Demand forecasting: an alternative approach based on technical indicator Pbands

Research background: Demand forecasting helps companies to anticipate purchases and plan the delivery or production. In order to face this complex problem, many statistical methods, artificial intelligence-based methods, and hybrid methods are currently being developed. However, all these methods have similar problematic issues, including the complexity, long computing time, and the need for high computing performance of the IT infrastructure. Purpose of the article: This study aims to verify and evaluate the possibility of using Google Trends data for poetry book demand forecasting and compare the results of the application of the statistical methods, neural networks, and a hybrid model versus the alternative possibility of using technical analysis methods to achieve immediate and accessible forecasting. Specifically, it aims to verify the possibility of immediate demand forecasting based on an alternative approach using Pbands technical indicator for poetry books in the European Quartet countries. Methods: The study performs the demand forecasting based on the technical analysis of the Google Trends data search in case of the keyword poetry in the European Quartet countries by several statistical methods, including the commonly used ETS statistical methods, ARIMA method, ARFIMA method, BATS method based on the combination of the Cox-Box transformation model and ARMA, artificial neural networks, the Theta model, a hybrid model, and an alternative approach of forecasting using Pbands indicator. The study uses MAPE and RMSE approaches to measure the accuracy. Findings & value added: Although most currently available demand prediction models are either slow or complex, the entrepreneurial practice requires fast, simple, and accurate ones. The study results show that the alternative Pbands approach is easily applicable and can predict short-term demand changes. Due to its simplicity, the Pbands method is suitable and convenient to monitor short-term data describing the demand. Demand prediction methods based on technical indicators represent a new approach for demand forecasting. The application of these technical indicators could be a further forecasting models research direction. The future of theoretical research in forecasting should be devoted mainly to simplifying and speeding up. Creating an automated model based on primary data parameters and easily interpretable results is a challenge for further research.

Trend identification with the relative strength index (RSI) technical indicator –A conceptual study

We all must agree that the word "trend" is now the buzzword of the stock market. As a part of investment strategy and analysis, it is always suggested that the investors should keep an eye on medium-term and short-term changes in addition to longer-term (secular) patterns. Traders and investors use the RSI as a momentum indicator. Overbought and oversold situations are indicated by RSI values between 70 and 30. Over the past two decades, several techniques have been developed to analyze NIFTY 50 data for investment purposes. In this paper, we have estimated the returns by looking at the two trends i.e., 50-50 and 60-40. In addition to this, how to trade and back-test our strategy is also explained. Applying these two RSI strategies to the NIFTY 50 chart revealed that 50-50 offers a higher long-term return, while 60-40 provides a superior short-term return. Finally, the strategies' returns F-statistics and P-values were calculated and analyzed to determine their significance level and acceptability.

Reassessment of the Concept of Momentum in Econophysics

We examine the most basic feature of the economic process - momentum - under the point of view of analogies with physical laws, as they were reformulated recently [1]. Our approach is applied with minimal assumptions and we conclude that the inclusion of entropy as an effective variable in econophysics may bring a new vision of economic progress and the possibility to harness economic waves as a means to transport development from rich to poor countries using trade and technological progress. A new technical indicator for the stock market is proposed offering double opportunities on enter and exit trades, when compared to the Relative Strength Index usually used in analysis of financial markets.

Combined deep learning classifiers for stock market prediction: integrating stock price and news sentiments

PurposeStock market forecasters are focusing to create a positive approach for predicting the stock price. The fundamental principle of an effective stock market prediction is not only to produce the maximum outcomes but also to reduce the unreliable stock price estimate. In the stock market, sentiment analysis enables people for making educated decisions regarding the investment in a business. Moreover, the stock analysis identifies the business of an organization or a company. In fact, the prediction of stock prices is more complex due to high volatile nature that varies a large range of investor sentiment, economic and political factors, changes in leadership and other factors. This prediction often becomes ineffective, while considering only the historical data or textural information. Attempts are made to make the prediction more precise with the news sentiment along with the stock price information.Design/methodology/approachThis paper introduces a prediction framework via sentiment analysis. Thereby, the stock data and news sentiment data are also considered. From the stock data, technical indicator-based features like moving average convergence divergence (MACD), relative strength index (RSI) and moving average (MA) are extracted. At the same time, the news data are processed to determine the sentiments by certain processes like (1) pre-processing, where keyword extraction and sentiment categorization process takes place; (2) keyword extraction, where WordNet and sentiment categorization process is done; (3) feature extraction, where Proposed holoentropy based features is extracted. (4) Classification, deep neural network is used that returns the sentiment output. To make the system more accurate on predicting the sentiment, the training of NN is carried out by self-improved whale optimization algorithm (SIWOA). Finally, optimized deep belief network (DBN) is used to predict the stock that considers the features of stock data and sentiment results from news data. Here, the weights of DBN are tuned by the new SIWOA.FindingsThe performance of the adopted scheme is computed over the existing models in terms of certain measures. The stock dataset includes two companies such as Reliance Communications and Relaxo Footwear. In addition, each company consists of three datasets (a) in daily option, set start day 1-1-2019 and end day 1-12-2020, (b) in monthly option, set start Jan 2000 and end Dec 2020 and (c) in yearly option, set year 2000. Moreover, the adopted NN + DBN + SIWOA model was computed over the traditional classifiers like LSTM, NN + RF, NN + MLP and NN + SVM; also, it was compared over the existing optimization algorithms like NN + DBN + MFO, NN + DBN + CSA, NN + DBN + WOA and NN + DBN + PSO, correspondingly. Further, the performance was calculated based on the learning percentage that ranges from 60, 70, 80 and 90 in terms of certain measures like MAE, MSE and RMSE for six datasets. On observing the graph, the MAE of the adopted NN + DBN + SIWOA model was 91.67, 80, 91.11 and 93.33% superior to the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively for dataset 1. The proposed NN + DBN + SIWOA method holds minimum MAE value of (∼0.21) at learning percentage 80 for dataset 1; whereas, the traditional models holds the value for NN + DBN + CSA (∼1.20), NN + DBN + MFO (∼1.21), NN + DBN + PSO (∼0.23) and NN + DBN + WOA (∼0.25), respectively. From the table, it was clear that the RMSRE of the proposed NN + DBN + SIWOA model was 3.14, 1.08, 1.38 and 15.28% better than the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively, for dataset 6. In addition, he MSE of the adopted NN + DBN + SIWOA method attain lower values (∼54944.41) for dataset 2 than other existing schemes like NN + DBN + CSA(∼9.43), NN + DBN + MFO (∼56728.68), NN + DBN + PSO (∼2.95) and NN + DBN + WOA (∼56767.88), respectively.Originality/valueThis paper has introduced a prediction framework via sentiment analysis. Thereby, along with the stock data and news sentiment data were also considered. From the stock data, technical indicator based features like MACD, RSI and MA are extracted. Therefore, the proposed work was said to be much appropriate for stock market prediction.

Design of a Ku-band Tile T/R Module

This paper introduces a 16 channel Ku-band tile T/R module design in 3D highly integrated packaging. The technical indicator design of T/R module is completed by using four channel amplitude phase multifunctional chip and various design methods. Through reasonable indicator distribution and structure layout, the miniaturization design of T/R module is realized by using high-density substrate and 3D interconnection structure. Through process optimization, structural design and simulation verification, the reliability and producibility of T/R module is studied, and the consistency of products is improved. The test results show that the design has good microwave performance and can meet the requirements of high performance, low cost and batch production of Ku-band T/R module.

A New Stock Trade Technical Indicator

This paper is focused on the new stock trade technical indicator that I developed to help indicate whether the stock is in an uptrend or downtrend. The new technical indicator is compared with the moving average convergence divergence (MACD) crossover and histogram to determine the accuracy of each technical indicator. The comparison shows that the new technical indicator had a higher accuracy percentage, as well as fewer losses, compared to the MACD crossover and histogram.

Cognitive Robotics on 5G Networks

Emotional cognitive ability is a key technical indicator to measure the friendliness of interaction. Therefore, this research aims to explore robots with human emotion cognitively. By discussing the prospects of 5G technology and cognitive robots, the main direction of the study is cognitive robots. For the emotional cognitive robots, the analysis logic similar to humans is difficult to imitate; the information processing levels of robots are divided into three levels in this study: cognitive algorithm, feature extraction, and information collection by comparing human information processing levels. In addition, a multi-scale rectangular direction gradient histogram is used for facial expression recognition, and robust principal component analysis algorithm is used for facial expression recognition. In the pictures where humans intuitively feel smiles in sad emotions, the proportion of emotions obtained by the method in this study are as follows: calmness accounted for 0%, sadness accounted for 15.78%, fear accounted for 0%, happiness accounted for 76.53%, disgust accounted for 7.69%, anger accounted for 0%, and astonishment accounted for 0%. In the recognition of micro-expressions, humans intuitively feel negative emotions such as surprise and fear, and the proportion of emotions obtained by the method adopted in this study are as follows: calmness accounted for 32.34%, sadness accounted for 34.07%, fear accounted for 6.79%, happiness accounted for 0%, disgust accounted for 0%, anger accounted for 13.91%, and astonishment accounted for 15.89%. Therefore, the algorithm explored in this study can realize accuracy in cognition of emotions. From the preceding research results, it can be seen that the research method in this study can intuitively reflect the proportion of human expressions, and the recognition methods based on facial expressions and micro-expressions have good recognition effects, which is in line with human intuitive experience.

Stock Trend Prediction Using Deep Learning Approach on Technical Indicator and Industrial Specific Information

A stock trend prediction has been in the spotlight from the past to the present. Fortunately, there is an enormous amount of information available nowadays. There were prior attempts that have tried to forecast the trend using textual information; however, it can be further improved since they relied on fixed word embedding, and it depends on the sentiment of the whole market. In this paper, we propose a deep learning model to predict the Thailand Futures Exchange (TFEX) with the ability to analyze both numerical and textual information. We have used Thai economic news headlines from various online sources. To obtain better news sentiment, we have divided the headlines into industry-specific indexes (also called “sectors”) to reflect the movement of securities of the same fundamental. The proposed method consists of Long Short-Term Memory Network (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) architectures to predict daily stock market activity. We have evaluated model performance by considering predictive accuracy and the returns obtained from the simulation of buying and selling. The experimental results demonstrate that enhancing both numerical and textual information of each sector can improve prediction performance and outperform all baselines.

Preliminarily Screening Geographical Hotspots for New Rooftop PV Installation: A Case Study in Thailand

As rooftop PV deployment accelerates around the world, forecasts of rooftop PV penetration by geographical region and customer group are essential to guide policy and decision-making by utilities. However, most state-of-the-art forecasting tools require detailed data that are often unavailable for developing countries. A simplified analytical tool with limited data is proposed to preliminarily identify the rooftop PV “hotspots”—that is, geographical areas where many new investments into rooftop PV investments are likely to occur. The tool combines the assessment of financial and technical indicator in form of the optimal PV-to-load ratio indicating the maximum penetration of solar PV, and the capital-to-expenditure ratio indicating the ease of such investment. Using Thailand as a case study, the results from this tool show that under the self-consumption and net-billing scheme, the Northern and Northeastern regions are marked as the potential hotspots where the utility’s impact will be realized early or strongly or both. The average LCOE and self-consumption level for all customer classes and regions are in the range of 0.084–0.112 USD/kWh and 41.33–73.13% of PV production, respectively.