Journal articles: 'Stock loss'

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Falsetta, Diana, and Richard A. White. "The Impact of Income Tax Withholding Position and Stock Position on the Sale of Stock." Journal of the American Taxation Association 27, no. 1 (March 1, 2005): 1–23. http://dx.doi.org/10.2308/jata.2005.27.1.1.

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The objective of this study is to investigate the effect that stock position (gain or loss) and income tax withholding position (tax payment or tax refund) have on the sale of stock at the end of the year. Prior investigations of stock position have shown that individuals are more likely to sell gain stocks and hold loss stocks (e.g., the disposition effect). However, studies also have found this pattern of behavior to reverse at year-end in an effort to reduce tax liabilities. We conduct two experiments (baseline and primary) to compare the sell or hold decision of participants with either a gain or loss stock. Results of the baseline experiment confirm the disposition effect. However, when participants become more sensitive to tax considerations, the results of the primary experiment support the tax-loss selling hypothesis. That is, participants tend to sell loss stocks and hold gain stocks. These results, while consistent with the tax-loss selling hypothesis, are contrary to the disposition effect, indicating that these effects are strongest when tax considerations are not a primary factor in the decision process. Furthermore, contrary to expectations, participants are not influenced by income tax withholding position. Their propensity to sell loss stocks relative to gain stocks at year-end is the same whether they are faced with a tax payment or a tax refund.

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Sharma, Y. K., Arakshita Majhi, V. S. Kukreti, and M. O. Garg. "Stock loss studies on breathing loss of gasoline." Fuel 89, no. 7 (July 2010): 1695–99. http://dx.doi.org/10.1016/j.fuel.2009.08.006.

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Fadhilah, Penny Rahmah, and Ananta Hagabean. "OPTIMISASI POTENTIAL LOSS PORTOFOLIO SAHAM BLUE CHIPS SYARIAH DENGAN VALUE AT RISK." Jurnal Orientasi Bisnis dan Entrepreneurship (JOBS) 2, no. 2 (November 18, 2021): 72–85. http://dx.doi.org/10.33476/jobs.v2i2.2050.

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The importance of analyzing risk in an investment in order to provide information about the risks that investors will analyze before deciding to carry out their investment activities. In investing in the capital market, investors can form a stock portfolio with stock diversification, namely selecting stocks based on returns and risks and then forming an optimal portfolio of these stocks. The formation of a portfolio determines the assets that will be selected for investment and determines the proportion of the amount of funds that will be placed into the investment. The use of the VaR model will reflect how much loss that might occur if investors invest their funds into stock assets that have been selected based on blue chips shares. Determination of the sample in this study is the shares of sharia-based companies with the blue chip category taken from JII, JII 70 and ISSI. This study chose a time limit for observations from June 2018 to June 2019, which was then retested by validating the model. The results showed that the optimal portfolio of Islamic blue chips stocks after optimization has a smaller risk potential value than the risk of each stock in the VaR calculation. The results of this study can be used as a consideration for investors in determining investments in Islamic-based company stocks

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Evans, David O., and Campbell C. Willox. "Loss of Exploited, Indigenous Populations of Lake Trout, Salvelinus namaycush, by Stocking of Non-Native Stocks." Canadian Journal of Fisheries and Aquatic Sciences 48, S1 (December 19, 1991): 134–47. http://dx.doi.org/10.1139/f91-312.

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A deterministic, age-structured population model was developed to explore the combined effects of exploitation and stocking of hatchery-reared lake trout on native lake trout populations. Stocking of non-native lake trout at moderate to high exploitation rates caused loss of the recipient population, even when the stocked fish did not reproduce or interbreed with the native fish. If hatchery fish reproduce, their progeny mask the loss of the wild stock. At exploitation and stocking rates typical of lake trout populations in Ontario, the wild stock was replaced by the hatchery stock in a few generations. Native stocks having weak recruitment are least resistant to displacement by hatchery stocks and are also the most likely to be subjected to stocking. Evidence from several field studies in Ontario generally provided support for our stock displacement hypothesis and modelling results. Extensive stocking of a few domesticated hatchery stocks has the potential to reduce genetic heterogeneity through loss of locally adapted stocks of lake trout. We suggest that emphasis should be placed on protecting the genetic integrity of the remaining self-sustaining wild stocks, and that a moratorium be considered on supplementation stocking of these populations, especially with non-native hatchery-reared stocks.

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Addinpujoartanto, Nur Ariefin. "ANALYSIS OF JANUARY EFFECT ON BIG STOCK COMPANIES AND SMALL STOCK COMPANIES AT INDONESIA STOCK EXCHANGE." International Journal of Business, Humanities, Education and Social Sciences (IJBHES) 1, no. 2 (December 31, 2019): 47–56. http://dx.doi.org/10.46923/ijbhes.v1i2.40.

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January Effect is one of market anomaly where the stock returns in January are higher than other months. Some of causes the January Effect are the actions of investor who carry out tax-loss selling and windows dressing. In addition, investors have different views to choose stocks, based on market capitalization dan risk. This study is purposed to find the January Effect in the Indonesia Stock Exchange and January Effect on small company stock is stronger than large company stock. The data is normally distributed using the One-Sample Kolmogorov-Smirnov test. The test using the OLS method with dummy variable at five percent significance level. By using a sample of 30 large company stocks and 30 small company stocks based on market capitalization during period 2013-2017, the result of this study found a January Effect in the Indonesia Stock Exchange. But the January Effect doesn’t occur on small company stock, except on large company stock during that periode.

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Ivković, Zoran, James Poterba, and Scott Weisbenner. "Tax-Motivated Trading by Individual Investors." American Economic Review 95, no. 5 (November 1, 2005): 1605–30. http://dx.doi.org/10.1257/000282805775014461.

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We analyze stock trades made by individuals holding stock in both taxable and tax-deferred accounts. By comparing trades across these two types of accounts, we uncover a capital gains lock-in effect in taxable accounts. The lock-in effect is more pronounced for large stock transactions and for stocks held for at least 12 months. Over shorter horizons, the disposition effect outweighs the lock-in effect. Comparison of loss realizations in taxable and tax-deferred accounts yields evidence of tax-loss selling throughout the year. Effective accrual tax rates for stocks that experience substantial appreciation are substantially below the statutory tax rate on long-term gains.

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Fernandez, Kylie, Melissa Merz, Camelia Kuhnen, Joseph Schmidt, and Nichole Lighthall. "Gain/Loss Framing Effects on Learning in Economic Decision Making Investigated with Eye Tracking." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 1166–67. http://dx.doi.org/10.1177/1541931213601775.

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Previous research has revealed a domain-based bias when people estimate payout likelihoods for probabilistic choice options that minimize losses versus those that maximize gains (Kuhnen, 2015). For instance, in economic boom situations, people overestimate how valuable a low profit stock is. Conversely, in economic recession situations, individuals underestimate stocks that minimize losses. Cognitive neuroscience posits that gain and loss information is processed differently in the brain (Knutson and Bossaerts, 2007; Kuhnen and Knutson, 2005), but the precise mechanisms of these domain differences are still unclear. The current study investigated two potential causes of this domain-based bias. Bias may be driven by a high magnitude effect, owing to greater salience for large gains and losses and subsequent overweighting in probability estimations. This would be evidenced by enhanced attention and memory for stimuli associated with high-magnitude dividend choice options and payouts. Domain-based bias in probability estimations could also be driven by incongruence between objective probabilities and dividend payout valence (e.g., “bad” choice options in the gain domain; “good” choice options in the loss domain; valence incongruence effect). If true, we would expect enhanced estimation errors, reaction times (RT), and attention when there is incongruence between the valence of choice payout probabilities and their payouts. To test these hypotheses, 26 students from the University of Central Florida (UCF) participated in an economic decision-making study. The main task involved choosing between pairs of stocks (probabilistic payouts) and bonds (sure-thing payouts). Choice pairs were embedded in either a gain or loss block, with both choice options paying either positive or negative dividends, respectively. Stocks within a block were pseudorandomly drawn from either the “good distribution” (70% high payouts) or the “bad distribution” (30% high payouts). After choosing a security, the stock payout was shown and participants were then asked to estimate the probability that the current stock was drawn from the good distribution. Performance bonus payments were paid based on accurate stock probability estimates and 10% of the total earned from stock/bond choices. The study was approved by the UCF Institutional Review Board. Eye movements were recorded throughout to measure overt visual attention as a potential mechanism of domainbased bias. Measures included the fixation duration on each stimulus (dwell time) and average number of oscillations between choice options to determine when and where one looks (Carpenter and McDonald, 2007). Interest areas were created a priori around each critical stimulus in the choice and stock payout phases. To test memory for choice and stock payout phases, participants completed an incidental memory test at the end of the experiment. Here memory was assessed for fractal images associated with each stock and bond option, as well as face images associated with each stock payout (“stockbrokers”). The critical dependent variables to measure domain-based bias were estimation error, response time, oscillation between choice stimuli, and stimulus dwell time. The impact of memory, attention, and congruence of information on measures of domain-based estimation bias was examined with 2 x2 domain (gain, loss) by dividend payout (high, low payout) repeated-measures analysis of variance models (ANOVA). Mixed effects modeling was used to examine the power of outcome RT and visual dwell time to predict probability estimate bias. Behavioral results. Consistent with the valence incongruence hypothesis, absolute errors for stock payout probabilities were relatively higher when gain-domain stocks had worse expected values (gain stock was “bad”) than associated bonds and when loss-domain stocks had better expected values than associated bonds (loss stock was “good”). In addition, RT during the choice phase was greater in the loss domain, as participants had to update their estimations the stock came from the “good distribution” even though it only lost money. For stock payout RT, the mixed effects model found an interaction of domain, payout magnitude, and outcome RT where the longer participants spent on gain outcome screens, the more positive their bias and the longer they spent on loss outcome screens, the more negative their bias. Results from the two incidental memory test scores did not reveal any main effects or interactions of domain or dividend payout, lessening support for the high magnitude hypothesis. The data provide support for both attentional effects. Eye tracking data. Greater oscillations between stock and bond options at choice was observed in the loss condition, suggesting greater choice uncertainty when stocks lose money. Stimulus dwell times were higher in the loss domain during the choice phase but did not differ by dividend payout. However, the mixed effects model found an interaction of domain and stock dwell times where the longer participants spent on gain information, the more positive their bias and the longer they spent on loss information, the more negative their bias. The mix of results provide support for both attentional effects. The behavioral results were in line with previous research (Kuhnen, 2015). Together with the eye tracking data, the results support the both the valence incongruence and high magnitude effects. We have evidence that one effect influences overall error rate (incongruence) and the other drives the direction of the error (magnitude). Thus, future interventions should consider both effects when seeking to improve decision making.

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Aramrueng, Thanchanok, and Peera Tangtammaruk. "An Experimental Economic Study of Loss Aversion in Stock Trading Decisions." Humanities and Social Sciences Letters 9, no. 4 (December 14, 2021): 417–29. http://dx.doi.org/10.18488/journal.73.2021.94.417.429.

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The disposition effect is a form of behavioral bias that tends to result in investors holding on to their losing stocks for too long and selling winning stocks too soon. It can be explained by the behavioral economics theory of loss aversion. Even though many have studied this kind of behavioral bias in a variety of different countries, none of them have investigated the disposition effect in the case of Thailand. Therefore, the main objective of our study is to test the disposition effect among Thais by applying the experimental economic approaches of Weber & Camerer (1998) and Odean (1998) whilst also including the findings from questionnaires and interviews. We set up a simulation stock trading market to test the disposition effect of participants regardless of whether they had stock trading experienced or not. Subjects were required to trade among six stocks in 14 trading periods. We also added three more periods to test how different types of news impacted the subjects’ trading decisions. In addition, we analyzed socioeconomic factors that affect disposition effect behavior by using an econometric binary choice model. We found that this experiment can exhibit the disposition effect of subjects in terms of overall and individual measurement. In normal stock trading situations, we found that over 70% of subjects showed clear signs of the disposition effect, which seemed to decrease after they received fictional news.

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Verbrigghe, Niel, Niki I. W. Leblans, Bjarni D. Sigurdsson, Sara Vicca, Chao Fang, Lucia Fuchslueger, Jennifer L. Soong, et al. "Soil carbon loss in warmed subarctic grasslands is rapid and restricted to topsoil." Biogeosciences 19, no. 14 (July 20, 2022): 3381–93. http://dx.doi.org/10.5194/bg-19-3381-2022.

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Abstract. Global warming may lead to carbon transfers from soils to the atmosphere, yet this positive feedback to the climate system remains highly uncertain, especially in subsoils (Ilyina and Friedlingstein, 2016; Shi et al., 2018). Using natural geothermal soil warming gradients of up to +6.4 ∘C in subarctic grasslands (Sigurdsson et al., 2016), we show that soil organic carbon (SOC) stocks decline strongly and linearly with warming (−2.8 t ha−1 ∘C−1). Comparison of SOC stock changes following medium-term (5 and 10 years) and long-term (>50 years) warming revealed that all SOC stock reduction occurred within the first 5 years of warming, after which continued warming no longer reduced SOC stocks. This rapid equilibration of SOC observed in Andosol suggests a critical role for ecosystem adaptations to warming and could imply short-lived soil carbon–climate feedbacks. Our data further revealed that the soil C loss occurred in all aggregate size fractions and that SOC stock reduction was only visible in topsoil (0–10 cm). SOC stocks in subsoil (10–30 cm), where plant roots were absent, showed apparent conservation after >50 years of warming. The observed depth-dependent warming responses indicate that explicit vertical resolution is a prerequisite for global models to accurately project future SOC stocks for this soil type and should be investigated for soils with other mineralogies.

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Effendi, Fabiola Dinda, and Tantina Haryati. "Pengaruh Faktor Fundamental Keuangan dan Makro Ekonomi Terhadap Harga Saham pada Perusahaan Badan Usaha Milik Negara (BUMN) yang Terdaftar di Bursa Efek Indonesia (BEI) Periode 2016-2020." Kompak :Jurnal Ilmiah Komputerisasi Akuntansi 15, no. 1 (June 24, 2022): 49–60. http://dx.doi.org/10.51903/kompak.v15i1.601.

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Stock investment in the capital market promises two forms of profit, capital gains and dividends. In addition to high profits, stock investments also have a high risk of loss because stocks have a nature high return-high risk. One of the risks posed is the ups and downs of stock prices that occur at any time can cause losses such as capital loss. The purpose of this study was to determine the effect of ROE, DER, and exchange rates on stock prices in BUMN listed on the Indonesia Stock Exchange (IDX) for the 2016-2020 period. The sampling technique is a purposive sampling technique. The sample obtained amounted to 13 companies. The results of the analysis using SmartPLS 3.0 shows that ROE has a positive and significant effect, DER has a negative and significant effect, while the exchange rate has no significant effect on stock prices.

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Calcagnotto, Daniela, and Silvio de Almeida Toledo-Filho. "Loss of genetic variability at the transferrin locus in five hatchery stocks of tambaqui (Colossoma macropomum)." Genetics and Molecular Biology 23, no. 1 (March 2000): 127–30. http://dx.doi.org/10.1590/s1415-47572000000100023.

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Knowledge and conservation of the genetic variability in stocks maintained as live gene banks have become a high priority task for Brazilian fish culture. The aim of the present survey was to assess the transferrin allelic diversity of five hatchery stocks of tambaqui (Colossoma macropomum). The tambaqui stock from Pentecoste, the oldest maintained in Brazilian hatchery stations, retained three of the six alleles detected in wild populations of tambaqui from the Amazon River. Other hatchery stocks, directly or indirectly derived from the Pentecoste stock, did not show transferrin allelic variability. Insufficient number of founders and genetic drift due to sampling errors seem to be the main causes leading to loss of genetic diversity in tambaqui hatchery stocks. Appropriate management strategies are required in order to improve the genetic potential of tambaqui stocks in Brazil.

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Karman, Jan. "Profit and loss in a stock portfolio." ACM SIGAPL APL Quote Quad 26, no. 3 (March 1996): 8–10. http://dx.doi.org/10.1145/242711.242712.

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Shen, Shih-yu, and Andrew Minglong Wang. "On stop-loss strategies for stock investments." Applied Mathematics and Computation 119, no. 2-3 (April 2001): 317–37. http://dx.doi.org/10.1016/s0096-3003(99)00229-5.

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Karpio, Krzysztof, Magdalena A. Załuska–Kotur, and Arkadiusz Orłowski. "Gain–loss asymmetry for emerging stock markets." Physica A: Statistical Mechanics and its Applications 375, no. 2 (March 2007): 599–604. http://dx.doi.org/10.1016/j.physa.2006.10.003.

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Megawati, Resmawan, Boby Rantow Payu, and Amanda Adityaningrum. "Prediksi Pergerakan Saham Menggunakan Metode Simulasi Monte Carlo untuk Pembentukan Portofolio Optimal dengan Pendekatan Model Markowitz." Jurnal Statistika dan Aplikasinya 6, no. 1 (June 30, 2022): 86–95. http://dx.doi.org/10.21009/jsa.06108.

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Stock movements that follow a stochastic process move randomly at certain times, have led stock prices challenging to predict. For this reason, the monte carlo simulation method is used to get the possibilities of stock prices in the future. This case study focused on the shares listed on the Jakarta Islamic Index 70 in 2018, by simulating 10 times the daily closing price data, thus, the possible stock prices in 2019 were obtained. Portfolio optimization was then carried out using the markowitz model approach from the predicted data. Based on the prediction data, there are 20 stock have a positive expected return. The stocks that has the largest weight is ICBP.JK (Indofood CBP Sukses Makmur Tbk) stocks, with 0.1396, while the stocks with the smallest weight is INAF.JK (Indofarma (Persero) Tbk) at 0.0053. Histirical simulations calculate the Value a Risk of 20 stocks that provide optimal returns, if investors invest Rp. 100,000,000.00 the maximum risk or loss that will be obtained is Rp. 2,910,410.00 for 1 year.

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Wahyono, Wahyono, Chasandra Puspitasari, Muhammad Dzulfikar Fauzi, Kasliono Kasliono, Wahyu Sri Mulyani, and Laksono Kurnianggoro. "An Optimal Stock Market Portfolio Proportion Model Using Genetic Algorithm." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 12, no. 2 (July 31, 2018): 171. http://dx.doi.org/10.22146/ijccs.36154.

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To reduce the amount of loss due to investment risk, an investor or stockbroker usually forms an optimal stock portfolio. This technique is done to get the maximum return of investment on shares to be purchased. However, in forming a stock portfolio required a fairly complex calculations and certain skills. This work aims to provide an alternative solution in the problem of forming the optimal and efficient stock portfolio composition by designing a system that can help decision making of investors or stockbrokers in preparing stock portfolio in accordance with the policy and risk investment. In this work, determination of optimal stock portfolio composition is constructed by using Genetic Algorithm. The data used in this work are the 4 selected stocks listed on the LQ45 index in 2017. Meanwhile, the calculation of profit and loss rate utilizes a single index model theory. The efficiency of the algorithm has been examined against the population size and crossover and mutation probabilities. The experimental results show that the proposed algorithm can be used as one of solutions to select the optimal stock portfolio.

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Lee, Su Jeong, Young Jun Kim, Eugenia Y. Lee, and Ga-young Choi. "Market Reactions to Announcements of Valuation Losses on Conversion Rights Embedded in Convertible Instruments." Journal of Derivatives and Quantitative Studies 28, no. 1 (February 29, 2020): 35–61. http://dx.doi.org/10.37270/jdqs.28.1.2.

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Convertible instruments are financial instruments embedded with conversion rights such as convertible bonds or convertible preferred stocks. Under the Korean International Financial Reporting Standards (K-IFRS), the embedded conversion rights with certain conditions (i.e., a refixing clause) are recognized as derivative liabilities and are recognized at fair value in issuer’s financial statements. Since the value of convertible rights varies with the underlying stock value, an increase in the issuers’ stock price causes the issuers of convertible instruments to announce large derivative valuation losses. Using disclosures under the title of ‘Loss from Derivatives Trading’ from the KOREA EXCHANGE (KRX) during January 2016 through December 2019, this study examines market reactions to the disclosure of valuation losses on conversion rights embedded in convertible instruments. We find the following results. First, abnormal stock returns on the loss announcement date are significantly negative. Second, abnormal trading volumes peak on the loss announcement date. Third, abnormal stock returns persist in the long-term. Collectively, our findings suggest that investors perceive the loss disclosures as negative news, but fail to impound the information into issuer’s stock prices effectively. This study emphasizes the importance of education on convertible instruments and improvement in the disclosure requirements on valuation losses of conversion rights embedded in convertible instruments by providing evidence that investors face difficulty in understanding the related disclosures.

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Husniev, Ilshat, Vladimir Romanenkov, Olga Minakova, and Pavel Krasilnikov. "Modelling and Prediction of Organic Carbon Dynamics in Arable Soils Based on a 62-Year Field Experiment in the Voronezh Region, European Russia." Agronomy 10, no. 10 (October 20, 2020): 1607. http://dx.doi.org/10.3390/agronomy10101607.

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Organic carbon (OC) accumulation in soil mitigates greenhouse gases emission and improves soil health. We aimed to quantify the dynamics of OC stock in soils and to justify technologies that allow annual increasing OC stock in the arable soil layer by 4‰. We based the study on a field experiment established in 1936 in the 9-field crop rotation with a fallow on Chernozem in European Russia. The RothC version 26.3 was used for the reproducing and forecasting OC dynamics. In all fertilizer applications at FYM background, there was a decrease in the OC stock with preferable loss of active OC, except the period 1964–1971 with 2–5‰ annual OC increase. The model estimated the annual C input necessary to maintain OC stock as 1900 kg·ha−1. For increasing OC stocks by 4‰ per year, one should raise input to 2400 kg·ha−1. The simulation was made for 2016–2090 using climate scenarios RCP4.5 and RCP8.5. Crop rotation without fallowing provided an initial increase of 3‰ and 6‰ of stocks in the RCP8.5 and RCP4.5 scenarios accordingly, followed by a loss in accumulated OC. Simulation demonstrates difficulties to increase OC concentration in Chernozems under intensive farming and potential capacity to rise OC stock through yield management.

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Hersugondo, Hersugondo, Imam Ghozali, Eka Handriani, Trimono Trimono, and Imang Dapit Pamungkas. "Price Index Modeling and Risk Prediction of Sharia Stocks in Indonesia." Economies 10, no. 1 (January 6, 2022): 17. http://dx.doi.org/10.3390/economies10010017.

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This study aimed to predict the JKII (Jakarta Islamic Index) price as a price index of sharia stocks and predict the loss risk. This study uses geometric Brownian motion (GBM) and Value at Risk (VaR; with the Monte Carlo Simulation approach) on the daily closing price of JKII from 1 August 2020–13 August 2021 to predict the price and loss risk of JKII at 16 August 2021–23 August 2021. The findings of this study were very accurate for predicting the JKII price with a MAPE value of 2.03%. Then, using VaR with a Monte Carlo Simulation approach, the loss risk prediction for 16 August 2021 (one-day trading period after 13 August 2021) at the 90%, 95%, and 99% confidence levels was 2.40%, 3.07%, and 4.27%, respectively. Most Indonesian Muslims have financial assets in the form of Islamic investments as they offer higher returns within a relatively short time. The movement of all Islamic stock prices traded on the Indonesian stock market can be seen through the Islamic stock price index, namely the JKII (Jakarta Islamic Index). Therefore, the focus of this study was predicting the price and loss risk of JKII as an index of Islamic stock prices in Indonesia. This study extends the previous literature to determine the prediction of JKII price and the loss risk through GBM and VaR using a Monte Carlo simulation approach.

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Yokomatsu, Muneta. "A Commentary on “Recovery from Catastrophe and Building Back Better (Takeuchi and Tanaka, 2016)” – Structure of Damage of Production Capital Stock on Normative Economic Process." Journal of Disaster Research 13, no. 3 (June 1, 2018): 564–70. http://dx.doi.org/10.20965/jdr.2018.p0564.

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In the field of disaster prevention, disaster loss is often classified into “direct loss” and “indirect loss.” As such, “total loss” is often calculated as a sum of “direct loss” and “indirect loss,” where “direct loss” is defined as a “loss of capital (assets) as a stock” and “indirect loss” is defined as “loss arising out of decline in postdisaster production as a flow.” However, the loss here is calculated twice. The calculation is incorrect if “indirect loss” refers to, in particular, the lost profit of a firm that has lost a production facility that is considered as a stock. The reason is that the “value of capital stock” is nothing but the present value of a product that the stock will produce in the future. Therefore, an “indirect loss” defined in the above manner corresponds to a decrease in stock value. Using a dynamic economic model, this article provides a basic structure, “value of loss in capital stock lost by a disaster” = “total decline of production after a disaster.” This article also presents a relational expression in consideration of the restoration cost of a production facility, and concludes that a more multifaceted and functional damage information system needs to be developed in the future.

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Dodonova, Anna, and Yuri Khoroshilov. "Optimal Incentive Contracts for Loss-Averse Managers: Stock Options versus Restricted Stock Grants." Financial Review 41, no. 4 (November 2006): 451–82. http://dx.doi.org/10.1111/j.1540-6288.2006.00153.x.

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Berument, M. Hakan, and Nildag Basak Ceylan. "Soccer and Stock Market Risk: Empirical Evidence from the Istanbul Stock Exchange." Psychological Reports 112, no. 3 (June 2013): 763–70. http://dx.doi.org/10.2466/17.05.pr0.112.3.763-770.

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There is an emerging but important literature on the effects of sport events such as soccer on stock market returns. After a soccer team's win, agents discount future events more favorably and increase risk tolerance. Similarly, after a loss, risk tolerance decreases. This paper directly assesses risk tolerance after a sports event by using daily data from the three major soccer teams in Turkey (Beşiktaş, Fenerbahçe and Galatasaray). Results provide evidence that risk tolerance increases after a win, but similar patterns were not found after a loss.

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Liu, Jie, Xuanmei Fan, Xiaolu Tang, Qiang Xu, Erin L. Harvey, Tristram C. Hales, and Zhangdong Jin. "Ecosystem carbon stock loss after a mega earthquake." CATENA 216 (September 2022): 106393. http://dx.doi.org/10.1016/j.catena.2022.106393.

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Azar, Samih Antoine. "LOSS AVERSION IS CONSISTENT WITH STOCK MARKET BEHAVIOR." International Journal of Accounting & Finance Review 5, no. 4 (November 25, 2020): 60–73. http://dx.doi.org/10.46281/ijafr.v5i4.893.

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The purpose of this paper is to verify that discrete statistical distributions of the US stock market are consistent with loss aversion. Loss aversion has the following tenets: an S-shaped valuation function, characterized by diminishing sensitivity, a loss aversion coefficient higher than +1, probability weighting, and reference-dependence. Diminishing sensitivity implies that the exponent of the valuation function is between 0 and +1. It is expected that this exponent be higher for losses. Probability weighting replaces objective with subjective probabilities. Loss aversion is indicated by a coefficient higher than +1 for the valuation of losses. There are three parameters: the two exponents of the valuation function, and the loss aversion coefficient. There is one non-linear equation: the certainty equivalence relation. The procedure is to fix two parameters and find the third parameter by solving the non-linear certainty equivalence equation, using the EXCEL spreadsheet. The program is repeated for more than one case about the fixed parameters, and by enriching the analysis with probability weighting. The calibrations executed point strongly to the conclusion that loss aversion is consistent with six discrete distributions of the first two moments of returns of the US stock markets. The calibration process provides for reasonable estimates of the key parameters of loss aversion. These estimates suggest a more pronounced diminishing sensitivity, and a higher than expected coefficient of loss aversion, especially when probability weighting is imposed.

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Barberis, Nicholas, and Ming Huang. "Mental Accounting, Loss Aversion, and Individual Stock Returns." Journal of Finance 56, no. 4 (August 2001): 1247–92. http://dx.doi.org/10.1111/0022-1082.00367.

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Mckenzie, George. "Loan-loss provisions and bank buffer-stock capital." Applied Financial Economics 6, no. 3 (June 1996): 213–23. http://dx.doi.org/10.1080/096031096334231.

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Learmonth, I. D., G. P. Grobler, D. M. Dall, and V. Jandera. "Loss of bone stock with cementless hip arthroplasty." Journal of Arthroplasty 10, no. 3 (June 1995): 257–63. http://dx.doi.org/10.1016/s0883-5403(05)80171-8.

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Vihola, Jaakko, Jaakko Sorri, Juhani Heljo, and Paavo Kero. "Heat Loss Rate of the Finnish Building Stock." Procedia Economics and Finance 21 (2015): 601–8. http://dx.doi.org/10.1016/s2212-5671(15)00218-x.

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Agrawal, P. K. "Determining stock-sheet-sizes to minimise trim loss." European Journal of Operational Research 64, no. 3 (February 1993): 423–31. http://dx.doi.org/10.1016/0377-2217(93)90131-6.

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Pradaswara, Hazelino Rafi, Dwi Susanti, and Sukono Sukono. "Company Stock Performance Analysis on IDX ESG Leaders Index Using the ARIMA-GARCH Model." International Journal of Quantitative Research and Modeling 3, no. 3 (September 3, 2022): 133–37. http://dx.doi.org/10.46336/ijqrm.v3i3.347.

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Stocks are one of the most popular forms of investment. In investing stocks, it is necessary to know the movement of stock prices and the investment risks that may occur. The purpose of this study is to predict the level of risk, see the characteristics of stock returns, and whether the ESG Risk Rating makes the company's stock performance better. The models used to predict stock returns are Auto Regressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticty (GARCH), and Value at Risk (VaR) is used to predict risk. Based on the research, the potential loss for Bank BCA is IDR29.800.000,00 and Bank Mandiri is IDR33.600.000,00 with the assumption that an investor invests as much as IDR1.000.000.000,00. In addition, Bank BCA has a lower ESG Risk Rating than Bank Mandiri, but has a better performance.

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Mahtab, Md Tanvir, A. G. M. Zaman, Montasir Rahman Mahin, Mohammad Nazim Mia, and Md. Tanjirul Islam. "Stock Price Prediction: An Incremental Learning Approach Model of Multiple Regression." AIUB Journal of Science and Engineering (AJSE) 21, no. 3 (December 31, 2022): 159–66. http://dx.doi.org/10.53799/ajse.v21i3.490.

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The endeavour of predicting stock prices using different mathematical and technological methods and tools is not new. But the recent advancements and curiosity regarding big data and machine learning have added a new dimension to it. In this research study, we investigated the feasibility and performance of the multiple regression method in the prediction of stock prices. Here, multiple regression was used on the basis of the incremental machine learning setting. The study conducted an experiment to predict the closing price of stocks of six different organizations enlisted in the Dhaka Stock Exchange (DSE). Three years of historical stock market data (2017-2019) of these organizations have been used. Here, the Multiple Regression, Squared Loss Function, and Stochastic Gradient Descent (SGD) algorithms are used as a predictor, loss function, and optimizer respectively. The model incrementally learned from the data of several stock-related attributes and predicted the closing price of the next day. The performance of prediction was then analysed and assessed on the basis of the rolling Mean Absolute Error (MAE) metric. The rolling MAE scores found in the experiment are quite promising.

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Miasary, Seftina Diyah. "PENERAPAN VECTOR AUTOREGRESSIVE (VAR) DALAM MEMPREDIKSI RETURN SAHAM DI INDONESIA." Jurnal Edukasi dan Sains Matematika (JES-MAT) 8, no. 2 (September 30, 2022): 171–80. http://dx.doi.org/10.25134/jes-mat.v8i2.6225.

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The rate of return (return) and risk are inseparable in investing activities. One equilibrium model that describes the relationship between return and risk assumes that the expected return is influenced by more than one macroeconomic factor. Furthermore, the causal relationship between stock returns and macroeconomic factor returns was analyzed using VAR. The application of VAR in this study is to predict stock returns through the stages of checking data stationarity, determining the optimal lag length, testing Granger causality between variables, estimating VAR model parameters and Portmanteau diagnostic tests, and predicting stock returns. The results show that the VAR (1) model is the most appropriate model to describe the relationship between stock returns and macroeconomic factor returns with a significant model owned by BBCA, ICBP, INTP, KLBF, and SMGR stocks. Furthermore, the VAR (1) model is used to predict the five stock returns. The prediction results show that INTP's stock returns are negative while the returns of the other four stocks are positive. This shows that INTP shares experienced a capital loss, while the stock returns of BBCA, ICBP, KLBF, and SMGR experienced capital gains

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Sawers, Kimberly, Arnold Wright, and Valentina Zamora. "Does Greater Risk-Bearing in Stock Option Compensation Reduce the Influence of Problem Framing On Managerial Risk-Taking Behavior?" Behavioral Research in Accounting 23, no. 1 (January 1, 2011): 185–201. http://dx.doi.org/10.2308/bria.2011.23.1.185.

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ABSTRACT: We examine the extent to which the behavioral agency model reflects the relation between greater risk-bearing in stock option compensation and managerial risk-taking. The behavioral agency model predicts that managers with greater wealth at stake will avoid risky projects that threaten their wealth. This greater risk-bearing effect moderates the problem-framing effect, which predicts that loss-averse managers will be more (less) risk-taking when choosing among loss (gain) projects. Using a 2 × 2 between-subjects experiment with 108 M.B.A. students acting as managers, we find that managers are more risk-taking in the loss context than in the gain context when they have at-the-money stock options but not when they have wealth at stake through in-the-money stock options. Further, we find that managers with in-the-money stock options are less risk-taking than managers with at-the-money stock options in the loss context. These findings support the behavioral agency model prediction that greater risk-bearing in stock option compensation (moving from at-the-money stock options to in-the-money stock options) reduces the problem framing effect on risk-taking behavior, particularly when the firm faces a loss decision context. Our results point to the importance of considering the implications of risk-bearing in stock option compensation for managers choosing risky projects that affect firm value.

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Whitlock, R. E., T. Pakarinen, S. Palm, M. L. Koljonen, J. Östergren, and J. Dannewitz. "Trade-offs among spatio-temporal management actions for a mixed-stock fishery revealed by Bayesian decision analysis." ICES Journal of Marine Science 78, no. 10 (November 11, 2021): 3625–38. http://dx.doi.org/10.1093/icesjms/fsab203.

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Abstract Management and conservation of populations that are harvested simultaneously present a unique set of challenges. Failure to account for differences in productivity and spatio-temporal abundance patterns can lead to over-exploitation of depleted populations and/or loss of potential yield from healthy ones. Mixed-stock fisheries (where a stock may comprise one or more populations of reared or wild origin) harvest multiple stocks, often in unknown proportions, and lack of tools for estimation of stock-specific harvest rates can hamper status evaluations and attainment of management goals. We present a method for evaluating stock-specific impacts of alternative harvest strategies, using coastal trap net fisheries for Atlantic salmon (Salmo salar) in the Baltic Sea as a case study. Our results demonstrate a large variation among stocks in coastal mixed fishery harvest rates, as well as large differences in harvest rates relative to stock-specific maximum sustainable yield (MSY) and recovery levels. Bayesian decision analysis showed that spatio-temporal management actions, such as delayed fishery opening and closed areas may be effective in improving probabilities of meeting management objectives for Baltic salmon. However, stocks did not respond uniformly to different management actions, highlighting the potential for trade-offs in reaching stock-specific targets that must be considered by managers.

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Hidayana, Rizki Apriva, Herlina Napitupulu, and Sukono Sukono. "Determination of Value-at-Risk in UNVR Stocks Using ARIMA-GJR-GA RCH Model." Operations Research: International Conference Series 2, no. 4 (December 6, 2021): 89–92. http://dx.doi.org/10.47194/orics.v2i4.181.

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Stocks are investment instruments that are in great demand by investors as a basis for storing finances. The most important thing in investing is the return and risk of loss obtained from investing in stocks. Risk measurement is carried out using Value-at-Risk and Conditional Value-at-Risk. The stock movements used are historical data and in the form of time series, so that a model can be formed to predict the next movement of stocks and risk measurements can be carried out. The purpose of this study is to determine the value of risk obtained by investors using time series analysis. The data used in this study is the daily closing price of stocks for 3 years. The stages of the analysis carried out to predict stock movements are to determine the ARIMA model for the mean model and the GJR-GARCH model for the volatility model. The mean value and variance are used to calculate the risk value of VaR. Based on the results of the Value-at-Risk calculation obtained, UNVR shares have a risk value of 0.01217. This means that if an investment is made in UNVR shares of IDR 100,000,000.00, the estimated maximum loss of potential loss that occurs is estimated to reach IDR 1,217,000.

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Djakaria, Ismail, Fenly B. Mohamad, and Djihad Wungguli. "Optimasi Trim Loss menggunakan Integer Linear Programming pada Cutting Stock Problem untuk industri meubel (Studi Kasus pada UD. Flybers)." AKSIOMA : Jurnal Matematika dan Pendidikan Matematika 12, no. 1 (April 22, 2021): 80–108. http://dx.doi.org/10.26877/aks.v12i1.7002.

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Trim loss merupakan kerugian yang timbul dari hasil pemotongan yang tidak optimal. Trim loss dipengaruhi beberapa faktor salah satunya yaitu peletakan pola pemotongan yang kurang tepat. Trim loss dapat diselesaikan dengan beberapa metode salah satunya menggunakan metode cutting stock. cutting stock digunakan pada pengoptimalan pemotongan sisa material yang tidak dapat digunakan lagi. Pada cutting stock dipengaruhi oleh masalah pola pemotongan disebut cutting stock problem(CSP). CSP dapat diselesaikan dengan menggunakan pendekatan integer linear programming (ILP). ILP adalah salah satu model dalam program linear yang variabel keputusannya berbentuk bilangan positif atau nol.

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Kallah-Dagadu, Gabriel, Victor Apatu, Felix Okoe Mettle, Dennis Arku, and Godwin Debrah. "Application of Markov Chain Techniques for Selecting Efficient Financial Stocks for Investment Portfolio Construction." Journal of Applied Mathematics 2022 (August 13, 2022): 1–9. http://dx.doi.org/10.1155/2022/2863302.

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In this paper, we apply Markov chain techniques to select the best financial stocks listed on the Ghana Stock Exchange based on the mean recurrent times and steady-state distribution for investment and portfolio construction. Weekly stock prices from Ghana Stock Exchange spanning January 2017 to December 2020 was used for the study. A three-state Markov chain was used to estimate the transition matrix, long-run probabilities, and mean recurrent times for stock price movements from one state to another. Generally, the results revealed that the long-run distribution of the stock prices showed that the constant state recorded the highest probabilities as compared to the point loss and point gain states. However, the results showed that the mean recurrent time to the point gain state ranges from three weeks to thirty-five weeks approximately. Finally, Standard Chartered Bank, GCB, Ecobank, and Cal Bank emerged as the top best performing stocks with respect to the mean recurrent times and steady-state distribution, and therefore, these equities should be considered when constructing asset portfolios for higher returns.

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Hersugondo, Hersugondo, Endang Tri Widyarti, Di Asih I. Maruddani, and Trimono Trimono. "ASEAN-5 Stock Price Index Valuation after COVID-19 Outbreak through GBM-MCS and VaR-SDPP Methods." International Journal of Financial Studies 10, no. 4 (November 30, 2022): 112. http://dx.doi.org/10.3390/ijfs10040112.

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In the economic globalization era, mainly since 2010, ASEAN countries’ financial and investment sectors have emerged to accelerate economic growth. The driving factor for the financial sector’s contribution is the public’s growing interest in financial asset investment products, of which the most chosen one in ASEAN is stocks. However, the COVID-19 pandemic at the end of 2019 affected the growth of stock investments, causing market conditions to be unstable. People held back their interest in investing in stocks because they thought this condition would bring significant losses. Therefore, in this study, the ASEAN-5 stock price index was evaluated to analyze the general stock price conditions for each stock market in the new standard era. The valuation included price predictions and risk of loss using the GBM-MCS and VaR-VC models. The results showed that the GBM-MCS model was more accurate than the GBM model because it had a more stable MAPE value. Referring to the VaR-VC value, the prediction of losses in the ASEAN topfive stock markets for 21–25 April 2022 ranged from 1% to 15%.

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Anwar, Chairil. "PENGARUH POSISI KEUANGAN, LABA RUGI, PERUBAHAN EKUITAS DAN ARUS KAS TERHADAP RETURN SAHAM PADA PERUSAHAAN SEKTOR AGRICULTURE YANG TERCATAT DI BEI PERIODE 2017 – 2019." GREENOMIKA 2, no. 2 (December 17, 2020): 122–29. http://dx.doi.org/10.55732/unu.gnk.2020.02.2.5.

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This study aims to determine: (1) Effect of Financial Position on Stock Returns for 2017-2019 period, (2) Effect of Profit and Loss on Stock Returns for 2017-2019 period, (3) Effect of Changes in Equity on Stock Returns for 2017-2019 period, (4) Effect of Cash Flow on Stock Returns for 2017-2019 period, (5) Effect of Financial Position, Profit and Loss, Changes in Equity and Cash Flow simultaneously on Stock Returns for 2017-2019 period. The data in this study are secondary data obtained from the annual financial statements of agriculture sector companies in the Indonesian Stock Exchange (IDX). This type of research is quantitative research. This research was conducted using a sample of 16 companies listed on the Indonesia Stock Exchange for the 2017-2019 period. Data analysis techniques used are descriptive statistics, classic assumption tests, multiple linear regression analysis, hypothesis testing and coefficient of determination tests. Based on hypothesis testing using the t test, it was concluded that the Financial Position, Profit and Loss, Changes in Equity and Cash Flow did not have a significant effect on Stock Return partially. Based on the F test it was concluded that the Financial Position, Profit and Loss, Changes in Equity and Cash Flow did not significantly influence the Stock Return simultaneously.

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Simanjuntak, Alberto. "Application of ARIMA-GARCH Model to Estimating Expected Shortfall in BMRI Stocks." Operations Research: International Conference Series 1, no. 4 (December 3, 2020): 100–105. http://dx.doi.org/10.47194/orics.v1i4.149.

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Stocks are one of the best-known forms of investment and are still used today. In stock investment, it is necessary to know the movement and risk of loss that may be obtained from the stock investment so that investors can consider the possibility of profit. One way of calculating risk is to use the Expected Shortfall (ES). Because the stock movement is in the form of a time series, a model can be formed to predict the movement of the stock which can then be used for ES calculations using time series analysis. The purpose of the study was to determine the expected shortfall value of BMRI shares using time series analysis. The data used for this research is the daily closing price of shares for three years. In the time series analysis stage, the models used in predicting stock movements are Autoregressive Integrated Moving Average (ARIMA) for the mean model and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) for the volatility model. The average value and variance obtained from the model are then used in calculating the ES on BMRI stock. Based on the results of the study, it was obtained that BMRI's stock had an ES of 0.008116. This means if an investment is made for BMRI shares of IDR 1,000,000.00 for 37 days (5% of 751 days) for an investment period with a 95% confidence level, the expected loss to be borne by the investor is IDR 8,116.00.

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Saputra, Jumadil, and Alberto Simanjuntak. "Estimating the Expected Shortfall in MYOR Stock Using the ARIMA-GARCH Model." International Journal of Global Operations Research 2, no. 3 (August 7, 2021): 118–23. http://dx.doi.org/10.47194/ijgor.v2i3.89.

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Stocks are one of the best-known forms of investment and are still used today. In stock investment, it is necessary to know the movement and risk of loss that may be obtained from the stock investment so that investors can consider the possibility of profit. One way of calculating risk is to use the Expected Shortfall (ES). Because the stock movement is in the form of a time series, a model can be formed to predict the movement of the stock which can then be used for ES calculations using time series analysis. The purpose of the study was to determine the expected shortfall value of MYOR shares using time series analysis. The data used for this research is the daily closing price of shares for three years. In the time series analysis stage, the models used in predicting stock movements are Autoregressive Integrated Moving Average (ARIMA) for the mean model and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) for the volatility model. The average value and variance obtained from the model are then used in calculating the ES on MYOR stock. Based on the results of the study, it was obtained that MYOR's stock had an ES of 0.050772. This means if an investment is made for MYOR shares of IDR 1,000,000.00 for 37 days (5% of 751 days) for an investment period with a 95% confidence level, the expected loss to be borne by the investor is IDR 50,772.00.

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Suryani, Ani Wilujeng, and Karina Dian Pertiwi. "Lombok’s Tsunami and Stock Abnormal Returns." Accounting Analysis Journal 10, no. 1 (February 24, 2021): 1–8. http://dx.doi.org/10.15294/aaj.v10i1.42584.

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Natural disaster often brings damage to the economy, including the decrease of stock’s market value. For this reason, this study aims to determine the effect of the tsunami earthquakes in Lombok in 2018 on abnormal returns and cumulative abnormal returns of insurance companies. This study used the event study approach, with three days window period after the three tsunami earthquakes from July to August 2018. The sample of this study is the stock price of 14 insurance companies listed on the Indonesia Stock Exchange. To test whether abnormal return exists, a one-sample t-test was used on the average abnormal and cumulative returns. The results show that the tsunami earthquake disasters in Lombok in 2018 have a significant effect on cumulative abnormal returns of insurance companies stocks, and this effect even bigger on the third tsunami. This finding shows that the market reacts to continuous disaster by considering the earthquake as negative information and thus decrease the stock price. This study implies that investors may buy the stocks after the disaster to get a cheaper price or hold the stocks to avoid loss. Keywords: abnormal return; event study; Lombok tsunami earthquake; signaling theory

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Ekramol, Islam, Sharif Uddin, and Mohammad Ataullah. "Stochastic inventory models with reworks." Yugoslav Journal of Operations Research 28, no. 4 (2018): 567–78. http://dx.doi.org/10.2298/yjor170922027i.

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In this paper, two stocks, for fresh and the returned things, are considered for the efficient stock management. Hence, we give two models: the first is for non-perishable and the second for perishable things. In addition, inventories kept in the stock may lose their fairly estimated worth, which we additionally viewed in model-II, for example, PC and versatile embellishments, or most current engine autos. In model-II, the stock decay (a loss of significant worth) in a steady rate ? is chosen arbitrarily. Though the models are more fitting where guarantees are accommodated to a settled time length after the deal for new things was made, they can be used to separate characteristics of a stock system for a broad scale production firm. It is expected that the stock level for both new and the returned things are pre-decided. When the stock level scopes at the re-order point s, a request for renewal is put with parameter for new things. The requests for both new and the returned things take after the Poisson process with parameter ? & ?, respectively. Service will be given according to Poisson process for returned things with parameter ?. The joint probability distribution for both returned and new things are derived in the steady state examination. A few system characteristics of two models are inferred here and the outcomes are outlined, based on some numerical cases.

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Knibb, W. R., J. S. F. Barker, and J. G. Oakeshott. "The genetics of abnormal abdomen, incomplete abdomen, and bobbed in Drosophila buzzatii." Genome 32, no. 5 (October 1, 1989): 754–61. http://dx.doi.org/10.1139/g89-508.

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Five stocks of Drosophila buzzatii with superficially similar abdominal disruptions including partial tergite and sternite loss were isolated by inbreeding. Three of the stocks have indistinguishable phenotypes, the inheritance of which is maternally influenced. This phenotype and its mode of inheritance bear similarities with those of Abnormal abdomen in D. melanogaster. The phenotype in the fourth stock is slightly different and is due to a single autosomal recessive gene, which we denote incomplete abdomen. In the fifth stock the trait is limited to females, and in appearance and mode of inheritance resembles bobbed in D. melanogaster. Furthermore, only in this stock are rDNA deletions evident. The combined frequencies of the three types of abdominal aberration were found to be around 1% in several samples from wild and laboratory populations of D. buzzatii.Key words: Drosophila, abnormal abdomen, ribosomal DNA, selection.

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Kang, Yun, and Stanley B. Gershwin. "Information inaccuracy in inventory systems: stock loss and stockout." IIE Transactions 37, no. 9 (September 2005): 843–59. http://dx.doi.org/10.1080/07408170590969861.

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Yanai, Aurora Miho, Euler Melo Nogueira, Paulo Maurício Lima de Alencastro Graça, and Philip Martin Fearnside. "Deforestation and Carbon Stock Loss in Brazil’s Amazonian Settlements." Environmental Management 59, no. 3 (October 24, 2016): 393–409. http://dx.doi.org/10.1007/s00267-016-0783-2.

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Saputra, Wisnowan Hendy, and Ika Safitri. "Expansion of Stock Portfolio Risk Analysis Using Hybrid Monte Carlo-Expected Tail Loss." Jurnal Varian 5, no. 2 (April 26, 2022): 149–60. http://dx.doi.org/10.30812/varian.v5i2.1813.

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Monte Carlo-Expected Tail Loss (MC-ETL) is the new expansion method that combines simulation and calculation to measure investment risk. This study models US stock prices using ARIMA-GARCH and forms an optimized portfolio based on Multi-Objective that aims to analyze the portfolio investment return. The next portfolio return will be simulated using the Monte Carlo (MC) method, measured based on the Expected Tail Loss (ETL) calculation. The optimized portfolio comprises 5 US stocks from 10 years of data, with the biggest capitalization market on February 25, 2021. MSFT has the most considerable weight in the optimized portfolio, followed by GOOG, AAPL, and AMZN, whereas TSLA shares have negligible weight. Based on the simulation result, the optimized portfolio has the smallest ETL value compared to its constituent stocks, which is ±0.029 or about 2.9%. This value means that the optimized portfolio is concluded as an investment choice for investors with a low level of risk.

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Ling, Pang Wen. "Creation of Stock Analysis System Depend on the Prediction Research of Individual Stock Price Behavior by Moving Average Method." Advanced Materials Research 601 (December 2012): 547–53. http://dx.doi.org/10.4028/www.scientific.net/amr.601.547.

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The Taiwan stock market has millions of investors. If each investor spends $5,000 NTD each month on a professional technical analysis system and it is conservatively estimated 100,000 investors would use the system, the market potential will be $500 million NTD. If we can seek out irregular general rules of stock price data on stock market and further make predictions to some extent, the return on investment may be effectively raised, and investment loss reduced for the investors. This study will take Taiwan stock market as data source and use Moving Average Method (MA) to conduct study and analysis of stochastic data of stock price. One stocks (TSMC) typical in Taiwan’s industries and with large-cap index weights are studied to explore whether there are any specific changes and regularities in seemingly irregular stock prices.. We should depend on the front research to develop an effective stock trading analysis system, and we can help investors obtain profits. At the same time, the system users can also make more considerable profits.

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Butts, Christopher L., and John S. Smith. "Shrinkage of Farmers Stock Peanuts During Storage1." Peanut Science 22, no. 1 (January 1, 1995): 33–41. http://dx.doi.org/10.3146/pnut.22.1.0008.

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Abstract A3-yr study was conducted during crop years 1989–91 to determine the value loss (shrinkage) experienced during long-term storage of farmers stock peanuts. Typical warehouses used to store each of the three major peanut market types were selected in each of the peanut production regions of the United States. Peanuts were purchased and stored according to conventional operating procedures for at least 60 d, unloaded, and hauled to the shelling plant (regrade). The loan value of the peanuts was determined using the 1990 Peanut Loan Schedule based on official grades and weights at load-in, bailout, and the shelling plant. Data from samples shelled at the National Peanut Research Laboratory Pilot Shelling Plant were used to determine the difference between outturns estimated from the official grade and the actual shelling outturns after storage. The 3-yr average change in value from load-in to regrade for runner peanuts was −2.04%; Spanish peanuts was −2.81%; and Virginia peanuts was −4.17%. Increases in foreign material and loose shelled kernels due to handling during loading and unloading, and changes in the kernel size distribution contributed equally to the value loss for the runner peanuts. Moisture loss to levels below 7% was responsible for approximately 20% of the value loss in both the Spanish and Virginia peanuts. Redistribution of kernel size, primarily the increase in split kernels, resulted in approximately 25% of the loss experienced in virginia-type peanuts. An increase in foreign material percentage accounted for approximately 50% of the Virginia peanut value loss. Increased foreign material and loose shelled kernels accounted for approximately 66% of the loss experienced in the Spanish peanuts. The total kernel weight obtained when the peanuts were shelled averaged 1.5% less than that estimated from the official grades at the time of original purchase. All market types had decreased outturn of whole edible kernels and increased split kernels. A net decrease in edible kernel outturns of 1.3% was observed.

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Simanjuntak, Alberto. "Estimating Value-at-Risk in TLKM Shares using the ARIMA-GARCH Model." Operations Research: International Conference Series 1, no. 3 (September 4, 2020): 86–91. http://dx.doi.org/10.47194/orics.v1i3.145.

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Stocks are one of the best-known forms of investment and are still used today. In stock investment, it is necessary to know the movement and risk of loss that may be obtained from the stock investment, so that investors can consider the possible losses. One way to calculate risk is to use Value-at-Risk (VaR). Since the stock movement is in the form of a time series, a model can be formed to predict the movement of the stock, which can then be used for VaR calculations using time series analysis. The purpose of the study was to determine the Value-at-Risk of Telekomunikasi Indonesia Persero Tbk.’s (TLKM) shares using time series analysis. The data used for this research is the daily closing price of shares for three years. At the time series analysis stage, the models used in predicting stock movements are Autoregressive Integrated Moving Average (ARIMA) for the mean model and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) for the volatility model. The average and variance values obtained from the model are then used in calculating the VaR of TLKM shares. Based on the results of the study, it was found that the TLKM stock has a VaR of 0.022876. In other words, if an investment of IDR 1,000,000.00 is made for TLKM shares for 37 days (5% of 751 days), the investment period with a 95% confidence level, the maximum loss that may be borne by the investor is IDR 22.876.00.

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Journal articles on the topic 'Stock loss'

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