| Abstract: | Decentralization, championed by international institutions, has been one of the most prominent public sector reforms of the last decades, particularly in sub-Saharan Africa. To date, few studies propose a quasi-experimental evaluation of its capacity to contribute to local development. We exploit the phase-in of decentralization at the commune level in Burkina Faso. We use satellite information on night-time light density as a proxy for local development levels, which has the advantage of being measured and comparable over time and space. The communes that were decentralized first can be compared to the others after the reform relative to the pre-reform situation. The difference-in-difference approach includes commune fixed effects and inverse propensity score reweighting to account for time-varying differences across communes. We find a positive impact of decentralization on the night-light intensity trends of the early-decentralized communes. This is supported by alternative measures (remote sensing of built-up settlements and a welfare index), which shows the possibly broader scope of decentralization gains. We show that decentralization did not lift all boats: only the communes with the ability to generate own-source revenues benefited from effective decentralization. |
| Keywords: | decentralization, economic development, local development, Africa, Burkina Faso |
| JEL: | H00 H70 H71 H72 O10 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17459 |