scholarly journals Is Financial Analysis Doomed? The Birth of “Reactive Valuation” Analysis

2017 ◽  
Vol 6 (3) ◽  
pp. 39
Author(s):  
Paul A. Griffin ◽  
Mohammedi Padaria
Keyword(s):  
Language Processing ◽  
Financial Analysis ◽  
Detailed Knowledge ◽  
Information Costs ◽  
Short Term ◽  
Traditional Concept ◽  
Statistical Arbitrage ◽  
Media Channels ◽  
Short Time ◽  
Information Landscape

The purpose of this paper is to examine how firms’ information landscape has changed in recent years and why this could be problematic for those engaged in financial analysis and equity valuation. Our central contention is that two main forces of change – lower information costs and faster information processing – have completely disrupted the traditional concept of financial analysis. In response to this disruption, financial analysis will now increasingly take the form of “reactive valuation.” In addition to examining our main contention, we introduce a new term into the literature, called “reactive valuation,” which we define as the ultra short-term valuation of an equity, lasting from a few seconds to a few hours, based on information primarily published through social media channels. It may be later corroborated by factually based information or remain unsubstantiated. It may or may not be from an authoritative source. It also may not relate clearly or directly to the valuation of the underlying asset. However, based mostly on the tools of artificial intelligence and natural language processing, “reactive valuation” will invariably provide an opportunity for statistical arbitrage during the short time it takes for the market to digest the information. Financial analysts who survive these two forces of change will have detailed knowledge of this new form of financial analysis.

scholarly journals Fake News Detection Using NLP

2021 ◽  
Vol 9 (12) ◽  
pp. 2022-2031
Author(s):  
Samrudhi Naik
Keyword(s):  
Language Processing ◽  
Good Accuracy ◽  
Short Term Memory ◽  
Fake News ◽  
Short Term ◽  
Traditional Media ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Media Channels ◽  
Better Than

Abstract: The spreading of fake news has given rise to many problems in society. It is due to its ability to cause a lot of social and national damage with destructive impacts. Sometimes it gets very difficult to know if the news is genuine or fake. Therefore it is very important to detect if the news is fake or not. "Fake News" is a term used to represent fabricated news or propaganda comprising misinformation communicated through traditional media channels like print, and television as well as nontraditional media channels like social media. Techniques of NLP and Machine learning can be used to create models which can help to detect fake news. In this paper we have presented six LSTM models using the techniques of NLP and ML. The datasets in comma-separated values format, pertaining to political domain were used in the project. The different attributes like the title and text of the news headline/article were used to perform the fake news detection. The results showed that the proposed solution performs well in terms of providing an output with good accuracy, precision and recall. The performance analysis made between all the models showed that the models which have used GloVe and Word2vec method work better than the models using TF-IDF. Further, a larger dataset for better output and also other factors such as the author ,publisher of the news can be used to determine the credibility of the news. Also, further research can also be done on images, videos, images containing text which can help in improving the models in future. Keywords: Fake news detection, LSTM(long short term memory),Word2Vec,TF-IDF,Natural Language Processing.

Evaluating the Financial Performance Through ‘Consumer Centric Decision’ Approach in Pharmaceutical Companies of Bangladesh: a Business Planning Perspective

2017 ◽  
Vol 9 (1) ◽  
pp. 168
Author(s):  
Md Mostafizur Rahman ◽  
Mahmud Uz Zaman
Keyword(s):  
Market Share ◽  
Swot Analysis ◽  
Financial Analysis ◽  
Pharmaceutical Companies ◽  
Short Term ◽  
The Public ◽  
Investment Choice ◽  
Good Investment ◽  
Business Analysis

Pharmaceuticals agglomerations consistently use their brand image and versatile product portfolios to consolidate their position in the financial sector, which is evident in their continuous profit making and expansion in market share. This paper explores the short-term and long-term investment attractiveness through ‘consumer centric decision’ approach in two selected pharmaceutical companies, Renata Limited and Orion Pharma Limited, of Bangladesh over the last three years’ period. This research adopts a systematic approach which primarily addresses the various concerns of investors to illustrate the decision-making process of the existing and future investors. Using primarily domestic transaction data, this study explores how the leading pharmaceuticals companies of Bangladesh effectively use the wide array of drug portfolios mix with appropriate branding techniques to increase their financial profit and market share simultaneously. Both SWOT analysis and Porters Five Forces Model explore the business analysis of Renata Limited in compare to Orion Pharma Limited that provides a conclusion regarding investors’ decision to invest in Renata Limited. Considering the financial analysis, Renata’s financial liquidity is not very satisfactory and could have been improved further if management is prudent on financial strategy settings. Findings of the business analysis indicate that Renata Limited would be a good investment choice for existing and prospective shareholders based on its opportunities for long term and short term growth and further expansion in developing the market. The results suggest that even lower liquidity coupled with higher interest borrowings can be balanced by posing positive picture to the public shareholders by returning the positive dividend to them.

Effect of short-term use of oral contraceptive pills on intraocular pressure

2020 ◽  
Vol 35 (3) ◽  
Author(s):  
Tayyaba Gul Malik ◽  
Hina Nadeem ◽  
Eiman Ayesha ◽  
Rabail Alam
Keyword(s):  
Oral Contraceptive ◽  
Childbearing Age ◽  
Short Term ◽  
Oral Contraceptive Pills ◽  
Contraceptive Pills ◽  
Time Period ◽  
Significant Difference ◽  
Group A ◽  
Group B ◽  
Short Time

Objective: To study the effect of short-term use of oral contraceptive pills on intra-ocular pressures of women of childbearing age.   Methods: It was a comparative observational study, conducted at Arif memorial teaching hospital and Allied hospital Faisalabad for a period of six months. Hundred female subjects were divided into two groups of 50 each. Group A, included females, who had been taking oral contraceptive pills (OCP) for more than 6 months and less than 36 months. Group B, included 50 age-matched controls, who had never used OCP. Ophthalmic and systemic history was taken. Careful Slit lamp examination was performed and intraocular pressures (IOP) were measured using Goldman Applanation tonometer. Fundus examination was done to rule out any posterior segment disease. After collection of data, we analyzed and compared the intra ocular pressures between the two groups by using ANOVA in SPSS version 21.   Results: Average duration of using OCP was 14.9 months. There was no significant difference of Cup to Disc ratios between the two groups (p= 0.109). However, significant difference was noted between the IOP of OCP group and controls. (p=0.000). Conclusion: OCP significantly increase IOP even when used for short time period.

scholarly journals Effects of white Gaussian noise on dynamic balance in healthy young adults

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ziyou Zhou ◽  
Can Wu ◽  
Zhen Hu ◽  
Yujuan Chai ◽  
Kai Chen ◽  
...  
Keyword(s):  
Young Adults ◽  
Gaussian Noise ◽  
Center Of Pressure ◽  
Dynamic Balance ◽  
White Gaussian Noise ◽  
Fluctuation Analysis ◽  
Balance Performance ◽  
Short Term ◽  
Crossover Point ◽  
Short Time

AbstractIt has been known that short-time auditory stimulation can contribute to the improvement of the balancing ability of the human body. The present study aims to explore the effects of white Gaussian noise (WGN) of different intensities and frequencies on dynamic balance performance in healthy young adults. A total of 20 healthy young participants were asked to stand at a dynamic balance force platform, which swung along the x-axis with an amplitude of ± 4° and frequency of 1 Hz. Their center of pressure (COP) trajectories were recorded when they were stimulated by WGN of different intensities (block 1) and different frequencies (block 2). A traditional method and detrended fluctuation analysis (DFA) were used for data preprocessing. The authors found that only with 75–85 dB WGN, the COP parameters improved. WGN frequency did not affect the dynamic balance performance of all the participants. The DFA results indicated stimulation with 75 dB WGN enhanced the short-term index and reduced the crossover point. Stimulation with 500 Hz and 2500 Hz WGN significantly enhanced the short-term index. These results suggest that 75 dB WGN and 500 Hz and 2500 Hz WGN improved the participants’ dynamic balance performance. The results of this study indicate that a certain intensity of WGN is indispensable to achieve a remarkable improvement in dynamic balance. The DFA results suggest that WGN only affected the short-term persistence, indicating the potential of WGN being considered as an adjuvant therapy in low-speed rehabilitation training.

Sentence similarity evaluation using Sent2Vec and siamese neural network with parallel structure

2021 ◽  
pp. 1-10
Author(s):  
Hye-Jeong Song ◽  
Tak-Sung Heo ◽  
Jong-Dae Kim ◽  
Chan-Young Park ◽  
Yu-Seop Kim
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Parallel Structure ◽  
Short Term ◽  
Similarity Estimation ◽  
Accurate Judgment ◽  
Proposed Model ◽  
Sentence Similarity ◽  
Long Short Term Memory

Sentence similarity evaluation is a significant task used in machine translation, classification, and information extraction in the field of natural language processing. When two sentences are given, an accurate judgment should be made whether the meaning of the sentences is equivalent even if the words and contexts of the sentences are different. To this end, existing studies have measured the similarity of sentences by focusing on the analysis of words, morphemes, and letters. To measure sentence similarity, this study uses Sent2Vec, a sentence embedding, as well as morpheme word embedding. Vectors representing words are input to the 1-dimension convolutional neural network (1D-CNN) with various sizes of kernels and bidirectional long short-term memory (Bi-LSTM). Self-attention is applied to the features transformed through Bi-LSTM. Subsequently, vectors undergoing 1D-CNN and self-attention are converted through global max pooling and global average pooling to extract specific values, respectively. The vectors generated through the above process are concatenated to the vector generated through Sent2Vec and are represented as a single vector. The vector is input to softmax layer, and finally, the similarity between the two sentences is determined. The proposed model can improve the accuracy by up to 5.42% point compared with the conventional sentence similarity estimation models.

Men's and Women's Mating Preferences: Distinct Evolutionary Mechanisms?

2002 ◽  
Vol 11 (3) ◽  
pp. 88-93 ◽  
Author(s):  
Lynn Carol Miller ◽  
Anila Putcha-Bhagavatula ◽  
William C. Pedersen
Keyword(s):  
Sexual Functioning ◽  
Large Female ◽  
Detailed Knowledge ◽  
Short Term ◽  
Evolutionary Mechanisms ◽  
Men And Women ◽  
Mating Preferences ◽  
Chemical Mechanisms ◽  
Species Comparisons

Have men and women evolved sex-distinct mating preferences for short-term and long-term mating, as postulated by some evolutionary theorists? Direct tests of assumptions, consideration of confounds with gender, and examination of the same variables for both sexes suggest men and women are remarkably similar. Furthermore, cross-species comparisons indicate that humans do not evidence mating mechanisms indicative of short-term mating (e.g., large female sexual skins, large testicles). Understanding human variability in mating preferences is apt to involve more detailed knowledge of the links between these preferences and biological and chemical mechanisms associated with sexual motivation, sexual arousal, and sexual functioning.

scholarly journals Lalabet Financial Analysis: Study of Islamic Economics at Micro Level

2021 ◽  
Vol 4 (1) ◽  
pp. 233-244
Author(s):  
Ainorrofiqie Ainorrofiqie ◽  
Umrotul Khasanah ◽  
Akhmad Djalaluddin
Keyword(s):  
Financial Management ◽  
Financial Analysis ◽  
Financial Statements ◽  
The Body ◽  
Short Term ◽  
The Family ◽  
Micro Level ◽  
The Impact ◽  
The Village

This research aims to explore the model of financial management tradition Lalabet in the village of Babbalan District Batuan Sumenep. This study is based on the fact that occurred in the community about the implementation of traditions carried out by the heirs to family members who died. Interpretative qualitative research is used and an in-depth understanding of a problem that occurs is emphasized more. Based on the results of this study, the financial management tradition Lalabet can be done based on accounting equations. The accounts contained in the accounting equation is not used in its entirety and are reported as are generally financial statements. In this case, the source of funds in carrying out Lalabet tradition is sourced from personal money, money and donations from the family, money from Muslimat, debt, and money or goods from Lalabet's proceeds. The impact is the onset of debt both short-term and long-term. While the expenditure is in the form of costs in taking care of the body, costs for tahlilan (petto'arean), pa'polo, nyatos, nyataon, nyaebu, mangaji, ngin-tangin, nyalenin mayyid, and ajege makam (kep-sekep).

scholarly journals Analisis Peluang Penyertaan Modal Pemerintah Daerah Kota XYZ pada PT Bank Jawa Barat dan Banten (Bank BJB)

2016 ◽  
Vol 2 (1) ◽  
pp. 54
Author(s):  
Dian Yudo Palupi ◽  
Farida Ratna Dewi ◽  
R. Dikky Indrawan
Keyword(s):  
Swot Analysis ◽  
Financial Analysis ◽  
Risk Exposure ◽  
Regional Government ◽  
Government Bond ◽  
Short Term ◽  
Small Risk ◽  
Short Term Investment ◽  
Stock Share

Economic growth and public welfare are the reason of regional autonomy regulation policy (UU No 22 year 1999). The policy allows regional economic resources managed by regional government to achieve its goal. One of the regional government strategies is investment strategy, which in this case investing in banking industry. The purposes of the study are 1) to identify the investment regulation on regional government 2) to identify the Bank BJB business and investment environment 3) to identify the comparison of investment feasibility on Bank BJB versus other banks 4) to identify the position of Stock Share A series owned by XYZ at Bank BJB. The data collection methods are using structured interview, in depth interview, field survey and literature study. The analysis tools are using institutional analysis, SWOT analysis and financial analysis. Institutional analysis showed XYZ regional government investment management is limited to regulation as follow 1) long term capital (stock share) investment limited only at BUMD (e.g. Bank BJB) 2) short term investment e.g. saving and deposit is limited only at healthy and feasible bank, and government bond which has small risk exposure. The financial analysis also showed the increasing performance of BJB Earning per Share (EPS) and Return on Equity (ROE) from 2006 until 2010. The SWOT analysis support other analysis that BJB Bank position in financial industry is suitable for long term and short term investment for XYZ regional government. Base on explanation above, the conclusions are the autonomy regulation limited XYZ regional government to invest as shareholders in A series (stock share) or B series (stock share) at BJB Bank only, and for short term investment is limited only at healthy and feasible bank, and government bond which has small risk exposure.

scholarly journals Analisis Peningkatan Upah Minimum Terhadap Penyerapan Tenaga Kerja di Provinsi Jawa Tengah

2021 ◽  
Vol 10 (3) ◽  
pp. 134-143
Author(s):  
Annisa Yulianti ◽  
Hadi Sasana
Keyword(s):  
Economic Growth ◽  
Minimum Wage ◽  
Time Series Data ◽  
Series Data ◽  
Short Term ◽  
Central Java ◽  
Meaningful Relationship ◽  
Short Time ◽  
Relationship Of

 This study aims to analyze the short-term and long-term relationship of increasing the minimum wage in Central Java on employment. The research method used is ECM. The variables of this study include labor, minimum wages, PMDN, and economic growth. The data used are time-series data from 1990-2020. The results show that the minimum wage has a positive and significant relationship to the employment in the long term but not significantly in the short time. PMDN has a negative but significant correlation in the short and long term. At the same time, the variable economic growth has a positive but not meaningful relationship to employment absorption in the long and short term.

scholarly journals Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Kazi Nabiul Alam ◽  
Md Shakib Khan ◽  
Abdur Rab Dhruba ◽  
Mohammad Monirujjaman Khan ◽  
Jehad F. Al-Amri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Confusion Matrix ◽  
Short Term ◽  
Learning Techniques ◽  
The World ◽  
Long Short Term Memory ◽  
Severe Anxiety

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people’s feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people’s minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public’s opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

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