Option hedging of stock indices: Benefits of signals of fundamental and technical analysis

2021 ◽  
Vol 57 (2) ◽  
pp. 106
Author(s):  
Denis Lopushansky
Keyword(s):  
Technical Analysis ◽  
Option Hedging ◽  
Stock Indices

scholarly journals ВИКОРИСТАННЯ СИСТЕМ ШТУЧНОГО ІНТЕЛЕКТУ У ЗАДАЧАХ ПРОГНОЗУВАННЯ ФІНАНСОВИХ ІНДЕКСІВ: ОГЛЯД НАУКОВИХ ДЖЕРЕЛ

2020 ◽  
pp. 108-117
Author(s):  
Николай Ярославович Кушнир ◽  
Катерина Токарева
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Large Scale ◽  
Scientific Literature ◽  
Financial Time Series ◽  
Technical Analysis ◽  
Soft Data ◽  
Scientific Publications ◽  
Financial Time ◽  
Stock Indices

The paper investigates methods of artificial intelligence in the prognostication and analysis of financial data time series. The usage of well-known methods of artificial intelligence in forecasting and analysis of time series is investigated. Financial time series are inherently highly dispersed, complex, dynamic, nonlinear, nonparametric, and chaotic nature, so large-scale and soft data mining techniques should be used to predict future values. As the scientific literature superficially describes the numerous artificial intelligence algorithms to be used in forecasting financial time series, a detailed analysis of the relevant scientific literature was conducted in scientometric databases Scopus, Science Direct, Google Scholar, IEEExplore, and Springer. It is revealed that the existing scientific publications do not contain a comprehensive analysis of literature sources devoted to the use of artificial intelligence methods in forecasting stock indices. Besides, the analyzed works, which are related in detail to the object of our study, have a limited scope because they focus on only one family of artificial intelligence algorithms, namely artificial neural networks. It was found that the analysis of the use of artificial intelligence systems should be based on two well-known approaches to predicting the behavior of financial markets: fundamental and technical analysis. The first approach is based on the study of economic factors that have a possible impact on market dynamics and more common in long-term planning. Representatives of technical analysis, on the other hand, argue that the price already contains all the fundamental factors that affect it. In this regard, technical analysis involves forecasting the dynamics of price changes based on the analysis of their change in the past, ie time series. Although today there are many developed models for forecasting stock indices using artificial intelligence algorithms, in the scientific literature there is no established methodology that defines the main elements and stages of the algorithm for forecasting financial time series. Therefore, this study has improved the methodology for forecasting financial time series.

scholarly journals Analyzing Performance of Technical Analysis on Stock Markets

2020 ◽  
pp. 53-62
Author(s):  
A. Stavytskyy ◽  
V. Taraba
Keyword(s):  
Hong Kong ◽  
Historical Data ◽  
Arima Model ◽  
Technical Analysis ◽  
Optimal Parameters ◽  
Arima Models ◽  
Optimal Model ◽  
Stock Indices ◽  
Analysis Methods ◽  
The Stability

The article analyzes the profitability of technical analysis methods for the seven stock indices during the last ten years. According to the analysis, the profitability of technical analysis has increased recently due to changes in market conditions. However, the efficiency of technical analysis methods was much lower during 2010-2018. The analysis showed that technical analysis methods demonstrated best results on the Chinese, Indian, and Hong Kong stock indices, the worst – on the American, European, and Japanese stock indices. However, the stability of these methods is quite low: their profitability varies greatly with the change of the sample. The issue of aggregation of technical analysis signals and ARIMA-model signals is also considered in this paper. The optimal parameters for the technical analysis methods were selected by testing on historical data; optimal ARIMA models were selected for each index. For 3 out of 7 indices the optimal model is WN (white noise). Most technical analysis methods showed poor results on the American (S&P 500) and European (Euronext 100) stock indices (except for the last two years). The results can be used to develop trading strategies.The analysis showed that technical analysis methods demonstrated best results on the Chinese, Indian, and Hong Kong stock indices, the worst – on the American, European, and Japanese stock indices. However, the stability of these methods is quite low: their profitability varies greatly with the change of the sample. The issue of aggregation of technical analysis signals and ARIMA-model signals is also considered in this paper. The optimal parameters for the technical analysis methods were selected by testing on historical data; optimal ARIMA models were selected for each index. For 3 out of 7 indices the optimal model is WN (white noise). Most technical analysis methods showed poor results on the American (S&P 500) and European (Euronext 100) stock indices (except for the last two years). The results can be used to develop trading strategies.

Back to the Future. A Behavioural Perspective on Technical Analysis into PIGS Countries

2015 ◽  
Vol 4 (1) ◽  
pp. 67-87
Author(s):  
Papathanasiou Spyros ◽  
Vasiliou Dimitrios ◽  
Eriotis Nikolaos
Keyword(s):  
Technical Analysis ◽  
The Future

Piano Performance Technical Analysis of The People United Will Never Be Defeated by Rzewski

2018 ◽  
Vol 5 (3) ◽  
pp. 161-171
Author(s):  
Liang Deng
Keyword(s):  
Technical Analysis ◽  
Piano Performance ◽  
The People ◽  
Performance Techniques

Abstract The piano variations The People United will Never be Defeated by Rzewski contains many modern piano performance techniques and skills. The difficulties of these techniques and skills in these enormous variations are far beyond the boundaries of traditional piano performance techniques and skills. This analysis will give a specific classification for these modern piano performance techniques and skills in order to provide a more comprehensive guide for the piano performers.

IDENTIFIKASI RASIO PARAMETER KAPAL PENUMPANG CATAMARAN BERBAHAN FRP

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Soegeng Hardjono
Keyword(s):  
Main Parameter ◽  
Slenderness Ratio ◽  
Technical Analysis ◽  
Preliminary Design ◽  
Naval Architecture

Parameter ratios are important information for the ship designer in the preliminary design stages. They can be used as a tool to identify the main parameter of vessel and their other technical characteristics. The information about parameter ratios of mono-hull vessel is currently available in Naval Architecture literatures, but it is not the case for the type of twin-hull vessel called catamaran. Having conducted technical analysis in this research, it has been identified that parameter ratios of passenger catamaran vessel made of FRP has values of L / B ratio from 2.52 up to 3.7, L / D ratio from 5.25 up to 11:24, slenderness ratio (Lwl/BwL) from  9 up to 12. Other values of various parameter ratios like B/T, D/T, L /√ L, and Displacement/L are also discussed.

ANALISIS DIVERSIFIKASI INTERNASIONAL : PEMBENTUKAN PORTOFOLIO OPTIMAL INDEKS SAHAM DUNIA (Studi Kasus Pada Indeks Saham Aktif Dunia Versi The Wall Street Journal)

2019 ◽  
Vol 6 (02) ◽  
Author(s):  
Rony Mahendra ◽  
Erwin Dyah Astawinetu
Keyword(s):  
Wall Street Journal ◽  
Optimal Portfolio ◽  
Stock Index ◽  
Wall Street ◽  
Expected Return ◽  
Single Index ◽  
Index Model ◽  
Single Index Model ◽  
Stock Indices ◽  
The Wall Street Journal

The research objective is to establish an optimal portfolio and know the difference between risk and return stock index portfolio candidates and non-candidates. Method used in the preparation of this research portfolio is the single index model, while the samples of this study are active world stock indices version of The Wall Street Journal during the period August 2012 - August 2016 and The Global Dow is used as the benchmark stock index. In establishing the optimal portfolio is used two perspectives: the Rupiah perspective and the U.S. Dollar perspective. The results showed there were three stock indices from the perspective of Rupiah and 8 share index menurutperspektif U.S. Dollar that make up the optimal portfolio, with the cut-of-pointsebesar 0,01393menurut Rupiah perspective and the perspective of 0.0078 US Dollars Based on the perspective of return expectations Rupiah obtained by 0.0258 with a risk of 0.06512. Berdarkan perspective of US Dollars, obtained return expectations at 0.0154 with a risk of 0.0292. From the test results showed that the hypothesis, the return on both perspectives there are significant differences between the index of the candidate, with a non-candidate. Then the risk of stock index, among the candidates, with a non-candidate, the Rupiah perspective there is no difference, but in the perspective of US Dollars, there are significant differences.Keywords: Single Index Model, candidate portfolio, optimal portfolio, expected return, excess return to beta, cut-off-point

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