Multiscale Partial Correlation Clustering of Stock Market Returns

This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct market movement characteristics and are therefore useful for portfolio diversification. The proposed procedure is demonstrated using all the major S&P 500 sector indices as well as precious metals and energy sector futures returns during the last decade. The results suggest cluster formations that vary by time scale, which entails different stock selection strategies for investors differing in terms of their investment horizon orientation.

Portfolio Investment in Malaysia and Saudi Arabia

Several strategies are adopted by investors in lowering the risk of investment while maximising its return. Graham's stock selection criteria are noted as one of the best strategies in selecting portfolios by investors. Although the model is universally accepted, it is less commonly practised and examined in emerging markets. Considering the growth of these emerging countries' financial markets, it is worthwhile to investigate the doctrine's effect on investment in these countries. This study endeavours to review the consequence of Graham's stock selection criteria on portfolio returns in the Malaysian and Saudi Arabian stock markets. Each country represents the fastest growing market in their region which justifies this study. The study found that the Malaysian stock market is capable of proffering abnormal returns to investors while the Saudi stock market is capable to offer abnormal returns to investors despite being an undeveloped and immature stock market. The study concludes that the model of stock selection remains beneficial and indeed valuable to regional investments.

ANALISIS KINERJA REKSA DANA SAHAM PADA PLATFORM PT. PHILLIP SEKURITAS INDONESIA

Penelitian ini bertujuan untuk menganalisis kinerja reksa dana saham yang terdaftar pada platform PT. Phillip Sekuritas Indonesia. Penelitian ini menggunakan metode deskriptif kuantitatif untuk jangka waktu 1 tahun. Populasi dalam penelitian ini adalah 44 reksa dana saham yang terdaftar pada platform PT. Phillip Sekuritas Indonesia. Pengambilan sampel pada penelitian ini menggunakan kriteria yang sudah di tetapkan oleh peneliti yaitu 44 reksa dana saham . Variabel yang digunakan pada penelitian ini adalah Asset Under Management (AUM), Stock Selection, Fee Broker, dan Nilai Aktiva Bersih (NAB). Penelitian ini menggunakan metode klasifikasi data dari masing-masing variabel. Hasil dari penelitian ini menunjukkan adanya besaran perhitungan dari masing-masing variabel terhadap kinerja reksa dana saham yang terdaftar di platform PT. Phillip Sekuritas Indonesia dengan keputusan memilih reksa dana saham tersebut. Selain itu penelitian ini menunjukkan besaran dari Asset Under Management (AUM), Stock Selection, Fee Broker, dan Nilai Aktiva Bersih (NAB) akan menjadi tolak ukur untuk investor untuk ber-investasi pada reksa dana saham yang terdaftar.

Optimal Returns in Indian Stock Market during Global Pandemic: A Comparative Study

This research is an extension of our previous work [Debnath and Srivastava (2021)]. In that paper, we designed a portfolio based on data taken from National Stock Exchange (NSE), India, during 1 January 2020 to 31 December 2020 and performance of that portfolio in real-life situation was examined during 1 January 2021 to 21 May 2021 assuming investments were made according to the proposed model. We observed that our proposed portfolio was efficient enough in that period to beat the performance of most of the in-demand mutual funds. It was also conjectured that this portfolio would be sustainable post the second wave of COVID-19 in India. In the present paper, our aim is to validate this conjecture. Here, we examine the performance of this portfolio during the period 1 January 2021 to 18 October 2021 using the same previous data set. We also investigate the performance of this portfolio if it was blindly adopted without applying the stock selection methodology during 1 January 2019 to 31 December 2019. Using paired t-test between the difference of means of the performances in the year 2019 and the year 2021, we show that the performance in 2021 was significantly enhanced because of selecting the stocks applying our proposed model.

Does an Exchange-Traded Fund Converge to its Benchmark in the Long Run? Evidence from Ishares MSCI in Asia-Pacific Countries

This study examines the linkages between exchange-traded funds (ETFs) and their benchmark indices from 2013 to 2019 using iShares MSCI of ten Asia-Pacific countries. Our results show, first, that there is a long-run causality running from the benchmark index to ETFs. These findings imply that ETFs may replicate the performance of the benchmark index over the long run. Second, there is a unidirectional causal relationship from ETFs to the benchmark index in the short run, which indicates that benchmark index prices respond to the short-run changes in the ETF prices when new information is available. Third, there is a significant tracking error between ETFs and the benchmark index. This finding justifies the existence of stock selection and market timing abilities among the ETF managers. Lastly, fund managers add value to the ETFs and generate better than the market returns. This paper provides new evidence to support this new stylized fact of ETFs.

Impact of Stock Market Volatility on Gold prices during the Covid-19 pandemic

The present research study examines the impact of Stock marketson Gold prices using daily data for pre and during COVID-19 period (January-October 2020). This study uses Unit root test, Granger causality test, GARCH method and Johansen’s co-integration test to evaluate difference in the Volatility as well as the relationship between them. The findings show that no causal relationship exists between Gold Prices and Stock market prices in the short run. The result of the Johansen Co-integration test for the long-run relationship between theGold price and Nifty Indices showno co-integration at all, but low co-integration inshort-run cannot be ruled out. With this study, an attempt has been made to reveal the relationship that exists between Gold and stock markets with empirical findings using the time series analysis which reveals the original side of work during the pandemic. The ARCH and GARCH coefficient explain significantly the persistence of information on stock return volatility. The present study recommends that the integration between Gold and Stock market price entails the need for investors globally to follow a portfolio stock selection strategy to add value from the investments in India.These findings have important implication for the investors seeking portfolio diversification.

Trading using Hidden Markov Models during COVID-19 turbulences

Obtaining higher than market returns is a difficult goal to achieve, especially in times of turbulence such as the COVID-19 crisis, which tested the resilience of many models and algorithms. We used a Hidden Markov Models (HMM) methodology based on monthly data (DAX returns, VSTOXX index Germany’s industrial production and Germany’s annual inflation rate) to calibrate a trading strategy in order to obtain higher returns than a buy-and-hold strategy for the DAX index., following Talla (2013) and Nguyen and Nguyen (2015). The stock selection was based on 26 stocks from DAX’s composition, which had enough data for this study, aiming to select the 15 best performing. The training period was January 2000 - December 2015, and the out-of-sample January 2016 - August 2021, including the period of high turbulence generated by COVID-19. Fitting the best model revealed that the following regimes are the most suitable: two regimes for DAX returns, two regimes for VSTOXX and three regimes for the inflation rate and for the industrial production, while the posterior transition probabilities were event-depending on the training sample. Furthermore, portfolios built using HMM strategy outperformed the DAX index for the out-of-sample period, both in terms of annualized returns and risk-adjusted returns. The results were in line with expectations and what other researchers like Talla (2013), Nguyen and Nguyen (2015) and Varenius (2020) found out. We managed to highlight that a strategy calibrated based on HMM methodology works well even in periods of extreme volatility such as the one generated in 2020 by COVID-19 pandemic.

Maximum Drawdown, Recovery, and Momentum

We empirically test predictability on asset price using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directions of asset prices but also in capturing cross-sectional return differentials. In monthly periods, the alternative portfolios ranked by maximum drawdown measures exhibit outperformance over other alternative momentum portfolios including traditional cumulative return-based momentum portfolios. In weekly time scales, recovery-related stock selection rules are the best ranking criteria for detecting mean-reversion. For the alternative portfolios and their ranking baskets, improved risk profiles in various reward-risk measures also imply more consistent prediction on the direction of assets in future. Moreover, turnover rates of these momentum/contrarian portfolios are also reduced with respect to the benchmark portfolios. In the Carhart four-factor analysis, higher factor-neutral intercepts for the alternative strategies are another evidence for the robust prediction by the alternative stock selection rules.