scholarly journals Forecasting Nestle Stock Price by using Brownian Motion Model during Pandemic Covid-19

2021 ◽  
Vol 7 (2) ◽  
pp. 58-64
Author(s):  
Siti Raihana Hamzah ◽  
Hazirah Halul ◽  
Assan Jeng ◽  
Umul Ain’syah Sha’ari
Keyword(s):  
Brownian Motion ◽  
Stock Prices ◽  
Stock Price ◽  
Investment Decision ◽  
Primary Objective ◽  
Percentage Error ◽  
Economic Downturn ◽  
Term Prediction ◽  
Investment Decision Making ◽  
Short Term Prediction

In the modern financial market, investors have to make quick and efficient investment decisions. The problem arises when the investor does not know the right tools to use in investment decision making. Different tools can be implemented in trading strategies to predict future stock prices. Therefore, the primary objective of this paper is to analyse the performance of the Geometric Brownian Motion (GBM) model in forecasting Nestle stock price by assessing the performance evaluation indicators. To analyse the stocks, two software were used, namely Microsoft Excel and Python.  The model is trained for 16 weeks (4 months) of data from May to August 2019 and 2020. The simulated sample is for four weeks (1 month) which is for September 2019 and 2020. The findings show that during the Pandemic Covid-19, short-term prediction using GBM is more efficient than long-term prediction as the lowest Mean Square Error (MSE) value is at one week period.  In addition, the Mean Absolute Percentage Error (MAPE) for all GBM simulations is highly accurate as it shows that MAPE values are less than 10%, indicating that the GBM method can be used to predict Nestle stock price during an economic downturn.

Pengaruh Rasio Keuangan terhadap Perubahan Harga Saham

2006 ◽  
Vol 1 (1) ◽  
pp. 106
Author(s):  
Umi Murtini ◽  
Shinta Mareta
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Investment Decision ◽  
Profit Margin ◽  
Financial Statement ◽  
Return On Assets ◽  
Stock Investment ◽  
Investment Decision Making ◽  
Net Profit ◽  
Equity Ratio

One factor that supports investors' trust on capital market istheir perception to the fiuingness of stock price. The more appropriate and quicker the information reflected by stock price delivered to investors, the more fficient the stock market. fhe information needed from the firm's financial statement if it's tooked from investor needs who would purchase a stock are stock price information, earning per sltare, total asset, earning after tax, net sale, total liabitity, and totat equity which is used to the company financing source. This research aims toexamine the effect of Price Earning Ratio (PER), Return on Assets (RoA), Net Profit Margin (NPM), Debt Equity Ratio (DER) changes to stock price changes either partially or simultaneously. This research proves that Price Earning ratio (PER) and Net Profit Margin (NpM) changes partially influence the change of stock price, whereas those four variables simultaneously influence the change of stock price. Thisresearch hopefully could give benefits to investors, emitens, and other partles as additional evaluating tools in the relation with the process of stock investment decision making when stock prices are fluctuative. This research is hopefully beneficial for emitens in making a wisdom relating to PER, RoA, NPM, and DER and can give additional lorcwledge and information for parties who need reference as well as literqture about financial management.Keywords: closing price, EAT, PER, DER, NPM, ROA

scholarly journals The Geometric Brownian Motion of Indosat Telecommunications Daily Stock Price During the Covid-19 Pandemic in Indonesia

2021 ◽  
Vol 2084 (1) ◽  
pp. 012012
Author(s):  
Tiara Shofi Edriani ◽  
Udjianna Sekteria Pasaribu ◽  
Yuli Sri Afrianti ◽  
Ni Nyoman Wahyu Astute
Keyword(s):  
Brownian Motion ◽  
Stock Price ◽  
Service Providers ◽  
Main Idea ◽  
Geometric Brownian Motion ◽  
Government Policies ◽  
Percentage Error ◽  
Price Movement ◽  
Data Movement ◽  
Total Data

Abstract One of the major telecommunication and network service providers in Indonesia is PT Indosat Tbk. During the coronavirus (COVID-19) pandemic, the daily stock price of that company was influenced by government policies. This study addresses stock data movement from February 5, 2020 to February 5, 2021, resulted in 243 data, using the Geometric Brownian motion (GBM). The stochastic process realization of this stock price fluctuates and increases exponentially, especially in the 40 latest data. Because of this situation, the realization is transformed into log 10 and calculated its return. As a result, weak stationary in variance is obtained. Furthermore, only data from December 7, 2020 to February 5, 2021 fulfill the GBM assumption of stock price return, as R t 1 * , t 1 * = 1 , 2 , 3 , … , 40 . The main idea of this study is adding datum one by one as much as 10% – 15% of the total data R t 1 * , starting from December 4, 2020 backwards. Following this procedure, and based on the 3% < p-value < 10%, the study shows that its datum can be included in R t 1 * , so t 1 * = − 4. − 3 , − 2 , … , 40 and form five other data groups, R t 2 * , … , R t 6 * . Considering Mean Absolute Percentage Error (MAPE) and amount of data from each group, R t 6 * is selected for modelling. Thus, GBM succeeded in representing the stock price movement of the second most popular Indonesian telecommunication company during COVID-19 pandemic.

Usec Inc.

2017 ◽  
pp. 1-11
Author(s):  
Kenneth M. Eades ◽  
Ben Mackovjak ◽  
Lucas Doe
Keyword(s):  
Stock Price ◽  
Capital Investment ◽  
Net Present Value ◽  
Cost Of Capital ◽  
Investment Decision ◽  
Weighted Average ◽  
Cash Flows ◽  
Primary Objective ◽  
Discounted Cash Flow ◽  
Important Input

This case is designed to present students with the challenges of formulating a discounted-cash-flow (DCF) analysis for a strategically important capital-investment decision. Analytically, the problem is representative of most corporate investment decisions, but it is particularly interesting because of the massive size of the American Centrifuge Project and the potential of the project to significantly affect the stock price. Students must determine the relevant cash flows, paying close attention to the treatment of input costs, selling prices, timing of investment outlays, depreciation, and inflation. An important input is the appropriate cost of uranium, which some students argue should be included at book value, while others argue that market value should be used. Although the primary objective of the case is to focus on the estimation of cash flows, students are provided with a straightforward set of inputs to estimate USEC's weighted average cost of capital. The case is designed for students who are learning, or need a refresher on, DCF analysis. Because of the basic issues covered, the case works well with undergraduate, MBA, and executive-education audiences. The case also affords the opportunity to explore a variety of issues related to capital-investment analysis, including relevant costs, incremental analysis, cost of capital, and sensitivity analysis. The case is an excellent example of the value of a firm as the value of assets in place plus the net present value of future growth opportunities.

scholarly journals Differences In Intrinsic Value With Stock Market Prices Using The Price Earning Ratio (Per) Approach As An Investment Decision Making Indicator (Case Study Of Manufacturing Companies In Indonesia Period 2016 - 2017)

2019 ◽  
Vol 1 (1) ◽  
pp. 82-92
Author(s):  
Ardy Indra Lekso Wibowo Putra ◽  
Aditya Dwiansyah Putra ◽  
Murni Sari Dewi ◽  
Denny Oktavina Radianto
Keyword(s):  
Decision Making ◽  
Stock Market ◽  
Stock Price ◽  
Stock Exchange ◽  
Intrinsic Value ◽  
Investment Decision ◽  
Manufacturing Companies ◽  
Market Prices ◽  
Investment Decision Making

An investor must be able to consider all kinds of steps that will be taken or that will be carried out, assessing stocks - shares that will provide optimal benefits in making an investment decision. By analyzing the intrinsic value of the price of a company's stock, investors can assess the fairness of the stock price. The method used to analize intrinsic value is fundamental analysis using the Price Earning Ratio (PER) approach. The samples to be taken in this research are manufacturing companies in Indonesia which are listed on the Indonesia Stock Exchange (IDX) for the period 2016 - 2017 with certain criteria. The results of this research will show that the shares of companies listed are in overvalued, undervalued or correctly valued conditions. So investors can decide to buy, hold or sell their shares.

scholarly journals Predicting Movement of Stock of Apple Inc. using Sutte Indicator

2017 ◽  
Author(s):  
Ansari Saleh Ahmar
Keyword(s):  
Decision Making ◽  
Moving Average ◽  
Investment Decision ◽  
Technical Analysis ◽  
Percentage Error ◽  
Decision Making Process ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Investment Decision Making ◽  
The Mean

The purpose of this study is to apply technical analysis e.g. Sutte Indicator in Stock Market that will assist in the investment decision-making process to buy or sell of stocks. This study took data from Apple Inc. which listed in the NasdaqGS in the period of 1 January 2008 to 26 September 2016. Performance of the Sutte Indicator can be seen with comparison with other technical analysis e.g. Simple Moving Average (SMA) and Moving Average Convergence/Divergence (MACD). Comparison of the reliability of prediction from Sutte Indicator, SMA, and MACD using the mean of square error (MSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE).

scholarly journals Application of Short-term Forecasting Models for Energy Entity Stock Price (Study on Indika Energi Tbk., JII)

2019 ◽  
Author(s):  
Fajrin Satria Dwi Kesumah ◽  
Rialdi Azhar ◽  
Edwin Russel
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Investment Decision ◽  
Share Price ◽  
Series Data ◽  
Arch Effect ◽  
Heterogeneous Variances ◽  
Short Term Forecasting ◽  
Autoregressive Conditional Heteroscedasticity

Share price as one of financial data is the time series data that indicates both a level of fluctuate movement and heterogeneous variances called heteroscedasticity. The method that can be used to overcome the effect of autoregressive conditional heteroscedasticity (ARCH effect) is GARCH model. This study aims to design the best model that can estimate the parameters, to predict share price based on the best model, and to show its volatility. In addition, this paper also discuss the predicted-based-model investment decision. The finding indicating the best model correspond to the data is AR(4) – GARCH(1,1). It is then implemented to forecast the stock prices of Indika Energy, Tbk, Indonesia, for upcoming 40 days that presents significantly good findings with the error percentage below the mean absolute.

scholarly journals Evidence Of Investors Responses To Managerial Investment Decision-Making

2011 ◽  
Vol 15 (3) ◽  
pp. 55
Author(s):  
Robert J. Walsh
Keyword(s):  
Decision Making ◽  
Stock Price ◽  
Investment Decision ◽  
Investment Decisions ◽  
Price Movement ◽  
Investment Expenditure ◽  
Price Reaction ◽  
Investment Decision Making ◽  
Significant Difference ◽  
Stock Price Reaction

<span>This paper investigates the stock price reaction of some pre-management buyout (MBO) investment decisions of managers. This paper finds that managers who engage in non-acquisition type investment expenditures (like plant expansions) in the pre-MBO period could lower the firms stock price. In addition, managers whose companies buy the assets or a stake in another firm give their company a positive stock price movement. Lastly, there is a statistically significant difference in stock price reaction depending on whether the investment expenditure is non-acquisition or acquisition.</span>

scholarly journals Implementation of Long Short-Term Memory and Gated Recurrent Units on Grouped Time-Series Data to Predict Stock Prices Accurately

2021 ◽  
Author(s):  
Armin Lawi ◽  
Hendra Mesra ◽  
Supri Amir
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Percentage Error ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract Stocks are an attractive investment option since they can generate large profits compared to other businesses. The movement of stock price patterns on the stock market is very dynamic; thus it requires accurate data modeling to forecast stock prices with a low error rate. Forecasting models using Deep Learning are believed to be able to accurately predict stock price movements using time-series data, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. However, several previous implementation studies have not been able to obtain convincing accuracy results. This paper proposes the implementation of the forecasting method by classifying the movement of time-series data on company stock prices into three groups using LSTM and GRU. The accuracy of the built model is evaluated using loss functions of Rooted Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results showed that the performance evaluation of both architectures is accurate in which GRU is always superior to LSTM. The highest validation for GRU was 98.73% (RMSE) and 98.54% (MAPE), while the LSTM validation was 98.26% (RMSE) and 97.71% (MAPE).

scholarly journals Investment Decisions through Total Asset Growth toward Price to Book Value on the Jakarta Islamic Index

2021 ◽  
Vol 12 (1) ◽  
pp. 63-74
Author(s):  
Syaugi Syaugi ◽  
Aulia Rahmah
Keyword(s):  
Stock Price ◽  
Time Series Data ◽  
Investment Decision ◽  
Investment Decisions ◽  
Study Data ◽  
Total Asset ◽  
Series Data ◽  
Book Value ◽  
Investment Decision Making ◽  
Asset Growth

This research analyzes the effect of investment decisions through Total Asset Growth (TAG) on Price to Book Value (PBV). Since PVB indicates stock measurement based on the ratio of stock price to book value, it is used by investors to assess the price offered. This research uses time-series data from 2014-2020 to examine seven companies selected using purposive sampling but based on fairly good asset developments from 2014 to 2020. Furthermore, this quantitative causal study data were collected using documentation from various sources and analyzed using a simple linear regression test. The results show that the TAG variable has no effect on PBV with a significance value of 0.89 0.05. This shows that TAG does not describe a stable company and is not always useful in investment decision-making.

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