1,518 research outputs found
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review
Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine learning and statistical approaches in this context. Unfortunately, several of these techniques are unable or slow to adapt to changes in the price-generation process. This study aims to survey the relevant literature on Machine Learning for financial prediction under regime change employing a systematic approach.
It reviews key papers with a special emphasis on technical analysis. The study discusses the growing number of contributions that are bridging the gap between two separate communities, one focused on data stream learning and the other on economic research. However, it also makes apparent that we are still in an early stage. The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant
Use of Artificial Intelligence (AI) in Managing Inventory of Medicine in Pharmaceutical Industry
Inventory is one of the vital components of current assets. Excess holdings of inventory may increase cost as well as wastage. As such, effective and efficient management of inventory is an integral part of supply chain. Especially, in the field of management of pharmaceutical products and medicine it bears more importance. Improper use of pharmaceutical products or shortage of medicine would not only cause financial loss but also may affect the patients adversely. Rather than using the traditional techniques of managing inventory use of Artificial Intelligence (AI) can make the process more effective and efficient. AI is the application of computer program that demonstrates action like a human being, learns from experience, gets new input and processes big data by reasoning. It can acquire large amount of data and create rules for turning the data into actionable information. This study has been conducted based mainly on secondary sources of data. It is a qualitative study that gives a conceptual idea regarding how the functions of AI can support managing inventory of medicine in pharmaceutical industry
The AI Revolution: Opportunities and Challenges for the Finance Sector
This report examines Artificial Intelligence (AI) in the financial sector,
outlining its potential to revolutionise the industry and identify its
challenges. It underscores the criticality of a well-rounded understanding of
AI, its capabilities, and its implications to effectively leverage its
potential while mitigating associated risks. The potential of AI potential
extends from augmenting existing operations to paving the way for novel
applications in the finance sector. The application of AI in the financial
sector is transforming the industry. Its use spans areas from customer service
enhancements, fraud detection, and risk management to credit assessments and
high-frequency trading. However, along with these benefits, AI also presents
several challenges. These include issues related to transparency,
interpretability, fairness, accountability, and trustworthiness. The use of AI
in the financial sector further raises critical questions about data privacy
and security. A further issue identified in this report is the systemic risk
that AI can introduce to the financial sector. Being prone to errors, AI can
exacerbate existing systemic risks, potentially leading to financial crises.
Regulation is crucial to harnessing the benefits of AI while mitigating its
potential risks. Despite the global recognition of this need, there remains a
lack of clear guidelines or legislation for AI use in finance. This report
discusses key principles that could guide the formation of effective AI
regulation in the financial sector, including the need for a risk-based
approach, the inclusion of ethical considerations, and the importance of
maintaining a balance between innovation and consumer protection. The report
provides recommendations for academia, the finance industry, and regulators
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