GTC ON-DEMAND

 
SEARCH SESSIONS
SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC ON-DEMAND

Presentation
Media
Abstract:

We'll discuss how applying deep learning to identifying market regimes can be valuable in helping anticipate and position a portfolio for significant structural shifts in the market. We will explain how we develop deep neural networks, including time delay and recurrent neural networks, and train them to identify and target intervals that delineate market state changes such as factor-based trends (e.g. growth vs. value), volatility regimes, and economic cycles. We'll also cover how the optimization algorithm used to drive the training relies on CUDA for high-performance computations on the GPU.

We'll discuss how applying deep learning to identifying market regimes can be valuable in helping anticipate and position a portfolio for significant structural shifts in the market. We will explain how we develop deep neural networks, including time delay and recurrent neural networks, and train them to identify and target intervals that delineate market state changes such as factor-based trends (e.g. growth vs. value), volatility regimes, and economic cycles. We'll also cover how the optimization algorithm used to drive the training relies on CUDA for high-performance computations on the GPU.

  Back
 
Topics:
Finance - Deep Learning, Deep Learning & AI Frameworks, AI & Deep Learning Business Track (High Level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9618
Streaming:
Download:
Share:
 
Abstract:
We develop and implement an approach using deep neural networks to process financial and macroeconomic signals to help identify key inflection points in equity market-based factor performance such as momentum and volatility. The model may be used to calibrate factor rotation strategies and better assess portfolio risks associated with factor-based exposures. The machine learning algorithm relies on the GPU for high-performance computations to drive an optimization framework within a deep neural network.
We develop and implement an approach using deep neural networks to process financial and macroeconomic signals to help identify key inflection points in equity market-based factor performance such as momentum and volatility. The model may be used to calibrate factor rotation strategies and better assess portfolio risks associated with factor-based exposures. The machine learning algorithm relies on the GPU for high-performance computations to drive an optimization framework within a deep neural network.  Back
 
Topics:
Deep Learning & AI Frameworks, Finance
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8520
Streaming:
Download:
Share:
 
Abstract:

We'll examine an innovative approach using an optimized algorithm to create a decision tree for the basis of regime dependent and pattern classification of financial and macroeconomic time-series data. Implemented in a supervised and unsupervised learning framework, the algorithm relies on the GPU for high performance computing and the host processor to further integrate the results in a deep learning framework. Also, we implement random number generation, in part, using a hardware quantum based true random number generator, balanced with the pseudo-random number generator in CUDA, so as to optimize overall speed where an exhaustive search is not feasible.

We'll examine an innovative approach using an optimized algorithm to create a decision tree for the basis of regime dependent and pattern classification of financial and macroeconomic time-series data. Implemented in a supervised and unsupervised learning framework, the algorithm relies on the GPU for high performance computing and the host processor to further integrate the results in a deep learning framework. Also, we implement random number generation, in part, using a hardware quantum based true random number generator, balanced with the pseudo-random number generator in CUDA, so as to optimize overall speed where an exhaustive search is not feasible.

  Back
 
Topics:
Finance, Accelerated Data Science, Artificial Intelligence and Deep Learning, Algorithms & Numerical Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7404
Download:
Share:
 
Abstract:

Our presentation this year will provide an update on the signal processing aspect of the presentation we gave last year - An Approach to Parallel Processing of Big Data in Finance for Alpha Generation and Risk Management. We will demonstrate the use of signal processing on financial time-series data to inform us of market patterns and signals that may be evolving in real time. We will implement a signal filtering algorithm on a real time basis on securities price time-series data and will develop a cluster chart organizing these patterns visually.

Our presentation this year will provide an update on the signal processing aspect of the presentation we gave last year - An Approach to Parallel Processing of Big Data in Finance for Alpha Generation and Risk Management. We will demonstrate the use of signal processing on financial time-series data to inform us of market patterns and signals that may be evolving in real time. We will implement a signal filtering algorithm on a real time basis on securities price time-series data and will develop a cluster chart organizing these patterns visually.

  Back
 
Topics:
Finance, Big Data Analytics, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5498
Streaming:
Download:
Share:
 
Abstract:
This session discusses the convergence of parallel processing and big data in finance as the next step in evolution of risk management and trading systems. We advocate a risk management approach in finance should evolve from more traditional inter-day top down metrics to intra-day bottom up approach using signal generation and pattern recognition. We have also determined that parallel processing is a key tool to absorb greater insights into market patterns providing "trading DNA" and more effective tools to manage risk in real time.
This session discusses the convergence of parallel processing and big data in finance as the next step in evolution of risk management and trading systems. We advocate a risk management approach in finance should evolve from more traditional inter-day top down metrics to intra-day bottom up approach using signal generation and pattern recognition. We have also determined that parallel processing is a key tool to absorb greater insights into market patterns providing "trading DNA" and more effective tools to manage risk in real time.   Back
 
Topics:
Finance, Big Data Analytics, Numerical Algorithms & Libraries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2014
Session ID:
S4536
Streaming:
Download:
Share:
 
 
Previous
  • Amazon Web Services
  • IBM
  • Cisco
  • Dell EMC
  • Hewlett Packard Enterprise
  • Inspur
  • Lenovo
  • SenseTime
  • Supermicro Computers
  • Synnex
  • Autodesk
  • HP
  • Linear Technology
  • MSI Computer Corp.
  • OPTIS
  • PNY
  • SK Hynix
  • vmware
  • Abaco Systems
  • Acceleware Ltd.
  • ASUSTeK COMPUTER INC
  • Cray Inc.
  • Exxact Corporation
  • Flanders - Belgium
  • Google Cloud
  • HTC VIVE
  • Liqid
  • MapD
  • Penguin Computing
  • SAP
  • Sugon
  • Twitter
Next