YIELDCO AS A PERSPECTIVE INVESTMENT VEHICLE

Purpose – the purpose of the article is to assess the investment potential of YieldСos as an innovative pension vehicle and determine the risks that may arise in connection with them. Methods used: empirical analyses, comparisons, statistical analyses. Research methodology – empirical research, comparative analysis, statistical analyses. Findings – in the paper we compared the new investment vehicle YieldCos (green) and a traditional investment vehicle – energy companies (non-green). It was found that the correlation of YieldCos with the market indices is similar to nongreen companies. But YieldCos are more exposed to risks than energy companies. That may offset their attractiveness as long term investment vehicle. It is necessary to continue research for this investment vehicle during the period of global financial volatility and crash of crude oil price. Research limitations – the authors study the raise of the new investment vehicle – YieldCos, during the period from 2013 to 2018 (pre Covid-19 Era). Practical implications – YieldCos focus on investors interests, raising money in an environmental projects (namely renewable energy), and provide combination of high yield and high income growth. Aforesaid characteristics are attractive for institutional investors that are currently experiencing a lack of resources to meet their obligations. Originality/Value – new investment vehicle is becoming a part of the overall socially responsible investment universe. We have taken the first step in the comparative evaluation of traditional and innovative types of investment instruments. Showed the prospects of a new environmentally oriented tool. It is necessary to continue research of this investment vehicle during the period of global financial volatility, changing landscape of energy resources and stakeholders rising influence.

Contagion, Exchange Rate, and Financial Volatility: Indonesian Case in Global Financial Turbulence

Global turbulence after the financial crisis has hit Indonesia and almost all emerging countries. Quantitative Easing (QE) normalization (tapering of) has caused the capital outflows from emerging countries. Trade war and increasing geopolitical tension together raise the pressure. Argentina and Turkey have been experiencing economic shock. Indonesia should identify the contagion possibility and refer to Thai baht contagion experience in 1997. This paper assesses the contagion, exchange rate, and financial volatility triggered by global turbulence and Argentina-Turkey crisis in 2018. We use vector autoregression (VAR), simple correlation, dynamic conditional correlation (DCC), and regression method. We will investigate the potential contagion both in stock and exchange rate markets and in the rupiah exchange rate determination from both contagion and fundamental factors regarding the balance of payment (BOP) condition. The empirical result shows the potential contagion from Argentina and Turkey’s financial crisis to the Indonesian economy, especially to the stock market and exchange rate. The regression and correlation result also shows that Turkey has a higher financial contagion effect than Argentina to Indonesian financial market. Balance of payment condition also has the significant effect to explain rupiah exchange rate depreciation.

Financial Risk Prediction with Multi-Round Q&A Attention Network

Financial risk is an essential indicator of investment, which can help investors to understand the market and companies better. Among the many influencing factors of financial risk, researchers find the earnings conference call is the most significant one. Predicting financial volatility after the earnings conference call has been critical to beneficiaries, including investors and company managers. However, previous work mainly focuses on the feature extraction from the word-level or document-level.The vital structure of conferences, the alternate dialogue, is ignored. In this paper, we introduced our Multi-Round Q&A Attention Network, which brings into account the dialogue form in the first place. Based on the data of earnings call transcripts, we apply our model to extract features of each round of dialogue through a bidirectional attention mechanism and predict the volatility after the earnings conference call events. The results prove that our model significantly outperforms the previous state-of-the-art methods and other baselines in three different periods.