Stock Price Prediction with Fluctuation Patterns Using Indexing Dynamic Time Warping and $$k^*$$-Nearest Neighbors

Nakagawa, Kei; Imamura, Mitsuyoshi; Yoshida, Kenichi

doi:10.1007/978-3-319-93794-6_7

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…Tsinaslanidis and Kugiumtzis [20] predicted the GBP/USD exchange rate using DTW and perceptually important points (PIP). Using DTW, Nakagawa et al [21] found a past time series pattern similar to the present pattern based on which future stock prices are predicted. Tsinaslanidis [22] used DTW to predict bullish and bearish markets for a number of NYSE-listed stocks, and Kim et al [23] constructed pattern matching trading system (PMTS) for KOSPI 200 futures index time series data using DTW.…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

“…DTW and GA have been widely used for developing various investment strategies. Previous studies proposed a pattern matching trading system using DTW to predict exchange rates and stock prices [20][21][22][23]. Additionally, GA has been used for predicting stock indices, real estate auction prices and appraisals, and was also used to optimize IPO investment strategies or trading strategies that hedge options [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

et al. 2021

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Research on stock market prediction has been actively conducted over time. Pertaining to investment, stock prices and trading volume are important indicators. While extensive research on stocks has focused on predicting stock prices, not much focus has been applied to predicting trading volume. The extensive trading volume by large institutions, such as pension funds, has a great impact on the market liquidity. To reduce the impact on the stock market, it is essential for large institutions to correctly predict the intraday trading volume using the volume weighted average price (VWAP) method. In this study, we predict the intraday trading volume using various methods to properly conduct VWAP trading. With the trading volume data of the Korean stock price index 200 (KOSPI 200) futures index from December 2006 to September 2020, we predicted the trading volume using dynamic time warping (DTW) and a genetic algorithm (GA). The empirical results show that the model using the simple average of the trading volume during the optimal period constructed by GA achieved the best performance. As a result of this study, we expect that large institutions will perform more appropriate VWAP trading in a sustainable manner, leading the stock market to be revitalized by enhanced liquidity. In this sense, the model proposed in this paper would contribute to creating efficient stock markets and help to achieve sustainable economic growth.

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Section: Dynamic Time Warpingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

et al. 2021

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“…In this section, we conduct experiments on real-world datasets, that predict the rise or fall of eight major world stock indices over a period from 1989 to 2021. Experiments in this section basically follow Nakagawa et al (2018), which conducts similar experiments with the stock indices before 2018.…”

Section: Application To Predictive Month-end Closing Price Classifica...mentioning

confidence: 99%

“…• (Experiments) In Section 4, our numerical experiment shows that LRLR outperforms LPoR and MSk-NN. In Section 5, we also apply LRLR to real-world datasets of daily stock indices called S&P 500, S&P/TSX, EURO STOXX 50, FTSE 100, DAX, CAC 40, TOPIX, and Hang Seng, by following Nakagawa et al (2018). We classify whether month-end closing prices of target months are going up or down by LRLR, by comparing to the previous fluctuation patterns of stock indices.…”

Section: Introductionmentioning

confidence: 99%

Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction

Cao¹,

Okuno²,

Nakagawa³

et al. 2021

Preprint

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For supervised classification problems, this paper considers estimating the query's label probability through local regression using observed covariates. Well-known nonparametric kernel smoother and knearest neighbor (k-NN) estimator, which take label average over a ball around the query, are consistent but asymptotically biased particularly for a large radius of the ball. To eradicate such bias, local polynomial regression (LPoR) and multiscale k-NN (MS-k-NN) learn the bias term by local regression around the query and extrapolate it to the query itself. However, their theoretical optimality has been shown for the limit of the infinite number of training samples. For correcting the asymptotic bias with fewer observations, this paper proposes a local radial regression (LRR) and its logistic regression variant called local radial logistic regression (LRLR), by combining the advantages of LPoR and MS-k-NN. The idea is simple: we fit the local regression to observed labels by taking the radial distance as the explanatory variable and then extrapolate the estimated label probability to zero distance. Our numerical experiments, including real-world datasets of daily stock indices, demonstrate that LRLR outperforms LPoR and MS-k-NN.

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“…Many studies on stock price prediction in terms of time-series analysis with machine learning have been published. For example, [17,18] showed that the shape of stock price fluctuation is an important feature in the prediction of future prices. They proposed a method to predict future stock prices with the past fluctuations similar to the current with indexing dynamic time warping method [19].…”

Section: Related Workmentioning

confidence: 99%

Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management

Abe

Nakagawa

2020

Proceedings of the 2020 Asia Service Sciences and Software Engineering Conference

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Stock price prediction has been an important research theme both academically and practically. Various methods to predict stock prices have been studied until now. The feature that explains the stock price by a cross-section analysis is called a "factor" in the field of finance. Many empirical studies in finance have identified which stocks having features in the cross-section relatively increase and which decrease in terms of price. Recently, stock price prediction methods using machine learning, especially deep learning, have been proposed since the relationship between these factors and stock prices is complex and non-linear. However, there are no practical examples for actual investment management. In this paper, therefore, we present a cross-sectional daily stock price prediction framework using deep learning for actual investment management. For example, we build a portfolio with information available at the time of market closing and invest at the time of market opening the next day. We perform empirical analysis in the Japanese stock market and confirm the profitability of our framework.

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Stock Price Prediction with Fluctuation Patterns Using Indexing Dynamic Time Warping and $$k^*$$-Nearest Neighbors

Cited by 7 publications

References 16 publications

Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction

Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management

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