Capturing the effects of Low-Probability, High-Impact "Black Swans" in the existing stochastic and deterministic models is tremendously Important. On this page, I would like to share with the members an open access, peer reviewed published research findings of my PhD thesis on how to capture the effects of Low-Probability, High-Impact Events in our existing economic and financial models. I shall begin with the incorporation of fat-tailed effects of the underlying assets probability distribution in the popular LOGIT and PROBIT MODELS. INTRODUCTION The Global financial markets have experienced series of financial and economic crises right from the inception and from generation to generation. Banks, Companies and the world economy experienced catastrophic deterioration and serious corporate failures by systemic risk effect.
FinancialForce this week is highlighting new ways it's using its alliance with Salesforce to help customers adjust to the seemingly ubiquitous "as-a-service" business model. First, the cloud enterprise resource planning (ERP) company, which was built on top of the Salesforce platform, is integrating its financial applications with Salesforce CPQ (configure-price-quote software) to help companies meet new accounting requirements for the "as-a-service" economy. Additionally, at the Salesforce Dreamforce conference this week, FinancialForce is highlighting how it will be using Salesforce's Einstein platform to deliver AI-powered insights to its customers. The predictive power of Einstein should help customers adopt the right business models to drive growth, FinancialForce CMO Fred Studer said to ZDNet. "The world has really changed really in the last year -- everything is a service," Studer said, referencing not only IT services but also the new service-based business models emerging for traditional industries like automotive.
Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field.
Richard Harmon, Managing Director of Financial Services at Cloudera, discusses the importance of relevant machine learning models in today's age, and how the financial sector can prepare for future changes. The past six months have been turbulent. Business disruptions and closures are happening at an unprecedented scale and impacting the economy in a profound way. In the financial services sector, S&P Global estimates that this year could quadruple UK bank credit losses. The economic uncertainty in the UK is heightened by Brexit, which will see the UK leave the European Union in 2021.
Learn the business thinking behind financial modeling and execute what you know effectively using Microsoft Excel. Many believe that sales and profitability projections shown in financial models are the keys to success in attracting investors. The truth is that investors will come up with their own projections. The investor wants to understand the assumptions, structure, and relationships within the modeling of a startup. If the investor is satiated, the entrepreneur has successfully demonstrated a complete understanding of the business side of the enterprise.