An Intelligent Machine Learning System for Identifying Financial Volatility in Corporate Entities

Authors: Dr. Amit Verma

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Abstract

Forecasting the volatility of financial assets can be useful because volatility is frequently used in several financial sectors. In this study, we employ long short-term memory (LSTM) and deep neural network (DNN) models to estimate stock index volatility. The majority of related research projects train machine learning models using the distance loss function, but this has two drawbacks. The first is that their models cannot be fairly compared to econometric models since they create mistakes when using estimated volatility as the forecasting aim. We also implement a probability-based loss function to train the deep learning models and test all the models against the likelihood of the test sample in order to address these two issues. The findings demonstrate that our deep learning models with likelihood-based loss functions are more accurate at forecasting volatility than the econometric model and the deep learning models with distance loss functions. Of the two deep learning models with likelihood-based loss functions, the LSTM model performs the best.

Introduction

Monitoring the volatility of market factors, such as commodity prices, interest rates, and the variables that determine the value of a portfolio, is one of the most crucial duties in finance. As the underlying asset for many derivatives, it is also a significant determinant of the pricing of many financial products. The stylized facts of volatility, the efficient market hypothesis, and the transient nature of financial relationships are only a few of the numerous reasons why it is not an easy process, as is the case with all financial forecasting and prediction (terms used interchangeably in this book). In spite of this, volatility can still be predicted to some extent.

Many various definitions have been put forth because volatility is frequently described as hidden and unobservable, even ex-post. Additionally, it can be difficult to quantify or predict volatility. Despite being simply approximate measures of latent volatility, these still have application since they offer a quantitative means of comparison and frequently coincide with market definitions.

There are numerous techniques that make an effort to model, comprehend, and forecast volatility in addition to the numerous volatility proxies. The generalized autoregressive conditional heteroscedasticity (GARCH) model and its family of variations are among the most frequently used models. In contrast to these conventional models, intelligent methods—which include techniques like machine learning (ML) and deep learning (DL), evolutionary algorithms (EAs), and fuzzy logic—have recently acquired significant attention. These methods are frequently nonlinear. Due to a flurry of successful neural network (NN) applications, machine learning (ML) and deep learning (DL) in particular have soared in popularity in recent years. This trend can also be seen in financial volatility forecasts.

We have the following three objectives in particular: The goals of this project are to: (1) produce a paper that may be used as an introduction to the subject of financial volatility forecasting; (2) offer a snapshot of the state-of-the-art in NN volatility forecasting; and (3) highlight some common problems, potential solutions, and future directions.

Conclusion

For the purpose of predicting the volatility of three US market indexes, we combine deep learning models, econometric models, and a straightforward statistical approach. We further offer a likelihood-based loss function to train the deep learning models and test all the approaches by the likelihood of the test sample, which is different from comparable research papers. When employing deep learning models, we can reduce errors in the volatility forecasting process by doing this. We can also compare the models we look at in a more equitable way. The empirical study\'s findings demonstrate that our deep learning models with likelihood-based loss functions predict volatility more accurately than the econometric model, and LSTM (likelihood-based loss) is the superior deep learning model of the two. The volatility series predicted by the six models exhibit remarkably comparable long-term tendencies. Under the harsh conditions of the US stock market, around March 2020, LSTM (likelihood-based loss) seems to capture the property better. Forecasting financial volatility will always be a crucial task. It is developing and getting more popular and accessible than ever because to the growth of AI research. Financial markets will, however, also continue to develop, revealing new relationships to exploit. These relationships will eventually burn out, making room for new ones that are hidden from view. This ongoing change will result in new possibilities and study directions.

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