scholarly journals Dynamic stop-loss rules as universal performance enhancers

2018 ◽  
Vol 15 (2) ◽  
pp. 1-16
Author(s):  
Dimitrios Thomakos ◽  
Rafael Yahlomi

This paper provides ample empirical evidence, using US equity and bond indices, why daily stop-loss rules can be considered as viable performance enhancers. While a longer-term stop-loss rule can help investors to avoid market crashes by being out of the market, investors may obviously lose on the up-market days too. Furthermore, a shorter-term stop-loss rule may not miss the good market days by allowing investors to stay for a longer time in the market at the obvious expense of increased risk and higher drawdowns. This paper illustrates how daily stop-loss rules can significantly outperform the buy and hold equity and bond benchmarks, their equally weighted portfolio and the trend following strategy, simple moving average, which is driven from those asset classes – for both long and short positions. The results are robust to a variety of variations on the initial theme and it’s shown that performance enhancements can come from a variety of other sources related to a static stop-loss rule.

Author(s):  
Chalermpon Jatuporn ◽  
Patana Sukprasert ◽  
Siros Tongchure ◽  
Vasu Suvanvihok ◽  
Supat Thongkaew

The purpose of this study is to forecast the import demand of table grapes of Thailand using monthly time series from January 2007 to April 2020. The ADF unit root test is used for stationarity checking, and seasonal autoregressive integrated moving average (SARIMA) is applied to forecast the import demand of table grapes. The results revealed that the integration of time series was in the first difference for non-seasonal and seasonal order. The best-fitted forecasting model was SARIMA(1,1,3)(2,1,0)12. The forecasted period for the next eight months showed the import demand of table grapes of Thailand that is slightly decreased by an average of 11.398 percent, with overall expected to decrease by an average of 15.218 percent in 2020.


2021 ◽  
Author(s):  
Salah Boussen ◽  
Pierre-Yves Cordier ◽  
Arthur Malet ◽  
Pierre Simeone ◽  
Sophie Cataldi ◽  
...  

Abstract BackgroundThe rapid spread of coronavirus disease COVID 19 calls for early screening and monitoring of these patients to distinguish those that are likely to worsen from stable patients that may be directed to intermediate care facilities. We designed a score for COVID-19 patients severity assessment, dynamic intubation and prolonged stay prediction using the Breathing Frequency (BF) and oxygen saturation (SPO2) signals.MethodsWe recorded BF, and SPO2 signals of confirmed COVID-19 patients admitted during the first and second outbreak of the pandemic in France (March to May 2020 and September 2020 to February 2021) in an ICU of a teaching hospital. We extracted four features from the signals that represent the four last hours before intubation for intubated patients and the mean of the four hours before the median intubation time for non-intubated patients. These data were used to train AI algorithms for intubation recognition. Algorithm robustness was checked on a validation set of patients. We selected the best algorithm that was applied every hour to predict intubation, thus a severity evaluation. We performed a 24h moving average of these predictions giving a S24 severity score that represent the patient's severity during the last 24 h. MS24, the maximum of S24 was confronted with the risk of intubation and prolonged ICU stay (>5 days).ResultsWe included 177 patients. Among the tested algorithms, the Logistic regression classifier had the best performance. The model had an accuracy of 88.9 % for intubation recognition (AUC=0.92). The accuracy on the validation set was 92.6 %. The S24 score of intubated patients was significantly higher than non-intubated patients 48h before intubation and increased 24 hours before intubation. MS24 score allows distinguishing three severity situations with an increased risk of intubation: green (3%), orange (30%) and red (76%). A MS24 score superior to 20 was highly predictive of an ICU stay greater than 5 day with an accuracy of 88.8% (AUC=0.95).ConclusionsThe score we designed uses simple signals and seems to be efficient to visualize the patient's respiratory situation and may help in decision-making. Real-time computation is easy to implement.


2009 ◽  
Vol XII (Issue 1) ◽  
pp. 63-72 ◽  
Author(s):  
Alexandru Todea ◽  
Adrian Zoicas-Ienciu ◽  
Angela-Maria Filip

2015 ◽  
Vol 33 (4) ◽  
pp. 374-392 ◽  
Author(s):  
Jaime Yong ◽  
Anh Khoi Pham

Purpose– Investment in Australia’s property market, whether directly or indirectly through Australian real estate investment trusts (A-REITs), grew remarkably since the 1990s. The degree of segregation between the property market and other financial assets, such as shares and bonds, can influence the diversification benefits within multi-asset portfolios. This raises the question of whether direct and indirect property investments are substitutable. Establishing how information transmits between asset classes and impacts the predictability of returns is of interest to investors. The paper aims to discuss these issues.Design/methodology/approach– The authors study the linkages between direct and indirect Australian property sectors from 1985 to 2013, with shares and bonds. This paper employs an Autoregressive Fractionally Integrated Moving Average (ARFIMA) process to de-smooth a valuation-based direct property index. The authors establish directional lead-lag relationships between markets using bi-variate Granger causality tests. Johansen cointegration tests are carried out to examine how direct and indirect property markets adjust to an equilibrium long-term relationship and short-term deviations from such a relationship with other asset classes.Findings– The authors find the use of appraisal-based property data creates a smoothing bias which masks the extent of how information is transmitted between the indirect property sector, stock and bond markets, and influences returns. The authors demonstrate that an ARFIMA process accounting for a smoothing bias up to lags of four quarters can overcome the overstatement of the smoothing bias from traditional AR models, after individually appraised constituent properties are aggregated into an overall index. The results show that direct property adjusts to information transmitted from market-traded A-REITs and stocks.Practical implications– The study shows direct property investments and A-REITs are substitutible in a multi-asset portfolio in the long and short term.Originality/value– The authors apply an ARFIMA(p,d,q) model to de-smooth Australian property returns, as proposed by Bond and Hwang (2007). The authors expect the findings will contribute to the discussion on whether direct property and REITs are substitutes in a multi-asset portfolio.


2020 ◽  
Vol 47 (2) ◽  
pp. 386-404 ◽  
Author(s):  
Minh Le ◽  
Viet-Ngu Hoang ◽  
Clevo Wilson ◽  
Thanh Ngo

PurposeThere is ample empirical evidence to show that larger banks are more efficient than smaller banks in developed countries. However, there is very little empirical evidence to show that in small developing economies, such as Vietnam, bank size is associated with increased risk, especially credit risk. This paper aim to provide empirical evidence to fill in this gap. This paper employs a slack-based directional distance function using the intermediation approach in measuring the inefficiency of banks in Vietnam during the period 2006–2015. Non-performing loans are used as an undesirable output to capture credit risk. The results show that small banks are more efficient than large banks at the mean level and across the entire distributions of inefficiency of the two groups. Input waste, output shortage and risk surplus of big banks are nearly three times higher than those of small banks. The results are robust under constant and variable returns to scale for production technologies. The study’s empirical results contribute to the ongoing debate on the merits of enlarging bank size in a small transitional economy and suggest that policy makers should pay attention to the risk and inefficiency of large banks to enhance the performance of Vietnam's banking system as a whole.Design/methodology/approachThis paper uses the non-radial slack-based directional technology distance function developed by Färe and Grosskopf (2010) to estimate the efficiency of banks using the data envelopment analysis technique. Data for 44 commercial banks are used.FindingsThe empirical results of the paper contribute to the ongoing debate on the merits of enlarging bank size in a small transitional economy and suggest that policy makers should pay attention to the risk and inefficiency of large banks to improve the performance of Vietnam's banking system as a whole.Originality/valueThis paper extends the extant literature by examining whether efficiency is associated with size in a typical transitional developing economy. The classic Cournot model, the structure-conduct-performance and the efficiency structure hypotheses state that larger banks are more efficient than smaller banks (Bikker and Bos, 2008). Empirical studies of Berger (2003), Mester (2005), Wheelock and Wilson (2012) lend support to the statement in developed countries. However, not much empirical literature focuses on small developing economies such as Vietnam to show that bank size is associated with increased risk, especially credit risk. The study’s empirical results show that size enlargement is not positively associated with risk-adjusted efficiency. Input waste, output shortage and risk surplus of big banks are nearly three times higher than those of small banks. The results are robust under constant and variable returns to scale for production technologies.


2017 ◽  
Vol 5 (4) ◽  
pp. 16
Author(s):  
Jishan Ma ◽  
Yuanbiao Zhang

This paper aims to establish a quantitative trading strategy of commodity futures based on market money flows. Firstly, we use Accumulation/Distribution index to respectively construct the CMF index which represents the ratio of total capital flows to total volume, and the CHO index which represents the exponential moving average of the cumulative capital flows. In view of the different flows of money between buyers and sellers, the establishment of the transaction net volume index VTL is used to describe respectively the flow of money between buyers and sellers. On this basis, the HMM model is introduced, and the above three kinds of indexes are combined to choose the time, at which we execute the stop-loss operation and risk control. Finally, all performance index values of the strategy are as follows: the rate of initial capital return is 193.77%, the annual rate of return is 99.86%, the maximum retracement rate is 15.73%, the Sharpe rate is 2.05 and the price earnings ratio is 4.01.


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