Big 5 ASEAN capital markets forecasting using WEMA method

ASEAN through ASEAN Economics Community (AEC) 2020 treaty has proposed financial integration via capital markets integration in order to aim comprehensive ASEAN economic integration. Therefore, the need to have a proper prediction of ASEAN capital market becomes a major issue. In this study, we took big 5 ASEAN capital markets, i.e. Straits Times Index (STI), Kuala Lumpur Stock Exchange (KLSE), Stock Exchange of Thailand (SET), Jakarta Stock Exchange (JKSE), and Philippine Stock Exchange (PSE) to be forecasted using WEMA method. Weighted Exponential Moving Average (WEMA) is a new hybrid moving average method which combines the weighting factor calculation in Weighted Moving Average (WMA) with the procedure of Exponential Moving Average (EMA). WEMA has successfully been implemented and used to forecaste discrete time series data, but never being used to forecast ASEAN capital markets. In this study, we took further action by implementing the WEMA method with brute force approach for scaling factor tuning on big 5 ASEAN capital markets. From the experimental results, we found that WEMA has successfully forecasted all those exchanges. By looking at the forecast error measurement, it gives the best performance on PSE and worst performance on SET dataset among all datasets being considered in this study

The Determinants of Capital Inflow in Developing Countries with Special Reference to Pakistan

Introduction: The capital mobility has increased in the developing countries after opening up of markets and WTO since 2001. Now the investors all over the world are free to move their capital and invest it in the country of their choice. Now the countries having big markets and number of customers are the target of big investors. This is the reason that capital has been flowing in the developing countries. Objectives of the study: The main objective is to investigate into the determinants of rising capital inflow into developing countries in general and Pakistan in particular and its impact on its economy. Main Research problem: Our main research problem is to study the determinant of capital inflow in Pakistan and the policies of the Government to attract foreign direct investment. Another problem is to study whether the policies are conducive for foreign investment. Methodology: In this study, we have taken the sample of eight developing countries of Asia such as Bangladesh, China, India, Malaysia, Indonesia Pakistan, South Korea and Thailand. The annual data of these countries is taken from International Financial Statistics and World Bank database for 23 years from 1990 to 2012. The E=views7 software has been used to analyze the data. Findings and Results: Our results specifically show that foreign exchange reserves, fiscal incentives, current account position, efficient capital market, strong infrastructure and efficient legal, judicial system and law and order situation play key role in attracting foreign capital inflow. At the moment Pakistan has been facing problem of twin deficits, political instability due to terrorism, and inefficient legal system, which are hampering large inflow of capital. Even then, Pakistan has been receiving more $12 billion remittance every years and ample foreign direct investment in telecommunication, banking and information technology sector. Our study show if government focus on political stability it can attract more capital flow. Our results are robust and consistent with other studies so far made on this subject. Key words: capital inflow, competitive edge, exchange rate, foreign exchange accumulation

Systemically Important or “Too Big to Fail” Financial Institutions

Although “too big to fail” (TBTF) has been a perennial policy issue, it was highlighted by the near-collapse of several large financial firms in 2008. Large financial firms that failed or required extraordinary government assistance in the recent crisis included depositories (Citigroup and Washington Mutual), government-sponsored enterprises (Fannie Mae and Freddie Mac), insurance companies (AIG), and investment banks (Bear Stearns and Lehman Brothers).1 In many of these cases, policy makers justified the use of government resources on the grounds that the firms were “systemically important” or “too big to fail.” TBTF is the concept that a firm’s disorderly failure would cause widespread disruptions in financial markets that could not easily be contained. While the government had no explicit policy to rescue TBTF firms, several were rescued on those grounds once the crisis struck. TBTF subsequently became one of the systemic risk issues that policy makers grappled with in the wake of the recent crisis. This report discusses the economic issues raised by TBTF, broad policy options, and policy changes made by the relevant Dodd-Frank provisions. This report also discusses recent legislation addressing the TBTF issue in the 113th Congress. The report ends with an Appendix reviewing the historical experience with TBTF before and during the recent crisis

Style analysis in real estate markets: beyond the sectors and regions dichotomy

While style analysis has been studied extensively in equity markets, applications of this valuable tool for measuring and benchmarking performance and risk in a real estate context are still relatively new. Most previous real estate studies on this topic have identified three investment categories (rather than styles): sectors, administrative regions and economic regions. However, the low explanatory power reveals the need to extend this analysis to other investment styles. We identify four main real estate investment styles and apply a multivariate model to randomly generated portfolios to test the significance of each style in explaining portfolio returns. Results show that significant alpha performance is significantly reduced when we account for the new investment styles, with small vs. big properties being the dominant one. Secondly, we find that the probability of obtaining alpha performance is dependent upon the actual exposure of funds to style factors. Finally we obtain that both alpha and systematic risk levels are linked to the actual characteristics of portfolios. Our overall results suggest that it would be beneficial for real estate fund managers to use these style factors to set benchmarks and to analyze portfolio returns