Stock Prediction Based on Technical Indicators Using Deep Learning Model

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

Can investors profit by utilizing technical trading strategies?Evidence from the Korean and Chinese stock markets

The idea of this study is derived from observing the profitability of stock investments following the phenomena of continuously rising(or falling)prices of stocks and continuously overbought(or oversold)signals emitted by technical indicators.We employ the standard event study approach and technical trading strategies to explore whether investors would exploit profits in trading the constituent stocks of the Korea Composite Stock Price Index 50 and Shanghai Stock Exchange 50 when the aforementioned continuous phenomena occur.We find that both the Korean and Chinese stock markets are not fully efficient;this finding may enhance the robustness of the existing literature.In addition,we reveal that contrarian strategies are appropriate for the trading stocks listed on the Korean stock market for all the cases investigated in this study.However,momentum strategies are appropriate for the Chinese stock market when continuously rising stock prices and overbought signals are simultaneously observed.These findings imply that the difference in investor behaviors between the Korean and Chinese stock markets might result in dissimilar trading strategies being employed for these two markets.

Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators

Forex(foreign exchange)is a special financial market that entails both high risks and high profit opportunities for traders.It is also a very simple market since traders can profit by just predicting the direction of the exchange rate between two currencies.However,incorrect predictions in Forex may cause much higher losses than in other typical financial markets.The direction prediction requirement makes the problem quite different from other typical time-series forecasting problems.In this work,we used a popular deep learning tool called“long short-term memory”(LSTM),which has been shown to be very effective in many time-series forecasting problems,to make direction predictions in Forex.We utilized two different data sets—namely,macroeconomic data and technical indicator data—since in the financial world,fundamental and technical analysis are two main techniques,and they use those two data sets,respectively.Our proposed hybrid model,which combines two separate LSTMs corresponding to these two data sets,was found to be quite successful in experiments using real data.

Research on stock trend prediction method based on optimized random forest

As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical analysis.Researchers in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock trend.This study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend prediction.This study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random forest.As the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as input.Then,the parameter combination of the model is optimized through random parameter search.The experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine model.Combined with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.

Study of The Technical Index of Online Learning Behavior Analysis of Nursing Majors on The Superstar Platform Based on The Kirkpatrick Evaluation Model

Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.

Stochastic Volatility Model and Technical Analysis of Stock Price

In the stock market, some popular technical analysis indicators （e.g. Bollinger Bands, RSI, ROC, ...） are widely used by traders. They use the daily （hourly, weekly, ...） stock prices as samples of certain statistics and use the observed relative frequency to show the validity of those well-known indicators. However, those samples are not independent, so the classical sample survey theory does not apply. In earlier research, we discussed the law of large numbers related to those observations when one assumes Black-Scholes＇ stock price model. In this paper, we extend the above results to the more popular stochastic volatility model.

Under false flag:using technical artifacts for cyber attack attribution

The attribution of cyber attacks is often neglected.The consensus still is that little can be done to prosecute the perpetrators–and unfortunately,this might be right in many cases.What is however only of limited interest for the private industry is in the center of interest for nation states.Investigating if an attack was carried out in the name of a nation state is a crucial task for secret services.Many methods,tools and processes exist for network-and computer forensics that allow the collection of traces and evidences.They are the basis to associate adversarial actions to threat actors.However,a serious problem which has not got the appropriate attention from research yet,are false flag campaigns,cyber attacks which apply covert tactics to deceive or misguide attribution attempts–either to hide traces or to blame others.In this paper we provide an overview of prominent attack techniques along the cyber kill chain.We investigate traces left by attack techniques and which questions in course of the attribution process are answered by investigating these traces.Eventually,we assess how easily traces can be spoofed and rate their relevancy with respect to identifying false flag campaigns.

Under false flag:using technical artifacts for cyber attack attribution

The attribution of cyber attacks is often neglected.The consensus still is that little can be done to prosecute the perpetrators–and unfortunately,this might be right in many cases.What is however only of limited interest for the private industry is in the center of interest for nation states.Investigating if an attack was carried out in the name of a nation state is a crucial task for secret services.Many methods,tools and processes exist for network-and computer forensics that allow the collection of traces and evidences.They are the basis to associate adversarial actions to threat actors.However,a serious problem which has not got the appropriate attention from research yet,are false flag campaigns,cyber attacks which apply covert tactics to deceive or misguide attribution attempts–either to hide traces or to blame others.In this paper we provide an overview of prominent attack techniques along the cyber kill chain.We investigate traces left by attack techniques and which questions in course of the attribution process are answered by investigating these traces.Eventually,we assess how easily traces can be spoofed and rate their relevancy with respect to identifying false flag campaigns.