Browsing by Author "Lapitskaya, Darya"

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Causal impact of science and technology parks on economic growth. The case of Belarus
(Tartu Ülikool, 2019) Lapitskaya, Darya; Mendes Pereira Vicente, Ricardo Alfredo; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
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.
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.
Predicting stock returns: ARMAX vs. machine learning
(2022) Lapitskaya, Darya; Eratalay, Hakan; Rajesh Sharma
In the modern world, online social and news media significantly impact society, economy, and financial markets. In this chapter, we compared the predictive performance of financial econometrics and machine learning and deep learning methods for the returns of the stocks of the SP100 index. The analysis is enriched by using COVID-19 related news sentiments data collected for a period of 10 months. We analyzed the performance of each model and found the best algorithm for such types of predictions. For the sample we analyzed, our results indicate that the autoregressive moving average model with exogenous variables (ARMAX) has a comparable predictive performance to the machine and deep learning models, only outperformed by the extreme gradient boosted trees (XGBoost) approach. This result holds both in the training and testing datasets.
Productivity and firm dynamics over the business cycle
(2022) Assefa, Abraham; Lapitskaya, Darya; Uusküla, Lenno
The paper studies the effects of technology shocks on the creation and destruction of firms. Using US data and a VAR model the paper finds Schumpeterian creative destruction for investment-specific technology shocks. A positive investment-specific technology shock increases the number of firms opening, but also leads to a higher number of firms closing. In contrast, labour-neutral technology shocks also benefit old firms. An increase in overall productivity leads to an increase in the number of new firms and a drop in the number of failures. Both margins contribute to an increase in the number of firms in the economy. A medium-scale DSGE model with endogenous entry and exit that is that is augmented with additional features is able to capture these stylised facts.