A study of financial volatility forecasting techniques in the FTSE/ASE 20 index

Forecasting financial market volatility is an important task that has absorbed the interest of many academics in the late twentieth and early twenty-first centuries. This strong interest of the academic world reflects the importance of volatility in several financial and business activities. Volatility forecast, crucially affects investment choice and is the most important parameter affecting prices of market listed options, of which trading volume has proliferated in the last years. The purpose of this article is to compare various volatility forecasting approaches using data on the Greek FTSE/ASE 20 stock index.

Homogeneous feature transfer and heterogeneous location fine-tuning for cross-city property appraisal framework

Most existing real estate appraisal methods focus on building accuracy and reliable models from a given dataset but pay little attention to the extensibility of their trained model. As different cities usually contain a different set of location features (district names, apartment names), most existing mass appraisal methods have to train a new model from scratch for different cities or regions. As a result, these approaches require massive data collection for each city and the total training time for a multi-city property appraisal system will be extremely long. Besides, some small cities may not have enough data for training a robust appraisal model. To overcome these limitations, we develop a novel Homogeneous Feature Transfer and Heterogeneous Location Fine-tuning (HFT+HLF) cross-city property appraisal framework. By transferring partial neural network learning from a source city and fine-tuning on the small amount of location information of a target city, our semi-supervised model can achieve similar or even superior performance compared to a fully supervised Artificial neural network (ANN) method.Comment: Accepted by AusDM 201