Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods

Altarawneh, Ghada A.; Hassanat, Ahmad B. A.; Tarawneh, Ahmad S.; Abadleh, Ahmad; Alrashidi, Malek; Alghamdi, Mansoor

doi:10.3390/economies10020043

Cited by 18 publications

(9 citation statements)

References 105 publications

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“…Similarly, Yogesh et al compared exponential moving average and ARIMA in forecasting prices of 15 stocks in the National Stock Exchange of India, 9 while Shahid et al compared exponential moving average, double exponential moving average, and Holt‐Winters method in forecasting stock prices of two IT companies 10 . Besides, Altarawneh et al compared moving average, weighted moving average, exponential moving average, and exponential decay weighted average in forecasting stock prices of Jordan insurance companies 11 …”

Section: Related Researchmentioning

confidence: 99%

“…10 Besides, Altarawneh et al compared moving average, weighted moving average, exponential moving average, and exponential decay weighted average in forecasting stock prices of Jordan insurance companies. 11 However, these methods of time series analysis tend to be dragged to previous values; it was pointed out that one-period-ahead forecast follows actual values of the preceding period, while n-periods-ahead forecast follows predicted values of previous periods; thus, there is a limit to improving forecast accuracy. 12 In this context, the present study is aimed at better forecasting of financial time series; particularly, an improved leading indicator is used to mathematically advance a F I G U R E 1 Moving average for monotone increasing function.…”

Section: Related Researchmentioning

confidence: 99%

“…Thus, the transfer function is adjusted toward inverse amplitude characteristic so that the amplitude almost does not change in low frequency range (gentle sloping), while changing greatly in high frequency region. To realize such an amplitude characteristic, the transfer function H(z) is defined as in Equation (11), and the smoothing constant α is set negative so that the 2nd term in the denominator becomes positive. 19…”

Section: Improved Leading Indicatormentioning

confidence: 99%

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Nonstationary time series change direction forecast method using improved leading indicator

Yoshida

2023

Elect Comm in Japan

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This paper presents a method for forecasting the change direction of nonstationary time series using the improved leading indicator. The leading indicator is a method developed by Ehlers that translates a time series mathematically in the future direction with respect to the time axis and calculates the leading value of the time series. However, this method has the problem that the leading value can be calculated only in the low frequency region with a normalized frequency of 0.06 or more (normalized period of 17 or more) at the maximum. In order to solve this problem, by the gentle slope the amplitude characteristics in the low frequency region of the leading indicator, it is possible to calculate the leading value in the frequency domain with a normalized frequency of 0.25 or more (normalized period of four or more) at the maximum. By applying the preceding value to the instantaneous periodic time series by the improved sine wave indicator developed by Ehlers, it is possible to forecast the change direction of the non‐stationary time series in the short term.

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Section: Related Researchmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

Section: Improved Leading Indicatormentioning

confidence: 99%

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Nonstationary time series change direction forecast method using improved leading indicator

Yoshida

2023

Elect Comm in Japan

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“…Altarawneh et al [ 33 ] found that prediction and analysis of financial data-like insurance premium are a difficult task for any time series data. In their study, they examine the stock prices performance of ten insurance companies of Jordan using higher-order nonparametric Exponential Decay Weighted Average (EDWA).…”

Section: Review Of Literaturementioning

confidence: 99%

Prediction of Life Insurance Premium during Pre-and Post-Covid-19: A Higher-Order Neural Network Approach

Das

Nayak

et al. 2022

J. Inst. Eng. India Ser. B

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The impact of Covid-19 drastically changed the economical, social, behavioral, and psychological aspects of human life. Just after the official announcement of this pandemic by the World Health Organization, the entire world has locked for several months. As people come across these unpredictable situations, insurance has emerged as one of the ways to financially protect themselves and their families. Consumers were confused in terms of financial gain and risk associated with this disease. In this critical situation, the study on the consumer decision-making process is an integral part to evaluate consumer behavior. An evolutionary improved FLANN is developed to analyze the growth of insurance during the pandemic and observed the insignificance of this pandemic effect on consumer behavior toward life insurance. The proposed study has undertaken an asymmetrical data analysis on 24 life insurance companies of India from January 2015 to December 2020 and predicted the premium collection for the future. The proposed method is compared with other similar machine learning methods and found to be superior.

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“…Another direction in further development of technical analysis is adapting its tools concerning volatile stock markets, including the emerging and crisis ones (Altarawneh, 2022;de Souza et al, 2018;Lubnau & Todorova, 2014;Chong et al, 2010). The paper's authors (de Souza et al, 2018) developed an automated trading system that simulated trades in this portfolio using the methods of technical analysis and fundamental indicators.…”

Section: Introductionmentioning

confidence: 99%

Digital Methods of Technical Analysis for Diagnosis of Crisis Phenomena in the Financial Market

Yashina¹,

Кашина²,

Yashin³

et al. 2022

IJTech

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Periodically occurring crisis phenomena in financial markets make urgent the issues related to prompt diagnostics and forecasting of crisis phenomena development to minimize financial risks. Modern technical analysis, based on the extensive use of digital methods, is one of the main approaches to the prompt processing of stock exchange real-time information. However, the current instability of the economy requires the improvement of existing technical methods. This study aims to develop the tools for technical analysis of financial assets to improve investment strategies in crisis periods. In this direction, using the methods of mathematical statistics, indicators of volatility and depth of economic crisis were developed to optimize portfolio investment management strategies. The offered technical analysis tools have been tested by the example of the financial crises in 2008-2009, 2014-2015, 2019-2020 concerning the Russian stock market. The authors presented the investment strategy results based on the developed approach. The results obtained demonstrate the positive economic efficiency of the suggested recommendations. The application of the developed algorithm based on technical analysis allows for monitoring the financial market situation. It reveals critical points of crisis phenomena (for example, periods of maximum volatility, the 'bottom' of the crisis, etc.). The algorithm is used for analysis of information transmitted by a trading platform.

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Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods

Cited by 18 publications

References 105 publications

Nonstationary time series change direction forecast method using improved leading indicator

Nonstationary time series change direction forecast method using improved leading indicator

Prediction of Life Insurance Premium during Pre-and Post-Covid-19: A Higher-Order Neural Network Approach

Digital Methods of Technical Analysis for Diagnosis of Crisis Phenomena in the Financial Market

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