Browsing by Author "Sharma, Rajesh"

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The effects of the ECB communications on financial markets before and during COVID-19 pandemic
(2022) Alfieri, Luca; Eratalay, Mustafa Hakan; Lapitskaya, Darya; Sharma, Rajesh
The paper aims to estimate the effects of the European Central Bank communications on the sectoral returns of STOXX Europe 600 from 2013 to 2021. Previous literature has investigated the effects of communications of central banks and checked their effects on macroeconomics and financial data. New opportunities offered by text mining analysis allow us to find new insights into these aspects. However, studies focusing on how text mining indices derived from central banks’ communications can affect different financial sectors are more limited. In this paper, we use different sentiment and topic indices derived from the European Central Bank’s speeches. The paper shows how these different topics and sentiment indices affect the returns on different financial sectors. Our results indicate that the topic of communications is more influential on returns of sectoral indices than the type of communications. Moreover, we find that monetary policy and financial stability topics are the most relevant. We also find that during the COVID-19 time, the number of negative speeches is relevant for almost all the sectoral index returns.
Improved Classification of Blurred Images with Deep-Learning Networks Using Lucy-Richardson-Rosen Algorithm
(Licensee MDPI, 2023) Jayavel, Amudhavel; Gopinath, Shivasubramanian; Angamuthu, Praveen Periyasamy; Arockiaraj, Francis Gracy; Bleahu, Andrei; Xavier, Agnes Pristy Ignatius; Smith, Daniel; Han, Molong; Slobozhan, Ivan; Ng, Soon Hock; Katkus, Tomas; Rajeswary, Aravind Simon John Francis; Sharma, Rajesh; Juodkazis, Saulius; Anand, Vijayakumar
Pattern recognition techniques form the heart of most, if not all, incoherent linear shift-invariant systems. When an object is recorded using a camera, the object information is sampled by the point spread function (PSF) of the system, replacing every object point with the PSF in the sensor. The PSF is a sharp Kronecker Delta-like function when the numerical aperture (NA) is large with no aberrations. When the NA is small, and the system has aberrations, the PSF appears blurred. In the case of aberrations, if the PSF is known, then the blurred object image can be deblurred by scanning the PSF over the recorded object intensity pattern and looking for pattern matching conditions through a mathematical process called correlation. Deep learning-based image classification for computer vision applications gained attention in recent years. The classification probability is highly dependent on the quality of images as even a minor blur can significantly alter the image classification results. In this study, a recently developed deblurring method, the Lucy-Richardson-Rosen algorithm (LR2A), was implemented to computationally refocus images recorded in the presence of spatio-spectral aberrations. The performance of LR2A was compared against the parent techniques: Lucy-Richardson algorithm and non-linear reconstruction. LR2A exhibited a superior deblurring capability even in extreme cases of spatio-spectral aberrations. Experimental results of deblurring a picture recorded using high-resolution smartphone cameras are presented. LR2A was implemented to significantly improve the performances of the widely used deep convolutional neural networks for image classification.
Predicting company innovativeness by analysing the website data of firms: a comparison across different types of innovation
(2022) Sõna, Sander; Masso, Jaan; Sharma, Shakshi; Vahter, Priit; Sharma, Rajesh
This paper investigates which of the core types of innovation can be best predicted based on the website data of firms. In particular, we focus on four distinct key standard types of innovation – product, process, organisational, and marketing innovation in firms. Web-mining of textual data on the websites of firms from Estonia combined with the application of artificial intelligence (AI) methods turned out to be a suitable approach to predict firm-level innovation indicators. The key novel addition to the existing literature is the finding that web-mining is more applicable to predicting marketing innovation than predicting the other three core types of innovation. As AI based models are often black-box in nature, for transparency, we use an explainable AI approach (SHAP - SHapley Additive exPlanations), where we look at the most important words predicting a particular type of innovation. Our models confirm that the marketing innovation indicator from survey data was clearly related to marketing-related terms on the firms' websites. In contrast, the results on the relevant words on websites for other innovation indicators were much less clear. Our analysis concludes that the effectiveness of web-scraping and web-text-based AI approaches in predicting cost-effective, granular and timely firm-level innovation indicators varies according to the type of innovation considered.
Predicting stock return and volatility with machine learning and econometric models: A comparative case study of the Baltic stock market
(2021) Nõu, Anders; Lapitskaya, Darya; Eratalay, Mustafa Hakan; Sharma, Rajesh
For stock market predictions, the essence of the problem is usually predicting the magnitude and direction of the stock price movement as accurately as possible. There are different approaches (e.g., econometrics and machine learning) for predicting stock returns. However, it is non-trivial to find an approach which works the best. In this paper, we make a thorough analysis of the predictive accuracy of different machine learning and econometric approaches for predicting the returns and volatilities on the OMX Baltic Benchmark price index, which is a relatively less researched stock market. Our results show that the machine learning methods, namely the support vector regression and k-nearest neighbours, predict the returns better than autoregressive moving average models for most of the metrics, while for the other approaches, the results were not conclusive. Our analysis also highlighted that training and testing sample size plays an important role on the outcome of machine learning approaches.