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Interlinkages between option-implied volatility and realized volatility : A case of S&P 500 and FTSE 100 index options
(2020)
This thesis re-evaluates the interlinkages between option-implied volatility derived from the call-options and realized volatility driven by underlying asset pricing movements in S&P 500 and FTSE 100 index options markets. ...
Development of factoring by using blockchains
(2020)
The thesis’s objective was to describe ways on how to develop traditional factoring through private and consortium blockchains in Finnish factoring companies located in Finland. The research was carried out using a qualitative ...
DAX index price prediction using artificial neural networks
(2020)
Artificial neural networks (ANNs) have become widely popular in the field of machine learning and are also increasingly used in the financial field, especially in asset price prediction. This thesis studies if the price ...
Timing the market with Google trends search volume data
(2020)
The purpose of this study is to find evidence on whether market timing is possible using search query-based information and how an investment strategy would perform over the selected time period. The approach is to analyze ...
Neural network based binary and multi-class trading strategies using probability thresholds for trading actions on S&P 500 index
(2020)
There have been many attempts to predict stock market returns using regression algorithms. However, from the viewpoint of an investor, the stock market can be interpreted as a classification problem with the decision to ...
Supervised feature selection methods for default prediction in P2P lending
(2020)
The purpose of this thesis is to investigate the performance of different feature selection (FS) methods in P2P lending default prediction. The tested FS methods include maximum-relevance-minimum-redundancy (MRMR) approach, ...
Utilization of personalization in marketing automation and email marketing
(2020)
The utilization of personalization in marketing automation and email marketing is yet to be studied meticulously. The purpose of the paper is to build on existing literature and examine on how marketers practice the ...
Applying machine learning in predicting gross domestic savings : the case of Finland
(2020)
The main objective of this quantitative study is to predict gross domestic savings in Finland with the help of machine learning algorithms. Machine learning-based models and the traditional time-series model were assessed ...
Pairs trading revisited : the case of OMX Helsinki
(2020)
This thesis examines pairs trading opportunities in OMX Helsinki Stock Exchange. Pairs trading is a self-financing trading strategy, where trader enters a long position and offsetting short position simultaneously in two ...
Pairs trading : an application of pairs selection and outranking in Norwegian stock market
(2020)
Statistical arbitrage is a known research topic with a wide body of research with various methodologies to apply. Pairs trading is a part of statistical arbitrage research, where the purpose is to identify co-moving assets ...