Sentiment analysis is a growing field in natural language processing to analyze and determine the... more Sentiment analysis is a growing field in natural language processing to analyze and determine the polarity of given text or data in sentence level or document level. Sentiment analysis is widely used in social media, as it became an excellent source for people and individual to analysis also known as feature based sentiment analysis is a fine grained analysis where the aim is to identify different aspects expressed in document or sentence level. This approach allows the user to extract the most important aspects from the opinion expressed. This paper presents a brief survey of aspect-based sentiment analysis and its various approaches, metrics used for evaluation and latest research
One of the most important parts of Artificial Neural Networks is minimizing the loss functions wh... more One of the most important parts of Artificial Neural Networks is minimizing the loss functions which tells us how good or bad our model is. To minimize these losses we need to tune the weights and biases. Also to calculate the minimum value of a function we need gradient. And to update our weights we need gradient descent. But there are some problems with regular gradient descent ie. it is quite slow and not that accurate. This article aims to give an introduction to optimization strategies to gradient descent. In addition, we shall also discuss the architecture of these algorithms and further optimization of Neural Networks in general.
This paper inquires on the options pricing modeling using Artificial Neural Networks to price App... more This paper inquires on the options pricing modeling using Artificial Neural Networks to price Apple(AAPL) European Call Options. Our model is based on the premise that Artificial Neural Networks can be used as functional approximators and can be used as an alternative to the numerical methods to some extent, for a faster and an efficient solution. This paper provides a neural network solution for two financial models, the BlackScholes-Merton model, and the calibrated-Heston Stochastic Volatility Model, we evaluate our predictions using the existing numerical solutions for the same, the analytic solution for the Black-Scholes equation, COS-Model for Heston’s Stochastic Volatility Model and Standard Heston-Quasi analytic formula. The aim of this study is to find a viable time-efficient alternative to existing quantitative models for option pricing.
Sentiment analysis is a growing field in natural language processing to analyze and determine the... more Sentiment analysis is a growing field in natural language processing to analyze and determine the polarity of given text or data in sentence level or document level. Sentiment analysis is widely used in social media, as it became an excellent source for people and individual to analysis also known as feature based sentiment analysis is a fine grained analysis where the aim is to identify different aspects expressed in document or sentence level. This approach allows the user to extract the most important aspects from the opinion expressed. This paper presents a brief survey of aspect-based sentiment analysis and its various approaches, metrics used for evaluation and latest research
One of the most important parts of Artificial Neural Networks is minimizing the loss functions wh... more One of the most important parts of Artificial Neural Networks is minimizing the loss functions which tells us how good or bad our model is. To minimize these losses we need to tune the weights and biases. Also to calculate the minimum value of a function we need gradient. And to update our weights we need gradient descent. But there are some problems with regular gradient descent ie. it is quite slow and not that accurate. This article aims to give an introduction to optimization strategies to gradient descent. In addition, we shall also discuss the architecture of these algorithms and further optimization of Neural Networks in general.
This paper inquires on the options pricing modeling using Artificial Neural Networks to price App... more This paper inquires on the options pricing modeling using Artificial Neural Networks to price Apple(AAPL) European Call Options. Our model is based on the premise that Artificial Neural Networks can be used as functional approximators and can be used as an alternative to the numerical methods to some extent, for a faster and an efficient solution. This paper provides a neural network solution for two financial models, the BlackScholes-Merton model, and the calibrated-Heston Stochastic Volatility Model, we evaluate our predictions using the existing numerical solutions for the same, the analytic solution for the Black-Scholes equation, COS-Model for Heston’s Stochastic Volatility Model and Standard Heston-Quasi analytic formula. The aim of this study is to find a viable time-efficient alternative to existing quantitative models for option pricing.
Uploads
Papers by Kaustubh yadav