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Operational and investment efficiency of investment trust companies:Do foreign firms outperform domestic firms?
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作者 Mohammad Nourani Qian Long Kweh +1 位作者 Wen-Min Lu Ikhlaas Gurrib 《Financial Innovation》 2022年第1期2170-2195,共26页
This study examines the efficiency of investment trust companies(ITCs)from 2011 to 2020 using a meta-frontier two-stage network data envelopment analysis(DEA)based on the directional distance function(DDF).We improved... This study examines the efficiency of investment trust companies(ITCs)from 2011 to 2020 using a meta-frontier two-stage network data envelopment analysis(DEA)based on the directional distance function(DDF).We improved the accuracy of the efficiency measurement and added a network-based ranking component to rank the topperforming entities.In the group-specific technology assessment,foreign ITCs excel in investment efficiency.Meanwhile,in the meta-technology assessment,domestic ITCs outperform foreign ITCs in terms of both investment and operational efficiencies.Group-specific technology efficiency scores were found to be lower than or equal to the meta-technology efficiency scores for both the operational and investment stages.Based on the network-based ranking approach,Yuan Ta,a domestic ITC that ranked fourth in the operational stage and first in the investment stage,can be used as a reliable benchmark.This study will enable practitioners to gain a better understanding of the performance of ITCs operating under heterogeneous technologies. 展开更多
关键词 Data envelopment analysis Metafrontier two-stage network Network-based ranking Directional distance function Investment trust companies
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Energy crypto currencies and leading U.S.energy stock prices:are Fibonacci retracementsprofitable?
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作者 Ikhlaas Gurrib Mohammad Nourani Rajesh Kumar Bhaskaran 《Financial Innovation》 2022年第1期187-213,共27页
This paper investigates the role of Fibonacci retracements levels,a popular technical analysis indicator,in predicting stock prices of leading U.S.energy companies and energy cryptocurrencies.The study methodology foc... This paper investigates the role of Fibonacci retracements levels,a popular technical analysis indicator,in predicting stock prices of leading U.S.energy companies and energy cryptocurrencies.The study methodology focuses on applying Fibonacci retracements as a system compared with the buy-and-hold strategy.Daily crypto and stock prices were obtained from the Standard&Poor’s composite 1500 energy index and CoinMarketCap between November 2017 and January 2020.This study also examined if the combined Fibonacci retracements and the price crossover strategy result in a higher return per unit of risk.Our findings revealed that Fibonacci retracement captures energy stock price changes better than cryptos.Furthermore,most price violations were frequent during price falls compared to price increases,supporting that the Fibonacci instrument does not capture price movements during up and downtrends,respectively.Also,fewer consecutive retracement breaks were observed when the price violations were examined 3 days before the current break.Furthermore,the Fibonacci-based strategy resulted in higher returns relative to the naïve buy-and-hold model.Finally,complementing Fibonacci with the price cross strategy did not improve the results and led to fewer or no trades for some constituents.This study’s overall findings elucidate that,despite significant drops in oil prices,speculators(traders)can implement profitable strategies when using technical analysis indicators,like the Fibonacci retracement tool,with or without price crossover rules. 展开更多
关键词 Performance evaluation Energy cryptos Energy stocks Fibonacci retracements
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