Candlestick Analysis based Short Term Prediction of Stock Price Fluctuation using SOM-CBR

Abstract

like commodity, crude just to name a few [12]. Unlike other Abstract-Stock market analysis and prediction has been one technical indicators, which try to find statistical relation of of the widely studied and most interesting time series analysis current price with future price, candlestick indicator tries to problems till date. Many researchers have employed many find the investor's sentiment on a given stock. The idea is different models, some of them are linear statistic based while simple "what will be the price of a stock tomorrow depends some non linear regression, rule, ANN, GA and Fuzzy logic on what people think about that stock at the end of the day". based. In this paper we have proposed a novel model that tries to I o .. v predict short term price fluctuation, using Candlestick Analysis. Iode tO indenif as a pattern s, we have This is a proven technique used for short term prediction of stock used SOM and CBR as a pattern recognizer and pattern price fluctuation and market timing since many years. Our matching mechanism. Decision derived by the CBR (SOM) is approach has been hybrid that combines Self Organizing Map in turn combined with other technical indicator like with Case Based Reasoning to indentify profitable patterns Stochastics and Volume, in order to improve the accuracy of (candlestick) and predicting stock price fluctuation based on the predication. pattern consequences. All available models for predicting stock price behavior can be divided into three category 1) Machine Learning based Index Terms-Candlestick Analysis, Short Term Stock (Black box), 2) Expert knowledge based (White box) and 3) Prediction, Self Organizing Map, Case-Base Reasoning Combination of both (Hybrid). In Black box models [3] [5] the system learns itself from previous experiences. Such a model does not require an expert, but the representation of the knowledge is complex and not verifiable (like ANN) where as S tock data analysis has remain one of the challenging time in White box models [2] [4] [6] the representation of the series analysis problem over the years. Because of the knowledge is simple and verifiable but requires expert to complexity and intrications, the problem has received impart knowledge in the system (like Fuzzy rule, CBR). attention from many researchers. Owing to its high expected System is only as good as the expert's knowledge. Hybrid returns, there are many starting from local investor to a big models [7] [8] try to combine the benefits of both. In this paper finance houses who are interested in finding out how the stock we have proposed a hybrid model which combines SOM with market works? Many techniques have been derived over a CBR. period of time by many researchers and investors to predict

Candlestick Pattern Research Analysis, Future and Beyond: A Systematic Literature Review Using PRISMA

“…The first group shows that there are at least seven articles that discuss candlesticks and include them in the realm of the Expert System. There are two articles that use Neural Networks in making stock price predictions by making candlesticks as their main parameter (J. H. Chen & Tsai, 2020; Ng et al, 2011), While the rest use fuzzy and learning techniques to look for similarities in candlestick patterns so that they can be predicted for movement in the next period (Carpentier & White, 2013;Goswami et al, 2009) However, apart from the SLR screening results, there are dozens of articles that have the same theme but have citations that are not very significant.…”

Forecasting Symbolic Candle Chart-Valued Time Series

“…The candle chart, which is widely used for forecasting direction of the stock index, is composed of four indexes, open (y o t ), close (y c t ), highest (y h t ) and lowest (y l t ) indexes as shown in Figure 1 (Goswamil et al, 2009). The stock indices dataset may become huge, since stock price information is usually expressed by time series of four indices, open, close, highest and lowest.…”

K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering

“…The literatures [4,5] introduce the existing patterns and their features in detail, such as Three Inside Up (TIU), Three Inside Down (TID), and Doji. Some papers [6][7][8][9][10] conclude from the experiment that the existing K-line patterns have a good forecasting capability for forecasting stock trends. Some other papers [11][12][13][14][15] have studied the stock prediction based on these patterns and have achieved some research results.…”