investment analysis
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2022 ◽  
Vol 5 (1) ◽  
pp. p7
Hugh Ching (USA) ◽  
Chien Yi Lee (China) ◽  
Benjamin Li (Canada)

The P/E Ratio (Price/Earning) is one of the most popular concepts in stock analysis, yet its exact interpretation is lacking. Most stock investors know the P/E Ratio as a financial indicator with the useful characteristics of being relatively time-invariant. In this paper, a rigorous mathematical derivation of the P/E Ratio is presented. The derivation shows that, in addition to its assumptions, the P/E Ratio can be considered the zeroth order solution to the rate of return on investment. The commonly used concept of the Capitalization Rate (Cap Rate = Net Income / Price) in real estate investment analysis      can also be similarly derived as the zeroth order solution of the rate of return on real estate investment. This paper also derives the first order solution to the rate of return (Return = Dividend/Price + Growth) with its assumptions. Both the zeroth and the first order solutions are derived from the exact future accounting equation (Cash Return = Sum of Cash Flow + Cash from Resale). The exact equation has been used in the derivation of the exact solution of the rate of return. Empirically, as an illustration of an actual case, the rates of return are 3%, 73%, and 115% for a stock with 70% growth rate for, respectively, the zeroth order, the first order, and the exact solution to the rate of return; the stock doubled its price in 2004. This paper concludes that the zero-th, the first order, and the exact solution of the rate of return all can be derived mathematically from the same exact equation, which, thus, forms a rigorous mathematical foundation for investment analysis, and that the low order solutions have the very practical use in providing the analytically calculated initial conditions for the iterative numerical calculation for the exact solution. The solution of value belongs to recently classified Culture Level Quotient CLQ = 10 and is in the process of being updated by fuzzy logic with its range of tolerance for predicting market crashes to advance to CLQ = 2.

Jie Zou ◽  
Wenkai Gong ◽  
Guilin Huang ◽  
Gebiao Hu ◽  
Wenbin Gong

Traditional investment analysis algorithms usually only analyze the similarity between financial time series and financial data, which leads to inaccurate and inefficient analysis of investment characteristics. In addition, the trading volume of financial securities market is huge, the amount of investment data is also very large, and the detection of abnormal transactions is difficult. The aim of feature extraction is to obtain mathematical features that can be recognized by machine. Different from the traditional methods, this paper studies and improves the big data investment analysis algorithm of abnormal transactions in financial securities market. After processing the captured trading data of financial securities market, the big data feature of abnormal trading is extracted. Combined with the abnormal trading and the financial securities market, the investment strategy is determined. The optimization objective function is set and the genetic algorithm is used to improve the investment analysis algorithm. The simulation experiment verifies the improved investment analysis algorithm, and the average Accuracy of investment analysis is increased by at least 11.24%, the ROI is significantly improved, and the efficiency is higher, which indicates that the proposed algorithm has ideal application performance.

2021 ◽  
Vol 11 (1) ◽  
pp. 51-65
Suryanto Suryanto

ABSTRACT Stock investment is an investment that has a high risk. An investor needs to do an investment analysis before deciding to invest. Investment analysis can be carried out using both fundamental and technical approaches. Technical analysis is often an option because it is fast and easy to apply. This study aims to examine the level of differences in the use of technical analysis with the moving average convergence-divergence (MACD) method and the relative strength index (RSI) as a means of making stock investment decisions. The research method used in this research is the descriptive analysis method. This research was conducted on a group of banking stocks that are included in LQ45. The results showed that there was no difference between the price of the buy signal and the sell signal before and after using the MACD and RSI methods. The results also show that there is no difference between the buy signal and the sell signal between MACD and RSI. Therefore, it can be stated that for the same object and period, the MACD and RSI methods produce the same investment decisions (buy signal and sell signal). Keywords: technical analysis, MACD, RSI, buy signal, sell signal   ABSTRAK Investasi saham merupakanjenis investasi yang memiliki resiko tinggi. Seorang investor perlu melakukan analisis investasi sebelum memutuskan untuk berinvestasi. Analisis investasi dapat dilakukan dengan menggunakan pendekatan fundamental dan teknikal. Analisis teknikal seringkali menjadi pilihan karena cepat dan mudah diterapkan. Penelitian ini bertujuan untuk menguji tingkat perbedaan penggunaan analisa teknikal dengan metode moving average convergence-divergence (MACD) dan relative strength index (RSI) sebagai alat pengambilan keputusan investasi saham. Metode penelitian yang digunakan dalam penelitian ini adalah metode analisis deskriptif. Penelitian ini dilakukan pada sekelompok saham perbankan yang termasuk dalam LQ45. Hasil penelitian menunjukkan bahwa tidak ada perbedaan harga antara sinyal beli dan sinyal jual sebelum dan sesudah menggunakan metode MACD maupun RSI. Hasil penelitian juga menunjukkan bahwa tidak ada perbedaan antara sinyal beli dan sinyal jual antara MACD dan RSI. Dengan demikian dapat dikatakan bahwa untuk objek dan periode yang sama, metode MACD dan RSI menghasilkan keputusan investasi yang sama (sinyal beli dan sinyal jual). Kata kunci: analisa teknikal, MACD, RSI, sinyal beli, sinyal jual

Michael Sharp ◽  
Mehdi Dadfarnia ◽  
Timothy Sprock ◽  
Douglas Thomas

Abstract Industrial artificial intelligence (IAI) and other analysis tools with obfuscated internal processes are growing in capability and ubiquity within industrial settings. Decision makers share concern regarding the objective evaluation of such tools and their impacts at the system level, facility level, and beyond. One application where this style of tool is making a significant impact is in Condition Monitoring Systems (CMSs). This paper addresses the need to evaluate CMSs, a collection of software and devices that alert users to changing conditions within assets or systems of a facility. The presented evaluation procedure uses CMSs as a case study for a broader philosophy evaluating the impacts of IAI tools. CMSs can provide value to a system by forewarning of faults, defects, or other unwanted events. However, evaluating CMS value through scenarios that did not occur is rarely easy or intuitive. Further complicating this evaluation are the ongoing investment costs and risks posed by the CMS from imperfect monitoring. To overcome this, an industrial facility needs to regularly and objectively review CMS impacts to justify investments and maintain competitive advantage. This paper's procedure assesses the suitability of a CMS for a system in terms of risk and investment analysis. This risk-based approach uses the changes in the likelihood of good and bad events to quantify CMS value without making any one-time pointwise estimates. Fictional case studies presented in this paper illustrate the procedure and demonstrate its usefulness and validity.

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