A Naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices

Nayak, Rudra Kalyan; Mishra, Debahuti; Rath, Amiya Kumar

doi:10.1016/j.asoc.2015.06.040

Cited by 140 publications

(62 citation statements)

References 14 publications

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“…Evolutionary algorithms, mostly GA, have been widely applied to neural network models, such as MLP and RNN, and used in various hybrid approaches for financial time series forecasting to optimize technical analysis or train the neural network [23,36]. Muhammad and King (1997) exploited evolutionary fuzzy networks in the foreign exchange market forecasting [51], and Kai and Wenhua (1997) proposed an ANN model trained with GA to predict the stock price [52].…”

Section: A Hybrid Approach To Optimization In Lstm Network With Gamentioning

confidence: 99%

Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

2018

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With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Especially in the field of finance, we have great opportunities to create useful insights by analyzing that information, because the financial market produces a tremendous amount of real-time data, including transaction records. Accordingly, this study intends to develop a novel stock market prediction model using the available financial data. We adopt deep learning technique because of its excellent learning ability from the massive dataset. In this study, we propose a hybrid approach integrating long short-term memory (LSTM) network and genetic algorithm (GA). Heretofore, trial and error based on heuristics is commonly used to estimate the time window size and architectural factors of LSTM network. This research investigates the temporal property of stock market data by suggesting a systematic method to determine the time window size and topology for the LSTM network using GA. To evaluate the proposed hybrid approach, we have chosen daily Korea Stock Price Index (KOSPI) data. The experimental result demonstrates that the hybrid model of LSTM network and GA outperforms the benchmark model.

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Section: A Hybrid Approach To Optimization In Lstm Network With Gamentioning

confidence: 99%

Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

2018

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“…Step 2: Smoothing Dataset: As raw data are noisy in nature. So we go for smoothing the raw data using technical indicators which help to eliminate outliers from a data set in order to make a pattern more visible [27][28].…”

Section: Results Discussionmentioning

confidence: 99%

Forecasting Foreign Currency Exchange Price using Long Short-Term Memory with K-Nearest Neighbor Method

Nayak,

Pavitra,

Tripathy

et al. 2019

IJEAT

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With the growing population in the world, economic stability varies day by day. In case of India all banking transaction rules and regulations are taken by Reserve bank of India (RBI) whereas for other countries it is different. Therefore numerous academicians have projected their research on forecasting the currency exchange rate for diverse countryside. Foreign currency exchange rate prediction is a very pivotal task for international market. Hence researchers have explored different methods for predicting foreign currency exchange rate. In this work, we have taken Indian rupees (INR) with two different country’s data set such as Japanese yen (JPY) andChinese Yuan (CNY)for daily, weekly and monthlyprediction beforehand. We implemented a hybrid model oflong short-term memory (LSTM) with K-nearest neighbour (KNN) which gives better opening price prediction accuracy on our dataset. The accuracy of the prediction results are measured by the help of performance standards such as mean absolute percentage error (MAPE) and root mean square error (RMSE).

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“…Here we have done our experiment using Intel i3 with 4GB Hard disk and windows 7 operating system and complete code is written using python 3. In essence, our analysis is [27][28][29] given in equations (9), (10), (11) and 12…”

Section: Experimental Analysismentioning

confidence: 99%

Rough Set Based Affinity Propagation Model for Prediction of Future Gold Price in Indian Scenario

Nayak*,

Teja,

Tripathy

et al. 2019

IJITEE

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In international market, trading of metals has played a vital role. Metal cost might affect the nation’s economy. There are so many base metals available which have been utilized in world trading for construction and manufacturing of goods. Among them gold, silver, platinum, palladium have been treated as precious metals which has economic values. Therefore today’s researchers have concentrated their investigation on metal prediction using diversified algorithms like Auto Regressive Integrated Moving Average (ARIMA), KNN (K-Nearest Neighbor),Artificial Neural Network (ANN) and Support Vector Machine (SVM) etc. In this paper our foremost objective is to predict gold price, so we put our research on this metal. In this work we have employed rough set based affinity propagation algorithm for predicting future gold price and we compared our proposed model with rough set and ARIMA model basing upon the performance measures such as root mean square error (RMSE) and mean absolute percentage error (MAPE). The experimental result shows that the proposed model outperforms rough set and ARIMA model

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A Naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices

Cited by 140 publications

References 14 publications

Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

Forecasting Foreign Currency Exchange Price using Long Short-Term Memory with K-Nearest Neighbor Method

Rough Set Based Affinity Propagation Model for Prediction of Future Gold Price in Indian Scenario

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