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2022 ◽  
Vol 10 (4) ◽  
pp. 595-604
Author(s):  
Endah Fauziyah ◽  
Dwi Ispriyanti ◽  
Tarno Tarno

The Composite Stock Price Index (IHSG) is a value that describes the combined performance of all shares listed on the Indonesia Stock Exchange. JCI serves as a benchmark for investors in investing. The method used to predict future conditions based on past data is forecasting . Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) is amodel time series that can be used for forecasting. Financial data has high volatility which causes the variance of the residual model which is not constant (heteroscedasticity). ARCH / GARCH model is used to solve the heteroscedasticity problem in the model. If the data is heteroscedastic and asymmetric, then the model can be used Threshold Autoregressive Conditional Heteroskedasticity (TARCH). The data used are the Composite Stock Price Index (IHSG) for the January 2000 - April 2020 period and the dollar exchange rate data for the January 2000 - April 2020 period asvariables independent from the ARIMAX model. The best model used to predict the JCI from the results of this study is the ARIMAX (1,1,0) -TARCH (1,2) model with an AIC value of -0.819074. 


2021 ◽  
Vol 6 (1) ◽  
pp. 17-28
Author(s):  
John Koirala ◽  
Swachhanda Aabhas Rai

Background: Stock market experts analyse various indicators to estimate the stock market, including historical prices, economic analysis, industry analysis and company analysis, but this study uses historical prices for the NEPSE index, making forecasting more precise. Purpose: The purpose of this study is to explore short-term stock market momentum using fuzzy logic. The study also aims to establish a suitable fuzzy model to predict stock momentum, reduce the risk, and make the right investment decision. Methodology/Design: This study employed exploratory research design to understand the problem of chaotic decision making in the stock market. The mathematical method employed in this study is membership functions, which are part of fuzzy logic. This includes only the commercial banks, as it has the highest market capitalization, 53.11% of total market capitalization. Using 14-day past data as a base, the suggested fuzzy model determines the stock index’s momentum over the next 5 days. Findings: The forecasted trend value for the Nabil, Civil, and Prime Commercial bank is 0.92, 0.92, and 0.80, which shows a bullish trend. Compared to previously collected data, the findings closely reflect the expected real-world values.


2021 ◽  
Vol 10 (3) ◽  
pp. 325-336
Author(s):  
Anes Desduana Selasakmida ◽  
Tarno Tarno ◽  
Triastuti Wuryandari

Palladium is one of the precious metal commodities with the best performance since 3 years ago. Palladium has many benefits, including being used in the electronics, medical, jewelry and chemical industries. The benefits of palladium in the chemical field are that it can help speed up chemical reactions, filter out toxic gases in exhaust gases, and convert the gas into safer substances, so palladium is usually used as a catalyst for cars. Forecasting is a process of processing past data and projected for future interest using several mathematical models. The model used in this study is the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods. The process of forecasting palladium prices using monthly data from January 2011 to December 2020 with the Double Exponential Smoothing Holt method and the Fuzzy Time Series Chen method will be carried out in this study to describe the performance of the two methods. Based on the results of the analysis, it can be concluded that the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods have equally good performance with sMAPE values of 6.21% for Double Exponential Smoothing Holt and 9.554% for Fuzzy Time Series Chen. Forecasting for the next 3 periods using these two methods generally produces forecasting values that are close to the actual data. 


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Farouk Metiri ◽  
Halim Zeghdoudi ◽  
Ahmed Saadoun

PurposeThis paper generalizes the quadratic framework introduced by Le Courtois (2016) and Sumpf (2018), to obtain new credibility premiums in the balanced case, i.e. under the balanced squared error loss function. More precisely, the authors construct a quadratic credibility framework under the net quadratic loss function where premiums are estimated based on the values of past observations and of past squared observations under the parametric and the non-parametric approaches, this framework is useful for the practitioner who wants to explicitly take into account higher order (cross) moments of past data.Design/methodology/approachIn the actuarial field, credibility theory is an empirical model used to calculate the premium. One of the crucial tasks of the actuary in the insurance company is to design a tariff structure that will fairly distribute the burden of claims among insureds. In this work, the authors use the weighted balanced loss function (WBLF, henceforth) to obtain new credibility premiums, and WBLF is a generalized loss function introduced by Zellner (1994) (see Gupta and Berger (1994), pp. 371-390) which appears also in Dey et al. (1999) and Farsipour and Asgharzadhe (2004).FindingsThe authors declare that there is no conflict of interest and the funding information is not applicable.Research limitations/implicationsThis work is motivated by the following: quadratic credibility premium under the balanced loss function is useful for the practitioner who wants to explicitly take into account higher order (cross) moments and new effects such as the clustering effect to finding a premium more credible and more precise, which arranges both parts: the insurer and the insured. Also, it is easy to apply for parametric and non-parametric approaches. In addition, the formulas of the parametric (Poisson–gamma case) and the non-parametric approach are simple in form and may be used to find a more flexible premium in many special cases. On the other hand, this work neglects the semi-parametric approach because it is rarely used by practitioners.Practical implicationsThere are several examples of actuarial science (credibility).Originality/valueIn this paper, the authors used the WBLF and a quadratic adjustment to obtain new credibility premiums. More precisely, the authors construct a quadratic credibility framework under the net quadratic loss function where premiums are estimated based on the values of past observations and of past squared observations under the parametric and the non-parametric approaches, this framework is useful for the practitioner who wants to explicitly take into account higher order (cross) moments of past data.


MAUSAM ◽  
2021 ◽  
Vol 64 (2) ◽  
pp. 231-250
Author(s):  
PULAK GUHATHAKURTA ◽  
AJIT TYAGI ◽  
B. MUKHOPADHYAY

lHkh mi;ksxdrkZvksa] ;kstuk cukus okyksa] vkink izca/ku dkfeZdksa] i;ZVu vkfn }kjk rkieku] vf/kdre rkieku] U;wure rkieku] ok;qeaMyh; nkc] o"kkZ vkfn tSls ekSle izkpyksa dh tyok;q foKku ij lwpukvksa dh mUur tkudkjh dh vR;kf/kd ek¡x jgh gSA fdlh LFkku fo'ks"k esa os/k’kkyk ds vHkko vkSj dHkh&dHkh nh?kZ vof/k ds igys ds vk¡dM+ksa dh vuqiyC/krk ds dkj.k ekSle foKku leqnk; ml LFkku fo’ks"k ds fy, visf{kr lwpukvksa dks miyC/k ugha djk ikrk gSA bl 'kks/k i= esa LFkkfud varosZ’ku ds {ks= esa U;wjy usVodZ ds rqyukRed u, vuqiz;ksx crk, x, gSaA iwjs ckjg eghuksa ds vf/kdre vkSj U;wure nksuksa rkiekuksa  ds fy, U;wjy usVodZ varosZ’ku fun’kZ fodflr fd, x, gSaA ;g ekWMy ml LFkku fo’ks"k ij lkekU; vf/kdre vkSj U;wure rkiekuksa dks rS;kj djus ds fy, lwpukvksa ds :i  esa v{kka’k] ns’kkUrj vkSj mUu;u tSlh HkkSxksfyd lwpukvksa dk mi;ksx djrk gSA varosZ’ku ds fy, LFkkfud ekWMyksa ds fu"iknuksa dh rqyuk vU; lkekU;r% iz;qDr i)fr ds lkFk dh xbZ gSA Advance knowledge of information on  climatology of meteorological parameters like temperature, maximum temperature, minimum temperature, atmospheric pressure, rainfall etc are of great demands from all the users, planners, disaster managements personals, tourism etc. The information is required at the place concerned but this cannot be fulfilled by the meteorological community due to absent of observatory at that place and also some time absent of past data of long period. The present paper has described a comparatively new application of the neural network in the field of spatial interpolation. Neural network interpolation models are developed for both maximum and minimum temperatures for all the twelve months. The model utilizes geographical information like latitude, longitude and elevation as inputs to generate normal maximum and minimum temperatures at a place. The performances of the models are compared with the other commonly used method for spatial interpolation.  


MAUSAM ◽  
2021 ◽  
Vol 49 (2) ◽  
pp. 235-246
Author(s):  
N. C. BISWAS ◽  
S. N. DUTTA

Statistical analysis based on past data on probability of normal/excess rainfall, the probable future rainfall for smaller areas at different stages of the monsoon period will serve as an appropriate information system for efficient management of available surface water resource at the state and the national levels. In this paper the author have made an attempt in that direction and have brought out some important features of the monsoon rainfall. It is found that the probability of monthly rainfall becoming normal or excess is high in maximum number of sub-divisions in July and August and is least in September. It is further observed that the normality of rainfall as highly probable to the north of the monsoon trough in July and that to the south of the trough in August besides the west coast. The rainfall extreme values over a long period will be useful in determining the minimum assured and maximum probable future rainfall at different stages of the monsoon period. These information will be valuable to decision makers, managers etc. in their decision making process on real time basis.  


2021 ◽  
Vol 14 (2) ◽  
pp. 224-233
Author(s):  
Eko siswanto ◽  
Eka Satria Wibawa ◽  
Zaenal Mustofa

Forecasting is an estimate of future demand based on several forecasting variables based on historical time series or a process of using historical data (past data) that has been owned to use this model and use this model to estimate future conditions.The Ivori mini market SME group is known to be a mini market that sells daily necessities. The goods provided by the ivori mini market are not focused on only one type of goods, but include all types of goods. Ivori mini market often runs out of stock because there is no inventory planning. The main purpose of making this application is to assist employees in determining inventory planning that must be provided next month. While the method used to make this forecast is a single moving average, one of the time series methods in forecasting. Single Moving Average is a forecasting method that is done by collecting a group of observed values, looking for the average value as a forecast for the future period. The result of this forecasting is to predict the number of sales that will occur in the coming month.


Author(s):  
Prof. Pradnya Mehta ◽  
Onkar Dongare ◽  
Rushikesh Tekale ◽  
Hitesh Umare ◽  
Rutwik Wanve

As India is moving fast towards digital economy, E-commerce industry has been on rise. Many platforms such as Amazon and Flipkart provide their customers with a shopping experience better than actual physical stores. Several E-commerce websites use different methods to improve the customer engagement and revenue. One such technique is the use of personalized recommendation systems which uses customer’s data like interests, purchase history, ratings to suggest new products which they may like. Recommendation systems are used by E-commerce websites to suggest new products to their users. The products can be suggested based on the top merchants on the website, based on the interests of the user or based the past purchase pattern of the customer. Recommender systems are machine learning based systems that help users discover new products. Due to the recent pandemic situation of 2020 and 2021, many of the local retail stores have been trying to shift their business to online platforms such as dedicated websites or social media. The proposed methodology based on Machine Learning aims to enable local online retail business owners to enhance their customer engagement and revenue by providing users with personalized recommendations using past data using methods such as Collaborative Filtering and Content-Based Filtering.


2021 ◽  
Vol 948 (1) ◽  
pp. 012045
Author(s):  
P F Arko ◽  
L I Sudirman ◽  
I Qayim

Abstract Dungus Iwul Nature Reserved (CADI) is a remnant patch of tropical rainforest converted into plantations with neither past data nor study in macrofungi. In this article, we explored and identified macrofungal fruitbody in CADI and PTPN VIII Oil Palm Plantation (PTPN) around the nature reserve. The inventory was carried out with the opportunistic sampling methods assisted by the line intercept. Morphological characteristics were used to identify the macrofungal fruitbodies species. We found that the species richness in the study area stood at 120, with 70 species found in CADI, 23 species in PTPN planted in 2003, and 57 species in PTPN planted in 2004. These 120 species consist of 76 genera, 41 families, 11 orders, and four classes in Subkingdom Dikarya. Schizophyllum commune and Marasmiellus candidus in CADI and S. commune in PTPN planted in 2003 and 2004 were the species found with the highest relative frequency. Neither sign of basal stem rot on oil palm trees nor Ganoderma fruitbodies were found in both PTPN study locations, even though the fruitbodies were found in CADI. Further research is needed to determine if nature reserve could be a barrier against pathogens of monoculture oil palm plantation in a similar landscape model.


Author(s):  
JEAN-LOUP DUPRET ◽  
DONATIEN HAINAUT

Affine Volterra processes have gained more and more interest in recent years. In particular, this class of processes generalizes the classical Heston model and the more recent rough Heston model. The aim of this work is hence to revisit and generalize the constant proportion portfolio insurance (CPPI) under affine Volterra processes. Indeed, existing simulation-based methods for CPPI do not apply easily to this class of processes. We instead propose an approach based on the characteristic function of the log-cushion which appears to be more consistent, stable and particularly efficient in the case of saffine Volterra processes compared with the existing simulation techniques. Using such approach, we describe in this paper several properties of CPPI which naturally result from the form of the log-cushion’s characteristic function under affine Volterra processes. This allows to consider more realistic dynamics for the underlying risky asset in the context of CPPI and hence build portfolio strategies that are more consistent with financial data. In particular, we address the case of the rough Heston model, known to be extremely consistent with past data of volatility. By providing a new estimation procedure for its parameters based on the PMCMC algorithm, we manage to study more accurately the true properties of such CPPI strategy and to better handle the risk associated with it.


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